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The number of Vehicle Equipment (VE) connected to the Internet is increasing, and these VEs generate tasks that contain large amounts of data. Processing these tasks requires a lot of computing resources. Therefore, it is a promising issue that offloading compute-intensive tasks from resource-limited vehicles to Vehicular Edge Computing (VEC) servers, which involves big data transmission, processing and computation. In a network, multiple providers provide VEC servers. When a vehicle generates a task, our goal is to make an intelligent decision on whether and when to offload this task to VEC servers to minimize the task completion time and total big data processing time. When each vehicle passes VEC servers, the vehicle can decide to offload its task to the VEC server in the current communication range, or continue to drive until it reaches the next server's communication range. This issue can be considered as an asset selling problem. It is a challenging issue to make a smart decision for the vehicle with a location view because the vehicle is not sure when the next VEC server will be available and how much about the available computing capacity of the next VEC server. Firstly, this paper formulates the problem as a Markov Decision Process (MDP), defines and analyzes the state set, action set, reward model, and state transition probability distribution. Then it uses Asynchronous Advantage Actor-Critic (A3C) algorithm to solve this MDP problem, builds the various elements of the A3C algorithm, uses Actor (the strategy function) to generate two actions of the vehicle: offloading and moving without offloading. Thirdly, it uses Critic (the value function) to evaluate Actor's behavior, and guide Actor's actions in subsequent stages. The Actor starts from the initial state in the state space until it enters the termination state, forming a complete decision-making process. It minimizes the completion time of task offloading through learning thereby reducing the delay of big data processing. Compared to the Immediately Offload (IO) scheme and Expect Offload (EO) scheme, the MIDP scheme proposed in this paper reduces the average task offloading delay to 29.93% and 29.99%, close to the EO scheme in terms of task completion rate and up to 66.6% improvement compared to the IO scheme. • A task offloading approach in Internet of Vehicles (IoV) is proposed. • An MDP-based Intelligent Big Data Processing (MIDP) Scheme is proposed. • Simulations demonstrate the better performances of MIDP scheme. [ABSTRACT FROM AUTHOR]

The impacts of climate change and development present significant challenges and complexities that require new solutions, wise choices, and multi-disciplinary integration. In this context, emotional intelligence (EI) plays a crucial role. However, traditional engineering education and practice overlook the importance of understanding and managing emotions. This research aims to determine the impact of EI as a tool to enhance proactive decision-making and implement sustainable measures within the engineering profession.The study makes three main research contributions. First, it confirms a positive relationship between EI and proactive sustainable decision-making among engineers. This means that engineers with high EI are more likely to consider the impacts of their decisions on various stakeholders and dimensions of sustainability. Second, it suggests that EI can enhance creativity and innovative thinking in engineering, helping engineers to develop effective solutions for challenges related to climate change. Third, the study advocates for incorporating EI training and assessment into engineering curriculums to foster a sustainable and ethical engineering culture. By improving EI, engineers can enhance their interpersonal skills, self-awareness, and emotional management, which in turn can significantly improve teamwork in addressing challenges related to climate change. • Innovative Approach: The paper introduces emotional intelligence (EI) as a novel factor in engineering decision-making for climate change adaptation, which provides a fresh perspective within the realm of sustainable practices. • Relevance to Sustainability: It emphasizes the role of EI in fostering proactive and strategic decision-making, which is crucial for mitigating and adapting to a changing climate, aligning with the journal's focus on sustainable solutions1. • Multidisciplinary Integration: The study bridges engineering, psychology, and environmental science, reflecting the journal's interdisciplinary approach to addressing sustainability challenges. • Practical Implications: By exploring the attitudes of engineers and their decision-making tendencies in relation to EI, the paper offers insights that can lead to targeted interventions, contributing to the journal's aim of promoting sustainable practices. • This paper does not constitute a bibliometric study; rather, it provides empirical research on the impact of EI on sustainable decision-making among engineers. [ABSTRACT FROM AUTHOR]

Effective engagement is crucial for enhancing environmental decision-making processes, fostering more sustainable and equitable outcomes. However, the success of engagement is highly variable and context-dependent. While theoretical frameworks have been developed to explain outcome variance in engagement in environmental decision-making, they have not yet been tested in digital contexts, leaving their applicability to digital engagement processes unclear. More broadly, there are unanswered questions about the effectiveness of digital tools in achieving the goals of engagement, which have become increasingly pertinent amidst growing concerns about the potential of digital technologies for exacerbating exclusions, ethical issues, and systematically undermining democratic progress. This paper addresses this evidence gap by presenting findings from interviews with practitioners in UK public, private, and third sector organisations. Our results provide empirical insights into the technical, ethical, and inclusivity debates surrounding digital tools and their effectiveness in promoting accessible engagement, high-quality social interaction, place-based decision-making, and more trustworthy and credible outcomes. Our findings indicate that while current engagement theories are applicable to digital environments, the key explanatory factors acquire new dimensions in digital compared to in-person contexts. Drawing on the findings, this study contributes novel insights to expand current theory for explaining "what works" in engagement in environmental decisions, enhancing its relevance and applicability in the digital age. The paper concludes with evidence-led recommendations for environmental practitioners to improve engagement processes in digital and remote settings. [Display omitted] • There is a lack of evidence on the benefits and drawbacks of digital tools for engagement. • The applicability of current theories for engagement to digital and remote settings is unclear. • Key factors known to shape outcomes in engagement take on new dimensions in digital, remote environments. • There are new considerations which make engagement theory more relevant and applicable in digital contexts. [ABSTRACT FROM AUTHOR]

An out-branching B u + (in-branching B u −) in a digraph D is a connected spanning subdigraph of D in which every vertex except the vertex u , called the root, has in-degree (out-degree) one. A good (u,v) - pair in D is a pair of branchings B u + , B v − which have no arc in common. Thomassen proved that it is NP-complete to decide if a digraph has any good pair. A digraph is semicomplete if it has no pair of non-adjacent vertices. A semicomplete composition is any digraph D which is obtained from a semicomplete digraph S by substituting an arbitrary digraph H x for each vertex x of S. Recently the authors of this paper gave a complete classification of semicomplete digraphs which have a good (u , v) -pair, where u , v are prescribed vertices. They also gave a polynomial algorithm which for a given semicomplete digraph D and vertices u , v of D , either produces a good (u , v) -pair in D or a certificate that D has no such pair. In this paper we show how to use the result for semicomplete digraphs to completely solve the problem of characterizing semicomplete compositions which have a good (u , v) -pair for given vertices u , v. Our solution implies that the problem of deciding the existence of a good (u , v) -pair and finding such a pair when it exists is polynomially solvable for all semicomplete compositions. We also completely solve the problem of deciding the existence of a good (u , v) -pair and finding one when it exists for digraphs that are compositions of transitive digraphs. Combining these two results we obtain a polynomial algorithm for deciding whether a given quasi-transitive digraph D has a good (u , v) -pair for given vertices u , v of D. This proves a conjecture of Bang-Jensen and Gutin from 1998. [ABSTRACT FROM AUTHOR]

