Merging Strategy Research Articles

Stochastic weighted particle control for electrostatic particle-in-cell Monte Carlo collision simulations in an axisymmetric coordinate system

The non-uniform grids in the axisymmetric coordinate system pose a significant challenge for electrostatic particle-in-cell/Monte Carlo collision (PIC/MCC) simulations because they require numerous macroparticles to manage numerical heating around the mid-axis. To address this, we have developed a stochastic weighted particle control method that selectively samples small-weight particles, effectively controlling the particle number without inducing numerical heating. This method is based on a rejection-acceptance probability merging scheme, which is easy to implement and has a low time complexity. We have also made essential modifications, including a corrected density deposition scheme, an energy conservation scheme, and the introduction of target weights. By applying this particle control method, the number of macroparticles in the simulation can be reduced by more than one order of magnitude, significantly reducing the required computing time and storage. Furthermore, appropriately setting target weights also enables enhanced resolution of dilute regions with an acceptable increase in computational cost.

Cooperative Ramp Merging Strategy at Multi-Lane Area for Automated Vehicles

Cooperative Ramp Merging Strategy at Multi-Lane Area for Automated Vehicles

OASR-WFBP: An overlapping aware start-up sharing gradient merging strategy for efficient communication in distributed deep learning

Wait-Free-Back-Propagation (WFBP) is a practical method for distributed deep-learning, but it suffers from a high communication overhead. To address this issue, the communication overhead can be reduced by overlapping gradient communication and computation, and sharing the startup time among multiple gradient communication phases. However, existing optimizations choose to share the startup time greedily and fail to coordinately exploit the overlapping opportunity between computation and communication. We propose an overlapping aware startup sharing Wait-Free-Back-Propagation (OASR-WFBP). An analytic model is designed to guide the sharing procedure. Evaluations show that OSAR-WFBP achieves a 5%-16% optimization in iteration time over the state-of-the-art WFBP algorithm.

Evaluating a multi-step collocation approach for an ensemble climatological dataset of actual evapotranspiration over Italy

Accurate estimations of actual evapotranspiration (ET) are key in a variety of water balance applications, but divergent results can be obtained due to the large range of available methodologies. The use of an ensemble approach is a suitable alternative, as it summarizes multiple sources in an optimized strategy. In this study, an expert-based multi-step collocation (MC) approach is tested to merge six ET datasets, with the aim of reconstructing a spatiotemporally-consistent monthly dataset for Italy in the climatological period 1991–2020. The merged products are: three water balance datasets (BIG BANG, LSA SAF, and LISFLOOD), two residual surface energy balance model datasets (SSEBop, and ALEXI), and the MODIS standard ET product. The merged product is analyzed for spatio-temporal consistency and evaluated using flux observations from 11 sites. On average, the merged product has higher accuracy (mean absolute difference = 0.47 ± 0.17 mm/d, relative difference = 27.9 ± 7.5 %) than any single base dataset, and it is characterized by limited bias (mean bias error = -0.17 ± 0.26 mm/d), high correlation (r = 0.83 ± 0.10), and more uniform performance across sites. The merged ET dataset is accompanied by an estimation of the ensemble spread, which highlights large differences in ET estimates in some areas and periods characterized by severe water stress, such as in southern Italy during the summer. This large spread seems to be mostly driven by systematic differences among datasets, which affect the estimation of the reference climatology, suggesting how inter-model spread can have a defining role in further improving the merging strategies.

Dynamic Scheduling Optimization of Automatic Guide Vehicle for Terminal Delivery under Uncertain Conditions

As an important part of urban terminal delivery, automated guided vehicles (AGVs) have been widely used in the field of takeout delivery. Due to the real-time generation of takeout orders, the delivery system is required to be extremely dynamic, so the AGV needs to be dynamically scheduled. At the same time, the uncertainty in the delivery process (such as the meal preparation time) further increases the complexity and difficulty of AGV scheduling. Considering the influence of these two factors, the method of embedding a stochastic programming model into a rolling mechanism is adopted to optimize the AGV delivery routing. Specifically, to handle real-time orders under dynamic demand, an optimization mechanism based on a rolling scheduling framework is proposed, which allows the AGV’s route to be continuously updated. Unlike most VRP models, an open chain structure is used to describe the dynamic delivery path of AGVs. In order to deal with the impact of uncertain meal preparation time on route planning, a stochastic programming model is formulated with the purpose of minimizing the expected order timeout rate and the total customer waiting time. In addition, an effective path merging strategy and after-effects strategy are also considered in the model. In order to solve the proposed mathematical programming model, a multi-objective optimization algorithm based on a NSGA-III framework is developed. Finally, a series of experimental results demonstrate the effectiveness and superiority of the proposed model and algorithm.

