Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks ...caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology development that supports Connected Vehicles (CVs), which act as an active mobile sensor for collecting traffic data, provides an opportunity to harness the large datasets to develop novel methods regarding traffic bottlenecks detection. This paper presents a speed transition matrix based model for bottleneck probability estimation on motorways. The method is based on the computation of the speed at the vehicle transition point between consecutive motorway segments, which forms a traffic pattern that is represented using transition matrices. The main feature extracted from the traffic patterns was the center of mass, whose position is used as an input to the fuzzy-based system for bottleneck probability estimation. The proposed method is evaluated on four different simulated motorway traffic scenarios: (i) traffic collision site, (ii) short recurring bottleneck, (iii) long recurring bottleneck, and (iv) moving bottleneck. The method achieves comparable bottleneck detection results on every scenario, with a total accuracy of 92% on the validation dataset. The results indicate possible implementation of the method in the motorway traffic environment with a high CVs penetration rate using them as the sensory input data for the control systems based on the machine learning algorithms.
Traditional bottleneck approaches often have a static view, neglecting dynamically shifting bottlenecks and logistical goals such as on-time delivery. In industry 4.0 factories, the amount of shop ...floor data increases dramatically – yet is often underutilized. Existing approaches for detection are based on the measuring machine states, buffer levels, or process times. However, prioritization and cause-based diagnosis of bottlenecks for targeted elimination are ongoing research. This paper proposes a data-driven approach for bottleneck detection, prioritization and diagnosis. For detection, the utilization method and the active period method are applied. For prioritization, the current backlog situation is relevant. For diagnosis, a cause-based machine and buffer perspective is used. This extension and combination of existing approaches in a extended value stream diagram enables data-driven analysis of dynamic bottlenecks and considers additional logistical goals. The practical approach is successfully tested in a steel carrier production.
We examined the rate and nature of mitochondrial DNA (mtDNA) mutations in humans using sequence data from 64,806 contemporary Icelanders from 2,548 matrilines. Based on 116,663 mother-child ...transmissions, 8,199 mutations were detected, providing robust rate estimates by nucleotide type, functional impact, position, and different alleles at the same position. We thoroughly document the true extent of hypermutability in mtDNA, mainly affecting the control region but also some coding-region variants. The results reveal the impact of negative selection on viable deleterious mutations, including rapidly mutating disease-associated 3243A>G and 1555A>G and pre-natal selection that most likely occurs during the development of oocytes. Finally, we show that the fate of new mutations is determined by a drastic germline bottleneck, amounting to an average of 3 mtDNA units effectively transmitted from mother to child.
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•Detection of 8,199 mutations in 116,663 mother-child mtDNA transmissions•Position and allele-specific mutation rates reveal asymmetric hypermutability•Evidence for both pre- and post-natal selection against mtDNA variants•Children inherit effectively only ∼3 units of mtDNA from their mothers
This large-scale pedigree study of human mtDNA mutations reveals substantial selection against deleterious variants both before and after birth, characterizes extensive differences in mutability by position and allele, and shows that children only inherit around 3 units of mtDNA from their mothers.
Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of ...traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method.
This paper considers combinatorial optimization problems with an objective of type bottleneck, so the objective is to minimize the maximum cost among all elements in a feasible solution. For these ...problems, the sensitivity of an optimal solution to changes in parameters has received much less attention in existing studies than the computation of an optimal solution. This paper introduces methods for computing upper and lower tolerances which measure the amount of cost change needed in an element inside and outside an optimal solution, respectively, before that solution becomes non-optimal. Our main contribution is the development of efficient computation methods for bottleneck versions of the Linear Assignment Problem and the Minimum Spanning Tree Problem.
•We study sensitivity of combinatorial problems with bottleneck objective (CBPs).•We investigate the range of element costs that keep current solutions optimal.•We derive algorithms for two CBPs in particular.•We determine the elements belonging to optimal solutions of CBPs efficiently.
•Provide a deep understanding of bottleneck analysis methods.•Propose a novel bottleneck prediction methods based on digital twin simulations.•Use predictive capabilities to adapt production control ...to the forecasted bottleneck.
Bottlenecks in manufacturing systems may significantly reduce their efficiency and productivity. Therefore, bottleneck analysis is a consolidated topic in Industrial Engineering, both in research and practice. Recently, traditional methods for bottleneck analysis have been enhanced with data-driven approaches, such as artificial intelligence and big data analytics. Nevertheless, their exploitation built on the full scope of technologies from digitalization is still not fulfilled. Indeed, the integration with simulation-based methods remains under-explored. This work aims to address bottleneck prediction leveraging on Digital Twin simulation capabilities to predict manufacturing system behavior. For this purpose, the work first offers an extensive review of bottleneck identification methods, inclusive of the ones based on Digital Twin. The main contribution of the work lies in the proposal of a novel Digital Twin-based bottleneck prediction framework with the end purpose to achieve performance improvements actuated through production control. The framework utilizes the Digital Twin for predicting and mitigating bottlenecks in manufacturing systems. The Digital Twin enables the simulation of the future system behavior, while accounting for the current conditions. This insight can then be used by a bottleneck identification method to infer future system bottleneck. The information on the predicted bottleneck is eventually used to support production control decisions, by adapting the order release and sequencing according to the predicted bottleneck. The benefits of adapting production control to the predicted bottleneck are evaluated quantitatively, highlighting how system performance is enhanced. By doing so, this research contributes to bridging the gap between Digital Twin-based performance analysis and production control, providing knowledge and a practical framework transferable to researchers and industrial practitioners.