In edge computing, application components can be placed over a range of computational devices from cloud data centers to nodes at the network edge. Application placement can have significant impact on important metrics like latency and resource utilization. Thus, application placement is an important optimization problem. In edge computing, the characteristics of both the infrastructure and the application may change over time, which may require the dynamic re-optimization of the application placement. Most algorithms suggested so far for the dynamic re-optimization of edge application placement are centralized, i.e., they rely on one entity collecting information from the whole infrastructure and making decisions centrally. However, centralized approaches suffer from limited scalability and are vulnerable to failures. In this paper, we present a decentralized approach for the dynamic re-optimization of edge application placement. We adopt an algorithm of Malek et al. for distributed systems and modify it to make it applicable to edge computing. In this approach, each node makes decisions autonomously, using auctions for coordination. Our empirical results demonstrate that the proposed algorithm is very effective in optimizing edge application placement. In an edge system with 637 edge nodes and 563 end devices, our algorithm achieves 54% higher reduction of application latency than a previous decentralized algorithm. • Optimized placement of application components on cloud and edge devices. • Decentralized optimization algorithm using local knowledge and local modifications. • Auctions and distributed locking for synchronization. • Minimization of application latency. [ABSTRACT FROM AUTHOR]

• We designed a resource provisioning framework to cloud services for varying VM load conditions. • We customized a resource provisioning framework based on control MAPE-K loop. • We enhanced the performance of the planning phase by using Bayesian learning approach. • The proposed framework is evaluated using real-time webserver traces for different metrics. Deciding the correct extent of resources needed to run the various cloud services is always a challenge. Often in such dynamic environments, there is a tremendous need for accurate predictions and timely decision making methodologies to estimate the future demands within a minimal cost. This brings in a need to elucidate the research divergence for optimal dynamic resource provisioning that predicts the future enumerated resources on the support of application's type. This paper proposes a framework to provision the resources in an optimal way, by combining the concepts of autonomic computing, linear regression and Bayesian learning. The use of Bayesian learning to the proposed model helps in a proactive decision making process and provide a solid theoretical framework to estimate the future predictions using the prior information available. The autonomic resource provisioning framework proposed here is developed using CloudSim toolkit inspired by a cloud layer model. The efficacy of the proposed technique is evaluated using real world workload traces from google followed by the traces from Clarknet. The model is evaluated for various parameters namely – response time, SLA violations, virtual machine usage hours and cost. It is found that the proposed model lowers the overall cost by 31% with the increase in the usage of resources by 12% when compared with the other existing approaches. [ABSTRACT FROM AUTHOR]

A formidable challenge in scheduling user applications lies in collecting and representing the user's goals and requirements. We introduce a "science goal" as a mechanism for users to define scientific objectives and conditions of interest. To provide an abstraction to run applications on an ensemble of edge computing nodes, we implement a two-layered scheduler—cloud and edge scheduler. In this scheduling model, the users submit their goals to the cloud scheduler. These goals are conveyed to the appropriate nodes based on a variety of constraints including geographical area, resource availability, node capabilities, and applicability. The edge scheduler, with complete understanding of the current conditions, assumes the responsibility for executing the applications on the nodes so that the users' science goals are met. This paper provides a framework for the two-layered scheduling model for goal-driven edge computing and motivates and informs its architecture through a case study. • Execution of user science applications is often context driven. • Edge computing needs local decision making for scheduling applications in time. • Representing science needs in logic enables context-aware computing at the edge. • Science goals are achieved through reasoning of current context at the edge. [ABSTRACT FROM AUTHOR]

In the study of rooted phylogenetic networks, analyzing the set of rooted phylogenetic trees that are embedded in such a network is a recurring task. Algorithmically, this analysis requires a search of the exponential-sized multiset S of rooted phylogenetic trees that are embedded in a rooted phylogenetic network N. In this paper, we introduce the notion of a non-essential arc, which is an arc whose deletion from N results in a rooted phylogenetic network N ′ that embeds the same set of rooted phylogenetic trees as N but whose associated multiset is smaller than S. We characterize non-essential arcs for the popular class of tree-child networks and show that identifying and deleting such arcs takes time that is cubic in the number of leaves of the network. Moreover, we show that deciding if a given arc of an arbitrary phylogenetic network is non-essential is Π 2 P -complete. [ABSTRACT FROM AUTHOR]

In order to deal with the fast changing requirements of container based services in clouds, auto-scaling is used as an essential mechanism for adapting the number of provisioned resources with the variable service workloads. However, the latest auto-scaling approaches lack the comprehensive consideration of variable workloads and hybrid auto-scaling for multi-type services. Firstly, the historical data based proactive approaches are widely used to handle complex and variable workloads in advance. The decision-making accuracy of proactive approaches depends on the prediction algorithm, which is affected by the anomalies, missing values and errors in the historical workload data, and the unexpected workload cannot be handled. Secondly, the trigger based reactive approaches are seriously affected by workload fluctuation which causes the frequent invalid scaling of service resources. Besides, due to the existence of scaling time, there are different completion delays of different scaling actions. Thirdly, the latest approaches also ignore the different scaling time of hybrid scaling for multi-type services including stateful services and stateless services. Especially, when the stateful services are scaled horizontally, the neglected long scaling time causes the untimely supply and withdrawal of resources. Consequently, all three issues above can lead to the degradation of Quality of Services (QoS) and the inefficient utilization of resources. This paper proposes a new hybrid auto-scaling approach for multi-type services to resolve the impact of service scaling time on decision making. We combine the proactive scaling strategy with the reactive anomaly detection and correction mechanism. For making a proactive decision, the ensemble learning model with the structure improved deep network is designed to predict the future workload. On the basis of the predicted results and the scaling time of different types of services, the auto-scaling decisions are made by a Deep Reinforcement Learning (DRL) model with heterogeneous action space, which integrates horizontal and vertical scaling actions. Meanwhile, with the anomaly detection and correction mechanism, the workload fluctuation and unexpected workload can be detected and handled. We evaluate our approach against three different proactive and reactive auto-scaling approaches in the cloud environment, and the experimental results show the proposed approach can achieve the better scaling behavior compared to state-of-the-art approaches. • We design a hybrid auto-scaling system architecture for multi-type cloud services. • We propose a workload prediction model based on ensemble learning. • We propose a DRL based auto-scaling mechanism for multi-type cloud services. • We propose a anomaly correction mechanism for alleviating scaling error impact. [ABSTRACT FROM AUTHOR]

Quantiles are used for decision making in investment analysis and in the mining, oil and gas industries. However, it is unknown how common quantile-based decision making actually is among typical individual decision makers. This paper describes an experiment that aims to (1) compare how common is decision making based on quantiles relative to expected utility maximization, and (2) estimate risk attitude parameters under the assumption of quantile preferences. The experiment has two parts. In the first part, individuals make pairwise choices between risky lotteries, and the competing models are fitted to the choice data. In the second part, we directly elicit a decision rule from a menu of alternatives. The results show that a quantile preference model outperforms expected utility for 32%–55%, of participants, depending on the metric. The majority of individuals are risk averse, and women are more risk averse than men, under both models. [ABSTRACT FROM AUTHOR]

