Complete Path Research Articles

Data Collection and Preprocessing in Web Usage Mining: Implementation and Analysis

Data collection and data preprocessing are crucial stages in web usage mining, mainly because of the unstructured, diverse, and noisy nature of log data. During data collection, log file datasets are loaded and merged. Effective and comprehensive data preprocessing plays a vital role in ensuring the efficiency and scalability of algorithms used in the pattern discovery phase of web usage mining. This work aims to address these phases by introducing two innovative approaches. The first approach focuses on determining the device used for accessing the web, distinguishing between computers and mobile devices. The second approach aims to determine user sessions and complete paths by utilizing the referrer URL. The entire preprocessing pipeline has been implemented using the C# programming language, and the source code is available on GitHub at the following link: https://github.com/Mohammed91/Web-Usage-Mining.

Recent developments in autonomous floor-cleaning robots: a review

Purpose Autonomous floor-cleaning robots (AFCRs) have become increasingly popular due to their ability to provide efficient and effective cleaning without the need for human intervention. These robots can perform various cleaning tasks, such as vacuum cleaning, mopping, scrubbing or sweeping, in domestic or industrial setups. As the use of floor-cleaning robots continues to grow, this paper aims to document key technological advancements. Design/methodology/approach The structure of the present work relies on published research articles excavated from general online research databases such as Google Scholar, Web of Science and Scopus. The authors use a variety of keywords and titles to search for research papers. Finally, 93 research articles are selected for review based on abstracts and key results that match AFCRs. Findings According to market trends, floor-cleaning robots dominate other cleaning areas. This review mainly focuses on five attributes of floor-cleaning robots: design and development of AFCR, complete coverage path planning, the application of machine learning (ML)/deep learning (DL), optimisation strategies for qualitative output and ethnographic studies. It also consists of discussions based on the results of reported technical works. Hence, AFCRs have dominated the market in the past decade and are likely to be more aggressive in the coming years. Originality/value To the best of the authors’ knowledge, only a survey article based on US-granted patents published in 2013 constitutes a review work in the research domain on AFCRs. In 2021, another review conducted a survey on the latest technological advancements in window-cleaning robots. It reviewed in detail the locomotion aspects, control mechanisms, adhesion mechanisms, sensors and actuators required for window-cleaning robots. In 2019, a comprehensive review was published on cleaning robots from a control strategy perspective for domestic applications. Therefore, the authors have crafted this review to understand the evolution of floor-cleaning robots in the past decade.

Kinematic Constrained RRT Algorithm with Post Waypoint Shift for the Shortest Path Planning of Wheeled Mobile Robots.

This paper presents a rapidly exploring random tree (RRT) algorithm with an effective post waypoint shift, which is suitable for the path planning of a wheeled mobile robot under kinematic constraints. In the growth of the exploring tree, the nearest node that satisfies the kinematic constraints is selected as the parent node. Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. Then, it is proved that the method can obtain an optimal path in terms of the shortest length. The optimized path has good convergence and almost does not depend on the state of the initial random path. The comparative test results show that the proposed method has significant advantages over traditional RRT-based methods in terms of the sampling point number, the tree node number, and the path node number. Subsequently, an efficient method is further proposed to avoid unknown obstacles, which utilizes the original path information and thus effectively improves the new path planning efficiency. Simulations and real-world tests are carried out to demonstrate the effectiveness of this method.

Research on Complete Coverage Path Planning of Agricultural Robots Based on Markov Chain Improved Genetic Algorithm

Due to the limitations of low coverage, high repetition rate, and slow convergence speed of the basic genetic algorithm (GA) in robot complete coverage path planning, the state transition matrix of the Markov chain is introduced to guide individual mutation based on the genetic mutation path planning algorithm, which can improve the quality of population individuals, enhancing the search ability and convergence speed of the genetic algorithm. The proposed improved genetic algorithm is used for complete coverage path planning simulation analysis in different work areas. The analysis results show that compared to traditional genetic algorithms, the improved genetic algorithm proposed in this paper reduces the average path length by 21.8%, the average number of turns by 6 times, the repetition rate by 83.8%, and the coverage rate by 7.76% in 6 different work areas. The results prove that the proposed improved genetic algorithm is applicable in complete coverage path planning. To verify whether the Markov chain genetic algorithm (MCGA) proposed is suitable for agricultural robot path tracking and operation, it was used to plan the path of an actual land parcel. An automatic navigation robot can track the planned path, which can verify the feasibility of the MCGA proposed.

