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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.

Since its implementation in 1935, the Brazilian frog supply chain has operated with a low availability of knowledge and information essential for its consolidation and expansion. This problem has had negative repercussions on the productive components of the supply chain and, in this sense, has contributed to the maintenance of the imbalance between supply and demand for frog products and derivatives. One solution created and adopted by supply chain actors was the construction of a socio-technical network. This article explores the developments and bottlenecks observed throughout the network construction process, using a single case study design in the Brazilian frog supply chain as well as drawing on the participatory research method and focusing on the dynamics of interactions between the various actors involved. It is argued that the socio-technical network is built through the approximation, motivation and training of its members, but that its performance faces limitations inherent to its composition.

Whether it be the passengers’ mobility demand in transportation systems, or the consumers’ energy demand in power grids, the primary purpose of many infrastructure networks is to best serve this flow demand. In reality, the volume of flow demand fluctuates unevenly across complex networks while simultaneously being hindered by some form of congestion or overload. Nevertheless, there is little known about how the heterogeneity of flow demand influences the network flow dynamics under congestion. To explore this, we introduce a percolation-based network analysis framework underpinned by flow heterogeneity. Thereby, we theoretically identify bottleneck links with guaranteed decisive impact on how flows are passed through the network. The effectiveness of the framework is demonstrated on large-scale real transportation networks, where mitigating the congestion on a small fraction of the links identified as bottlenecks results in a significant network improvement. Infrastructure networks are characterized by fluctuations of flow demand between different points and temporal congestion or overload on flow pathways. Hamedmoghadam et al. identify congestion bottlenecks in networks relevant to communication, transportation, water supply, and power distribution.

Characterizing and understanding the limitations of diffusion MRI fiber tractography is a prerequisite for methodological advances and innovations which will allow these techniques to accurately map the connections of the human brain. The so‐called “crossing fiber problem” has received tremendous attention and has continuously triggered the community to develop novel approaches for disentangling distinctly oriented fiber populations. Perhaps an even greater challenge occurs when multiple white matter bundles converge within a single voxel, or throughout a single brain region, and share the same parallel orientation, before diverging and continuing towards their final cortical or sub‐cortical terminations. These so‐called “bottleneck” regions contribute to the ill‐posed nature of the tractography process, and lead to both false positive and false negative estimated connections. Yet, as opposed to the extent of crossing fibers, a thorough characterization of bottleneck regions has not been performed. The aim of this study is to quantify the prevalence of bottleneck regions. To do this, we use diffusion tractography to segment known white matter bundles of the brain, and assign each bundle to voxels they pass through and to specific orientations within those voxels (i.e. fixels). We demonstrate that bottlenecks occur in greater than 50‐70% of fixels in the white matter of the human brain. We find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon, and show that even regions traditionally considered “single fiber voxels” often contain multiple fiber populations. Together, this study shows that a majority of white matter presents bottlenecks for tractography which may lead to incorrect or erroneous estimates of brain connectivity or quantitative tractography (i.e., tractometry), and underscores the need for a paradigm shift in the process of tractography and bundle segmentation for studying the fiber pathways of the human brain. \"Bottleneck\" pose a challenge for diffusion tractography, and hinder our ability to accurately map the structural connections of the brain. We demonstrate that bottlenecks occur in greater than 50–70% of fixels in the human brain white matter, and find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon. These results emphasize the use of caution when interpreting quantitative diffusion magnetic resonance imaging connectomics results, and underscore the need for a paradigm shift in the process of tractography for studying the fiber pathways of the human brain.

Abstract Panax vietnamensis Ha et Grushv. is a precious medicinal species native to the tropical forests of Vietnam. Due to habitat loss and over-harvesting, this species is endangered in Vietnam. To conserve the species, we investigated genetic variability and population structure using nine microsatellites for 148 individuals from seven populations across the current distribution range of P. vietnamensis in Vietnam. We determined a moderate genetic diversity within populations (HO = 0.367, HE = 0.437) and relatively low population differentiation (the Weir and Cockerham index of 0.172 and the Hedrick index of 0.254) and showed significant differentiation (P < 0.05), which suggested fragmented habitats, over-utilization and over-harvesting of P. vietnamensis. Different clustering methods revealed that individuals were grouped into two major clusters, which were associated with gene flow across the geographical range of P. vietnamensis. This study also detected that ginseng populations can have undergone a recent bottleneck. We recommend measures in future P. vietnamensis conservation and breeding programs. Resumo Panax vietnamensis Ha et Grushv. é uma espécie medicinal preciosa nativa das florestas tropicais do Vietnã. Por causa da perda de hábitat e da colheita excessiva, essa espécie está ameaçada de extinção no Vietnã. Para conservá-la, investigamos a variabilidade genética e a estrutura populacional usando nove microssatélites para 148 indivíduos de sete populações em toda a distribuição atual de P. vietnamensis no Vietnã. Determinamos uma diversidade genética moderada dentro das populações (HO = 0,367 e HE = 0,437) e diferenciação populacional relativamente baixa (índice de Weir e Cockerham de 0,172 e índice de Hedrick de 0,254), com diferenciação significativa (P < 0,05), o que sugeriu fragmentação de hábitats, sobreutilização e sobre-exploração de P. vietnamensis. Diferentes métodos de agrupamento revelaram que os indivíduos foram agrupados em dois agrupamentos principais, que foram associados ao fluxo gênico em toda a área geográfica de P. vietnamensis. Este estudo também detectou que as populações de ginseng podem ter sofrido um gargalo recente. Recomendamos medidas em futuros programas de conservação e melhoramento de P. vietnamensis.

In this paper we investigate a bottleneck model in which the capacity of the bottleneck is assumed stochastic and follows a uniform distribution. The commuters' departure time choice is assumed to follow the user equilibrium principle according to mean trip cost. The analytical solution of the proposed model is derived. Both the analytical and numerical results show that the capacity variability would indeed change the commuters' travel behavior by increasing the mean trip cost and lengthening the peak period. We then design congestion pricing schemes within the framework of the new stochastic bottleneck model, for both a time-varying toll and a single-step coarse toll, and prove that the proposed piecewise time-varying toll can effectively cut down, and even eliminate, the queues behind the bottleneck. We also find that the single-step coarse toll could either advance or postpone the earliest departure time. Furthermore, the numerical results show that the proposed pricing schemes can indeed improve the efficiency of the stochastic bottleneck through decreasing the system’s total travel cost.

With the advancement in global market integration, manufacturing enterprises face increasingly fierce competition, making the development of intelligent manufacturing systems a key factor in enhancing market competitiveness. However, manufacturing systems are characterized by varying characteristics of manufacturing resources and strong interdependencies, which make production control more complex. A bottleneck refers to the issue where the manufacturing system’s actual production capacity is less than or equal to the demand placed on a resource. After extensive research, scholars have concluded that the definition, identification methods, and related studies on bottlenecks are not fixed but depend on the specific research subject and the type of bottleneck. Therefore, scholars suggest that only by accurately defining the bottlenecks within a system can appropriate models be employed for prediction, thereby avoiding issues such as inefficient resource allocation and delivery delays, or implementing measures to minimize these negative impacts. Particularly under unstable production conditions, dynamic bottlenecks are likely to occur, making the prediction and identification of bottlenecks under dynamic environmental conditions crucial. Currently, there is still a lack of research in real-time state sensing and integration, as well as a lack of systematic review of related research. To fill this research gap, this review comprehensively introduces the current state and achievements in bottleneck research within manufacturing systems, focusing on bottleneck categories, identification, shifting, and management. It also provides an outlook on future research trends and directions in the study of manufacturing system bottlenecks.