Planning under complex uncertainty often asks for plans that can adapt to changing future conditions. To inform plan development during this process, exploration methods have been used to explore the performance of candidate policies given uncertainties. Nevertheless, these methods hardly enable adaptation by themselves, so extra efforts are required to develop the final adaptive plans, hence compromising the overall decision-making efficiency. This paper introduces Reinforcement Learning (RL) that employs closed-loop control as a new exploration method that enables automated adaptive policy-making for planning under uncertainty. To investigate its performance, we compare RL with a widely-used exploration method, Multi-Objective Evolutionary Algorithm (MOEA), in two hypothetical problems via computational experiments. Our results indicate the complementarity of the two methods. RL makes better use of its exploration history, hence always providing higher efficiency and providing better policy robustness in the presence of parameter uncertainty. MOEA quantifies objective uncertainty in a more intuitive way, hence providing better robustness to objective uncertainty. These findings will help researchers choose appropriate methods in different applications. • Introduce reinforcement learning to decision-making under deep uncertainty. • Compare reinforcement learning and evolutionary algorithm. • Reinforcement learning is consistently more computationally efficient. • Reinforcement learning provides better robustness to parameter uncertainty. • Evolutionary algorithm provides better robustness to objective uncertainty. [ABSTRACT FROM AUTHOR]

Coalitionary recruitment offers a window into animal social cognition. However, naturally observed coalitionary conflicts are challenging to analyse because the researcher has no control over the context in which they occurred, and observed behaviour patterns are typically consistent with multiple explanations. In this paper we analyse observational data of coalitionary solicitations during conflicts in wild capuchin monkeys, Cebus capucinus. We build upon previous work that focuses on identifying the cues that animals use to solicit allies in agonistic encounters. In contrast to previous studies, we applied a statistical technique that allows us to simultaneously compare different hypotheses regarding which cues animals use and how these cues interact. Our analysis shows that capuchin monkeys use information about both relationship quality and dominance when recruiting allies during conflicts. Monkeys primarily use rank when recruiting an ally, but will also use relationship quality, particularly when the potential ally has low rank. This study provides evidence that nonhuman primates are able to classify other group members using multiple criteria simultaneously. In addition, this paper presents a statistical technique that animal researchers can use to infer decision rules from observational data. • We evaluated factors underlying coalitionary solicitations in capuchin monkeys. • The relative value of rank and relationship quality were analysed in a joint model. • Capuchin monkeys use both rank and relationship quality when recruiting allies. • Capuchin monkeys weigh rank more heavily than relationship quality. [ABSTRACT FROM AUTHOR]

Highlights • We present an overview of the SA integration in the speech therapy domain, showing criteria that are consolidated. • we survey the most used methods along with descriptions of where each one is commonly applied. • We address the projection capability of each study selected, for evaluating how decision-making is presented in the domain. • We demonstrate how SA integration has evolved over time, comparing criteria evaluated in this paper and in previous work. • We provide directions for further research, pointing out challenges and opportunities in the speech therapy domain. Abstract Situation Awareness (SA) involves the correct interpretation of a situation, allowing a system to respond to the observed environment and providing support for decision making in many systems domains. Speech therapy is an example of domain where situation awareness can provide benefits, since practitioners should monitor the patient in order to perform therapeutic actions. However, there are a few studies in the area that address reasoning about a situation to improve these tasks. For this reason, this systematic mapping study aims to identify and compare different proposals in the speech therapy domain in order to verify which aspects related to obtaining and maintaining SA are supported. Our analyzes provide useful insights on whats aspects of SA are best integrated in the speech domain - such as knowledge bases and adaptation - and other aspects that remain to be improved - like the action support and projection abilities. Also, this paper includes statistics, methodologies used by different authors and other issues involving research processes. As main contributions, this work presents an overview of the SA integration in the speech therapy domain, discussing challenges in the area and providing directions for further research. [ABSTRACT FROM AUTHOR]

Abstract Recognition of the need to manage the water environment in more holistic ways has resulted in the global growth of Integrated Catchment Management (ICM). ICM is characterised by horizontal integration, encouraging interdisciplinary working between traditionally disparate management sectors, alongside vertical integration, characterised by the engagement of communities; central is the promotion of participatory governance and management decision-making. ICM has been translated into policy through, for example, the EU Water Framework Directive and at a national level by policies such as the Catchment Based Approach in England. Research exploring the implementation of these policies has reported success at a catchment level, but further research is required to explore practices of management at local level within catchments. This paper presents the findings of participatory research undertaken with a catchment partnership in the northeast of England to explore the integration of top-down policy translation with how local communities interact with management agencies at sub-catchment scale (a bottom-up perspective). The research found that supra-catchment scale drivers dominate the vertical interplay between management systems at more local levels. These drivers embed traditional practices of management, which establishes public participation as a barrier to delivery of top-down management objectives, resulting in practices that exclude communities and participatory movements at the local level. Although collaboration between agencies at the partnership scale offers a potential solution to overcoming these obstacles, the paper recommends changes to supra-catchment governance structures to encourage flexibility in developing local participatory movements as assets. Further research is necessary to develop new practices of management to integrate local people more effectively into the management process. Highlights • Integrated Catchment Management policies promotes active public participation. • How effectively ICM policy is translated into practices of participation is explored. • Management practices exclude local communities and obstruct participation. • Local practices show a dominant vertical interplay with supra-catchment drivers. • Research on governance reform and new participatory practices is recommended. [ABSTRACT FROM AUTHOR]

Abstract An effective and efficient stewardship of natural resources requires consistency across all decision-informing approaches and components involved, i.e., managerial, governmental, political, and legal. To achieve this consistency, these elements must be aligned under an overarching management goal that is consistent with current and well-accepted knowledge. In this article, we investigate the adoption by the US Bureau of Ocean Energy Management of an environmental resilience-centered system that manages for resilience of marine ecological resources and its associated social elements. Although the framework is generally tailored for this Bureau, it could also be adapted to other federal or non-federal organizations. This paper presents a dynamic framework that regards change as an inherent element of the socio-ecological system in which management structures, e.g., federal agencies, are embedded. The overall functioning of the management framework being considered seeks to mimic and anticipate environmental change in line with well-accepted elements of resilience-thinking. We also investigate the goal of using management for resilience as a platform to enhance socio-ecological sustainability by setting specific performance metrics embedded in pre-defined and desired social and/or ecological scenarios. Dynamic management frameworks that couple social and ecological systems as described in this paper can facilitate the efficient and effective utilization of resources, reduce uncertainty for decision and policy makers, and lead to more defensible decisions on resources. Highlights • A resilience-based framework is studied for efficient natural resource management. • The management framework is expanded and adapted to a specific organization. • Nested panarchies are used to couple social and ecological sub-systems. • The environmental management framework is used to analyze two practical examples. • The management framework could be adapted to other organizations or groups of them. [ABSTRACT FROM AUTHOR]