The Improvement of Density Peaks Clustering Algorithm and Its Application to Point Cloud Segmentation of LiDAR.

This work focuses on the improvement of the density peaks clustering (DPC) algorithm and its application to point cloud segmentation in LiDAR. The improvement of DPC focuses on avoiding the manual determination of the cut-off distance and the manual selection of cluster centers. And the clustering process of the improved DPC is automatic without manual intervention. The cut-off distance is avoided by forming a voxel structure and using the number of points in the voxel as the local density of the voxel. The automatic selection of cluster centers is realized by selecting the voxels whose gamma values are greater than the gamma value of the inflection point of the fitted γ curve as cluster centers. Finally, a new merging strategy is introduced to overcome the over-segmentation problem and obtain the final clustering result. To verify the effectiveness of the improved DPC, experiments on point cloud segmentation of LiDAR under different scenes were conducted. The basic DPC, K-means, and DBSCAN were introduced for comparison. The experimental results showed that the improved DPC is effective and its application to point cloud segmentation of LiDAR is successful. Compared with the basic DPC, K-means, the improved DPC has better clustering accuracy. And, compared with DBSCAN, the improved DPC has comparable or slightly better clustering accuracy without nontrivial parameters.

Social Capital in the Patron-Client Dimension towards the Sustainability of Seaweed Cultivation Businesses in Rural Areas, South Sulawesi, Indonesia

The system of livelihoods in seaweed cultivation becomes the subject of study because of the factor of production, and the distribution of the output of production requires a strategy of merging between something material and non-material that involves interaction with various actors in society. The social capital awakened by the actors in the seaweed cultivation business through the patron-client dimension is an effort to ensure the sustainability of the business. The study aims to characterize the components of social capital that bind, bridge, and connect in the context of the relationship between actors in seaweed cultivation, including the important role of the combination of these three types of social capital in the sustainability of the seaweed cultivation business. The Research methods use qualitative descriptive approaches. Data is collected through semi-structured interviews and analyzed with open, axial, and selective encoding. The results show that actors in seaweed cultivation enterprises combine the social capital components of bonding, bridging, and linking selectively, depending on the interests behind each interaction with different actors. Social bonding capital is owned by seaweed farmers in their interactions to gain access to financial capital (in the form of cash and loans). The patron-client relationship pattern is the most prominent of the reciprocity relationships, where the patron is the provider of corporate capital and the guarantor of subsistence is the cultivator. The social bridging and linking capital is owned in its interaction to gain access to social assistance and political support as well as networking. Mechanisms of sale of seaweed production are dominated by marketing networks using intermediary services (middlemen) through the concept of personalized exchange. In such market trading, the symptoms of “boro” (guarantees) are detected through subscription relationships between sellers and buyers characterized by regular personality contacts and through credit mechanisms known as “take now, pay later”. Therefore, social capital plays an important role in the sustainability of seaweed farming in the countryside. In a situation of economic or environmental change, social capital can help the farmer adapt and survive in his business and household life. We found that social capital plays an important role in the rural habitat system, where access to livelihoods depends on social relationships. Thus, support is needed to strengthen the capacity of social capital in societies because the configuration of the social capital elements can inherit the cognitive social capital, the social structural capital and the social relational capital necessary for the improved social, economic and cultural development of societies.

Enhancing Work Zone Safety: Evaluating Static Merge Strategies Through Microscopic Traffic Simulation

Background The utilization of merging control has been proposed as a strategy to maximize the capacity of roads in work zone areas. The static Early Merge (EM) and static Late Merge (LM) controls are extensively implemented among these strategies. Although many studies have investigated the efficacy of these controls through the analysis of field data or microscopic traffic simulations, such comparisons are frequently conducted under different work zone conditions, which can result in inconsistent, contradictory conclusions. Materials and Methods A simulation study was carried out using the VISSIM microscopic traffic simulator within a self-developed work zone featuring a 2-to-1 lane closure setup to comprehensively assess and contrast the traffic efficiency of the EM and LM controls. Furthermore, through a comprehensive analysis and comparison of network performance within the designed work zone across various scenarios, including queue length, vehicle delays, and travel time, the significant VISSIM parameters influencing the system's performance were identified. Results The analysis revealed that the parameters CC1 (headway time) and CC2 (longitudinal following threshold) from the car-following model exert more significant influence in the simulated work zone throughput than the Safety Distance Reduction Factor (SDRF) parameter from the lane-changing model. Discussion According to the simulation findings, implementing the EM control is preferable when drivers display aggressive behavior and maintain relatively short safety distances (i.e., low CC1 values). Conversely, opting for the LM control is more advisable in work zone areas where drivers demonstrate cautious driving tendencies and maintain longer safety distances (i.e., high CC1 values). Conclusion The efficacy of static EM and LM was analyzed in a 2-to-1 lane closure work zone on a freeway using the microscopic traffic simulator VISSIM. Simulation results were compared to identify the most relevant VISSIM parameters that influence work zone throughput. Our results indicate that the parameters CC1 and CC2 from the car-following model have a more substantial impact than the SDRF parameter from the lane-changing model. In particular, our comparison results suggest that the work zone throughput decreases in both the EM and LM scenarios as the values of CC1 and CC2 increase. Additionally, SDRF has a relatively negligible effect on the network performance of both merge strategies.