Federated learning (FL) plays an important role in collaborative distributed modeling. However, most studies cannot address poor generalization of out-of-distribution (OoD) data. Efforts have been ...exerted to address data heterogeneity among participants, but yielding limited success. Here, we propose an information bottleneck based FL method (FedIB), which aims to build a model with better OoD generalization. We extract the domain-invariance of different source domains to mitigate the domain heterogeneity under the cross-silo scenarios. Next, given the scale imbalance, we balance the representation importance of different domains with reweighting a better invariance across multiple domains. In addition, the convergence of FedIB is analyzed. As opposed to aligning distributions or eliminating redundancy by previous methods, FedIB achieves better domain generalization explicitly by eliminating the pseudo-invariant features. Finally, we conduct extensive experiments on various datasets revealing that FedIB has superior performances facing OoD and scale imbalance scenarios in distributed modeling.
•We extract domain-invariant representation via information bottleneck across different clients.•Pseudo-invariant features of different clients are eliminated in the form of mutual information.•We propose the reweighting technique to balance disturbances from data scales for a better approximation of domain invariance. invariance.
ABSTRACT
More than a century ago, William Morton Wheeler proposed that social insect colonies can be regarded as superorganisms when they have morphologically differentiated reproductive and nursing ...castes that are analogous to the metazoan germ‐line and soma. Following the rise of sociobiology in the 1970s, Wheeler's insights were largely neglected, and we were left with multiple new superorganism concepts that are mutually inconsistent and uninformative on how superorganismality originated. These difficulties can be traced to the broadened sociobiological concept of eusociality, which denies that physical queen–worker caste differentiation is a universal hallmark of superorganismal colonies. Unlike early evolutionary naturalists and geneticists such as Weismann, Huxley, Fisher and Haldane, who set out to explain the acquisition of an unmated worker caste, the goal of sociobiology was to understand the evolution of eusociality, a broad‐brush convenience category that covers most forms of cooperative breeding. By lumping a diverse spectrum of social systems into a single category, and drawing attention away from the evolution of distinct quantifiable traits, the sociobiological tradition has impeded straightforward connections between inclusive fitness theory and the major evolutionary transitions paradigm for understanding irreversible shifts to higher organizational complexity. We evaluate the history by which these inconsistencies accumulated, develop a common‐cause approach for understanding the origins of all major transitions in eukaryote hierarchical complexity, and use Hamilton's rule to argue that they are directly comparable. We show that only Wheeler's original definition of superorganismality can be unambiguously linked to irreversible evolutionary transitions from context‐dependent reproductive altruism to unconditional differentiation of permanently unmated castes in the ants, corbiculate bees, vespine wasps and higher termites. We argue that strictly monogamous parents were a necessary, albeit not sufficient condition for all transitions to superorganismality, analogous to single‐zygote bottlenecking being a necessary but not sufficient condition for the convergent origins of complex soma across multicellular eukaryotes. We infer that conflict reduction was not a necessary condition for the origin of any of these major transitions, and conclude that controversies over the status of inclusive fitness theory primarily emanate from the arbitrarily defined sociobiological concepts of superorganismality and eusociality, not from the theory itself.
Multipath TCP (MPTCP) has been widely adopted in today’s mobile devices. However, two types of congestion control algorithms, uncoupled congestion control (Uncoupled CC) and coupled congestion ...control (Coupled CC), cannot achieve both bottleneck friendliness and throughput maximization for both of the MPTCP subflow bottleneck sharing scenarios, shared bottleneck (SB) scenario and non-shared bottleneck (NSB) scenario, leading to performance degradation in practice. In this work, we seek to enable efficient MPTCP congestion control, by alternating between Uncoupled CC algorithms and Coupled CC algorithms via smartly detecting whether the two MPTCP subflows share the same bottleneck link. We propose SmartSBD, the first learning-based data-driven approach for shared bottleneck detection, which is accurate, adaptable, and easy-to-deploy. SmartSBD is based on the key insight that the properties of subflows that share the same bottleneck often have similar trends of variation or similar values. In the training phase, SmartSBD collects system logs when MPTCP is running in real-world heterogeneous networks, extracts features, and trains a binary classifier. In the runtime phase, SmartSBD makes periodic predictions on the bottleneck sharing condition of live MPTCP subflows, and uses the prediction results to alternate between Coupled CC and Uncoupled CC. Our evaluations demonstrate that SmartSBD outperforms existing approaches.
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