• An effective end-to-end deep neural network for integration of multi-omics data. • Divergence-based regularization can capture consensus information among modalities. • The performances are varied when integrating different modalities of -omics data. • Improved performance in cancer type classification and survival prediction. • Visualized the better agreement among modalities after consensus learning. Breast and ovarian cancers are the second and the fifth leading causes of cancer death among women. Predicting the overall survival of breast and ovarian cancer patients can facilitate the therapeutics evaluation and treatment decision making. Multi-scale multi-omics data such as gene expression, DNA methylation, miRNA expression, and copy number variations can provide insights on personalized survival. However, how to effectively integrate multi-omics data remains a challenging task. In this paper, we develop multi-omics integration methods to improve the prediction of overall survival for breast cancer and ovarian cancer patients. Because multi-omics data for the same patient jointly impact the survival of cancer patients, features from different -omics modality are related and can be modeled by either association or causal relationship (e.g., pathways). By extracting these relationships among modalities, we can get rid of the irrelevant information from high-throughput multi-omics data. However, it is infeasible to use the Brute Force method to capture all possible multi-omics interactions. Thus, we use deep neural networks with novel divergence-based consensus regularization to capture multi-omics interactions implicitly by extracting modality-invariant representations. In comparing the concatenation-based integration networks with our new divergence-based consensus networks, the breast cancer overall survival C-index is improved from 0.655 ± 0.062 to 0.671 ± 0.046 when combing DNA methylation and miRNA expression, and from 0.627 ± 0.062 to 0.667 ± 0.073 when combing miRNA expression and copy number variations. In summary, our novel deep consensus neural network has successfully improved the prediction of overall survival for breast cancer and ovarian cancer patients by implicitly learning the multi-omics interactions. [ABSTRACT FROM AUTHOR]

• An algorithm is designed to reduce the network blocking rate. • Markov Decision Process based state transfer is provided for online RMSA in real-time. • Offline Deep Q-Network based algorithm is provided to get a well-trained Q-network helping the RMSA state transfer. • The experience priority replay mechanism and Sumtree are introduced to speed up Deep Q-Network training. The continuous growth of the network communication demand puts higher requirements on the network infrastructures. The elastic optical network (EON) has great potential to support the continued demands for communication bandwidth. Efficient use of bandwidth resources of EON has been particularly important to alleviate network blocking, which depends on routing, modulation, and spectrum allocation processes (RMSA). However, the time-varying states of EON caused by the uncertainty of future demands make it a lot tougher to realize the online RMSA in real time. To solve the above problem, this paper proposes a kind of Deep Q Network (DQN) algorithm with prioritized experience replay mechanism to perform the RMSA process in real-time. The proposed algorithm includes two parts. One is the Markov Decision Process (MDP) based state transfer for online RMSA by a trained Q-network. The other is an offline DQN-based algorithm for getting a trained Q-network to help the decision-making of RMSA state transfer, where the experience priority replay mechanism and Sumtree are introduced to speed up DQN training. Simulation results show that compared with the traditional Deep Q Network algorithm, the proposed algorithm nearly doubles the Q-network training speed. And compared with the traditional sp + ff algorithm, the trained Q-network reduces the blocking rate by nearly 35 %. [ABSTRACT FROM AUTHOR]

• The brain creates internal representations of the world that are optimised for decision-making. • Here we used EEG and nonlinear decision-making tasks to identify when such representations emerge. • Stimulus-bound representations emerged at around 100 ms and were followed by task-relevant representations between 700 ms and 900 ms. • Participants who made better decisions had stronger task-relevant representations. This paper describes the relationship between performance in a decision-making task and the emergence of task-relevant representations. Participants learnt two tasks in which the appropriate response depended on multiple relevant stimuli and the underlying stimulus-outcome associations were governed by a latent feature that participants could discover. We divided participants into good and bad performers based on their overall classification rate and computed behavioural accuracy for each feature value. We found that participants with better performance had a better representation of the latent feature space. We then used representation similarity analysis on Electroencephalographic (EEG) data to identify when these representations emerge. We were able to decode task-relevant representations in a time window emerging 700 ms after stimulus presentation, but only for participants with good task performance. Our findings suggest that, in order to make good decisions, it is necessary to create and extract a low-dimensional representation of the task at hand. [ABSTRACT FROM AUTHOR]

Waste management has still been a developing and progressing field, which demands continual improvements in waste transportation as well as proper selection of locations and technical operation of new treatment facilities. Most of research papers on waste management planning have been dealing with optimisation of network flows, thus minimising the cost and improving economic criteria. The shortest paths to treatment facilities are considered together with detailed analysis of their operation including heat and electricity demands in their vicinity. The tasks sometimes include social and global environmental criterions, however, the direct local consequences also play an important role and should be examined. A decision-making strategy in waste management updated with the local emission impact on the population is proposed in this paper. The paper focuses on the first move in analysing the production, dispersion, and impact of pollutants, originating in transport, with regards to the population living close to routes. The calculation of emission produced during the transport of waste takes into consideration the altitude profiles of routes, container loads, and specific types of vehicles. The consecutive estimated impact on the population reckons with the distances between routes and municipalities as well as their sizes in terms of the numbers of inhabitants, where the transportation routes are divided into smaller segments and dispersion is limited with threshold value. The proposed approach describing the emission effect has been tested using real-life operating data corresponding to the specific, 81 km long route along which approximately 25 t of waste is transported 800 times a year. The impact of pollutants on the population was evaluated and discussed. Results of the analysis were quantified for this route to create an edge characterisation needed for further calculations. This approach applied to the whole network then yields input data needed for future research of novel strategies in facility location problems. Other possible extensions of the presented approach include more accurate dispersion function or detailed calculation of the impact of pollutants with respect to specific locations of residential houses. [ABSTRACT FROM AUTHOR]

This paper presents an atmospheric vulnerability assessment framework based on CAMx that should be helpful to assess potential impacts of changes in human, atmospheric environment, and social economic elements of atmospheric vulnerability. It is also a useful and effective tool that can provide policy-guidance for environmental protection and management to reduce the atmospheric vulnerability. The developed framework was applied to evaluate the atmospheric environment vulnerability of 13 cities in the Beijing-Tianjin-Hebei (BTH) region for verification. The results indicated that regional disparity of the atmospheric vulnerability existed in the study site. More specifically, the central and southern regions show more atmospheric environment vulnerability than the northern regions. The impact factors of atmospheric environment vulnerability in the BTH region mainly derived from increasing population press, frequently unfavorable meteorological conditions, extensive economic growth of secondary industry, increased environmental pollution, and accelerating population aging. The framework shown in this paper is an interpretative and heuristic tool for a better understanding of atmospheric vulnerability. This framework can also be replicated at different spatial and temporal scales using context-specific datasets to straightly support environmental managers with decision-making. [ABSTRACT FROM AUTHOR]

While food variety continues to be of major interest to those studying eating and health, research has been mainly limited to laboratory research of simple meals. This paper seeks to enlarge the scope of eating research by examining the food offered in the earliest menus in United States restaurants and hotels of the early and mid-19th c, when restaurants began. This reveals a very large variety in what food was offered. The paper discusses why variety has declined in the US and probably elsewhere, including changes in the customer, changes in food service, changes of food availability, and the industrialization of the food supply. Menu analysis offers another approach to studying dietary variety across cultures and across time. [ABSTRACT FROM AUTHOR]