Game-Based Flexible Merging Decision Method for Mixed Traffic of Connected Autonomous Vehicles and Manual Driving Vehicles on Urban Freeways

Connected Autonomous Vehicles (CAVs) have the potential to revolutionize traffic systems by autonomously handling complex maneuvers such as freeway ramp merging. However, the unpredictability of manual-driven vehicles (MDVs) poses a significant challenge. This study introduces a novel decision-making approach that incorporates the uncertainty of MDVs’ driving styles, aiming to enhance merging efficiency and safety. By framing the CAV-MDV interaction as an incomplete information static game, we categorize MDVs’ behaviors using a Gaussian Mixture Model–Support Vector Machine (GMM-SVM) method. The identified driving styles are then integrated into the flexible merging decision process, leveraging the concept of pure-strategy Nash equilibrium to determine optimal merging points and timing. A deep reinforcement learning algorithm is employed to refine CAVs’ control decisions, ensuring efficient right-of-way acquisition. Simulations at both micro and macro levels validate the method’s effectiveness, demonstrating improved merging success rates and overall traffic efficiency without compromising safety. The research contributes to the field by offering a sophisticated merging strategy that respects real-world driving behavior complexity, with potential for practical applications in urban traffic scenarios.

Preferences of Iranian smokers regarding smart smoking cessation technologies: a parallel convergent mixed methods study

BackgroundConsidering the values and preferences of individuals who attempt to quit smoking is a crucial step in the development of smoking cessation technologies. This study aimed to explore preferences regarding smart smoking cessation technologies.MethodsThis parallel convergent mixed-methods study was conducted in two phases: quantitative and qualitative. In the quantitative phase, a cross-sectional study was conducted with 360 participants selected through stratified random sampling from technology-based smoking cessation clinics in Tabriz, Tehran, and Karaj cities in Iran. Data on demographic characteristics and preferences for smart smoking cessation technologies were collected using questionnaires and analyzed using descriptive statistics. In the qualitative phase, 25 users of these technologies were selected through purposeful and snowball sampling. The data were gathered through in-depth semistructured interviews and analyzed using qualitative content analysis with a conventional approach. Quantitative and qualitative data were integrated using the merging strategy and convergence model.ResultsThe quantitative phase results indicated that the highest preference was related to wearing and using a smartwatch for smoking cessation and using mobile apps. In the qualitative phase, 17 subcategories were extracted and classified into 8 main categories: high effectiveness, better management of the smoking cessation process, personalized technology, safe and uncomplicated technologies, attractiveness and innovative design, scientific basis, mobile applications, and smart monitoring devices.ConclusionBy combining and integrating quantitative and qualitative results, it can be concluded that users are more interested in wearable technologies and interactive mobile applications. The findings of this study can assist smoking cessation technology developers in designing and improving their tools based on user needs and preferences to enhance their effectiveness and acceptability.

Alleviating bus bunching via modular vehicles

The notorious phenomenon of bus bunching prevailing in uncontrolled bus systems produces irregular headways and downgrades the level of service by increasing passengers’ expected waiting time. Modular autonomous vehicles (MAVs), due to their ability to split and merge en route, have the potential to help both late and early buses recover from schedule deviation while providing continuous service. In this paper, we propose a novel bus bunching alleviation strategy for MAV-aided transit systems. We first consider a soft vehicle capacity constraint and establish a continuum approximation (CA) model (Model I) to capture the system dynamics intertwined with the MAV splitting and merging operations, and then establish an infinite-horizon stochastic optimization model to determine the optimal splitting and merging strategy. To capture the reality that passengers may fail to board an overcrowded bus, we propose a second model (Model II) by extending Model I to accommodate a hard vehicle capacity constraint. Based on the characteristics of the problem, we develop a customized deep Q-network (DQN) algorithm with multiple relay buffers and a penalized ruin state applicable for both models to optimize the strategy for each MAV. Numerical results show that the strategy obtained via the DQN algorithm is an effective bunch-proof strategy and has a better performance than the myopic strategy for MAV-aided systems and the two-way-looking strategy for conventional bus systems. Sensitivity analyses are also conducted to examine the effectiveness and benefits of the proposed strategy across different operation scenarios.

