Balancing conflicting requirements for grid and particle decomposition in continuum-Lagrangian solvers
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First principles studies on the geometric structure, stability, and electronic structure of WSi{sub n} clusters, n = 6-16, have been carried out to show that the observed differing “magic sizes” for WSi{sub n} clusters are associated with the nature of the growth processes. The WSi{sub 12} cluster, observed as a magic species in experiments reacting transition metal ions with silane, is not stable due to a filled shell of 18 electrons, as previously proposed, but due to its atomic structure that arrests further growth because of an endohedral transition metal site. In fact, it is found that all of these clusters, n = 6-16, have filled 5d shells except for WSi{sub 12}, which has a 5d{sup 8} configuration that is caused by crystal field splitting. The stability of WSi{sub 15}{sup +}, observed as highly stable in clusters generated by vaporizing silicon and metal carbonyls, is shown to be associated with a combination of geometric and electronic features. The findings are compared with previous results on CrSi{sub n} clusters.
After three decades of coexistence at a distance, model residential energy codes and residential energy rating systems have come together in the 2015 International Energy Conservation Code. At the October, 2013, International Code Council’s Public Comment Hearing, a new compliance path based on an Energy Rating Index was added to the IECC. Although not specifically named in the code, RESNET’s HERS rating system is the likely candidate Index for most jurisdictions. While HERS has been a mainstay in various beyond-code programs for many years, its direct incorporation into the most popular model energy code raises questions about the equivalence of a HERS-based compliance path and the traditional IECC performance compliance path, especially because the two approaches use different efficiency metrics, are governed by different simulation rules, and have different scopes with regard to energy impacting house features. A detailed simulation analysis of more than 15,000 house configurations reveals a very large range of HERS Index values that achieve equivalence with the IECC’s performance path. This paper summarizes the results of that analysis and evaluates those results against the specific Energy Rating Index values required by the 2015 IECC. Based on the home characteristics most likely to result in disparities between HERS-based compliance and performance path compliance, potential impacts on the compliance process, state and local adoption of the new code, energy efficiency in the next generation of homes subject to this new code, and future evolution of model code formats are discussed.
The potential for conflict is omnipresent in all projects, and even in all human interactions, and conflict itself leads to many second-order social impacts. This article examines the contribution of the methodological approach used in social impact assessment (SIA) to conflict management. We view conflict as a process that has its own dynamic, and is to be expected in all situations. By using game theory (prisoner's dilemma), we describe and conceptualize this process and highlight the importance of communication in managing conflict. We demonstrate the potential use of SIA in preventing, managing and resolving conflict. Emphasis is placed on the participatory character of SIA and the role of public media. In contrast to existing literature, our focus is not restricted to the typical fields of study of SIA (e.g. environmental conflicts), but understands conflict itself as a field of application. In this sense, conflict-sensitive SIA can be understood both as an extension to the SIA tool kit and a broadening of the scope of SIA application. -- Highlights: • Conflict is omnipresent and creates both positive and negative social impacts. • Conflict itself represents a possible field of application for SIA. • Conflict escalation is a process that can be modeled in a game-theoretic framework. • There needs to be concerted effort to prevent escalation to avoid harmful outcomes. • Conflict-sensitive SIA can support conflict management and sustainable resolution.
In this paper, we address the aircraft conflict resolution problem in air traffic control. We introduce new mixed-integer programming formulations for aircraft conflict resolution with speed, heading and altitude control which are based on disjunctive linear separation conditions. We first examine the two-dimensional aircraft conflict resolution problem with speed and heading control represented as continuous decision variables. We show that the proposed disjunctive linear separation conditions are equivalent to the classical nonlinear conditions for aircraft separation. Further, we characterise conflict-free trajectories based on aircraft velocity bounds and propose a simple pre-processing algorithm to identify aircraft pairs which are either always conflict-free, or which cannot be separated using speed and heading control only. We then incorporate altitude control and propose a lexicographic optimisation formulation that aims to minimise the number of flight level changes before resolving outstanding conflicts via two-dimensional velocity control. The proposed mixed-integer programming formulations are nonconvex, and we propose convex relaxations, decomposition methods and constraint generation algorithms to solve the two-dimensional and lexicographic optimisation formulations to guaranteed optimality. Numerical experiments on four types of conflict resolution benchmarking instances are conducted to test the performance of the proposed mixed-integer formulations. Further, the proposed method is compared against two benchmarks based on state-of-the-art approaches for the aircraft conflict resolution problem. Our numerical results show that the proposed method largely outperforms both benchmarks in terms of runtime and is able to solve significantly more instances to global optimality.