One way to engage people with green infrastructure (GI) is as environmental volunteers. Previous studies explored the nature of such groups/projects in terms of the benefits they deliver such as their impact on levels of social capital within a pre-defined community. However, existing literature contributes little to our understanding of the composition, characteristics and mechanisms used to form and maintain these groups. As such, it is difficult to establish the influencing factors determining the capacity of a group to sustain its provision over time. This paper serves to offer a more nuanced understanding of local-scale environmental stewardship by outlining the diversity of volunteer-led GI activities observed at the community-scale. Evidence presented from a desk-based examination of observable activity within The Mersey area Forest in North-West England represents a re-conceptualisation of existing definitions of Community-Scale GI (CSGI). Using thematic criteria, the paper clusters characteristics into key classification affecting group dynamics, composition and objectives. Initial findings identified the following categories as being significant descriptors for community-scale green infrastructure: status, location, timeframe, membership, activity focus, governance, resources and recognition, and communications. Thus, we classify four distinct types of group engaged in voluntary activity contributing to local level GI creation and long-term management: ‘Formal Group (Active), Formal Group (Inactive), Formal Project and Informal Group. Creating a nuanced typology of CSGI provides further opportunities to analysis the creation and long-term management of GI at a site, neighbourhood and city-scale. In turn, this contributes to our understanding of how multiple actors remain engaged in the decision-making processes of GI management and maintenance. [ABSTRACT FROM AUTHOR]

Behaviour and physiological responses (e.g. respiratory rate and cloacal temperature) could be an indication of the thermal comfort or discomfort of broilers chicks. This study aimed to estimate the cloacal temperature (CT) of chicks in response to different intensities and durations of thermal exposure during the first week of life using a fuzzy inference system (FIS) and a fuzzy genetic algorithm (Fuzzy-GA). The experiment was conducted in four temperature-controlled wind tunnels located at the environmental laboratory of the Federal University of Lavras (UFLA; Minas Gerais, Brazil). The experimental database is composed of 114 laboratory-based observations. The duration of thermal challenge (CD; days) and dry bulb temperature (t db ; °C) were used as input variables for FIS. This paper proposes a theoretical framework for the development of Fuzzy-GA systems via two different approaches: the Mogul approach and the Pittsburgh approach. According to our results, the predicted CT values for both models (FIS and Fuzzy-GA) were similar to the experimentally-observed CT values. However, we noted that the model based on Fuzzy-GA exhibited better statistical results than the manual FIS in terms of CT-predicting capability. Thus, the model based on Fuzzy-GA can be used to predict CT for chicks exposed to thermal challenges and can therefore aid in decision-making processes. • To estimate and compare cloacal temperature of chicks submitted to thermal stress. • Fuzzy Inference System and Fuzzy Genetic algorithm were developed and compared. • Descriptive statistical indices computed to evaluate improvement to Fuzzy systems. • The fuzzy-GA exhibited better predicting capacity statistical results than the FIS. • Thermal challenge may affect chick welfare and they may not recover adequately. [ABSTRACT FROM AUTHOR]

The popular brain monitoring method of electroencephalography (EEG) has seen a surge in commercial attention in recent years, focusing mostly on hardware miniaturization. This has led to a varied landscape of portable EEG devices with wireless capability, allowing them to be used by relatively unconstrained users in real-life conditions outside of the laboratory. The wide availability and relative affordability of these devices provide a low entry threshold for newcomers to the field of EEG research. The large device variety and the at times opaque communication from their manufacturers, however, can make it difficult to obtain an overview of this hardware landscape. Similarly, given the breadth of existing (wireless) EEG knowledge and research, it can be challenging to get started with novel ideas. Therefore, this paper first provides a list of 48 wireless EEG devices along with a number of important—sometimes difficult-to-obtain—features and characteristics to enable their side-by-side comparison, along with a brief introduction to each of these aspects and how they may influence one's decision. Secondly, we have surveyed previous literature and focused on 110 high-impact journal publications making use of wireless EEG, which we categorized by application and analyzed for device used, number of channels, sample size, and participant mobility. Together, these provide a basis for informed decision making with respect to hardware and experimental precedents when considering new, wireless EEG devices and research. At the same time, this paper provides background material and commentary about pitfalls and caveats regarding this increasingly accessible line of research. [ABSTRACT FROM AUTHOR]

Value Iteration (VI) is a core method to find optimal policies, allowing a robot to act autonomously in environments where the effects of its actions are not deterministic. Although there are extensive studies on VI's theoretical properties and computational cost, its energy performance — an essential indicator for its use in the physical world — has not been evaluated. In this paper, we explore both the energy and runtime performance of five parallel implementation strategies of VI on representative low-power heterogeneous computing platforms that integrate CPUs and GPUs, for the use-case scenario of indoor autonomous robot navigation. We provide a statistical analysis of their performance depending on the problem size, parallel implementation and computing platform. Our study shows that CPU–GPU heterogeneous strategies reduce computation time and energy considerably, given large enough problem sizes, regardless of the computation platform. This work also provides practical guidelines to assist in the application of the most efficient implementation, either in terms of energy consumption or time, to a low-power heterogeneous platform. • Heterogeneous parallelisation strategies to implement Value Iteration algorithm. • Autonomous navigation use-case on low-power HCPs for real-world MPD sizes. • Compressed MDP representation reducing up to 9x the memory and computations required. • Reduce energy and computation time using CPU–GPU heterogeneous implementations. • Sound statistic analysis and validation of the results. [ABSTRACT FROM AUTHOR]

Opportunistic Networks exploit portable handheld devices to collect delay-tolerant data from sensors to gateways for realizing various Smart City applications. To obtain knowledge for determining suitable routing paths as users go about their daily routine, nodes maintain history of every encounter and exchange the information through summary vectors. Due to large node populations, the size of summary vectors makes it challenging to implement real-world city-scale applications with the technology. In this paper, we take the technology a step towards real-world implementation by proposing a set of adaptive and privacy-preserving mechanisms that can be incorporated into existing encounter-based routing protocols to reduce summary vector sizes without compromising delivery guarantees. We validate our proposals with real-world human movement traces and simulation experiments. In terms of network performance, our proposals reduce the average summary vector size by 75% to achieve up to 21% less energy consumption with about 28% improvement in throughput. • Adaptive, privacy-preserving, flexible and resource-aware forwarding technique. • Makes forwarding decisions without maintaining history of every encounter. • Reduces summary vector size and related overheads in high node population. • Can be incorporated into existing routing protocols to improve their performance. • Reduces energy, memory and computational resource consumption on user devices. [ABSTRACT FROM AUTHOR]

In this paper, we formulate configurable cloud-based VoIP call allocation problem as a special case of dynamic multi-objective bin-packing. We consider voice quality influenced by CPU stress, cost contributed by the number of billing hours for Virtual Machines (VMs) provisioning, and calls placed on hold due to under-provisioning resources. We distinguish call allocation strategies by the type and amount of information used for allocation: knowledge-free, utilization-aware, rental-aware, and load-aware. We propose and study a variety of strategies with static and dynamic policies of VM provisioning. To study realistic scenarios, we consider startup delays for VM provisioning, and three configurable parameters: utilization threshold, rental threshold, and prediction interval. They can be configured and dynamically adapted to cope with different objective preferences, workloads, and cloud properties. We conduct comprehensive simulation on the real workload of the MIXvoip company and show that the proposed strategies outperform ones currently in-use. • We address concepts of simulation visualization as a pedagogical tool for supporting computer science undergraduates. • We demonstrate the practicability and benefits of the proposed approach on allocation of calls in Cloud VoIP model. • We discuss call allocation strategies with different types and amounts of information used for provision on cloud environments with configurable parameters. • We describe how simulation model enhance the decision making of VoIP providers respect to quality of service and cost of infrastructure. [ABSTRACT FROM AUTHOR]