Application of Path Planning and Tracking Control Technology in Mower Robots

Path planning and tracking is the most basic technology for mowing robots, among which the performance of algorithms has a great impact on their intelligence and efficiency. Based on the research of relevant references on mower robots, it mainly focuses on complete coverage path planning, path tracking control, and obstacle avoidance path planning. In complete coverage path planning, three methods were introduced, including simple complete coverage planning, optimal complete coverage planning, and hybrid complete coverage planning. In the path tracking control section, the control methods are divided into three types based on whether the control method depends on the robot model and the type of model, namely model free control method, kinematic model-based control method, and dynamic model-based control method. In obstacle avoidance path planning, we introduce the environment detection device and obstacle avoidance planning algorithm. Then the relevant research papers are analyzed in classification, comparing the research and validation methods adopted by the researchers in the form of charts. Finally, we pointed out the limitations of path planning technology in the application of mower robots. Meanwhile, future development trends are predicted.

Loneliness, Internalizing and Externalizing Problems, and Suicidal Ideation Among Chinese Adolescents: A Longitudinal Mediation Analysis

PurposeResearch indicates that loneliness and emotional and behavioral problems increase the risk of suicidal ideation in adolescents, but less is known about the distinct contributions of these problems. This study aimed to distinguish the pathways through which loneliness, internalizing problems, and externalizing problems contribute to suicidal ideation in Chinese adolescents. MethodsWe did a longitudinal mediation analysis with data collected at 3 time points (2021.05, 2021.10, and 2022.05) from 28 Taizhou high schools. Loneliness and suicidal ideation were assessed using the UCLA 3-Item Loneliness Scale and one suicide item from the Children’s Depression Inventory, respectively. The Strength and Difficulties Questionnaire assessed internalizing (emotional and peer problems) and externalizing problems (conduct and hyperactivity problems). Structural equation modeling was used to construct complete longitudinal path models. ResultsUsing data from 2,190 adolescents in junior and senior high schools, we found that loneliness, internalizing problems, and externalizing problems separately contributed to subsequent higher levels of suicidal ideation. Most notably, loneliness predicted worse subsequent internalizing problems (β = 0.279, p < .001) and externalizing problems (β = 0.159, p < .001), which in turn predicted more severe suicidal ideation (β = 0.019, p < .001; β = 0.018, p < .001). Loneliness also partially mediated the association between internalizing or externalizing problems and suicidal ideation. DiscussionLoneliness, internalizing problems, and externalizing problems were strongly intertwined with suicidal ideation in adolescents. Public health initiatives could reduce loneliness and emotional and behavioral problems by implementing multifaceted interventions, thereby breaking the vicious circle and protecting against the development of suicidal ideation.

Using a 1D Radially Symmetric Coda Envelope Model for Robust Moment Magnitude (Mw) Estimation in Iraq’s Tectonically Diverse Zones