Optimization of TiO2-natural hydrogels for paracetamol and ibuprofen degradation in wastewaters.

Agarose/micrometer titanium dioxide (TiO2) beads were essayed to test the photocatalytic capacity of two of the most widely prescribed drugs worldwide: paracetamol and ibuprofen. Although the initial tests demonstrated promising degradation rates for both drugs, the presence of turbidity, due to TiO2 leakage, during the photocatalytic essays induced to improve the stability of the photocatalytic composites. Among the different strategies adopted to strengthen such materials, crosslinking with citric acid and the use of alternative gelling agents: gellan, agargel™, and agar were chosen. Composites obtained by merging both strategies were characterized and employed to degrade both drugs under a simulated light that mimics the solar spectrum (indoor). Considering the superior degradation rates obtained when agar and agarose were used to shape the titanium oxide particles (up to 70-75% of drug destruction), such composites were subjected to a more realistic experiment (outdoor): solar illumination, tap water, and higher volumes, that should facilitate its ulterior scale up as a real wastewater depollution procedure. Degradation rates between 80 and 90% are attained under such conditions for both drugs.

Differences in carbonyl groups and boron acceptors in MR‐TADF and full‐color emission merging strategies: A theoretical study

AbstractMulti‐resonant thermally activated delayed fluorescent (MR‐TADF) materials, which combine large oscillator strengths, small singlet‐triplet energy gaps, high photoluminescence quantum yields, and color purity, have attracted great interest in both experimental and theoretical research in recent years. However, the differences between two classes of MR‐TADF, utilizing carbonyl groups and boron atoms as acceptors respectively, have not been clearly delineated, and the implementation of strategies combining both is extremely limited. This limitation hampers the diversity in composition and structure of MR‐TADF. In this study, we employed boron as the central acceptor and carbonyl groups as peripheral acceptors, designing and investigating 7 merged systems of MR‐TADF molecules. Calculations revealed that, in contrast to the strong acceptor characteristics of boron atoms, carbonyl groups do not exhibit absolute acceptor features, and their resonance effects depend on the surrounding environment. This unique resonance effect induces LRCT features to varying degrees, enabling the emission coverage of these molecules across almost the entire visible spectrum (theoretical emission wavelengths covering 452–751 nm). We gained an understanding of the differences between boron acceptors and carbonyl groups, achieving full‐color emission by adjusting only the MR cores. This provides insights into the rational design of complex‐component full‐color MR‐TADF emitters.

How Do Butterflies Use Silk to Attach their Pupae to Trees?

Butterflies constitute approximately 10% of lepidopteran insects, and along with silkworms, they can produce silk; however, this feature is often ignored. In the present study, we observed two primary methods used by butterflies to hang pupae on trees using silk: pupa adheraena (Danaus chrysippus) and pupa contigua (Papilio polytes). Anchoring the abdominal ends of pupae with a silk pad was the most common method used in both cases, whereas wrapping silk around the body using a silk girdle was a method unique to pupa contigua. The connection between the cremaster and silk pad was observed to be similar to that between the hook and loop of a Velcro fastener, except that the cremaster hook is anchor-shaped rather than being a single hook. Such a connection will remain secure, ensuring the safety of the pupae during exposure to wind and rain. Through determining the mechanical properties of silk, the performance of butterfly silk was found to be weaker than that of silkworm silk. Therefore, the P. polytes silk girdle adopts the strategy of merging a dozen silk threads to improve its strength and toughness, thereby making it difficult to break. In addition, we explained how the protein sequence and structure of butterfly silk impact its performance. In conclusion, we discovered that butterfly pupae develop unique body features to establish secure bonds with silk. This enables them to effectively undergo metamorphosis and endure harsh weather conditions and surroundings.