Food waste is a significant contemporary issue in the UK, with substantial environmental, social and economic costs to the nation. Whilst efforts to reduce food waste are laudable, a significant proportion of food and drink manufacturer waste is unavoidable. On the one hand, there is a drive from industry to reclaim as much value from this waste as possible, for example, by conversion to valuable products in what is known as "valorisation". At the same time, growing social and legislative pressures mean that any attempts to valorise food waste must be performed in a sustainable manner. However, for every company and its specific food wastes, there will be multiple valorisation possibilities and few tools exist that allow food and drink manufacturers to identify which is most profitable and sustainable for them. Such a decision would need to not only consider environmental, social and economic performance, but also how ready the technology is and how well it aligns with that company's strategy. In response, this paper develops and presents a hybrid framework that guides a company in modelling the volumes/seasonality of its wastes, identifying potential valorisation options and selecting appropriate indicators for environmental, social and economic performance as well as technological maturity and alignment with company goals. The framework guides users in analyzing economic and environmental performance using Cost-Benefit Analysis and Life Cycle Assessment respectively. The results can then be ranked alongside those for social performance, technological maturity and alignment with company goals using a weighted sum model variant of Multi-Criteria Decision Analysis to facilitate easy visual comparison. This framework is demonstrated in the form of a case study with a major UK fruit consolidator to identify the optimal strategy for managing their citrus waste. Possibilities identified included sale of imperfect but still edible waste via wholesale at a significantly reduced profit and the investment in facilities to extract higher value pectin from the same waste stream using a microwave assisted pectin extraction process. Results suggest that continued sale of waste to wholesale markets is currently the most beneficial in terms of economic viability and environmental performance, but that in the medium to long term, the projected growth in the market for pectin suggests this could become the most viable strategy. • When food waste cannot be avoided, valorisation can increase company resilience. • Valorisation strategies must try to avoid negative supply chain ramifications. • The SWaVI pragmatic framework allows companies to identify their optimal valorisation route. • The SWaVI pragmatic framework facilitates initial selection, not implementation of valorisation. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the process by which new experiences reactivate and potentially update old memories. Such memory reconsolidation appears dependent on the extent to which current experience deviates from what is predicted by the reactivated memory (i.e. prediction error). If prediction error is low, the reactivated memory is likely to be updated with new information. If it is high, however, a new, separate, memory is more likely to be formed. The temporal parietal junction TPJ has been shown across a broad range of content areas (attention, social cognition, decision making and episodic memory) to be sensitive to the degree to which current information violates the observer’s expectations – in other words, prediction error. In the current paper, we investigate whether the level of TPJ activation during encoding predicts if the encoded information will be used to form a new memory or update a previous memory. We find that high TPJ activation predicts new memory formation. In a secondary analysis, we examine whether reactivation strength – which we assume leads to a strong memory-based prediction – mediates the likelihood that a given individual will use new information to form a new memory rather than update a previous memory. Individuals who strongly reactivate previous memories are less likely to update them than individuals who weakly reactivate them. We interpret this outcome as indicating that strong predictions lead to high prediction error, which favors new memory formation rather than updating of a previous memory. [ABSTRACT FROM AUTHOR]

This paper presents one emerging social-technical innovation: The evolution of citizen-sensor-networks where citizens organize themselves from the ‘bottom up’, for the sake of confronting governance officials with measured information about environmental qualities. We have observed how citizen-sensor-networks have been initiated in the Netherlands in cases where official government monitoring and business organizations leave gaps. The formed citizen-sensor-networks collect information about issues that affect the local community in their quality-of-living. In particular, two community initiatives are described where the sensed environmental information, on noise pollution and gas-extraction induced earthquakes respectively, is published through networked geographic information methods. Both community initiatives pioneered in developing an approach that comprises the combined setting-up of sensor data flows, real-time map portals and community organization. Two particular cases are analyzed to trace the emergence and network operation of such ‘networked geo-information tools’ in practice: (1) The Groningen earthquake monitor, and (2) The Airplane Monitor Schiphol. In both cases, environmental 'externalities' of spatial-economic activities play an important role, having economic dimensions of national importance (e.g. gas extraction and national airport development) while simultaneously affecting the regional community with environmental consequences. The monitoring systems analyzed in this paper are established bottom-up, by citizens for citizens, to serve as ‘information power’ in dialogue with government institutions. The goal of this paper is to gain insight in how these citizen-sensor-networks come about: how the idea for establishing a sensor network originated, how their value gets recognized and adopted in the overall ‘system of governance’; to what extent they bring countervailing power against vested interests and established discourses to the table and influence power-laden conflicts over environmental pressures; and whether or not they achieve (some form of) institutionalization and, ultimately, policy change. We find that the studied-citizen-sensor networks gain strength by uniting efforts and activities in crowdsourcing data, providing factual, ‘objectivized data’ or ‘evidence’ of the situation ‘on the ground’ on a matter of local community-wide concern. By filling an information need of the local community, a process of ‘collective sense-making’ combined with citizen empowerment could grow, which influenced societal discourse and challenged prevailing truth-claims of public institutions. In both cases similar, ‘competing’ web-portals were developed in response, both by the gas-extraction company and the airport. But with the citizen-sensor-networks alongside, we conclude there is a shift in power balance involved between government and affected communities, as the government no longer has information monopoly on environmental measurements. [ABSTRACT FROM AUTHOR]

This paper studies the costs and benefits of delegating decisions to superiorly informed agents, that is of adopting flexible contracts, relative to the use of rigid, non-discretionary contracts. The main focus of the paper lies in the analysis of the costs of delegation, primarily agency costs, versus their benefits, primarily the flexibility of the action choice in two different environments, one with risk and one with ambiguity. We first determine and characterize the properties of the optimal flexible contract. We then show that the higher the agent's degree of risk aversion, the higher is the agency costs of delegation and the less profitable a flexible contract relative to a rigid one. When the parties have imprecise probabilistic beliefs, the agent's degree of imprecision aversion introduces another agency cost, which again reduces the relative profitability of flexible contracts. [ABSTRACT FROM AUTHOR]

This paper assesses the discovery of the dose-rate effect in radiation genetics and how it challenged fundamental tenets of the linear non-threshold (LNT) dose response model, including the assumptions that all mutational damage is cumulative and irreversible and that the dose-response is linear at low doses. Newly uncovered historical information also describes how a key 1964 report by the International Commission for Radiological Protection (ICRP) addressed the effects of dose rate in the assessment of genetic risk. This unique story involves assessments by two leading radiation geneticists, Hermann J. Muller and William L. Russell, who independently argued that the report's Genetic Summary Section on dose rate was incorrect while simultaneously offering vastly different views as to what the report's summary should have contained. This paper reveals occurrences of scientific disagreements, how conflicts were resolved, which view(s) prevailed and why. During this process the Nobel Laureate, Muller, provided incorrect information to the ICRP in what appears to have been an attempt to manipulate the decision-making process and to prevent the dose-rate concept from being adopted into risk assessment practices. [ABSTRACT FROM AUTHOR]

