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In recent years, high-utility itemset mining has emerged as an important data mining task. However, it remains computationally expensive both in terms of runtime and memory consumption. It is thus an important challenge to design more efficient algorithms for this task. In this paper, we address this issue by proposing a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discover high-utility itemsets. EFIM relies on two new upper bounds named revised sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper bounds in linear time and space. Moreover, to reduce the cost of database scans, EFIM proposes efficient database projection and transaction merging techniques named High-utility Database Projection and High-utility Transaction Merging (HTM), also performed in linear time. An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster than the state-of-art algorithms d 2 HUP, HUI-Miner, HUP-Miner, FHM and UP-Growth+ on dense datasets and performs quite well on sparse datasets. Moreover, a key advantage of EFIM is its low memory consumption.

The fundamental challenge of multi-sample structural variant (SV) analysis such as merging and benchmarking is identifying when two SVs are the same. Common approaches for comparing SVs were developed alongside technologies which produce ill-defined boundaries. As SV detection becomes more exact, algorithms to preserve this refined signal are needed. Here, we present Truvari—an SV comparison, annotation, and analysis toolkit—and demonstrate the effect of SV comparison choices by building population-level VCFs from 36 haplotype-resolved long-read assemblies. We observe over-merging from other SV merging approaches which cause up to a 2.2× inflation of allele frequency, relative to Truvari.

We consider the Z2 topology of the Dirac lines, i.e., lines of band contacts, on an example of graphite. Four lines-three with topological charge each and one with -merge together near the H-point and annihilate due to summation law . The merging point is similar to the real-space nexus, an analog of the Dirac monopole at which the Z2 strings terminate.

The current impacts of humanity on nature are rapid and destructive, but species turnover and change have occurred throughout the history of life. Although there is much debate about the best approaches to take in conservation, ultimately, we need to permit or enhance the resilience of natural systems so that they can continue to adapt and function into the future. In a Review, Barnosky et al. argue that the best way to do this is to look back at paleontological history as a way to understand how ecological resilience is maintained, even in the face of change. Science , this issue p. eaah4787 Conservation of species and ecosystems is increasingly difficult because anthropogenic impacts are pervasive and accelerating. Under this rapid global change, maximizing conservation success requires a paradigm shift from maintaining ecosystems in idealized past states toward facilitating their adaptive and functional capacities, even as species ebb and flow individually. Developing effective strategies under this new paradigm will require deeper understanding of the long-term dynamics that govern ecosystem persistence and reconciliation of conflicts among approaches to conserving historical versus novel ecosystems. Integrating emerging information from conservation biology, paleobiology, and the Earth sciences is an important step forward on the path to success. Maintaining nature in all its aspects will also entail immediately addressing the overarching threats of growing human population, overconsumption, pollution, and climate change.

An objective methodology that does not require any user‐defined parameter assumptions is introduced to obtain an improved soil moisture product along with associated uncertainty estimates. This new product is obtained by merging model‐, thermal infrared remote sensing‐, and microwave remote sensing‐based soil moisture estimates in a least squares framework where uncertainty estimates for each product are obtained using triple collocation. The merged anomaly product is validated against in situ based soil moisture data and shows higher correlations with observations than individual input products; however, it is not superior to a naively merged product acquired by averaging the products using equal weighting. The resulting combined soil moisture estimate is an improvement over currently available soil moisture products due to its reduced uncertainty and can be used as a standalone soil moisture product with available uncertainty estimates. Key Points Errors are objectively estimated Uncertainty estimate of soil moisture is obtained Improved soil moisture product is obtained

With the rapid development of Information and Communication Technologies, vehicular networks that communicate with each other will have an innovative application in traffic safety and congestion. This study describes a preliminary study on an automated on-ramp merging control algorithm for vehicles on freeways under condition of Internet-connected vehicles. On the basis of vehicular operation characteristics during the merging process analysis, a cooperative driving algorithm based on Internet of vehicles was designed to achieve ramp merging without collision. Then two on-ramp merging cases, including one vehicle and two vehicles merging into the platoon on main lane, were discussed in detail. Simulation works were carried out and the results proved that the on-ramp merging algorithm was effective, but the vehicle following the leading vehicle on ramp lane is disturbed seriously by the leading vehicle. At the same time, the simulation results also showed the scenario that merging a platoon into the two vehicles on main lane affects the traffic flow more seriously than letting each individual vehicle on ramp lane consecutively to merge in between the two vehicles in the main lane under the same initial condition.

In this study, we construct a method for selecting a target space when changing lanes from the merging lane to the main lane, assuming a merging scene on a congested highway. The proposed method predicts the driving trajectory for a few seconds ahead based on the state variables of the ego vehicle and surrounding vehicles, which is used to evaluate the lane change target. We determine the reachable range of the ego vehicle and select a candidate group of inter-vehicle spaces that will serve as the target for the lane change. Next, the proposed method evaluates the candidate group using indicators such as the size of the inter-vehicle space, the distance from the ego vehicle, and the remaining distance of the merging lane, selecting the inter-vehicle space with the highest evaluation as the target for the lane change. Through simulation experiments where the diversity of driving characteristics of human drivers on the main lane is considered, we confirmed that the proposed method has sufficient safety and stability.

To solve the problems of congestion and accident risk when multiple vehicles merge into the merging area of a freeway, a platoon split collaborative merging (PSCM) method was proposed for an on-ramp connected and automated vehicle (CAV) platoon under a mixed traffic environment composed of human-driving vehicles (HDV) and CAVs. The PSCM method mainly includes two parts: merging vehicle motion control and merging effect evaluation. Firstly, the collision avoidance constraints of merging vehicles were analyzed, and on this basis, a following–merging motion rule was proposed. Then, considering the feasibility of and constraints on the stability of traffic flow during merging, a performance measurement function with safety and merging efficiency as optimization objectives was established to screen for the optimal splitting strategy. Simulation experiments under traffic demand of 1500 pcu/h/lane and CAV ratios of 30%, 50%, and 70% were conducted respectively. It was shown that under the 50% CAV ratio, the average travel time of the on-ramp CAV platoon was reduced by 50.7% under the optimal platoon split strategy compared with the no-split control strategy. In addition, the average travel time of main road vehicles was reduced by 27.9%. Thus, the proposed PSCM method is suitable for the merging control of on-ramp CAV platoons under the condition of heavy main road traffic demand.

The report examines and analyzes the purpose of acceleration and deceleration lanes at two-level road interchanges. For the preparation of the current report are used the regulatory framework for the design of acceleration and deceleration lanes from Bulgaria, Austria, Germany and USA – California. The design elements of the acceleration and deceleration lanes are compared in detail. Widths and lengths of lanes, merging zones, angles and slopes. The conclusions of the report are given as guidelines for good practice in the design of road interchanges at one and two levels.

For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is challenging, wherein it suffers from dynamic objects and continuous environmental changes. The issue grows worse due to interrupting the quality of perception via adverse weather such as snow, rain, fog, night light, sand storms, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous-driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights was collected from training on the individual datasets, their merged versions, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the datasets mentioned earlier and their subsets. The evaluation revealed that using custom datasets for training significantly improved the detection performance compared to the YOLOv8 base weights. Furthermore, using more images through the feature-related data merging technique steadily increased the object detection performance.