Prokaryotic genomes are often considered to be mosaics of genes that do not necessarily share the same evolutionary history due to widespread horizontal gene transfers (HGTs). Consequently, representing evolutionary relationships of prokaryotes as bifurcating trees has long been controversial. However, studies reporting conflicts among gene trees derived from phylogenomic data sets have shown that these conflicts can be the result of artifacts or evolutionary processes other than HGT, such as incomplete lineage sorting, low phylogenetic signal, and systematic errors due to substitution model misspecification. Here, we present the results of an extensive exploration of phylogenetic conflicts in the cyanobacterial order Nostocales, for which previous studies have inferred strongly supported conflicting relationships when using different concatenated phylogenomic data sets. We found that most of these conflicts are concentrated in deep clusters of short internodes of the Nostocales phylogeny, where the great majority of individual genes have low resolving power. We then inferred phylogenetic networks to detect HGT events while also accounting for incomplete lineage sorting. Our results indicate that most conflicts among gene trees are likely due to incomplete lineage sorting linked to an ancient rapid radiation, rather than to HGTs. Moreover, the short internodes of this radiation fit the expectations of the anomaly zone, i.e., a region of the tree parameter space where a species tree is discordant with its most likely gene tree. In this work, we demonstrated that concatenation of different sets of loci can recover up to 17 distinct and well-supported relationships within the putative anomaly zone of Nostocales, corresponding to the observed conflicts among well-supported trees based on concatenated data sets from previous studies. Our findings highlight the important role of rapid radiations as a potential cause of strongly conflicting phylogenetic relationships when using phylogenomic data sets of bacteria. We propose that polytomies may be the most appropriate phylogenetic representation of these rapid radiations that are part of anomaly zones, especially when all possible genomic markers have been considered to infer these phylogenies.
Traffic signal control and cooperation are extremely important to alleviate traffic congestion in a large traffic network. This study develops a higher-order conflict graph approach for network-wide traffic signal control and cooperation. A conflict graph is applied to model the traffic signal configurations, which identifies the conflict and unconflicted movements for each intersection. In conflict graph, the node represents each movement. The weight of each node can be defined as traffic volume, queue length, fuel consumption, or any weighted combinations of these measurements. The calculation of the optimal green light duration and green light sequence (for different movements) is equivalent to sequentially finding the maximum weight independent set (MWIS) in the conflict graph. The conflict graph also provides a uniform and efficient way to connect traffic signal operations among nearby intersections spatially. Then, we introduced the concept of the k -th order neighborhood to model the degree of connectivity between each movement to the movements at upstream or downstream intersections. The weight of each node in the higher-order conflict graph not only represents its own congestion level, but also relates to the traffic conditions of nearby intersections. Through this approach, the cooperation of multiple intersections can be realized by incorporating their spatial connectivity into conflict graph and solving the MWIS problem. A simulation network is built in SUMO to test the effectiveness of the proposed method. Results suggested that the proposed model outperformed other state-of-the-art signal control methods. Also, the scheme maintains good performance under varying traffic demands.