Understanding homeowners' energy-efficiency retrofit (EER) decision-making is a critical priority for reducing the adverse environmental impacts of the building sector and promoting a sustainable consumption transition. Existing research lacks attention to the dynamics and social interactions in the decision-making process of homeowner EER adoption. This paper applies the complex network-based evolutionary game approach with agent-based modeling to construct an evolutionary dynamics model for homeowners' EER adoption decision-making. Through simulation experiments, this paper examines the effects of various key factors, including government incentives, retrofit costs, retrofit uncertainty, and network size, on the evolution of EER adoption. The results suggest that government incentives facilitate EER adoption, but their effects require a sufficiently long period of policy implementation and extensive social interaction to be realized. Reducing retrofit costs is a robust and effective way to encourage EER adoption, especially when uncertainty is high. Retrofit uncertainty has a significant impact on the adoption evolution. Increased uncertainty can hinder adoption decisions. In particular, the combination of high uncertainty and incentives is prone to lead to incentive failure. The increase in network size contributes to EER adoption, but attention needs to be paid to the impact of potential incentive redundancy in large-scale networks. • Using complex network-based evolutionary game to study energy-efficiency retrofit. • The effects of incentive, retrofit cost, and retrofit uncertainty are simulated. • The microscopic mechanisms underlying retrofit decision evolution are revealed. • Uncertainty and network size can affect the effectiveness of government incentives. • Policymakers need to pay attention to social network size and retrofit risks. [ABSTRACT FROM AUTHOR]

For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction. [ABSTRACT FROM AUTHOR]

Classic optimization models explain adaptive behaviours in terms of optimal cost–benefit trade-offs. They assume flexibility and make testable predictions about what animals should do in order to maximize their fitness. But they do not usually ask how animals can do it. Since flexible behaviours can be globally optimal but locally suboptimal, the optimality models should directly focus on the underlying mechanisms of behavioural flexibility, such as learning and decision making. In this paper, I use the classic 'diet' model of optimal foraging theory (OFT) to investigate the evolution of decision-making mechanisms at both the computational and the algorithmic level. At the computational level, I define benefits (the expected rate of net energy intake) and costs (lost opportunity) and formalize the decision rule. At the algorithmic level, I present two sequential sampling models, which differ in the way information is internally represented and used. The first model represents the prospective items along a one-dimensional scale of values (benefit–cost differences) and it uses a fixed amount of sensory information (and time) to make decisions. The second model represents items in the two-dimensional plane of benefits and costs, and it uses a variable amount of information. I test the models along a gradient of resource abundance. In each environment, I use OFT to classify resources as either profitable or unprofitable and describe the model performance in terms of decision time and accuracy. At high resource density, both models predict foraging choice to be more selective, but less accurate, than at low density, because decisions are strongly biased in the false-positive direction. At low resource density, the two-dimension model performs better than the one-dimension alternative, because it takes less time to make more accurate decisions. These differences, however, disappear when resources are abundant. [ABSTRACT FROM AUTHOR]

Research has shown that natural language communication is more effective than intention-signaling in promoting coordination. Our paper studies the reasons behind this finding. We hypothesize that, when communicating with natural language, people use and respond to both intentions and attitudes, with attitude indicating the strength of a message sender's desire to have her message followed. We test our hypothesis using controlled laboratory experiments. We find that: (i) free-form messages include both signaled intentions and attitudes; (ii) people respond to both intentions and attitudes when making decisions; and (iii) the use of attitude in natural language messages significantly improves coordination. [ABSTRACT FROM AUTHOR]

This paper examines how a commonly experienced cognitive state, sleepiness, impacts choice in a Bayesian task. We implement an ecologically valid sleep manipulation, and our main result is the increased use of a simple reinforcement-heuristic when sleepy. While use of this heuristic leads to errors in difficult decision environments where Bayes rule and reinforcement conflict, it does not harm choice accuracy in easier environments where Bayes rule and reinforcement coincide. Our results overall are consistent with the hypothesis that individuals make decisions in a relatively less deliberative manner under common adverse sleep states, which gives rise to a relative increase in automatic or heuristic-based decision making. While such heuristic-based decisions that are promoted by sleepiness need not be suboptimal in all contexts, our results show that the accuracy of a Bayesian assessment in more complex decision environments will likely suffer when sleepy. [ABSTRACT FROM AUTHOR]

Abstract Solutions that effectively promote sustainable development in urban environments require an understanding of the complexity of human-environment interactions. This paper adopted a systems-based framework to understanding how social, organisational, and environmental factors interact within an urban system in China. Drawing upon a case study from the industrial city of Jinchang, it applied a qualitative bottom-up approach to understand these key factors operating from three perspectives: the commitment of stakeholders, institutional development and personal development of social systems. It revealed that decision-making mechanisms play a key role in improving the sustainability of governance, and that public-private cooperation and public participation are critical for innovative knowledge-based strategies. The key for improving institutional development is to reform the evaluation system of governance thus enhancing the capacities of decision and policy makers, and consequently improve the process of decision-making. Cultural and personal development are important soft power components that influence individual pro-environmental behaviour and performance toward sustainability. The results demonstrated how, in a Chinese context, the urban social system can be understood and urban sustainability can be promoted through environmental adaptation supported by joint efforts from governments, businesses, society and individuals. In this, China does not differ from other nations in spite of significant differences in cultural and political systems. This study contributed to a better understanding of complex urban systems, providing a bottom-up approach and knowledge for enhancing the adaptive capacity toward urban sustainability. Highlights • Adaptive capacity driven by urban social system is essential for sustainability. • A bottom-up approach is effective to understand urban social system. • Multi-stakeholders cooperation benefits innovative knowledge-based strategies. • Reforming evaluation system of governance is critical for governance development. • Cultural and personal development are soft power for individual initiatives. [ABSTRACT FROM AUTHOR]

Abstract The contradiction between urban growth and environmental sustainability is a fundamental question regarding the welfare of human beings and the future of the earth. Human labor transforms the earth into factors of production by consuming its natural resources. The rational way to minimize the negative effects of such transformative intervention is advanced land use planning. Nevertheless, it is not possible to say that traditional modes of land use planning have fulfilled this task. The main reason for this failure is the habit of using traditional decision making tools which, to a significant level, are technically incompatible to deal with such a task. Hopefully, recent improvements in planning support tools have potential to process and evaluate complex geographical data. This paper benefits from these tools and moves one step further by performing a land assignment model, which operates binary integer programming and geographical information systems in a way that it can deal with different land use scenarios. This new methodology, which named as inquired land assignment model (ILAM), is applied for three different land use scenarios at upper level planning scale for a case area – Hillsborough County in Florida, USA. Outputs represent reasonable and consistent land use assignment and allocation results, and the model is potentially applicable to various fields and lower scales of planning practices by simple modifications. Highlights • Land use assignment decisions were optimized. • The method developed was used to determine capacity in urban development. • Land use assignment decisions were assessed and quantitatively tested. • Opportunity costs of different land use assignment scenarios were computed. [ABSTRACT FROM AUTHOR]