Merging Strategy for Autonomous Vehicles on Highways Based on Acceptance Gaps and Model Predictive Control

Merging Strategy for Autonomous Vehicles on Highways Based on Acceptance Gaps and Model Predictive Control

DCoMA: A dynamic coordinative merging assistant strategy for on-ramp vehicles with mixed traffic conditions

Merging sections on highways are identified as traffic bottlenecks, leading to congestion and accidents. The emergence of Connected and Autonomous Vehicles (CAVs) technology promises an optimized solution to on-ramp merging issues. Existing cooperative merging strategies typically focus on determining the merging sequence of vehicles in specific merging areas, overlooking the influence of the macroscopic traffic flow conditions on the merging process. In this paper, we innovatively introduce the Dynamic Cooperative Merging Assistance (DCoMA) strategy, a traffic management approach designed to enhance merging operations under variable traffic demands. At the macroscopic level, DCoMA employs the fundamental diagram of traffic flow to develop a platoon formation algorithm for mainline vehicles, tailored to the dynamic macroscopic states of traffic. The algorithm adaptively adjusts the size of platoons based on the volumes of both the mainline and the on-ramp. Subsequently, the spatio-temporal dynamics of these platoons function as the ’red phase’ of traffic signals, with the intervals between platoons analogous to the ’green phase’. This information is then converted into a time-series format and transmitted to all vehicles on the on-ramp. At the microscopic level, vehicles on the on-ramp alter their driving strategies in response to this time-series data, ensuring a seamless merging into the mainline flow without causing halting. Through simulations and comparative analysis with three existing strategies (i.e., CoopMA, ALINEA, and X-ALINEAQ), the proposed DCoMA strategy exhibits considerable potential for application across diverse traffic volumes and CAV penetration rates. The results indicate that the proposed approach can significantly improve the efficiency of the mainline and on-ramp, achieving a maximum improvement of 18.32%. More importantly, the DCoMA strategy effectively enlarges merging gaps and, coupled with the speed control capabilities of CAVs, significantly mitigates the risk of accidents and reduces exhaust emissions.

On Expectation-Maximization Algorithm with Split and Merge in R

This paper presents a comprehensive study on the implementation of the Expectation-Maximization (EM) algorithm for a 4-component bivariate Gaussian Mixture Model (GMM) with a focus on incorporating the split and merge techniques. The bivariate GMM is widely utilized in various fields, such as pattern recognition, image processing, and data clustering, due to its flexibility in capturing complex data distributions. Our proposed implementation leverages the versatility of the R programming language to create a robust and efficient framework for modeling and optimizing the parameters of the 4-component GMM. The EM algorithm is employed as a powerful tool for iteratively estimating the model parameters, ensuring convergence to a local maximum of the likelihood function. To enhance the model’s flexibility, we introduce the split and merge strategies, enabling the algorithm to adapt to diverse data structures and efficiently manage the complexity of the mixture components. The split operation allows for the subdivision of components when needed, while the merge operation facilitates the combination of components that represent similar patterns. This adaptive approach contributes to the model’s ability to capture intricate data patterns and improve convergence during the optimization process. The implementation is validated through extensive experimentation on synthetic, demonstrating its effectiveness in accurately estimating the parameters of the 4-component bivariate GMM. The proposed methodology proves to be a valuable addition to the existing tools available for GMM based modeling, providing researchers and practitioners with a flexible and powerful framework for analyzing complex data structures in diverse applications

Merging or Computing Saturated Cost Partitionings? A Merge Strategy for the Merge-and-Shrink Framework

Merging or Computing Saturated Cost Partitionings? A Merge Strategy for the Merge-and-Shrink Framework

Improving estimates of water resources availability over North Tropical South America: comparison of two satellite precipitation merging schemes

Low-density precipitation measurements impair the ability of hydrological models to estimate surface water resources accurately. Remote sensing techniques and climate models can help to improve the estimation of the space-time rainfall variability. However, they alone are not good enough to be used in surface models built to support water management. In this research, we test the improvement of rainfall field estimation by using hydrological modelling based on the premise that a higher hydrological performance generally implies that precipitation is more consistent with streamflow observations and evaporation estimates in the basin. The SWAT model was forced with two satellite and rain gauge blending techniques and with the traditional IDW deterministic interpolation method from stations. The three simulated streamflows were compared separately against observed records. We do not only focus the comparison on one hydrological performance metric but also conduct a deeper evaluation using several hydrological signatures and statistics. We included the bias, the temporal correlation, the relation of general variability, and an analysis of the Flow Duration Curves (we found that low and medium segments were estimated correctly, whereas the high segments were underestimated). We conclude that either combination technique has its advantages over the other and that both outperform the performance achieved by the IDW in most of the defined criteria, with an overall 10% improvement and with individual streamflow gauge performance enhancement up to 50%.

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Data Sets Built on FragPipe.

Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a data merging scheme and a protein-centric multiple hypothesis correction scheme, enabling processed LiP-MS data sets to be more robust and less redundant. These improvements strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.