Climate change and armed civil conflict are both linked to socioeconomic development, although conditions that facilitate peace may not necessarily facilitate mitigation and adaptation to climate change. While economic growth lowers the risk of conflict, it is generally associated with increased greenhouse gas emissions and costs of climate mitigation policies. Here, this study investigates the links between growth, climate change, and conflict by simulating future civil conflict using new scenario data for five alternative socioeconomic pathways with different mitigation and adaptation assumptions, known as the shared socioeconomic pathways (SSPs). We develop a statistical model of the historical effect of key socioeconomic variables on country-specific conflict incidence, 1960–2013. We then forecast the annual incidence of conflict, 2014–2100, along the five SSPs. We find that SSPs with high investments in broad societal development are associated with the largest reduction in conflict risk. This is most pronounced for the least developed countries—poverty alleviation and human capital investments in poor countries are much more effective instruments to attain global peace and stability than further improvements to wealthier economies. Moreover, the SSP that describes a sustainability pathway, which poses the lowest climate change challenges, is as conducive to global peace as the conventional development pathway.
A data processing system includes a memory that includes a first memory bank and a second memory bank. The data processing system also includes a conflict detector connected to the memory and adapted to receive memory access information. The conflict detector tracks memory access statistics of the first memory bank, and determines if the first memory bank contains frequent row conflicts. The conflict detector also remaps a frequent row conflict in the first memory bank to the second memory bank. An indirection table is connected to the conflict detector and adapted to receive a memory access request, and redirects an address into a dynamically selected physical memory address in response to a remapping of the frequent row conflict to the second memory bank.
Reducing greenhouse gas emissions (GHG) through renewable energy deployment is a goal for many cities and nations to mitigate the effects of a rapidly changing climate while securing energy needs. Given national and state level policies on green energy to achieve GHG reduction targets, localities are considering utility-scale renewable energy (USRE) facilities on remote lands, rural areas, oceans, coastal waters and large rivers to meet the energy needs for urban residents and industries. Although these USRE facilities generate much “greener” electricity than fossil fuel power plants, locating them can pose conflicts with wildlife, including endangered, threatened and/or special status species, and the habitats that support wildlife populations (Brunette et al., 2013; Gasparatos et al. 2016; Mulvaney 2017), which lead to a “Conflict of Greens” (Ko et al., 2011). In addition to ecological impacts, conflicts over social and cultural resources in local communities lead to concerns over environmental justice, in this context “energy justice,” where negative environmental, social, and economic impacts of energy projects fall more heavily upon marginalized, vulnerable, or indigenous communities. The Pacific Rim region is a center of major and growing economies, and it has become a battlefield for competing “green” objectives. In addition to progressive renewable energy goals, such as in California, Hawaii, and Taiwan, countries around the Pacific Rim have the majority of the growth in manufacturing of wind and solar energy devices and also some of the highest levels of renewables deployment with China, India, and the US leading the way in wind and solar installations. Developing renewable energy source in the ocean such as offshore wind, tidal, and wave energy, are beginning to add to this mix. Given this challenge, the APRU SCL energy working group provides six case studies that address questions about the “Conflict of Greens” across the Pacific Rim including South Korea, Taiwan, and the United States (California, Hawaii, and Massachusetts). The literature review highlights the emerging ecological, social, and economic aspects of conflicts over siting renewables on the landscape. Using case studies, we examine the trial and errors in least-conflict spatial planning, data collection and analysis, public participation in decision-making, mitigation, social planning for energy transitions, and multi-scalar approaches. Lastly, we recommend interdisciplinary policy, planning, and design actions for sustainable energy landscapes across the Pacific Rim and beyond.
The purpose of safety and security for nuclear activities are the same. They are both necessary measures to protect people and the environment from radioactive material. There are many measures that enhance both the safety and the security but there are also measures serving one area that are in direct conflict with the other. How information is handled is maybe the best example to demonstrate this conflict between interests. In the security field, information is handled on a need-to-know basis, preventing valuable details to fall into hands of antagonists, terrorists etc. The safety culture, on the contrary, is based on knowledge and transparency and it encourages the sharing of information and experience to improve the safety. There is little guidance on how to manage the conflicts between safety and security for a transport system moving nuclear fuel and radioactive waste and especially when there are conflicts between different legislation areas, for example the Act on Nuclear Activities and Safety of Life at Sea. The Swedish Nuclear Fuel and Waste Management Co, SKB, transports all Swedish spent fuel and operational waste from the Nuclear Power Plants to the central storage facilities in Sweden. To manage the transport system, SKB owns a fleet of packages, road vehicles and an INF 3 ship. For the shipments of nuclear material, the security focus has increased rapidly over the years after the 9/11 attack, followed by regulatory requirements on enhanced security. SKB is currently running a project to further strengthen the transport security, an activity that requires a careful balance between safety and security. This paper describes the methods used and the challenges experienced trying to manage the conflicts between safety and security for the Swedish nuclear transport system. (authors)
Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.
Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.
We analyze a class of explicitly solvable models for the problems of redistribution of the conflict space between two alternative opponents. The existence of equilibrium state is proved for a complex nonlinear system whose time evolution is generated by the conflict interaction between its components. Explicit formulas are obtained for the limit compromise distributions in terms of the densities of probability measures. We consider a number of specific model examples of the dynamics of redistribution of the conflict territory and formation of an equilibrium (compromise) distribution of the space. We propose an interpretation of the results for the case of social and territorial conflicts.
In memory hierarchies, caches perform an important role in reducing average memory access latency. Minimizing cache misses can yield significant performance gains. As set-associative caches are widely used in modern architectures, capacity and conflict cache misses co-exist. These two types of cache misses require different optimization strategies. While cache misses are commonly studied using cache simulators, state-of-the-art simulators usually incur hundreds to thousands of times a program’s execution runtime. Moreover, a simulator has difficulty in simulating complex real hardware. To overcome these limitations, measurement methods are proposed to directly monitor program execution on real hardware via performance monitoring units. However, existing measurement-based tools either focus on capacity cache misses or do not distinguish capacity and conflict cache misses. In this paper, we design and implement CCProf, a lightweight measurement-based profiler that identifies conflict cache misses and associates them with program source code and data structures. CCProf incurs moderate runtime overhead that is at least an order of magnitude lower than simulators. With the evaluation on a number of representative programs, CCProf is able to guide optimizations on cache conflict misses and obtain nontrivial speedups.
Open Radio Access Networks (O-RAN) are emerging, software-driven cellular architectures that promote flexibility by enabling components from different vendors to interoperate. Multiple control applications called xApps can independently adjust network parameters in near real time, often without awareness of each other's actions. This creates a system highly prone to unintended conflicts and performance degradation due to the inherent complexity of such openness. To model such systems and ultimately prevent or mitigate xApp conflicts, it is essential to understand the dynamic relationships between xApps (A), the control parameters they adjust (P), and the resulting KPI responses (K). While the mappings from A to P and from K to A can often be derived from xApp specifications, the relationship from P to K is typically hidden within the system’s dynamics and must be inferred from observed data. We propose a novel data-driven Boolean inference framework that uncovers the hidden P?K dependencies using machine learning and interpretable rule induction. Continuous parameters and KPIs are first binarized using decision tree classifiers, and a binary influence matrix L is then inferred by solving Boolean matrix equations over time. This compact representation improves interpretability and enables real-time tracking of dynamically evolving parameter-KPI dependencies. We demonstrate the effectiveness of our method in a realistic mobile handover scenario, where it accurately recovers the underlying logic and enables proactive conflict detection.
This study characterizes navigation conflicts in a region with a large traffic volume along the US Atlantic Coast, utilizing Automated Identification System (AIS) data for 2010. The region includes areas proposed for wind energy development. The characterization could be useful in evaluating the effect of offshore wind areas on navigation conflicts. The study processes the AIS data to provide pairwise comparisons of vessel interactions (encounters and near-misses) as they occurred. Using the vessel encounter data, analyses are made using a ‘blind’ vessel assumption to evaluate the potential for both near-misses and collisions. Then statistical analyses are made to estimate the point values and uncertainty for each type of encounter (crossing, head-on, overtaking). Examination of the frequency/number of collisions from actual observations is made. The examination of actual near-misses, potential near-misses, and potential collisions provides comparable results in the number of near-misses and collisions. The potential near-miss analyses include an examination of the timing of responses made by vessels to prevent near-misses. This informed the statistical analysis but may also have utility in the simulation of navigation conflicts.