Highlights • We model the RD cost comparison as a MDP for low complexity video coding. • We learn a CU early termination classifier with end-to-end actor-critic RL algorithm. • A CU early termination algorithm independent of depths is derived for whole CTU. Abstract In this paper, we propose a Reinforcement Learning (RL) based Coding Unit (CU) early termination algorithm for High Efficiency Video Coding (HEVC). RL is utilized to learn a CU early termination classifier independent of depths for low complexity video coding. Firstly, we model the process of CU decision as a Markov Decision Process (MDP) according to the Markov property of CU decision. Secondly, based on the MDP, a CU early termination classifier independent of depths is learned from trajectories of CU decision across different depths with the end-to-end actor-critic RL algorithm. Finally, a CU decision early termination algorithm is introduced with the learned classifier, so as to reduce computational complexity of CU decision. We implement the proposed scheme with different neural network structures. Two different neural network structures are utilized in the implementation of RL based video encoder, which are evaluated to reduce video coding complexity by 34.34 % and 43.33 %. With regard to Bj ø ntegaard delta peak signal-to-noise ratio and Bj ø ntegaard delta bit rate, the results are −0.033 dB and 0.85 % , −0.099 dB and 2.56 % respectively on average under low delay B main configuration, when compared with the HEVC test model version 16.5. [ABSTRACT FROM AUTHOR]

Abstract Reputation is crucial to enabling human or software agents to select among alternative providers. Although several effective reputation assessment methods exist, they typically distil reputation into a numerical representation, with no accompanying explanation of the rationale behind the assessment. Such explanations would allow users or clients to make a richer assessment of providers, and tailor selection according to their preferences and current context. In this paper, we propose an approach to explain the rationale behind assessments from quantitative reputation models, by generating arguments that are combined to form explanations. Our approach adapts, extends and combines existing approaches for explaining decisions made using multi-attribute decision models in the context of reputation. We present example argument templates, and describe how to select their parameters using explanation algorithms. Our proposal was evaluated by means of a user study, which followed an existing protocol. Our results give evidence that although explanations present a subset of the information of trust scores, they are sufficient to equally evaluate providers recommended based on their trust score. Moreover, when explanation arguments reveal implicit model information, they are less persuasive than scores. [ABSTRACT FROM AUTHOR]

Abstract The level set method for modeling multi-fluid interfaces gives a simple approach for the simulation of multiphase flow problems. However, many of the components of this method, such as the redistancing of the level set function and discretization of the singular surface tension force, become more difficult on unstructured meshes in a control volume framework; furthermore, these operations are shown to lead to errors in conservation of mass of the individual phases, as well as unphysical currents near the interface. This paper presents a novel formulation for the level set method combined with a balanced-force algorithm using a control volume finite element method (CVFEM) for unstructured grids. A diffuse interface description is used, allowing a straightforward inclusion of surface tension forces; we show that by consistently evaluating the pressure gradient and surface tension, we are able to discretely balance these two forces and suppress any parasitic currents given a prescribed curvature. By converting the redistancing equation to a form that is suitable for CVFEM and enforcing a constraint directly linked with the phase fraction in each cell, we are able conserve mass and preserve the shape of the interface to a high degree of accuracy in multiphase flow problems with high density and viscosity ratios. The accuracy of the of our proposed method is assessed by comparing with benchmark results from the literature. Highlights • A novel level set formulation utilizing CVFEM is proposed. • A balanced-force algorithm is formulated for purely unstructured meshes. • The redistancing equation is reformulated to allow higher order upwinding methods. • A new constraint is introduced to preserve the shape of the interface. • Benchmark examples show convergence for high density and viscosity ratio problems. [ABSTRACT FROM AUTHOR]

Abstract The volume of solid waste has increased significantly in the past century, directly contributing to global environmental problems. Public engagement with waste sorting is crucial to the diversion of solid waste from landfill and the reduction of contamination in waste streams. The current study aims to promote recycling and composting accuracy via a digital sorting game that provides immediate feedback. In this game, participants manually sorted items into four bins (food scraps, recyclable container, paper, and garbage) via a computer interface, and received immediate feedback on their performance after each trial. We found that immediate feedback improved sorting accuracy as measured by correct key presses (Experiment 1) or motion trajectory (Replication 1), even when feedback was no longer provided. This improvement in sorting accuracy remained a week after playing the game (Replication 2). We then implemented this game in residences, and found that after residents played this game, the weight of compost materials increased while the contamination rate decreased (Experiment 2). These findings suggest that providing immediate feedback in a digital sorting game can be an effective tool to engage and educate the public to increase recycling and composting rates. Highlights • We developed and tested a digital sorting game to improve sorting accuracy. • Immediate feedback on sorting accuracy was provided after each trial in the game. • The game increased recycling and composting accuracy even after one week. • The game increased compost rates and reduced contamination in student residences. • This game can be a useful education tool to increase recycling and composting adherence. [ABSTRACT FROM AUTHOR]

Abstract The expansion of wind energy poses challenges to policy- and decision-makers to address conflicts with wildlife. Conflicts are associated with impacts of existing and planned projects on wildlife, and associated difficulties of prediction where impacts are subject to considerable uncertainty. Many post-construction studies have demonstrated adverse effects on individuals of various bird and bat species. These effects may come in the form of collision-induced mortality or behavioral or physiological changes reducing the fitness of individuals exposed to wind energy facilities. Upscaling these individual effects to population impacts provides information on the true value of interest from a conservation point of view. This paper identifies methodological issues associated when moving from individual effects to population impacts in the context of wind energy. Distinct methodological approaches to predict population impacts are described using published case studies. The various choices of study design and metrics available to detect significant changes at the population level are further assessed based on these. Ways to derive impact thresholds relevant for decision-making are discussed in detail. Robust monitoring schemes and sophisticated modelling techniques may inevitably be unable to describe the whole complexity of wind and wildlife interactions and the natural variability of animal populations. Still, they will provide an improved understanding of the response of wildlife to wind energy and better-informed policies to support risk-based decision-making. Policies that support the use of adaptive management will promote assessments at the population level. Providing information to adequately balance the development of wind energy with the persistence of wildlife populations. Highlights • Wind energy development may cause impacts on wildlife populations. • Methodological issues to upscale to population impacts are discussed. • Assessments require ways to derive impact thresholds relevant for decision-making. • Adaptive management approaches may balance wind energy with wildlife populations. [ABSTRACT FROM AUTHOR]

Highlights • We propose a social recommendation method based on integration of interaction, trust relationships and product popularity. • The proposed method focuses on the analysis of users' interaction behavior to infer users' latent interaction relationships. • Users' co-rated items and the role importance on a social networks are used to infer their implicit trust relationship. • The proposed method aims to accurately analyze users' preferences in social networks for improving the prediction accuracy. • The proposed method can accurately predict users' preference and support users' purchase decision making. Abstract Web 2.0 technology fosters the flourishing growth and development of social networks. More and more people are participating in the activities on social networks to interact and share information with each other. Thus, consumers are often making their purchasing decisions based on information from the Internet such as reviews, ratings, and comments on products, especially from their trusted friends. However, a great amount of available information may cause the problem of information overload for consumers. In seeking to attain a good recommendation performance by taking the high-potential factors into account as far as possible, this paper proposes a novel social recommendation method on the basis of the integration of interactions, trust relationships and product popularity to predict user preferences, and recommend relevant products in social networks. In addition, the proposed method mainly focuses on analyzing user interactions to infer their latent interactions in accordance with the user ratings and corresponding reviews. Additionally, users may be affected by the popularity of products, so this factor has also been taken into consideration in this work. The experimental results show that the proposed recommendation method has a better recommendation performance in comparisons to other methods because the proposed method can accurately analyze user preferences and further recommend high-potential products to target users in social networks to support their purchase decision making. Furthermore, the proposed method can not only reduce the time and effort users spend on querying information, but also positively relieve the problem of information overload. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]