ABSTRACT Robust estimation of moment magnitude (Mw) can be challenging for Iraq due to the strong lateral heterogeneity across diverse tectonic zones. We aim to improve moment magnitude estimation by investigating the reliability of using a 1D coda envelope model in diverse tectonic zones of different lateral effects and offer a way forward for reliable estimates of Mw for small events that are difficult to waveform model. Iraq comprises two main tectonic zones: (1) the Outer platform, consisting of the northwestern Zagros fold-thrust belt and the Mesopotamian foredeep, and (2) the Inner Arabian platform which is overlain by the Iraqi desert. A simple 1D coda envelope model was used because coda waves have a low sensitivity to the source and path heterogeneity. Three separate coda calibrations were conducted to investigate the robustness of a single 1D calibration to fit the country: Whole-region calibration, Zagros calibration, and Mesopotamia calibration. In the whole-region calibration, we used stations from both the Zagros and Mesopotamia zones. In the two other calibration models, we used only stations that were in those particular zones. Ground-truth reference spectra derived from the coda spectral ratio method were used to constrain high-frequency site terms. There was no drastic difference when comparing the moment magnitudes calculated from the waveform modeling and the three calibration models. The results show that the 1D coda envelope model is a reliable method even for a region with diverse tectonic zones. Hence, we recommend using the whole-region calibration model for moment magnitude estimation that provides more complete path coverage and avoids biases introduced by path correction failures. The proposed calibration is a fundamental step in updating the comprehensive earthquake catalog and probabilistic seismic hazard assessments for Iraq.

Uniformly resolvable ${P_4, C_k}$-decomposition of $K_n$ - a complete solution

Let $K_n,$ $C_{n}$, and $P_{n}$ respectively denote the complete graph, cycle and path on $n$ vertices. Uniformly resolvable decomposition of $K_n$ is a decomposition of $K_n$ into subgraphs which can be partitioned into factors containing pairwise isomorphic subgraphs. In this paper, we determine necessary and sufficient conditions for the existence of uniformly resolvable decomposition of $K_n$ into $P_4$ and $C_k,~ k\geq3.$

Heavy metal contamination of surface soils by anthropogenic activities: concomitant ecological and health risk assessment

ABSTRACT The heavy metals in the soil are a condemnatory environmental threat as they expand their toxic effects on the environment as well as human health. The present study is intended to analyse the concentrations of heavy metals (K, Ca, Mg, Ti, Fe, Co, Cu, As, Zn, Rb, Sr, Zr, Pb, and Th) in soil samples congregating near a urea fertiliser factory utilising Energy Dispersive X-ray Fluorescence (EDXRF) technique and evaluate their distinctiveness, emergence and subjection to human. Mean value of Mg, K, Ca, Ti, Fe, Co, Cu, Zn, As, Rb, Sr, Zr, Pb and Th in the sampling area was found 14712, 11828, 8607, 4378, 47358, 10.93, 17.04, 151.57, 8.65, 191.08, 191.42, 263.64, 88.69 and 17.55 mg kg−1 respectively. Geo-accumulation index (Igeo), enrichment factor (EF) and contamination factor (CF) revealed the sampling sites were practically uncontaminated to moderately contaminated, and minimum to moderately enriched by the elements. Besides, a low level of risk was evaluated from the estimated value of the Ecological risk index (RI). Principal Component Analysis (PCA), Correlation Analysis (CA) and correlation coefficient indicated that the geological and the anthropogenic origins have boosted the elemental concentration in the soil samples. Regardless of age, the Hazard quotient (HQ) and Hazard index (HI) values in all three exposure pathways (HQ/HI < 1) and total lifetime carcinogenic risk (TCR < 1E–06) from the complete exposure path stipulated negligible risk and could cause no obvious health hazards on the surrounding populations. However, children manifested potential receptors of heavy metals compared to adults and were more susceptible to carcinogenic and non-carcinogenic risks.

Very high-T hydration in the lower gabbro section of Semail ophiolite, Hydrous components of the lower crust at fast-spreading ridge

Totally fresh black gabbros are observed locally at the very base of the gabbro section in southern Semail ophiolite. Devoid of medium to low-temperature diffuse alteration, the black gabbros are characterized by cloudy plagioclase marked by a high density of hydrothermal inclusions below the optical microscope resolution, evolving into diopside and pargasite crystals ∼10 μm sized. They record hydration at temperature over 900 °C as shown by their major elements composition. Focused Electron Backscattered Electron Diffraction (EBSD) study of the crystalline inclusions, suggests a phase transition from diopside to amphibole, and traces consistent crystallographic relationships of diopside inclusions with their host plagioclase. The detailed geometry of the microcrack system traces a complete fluid path from inter-grain cracking, to plagioclase intra-grain network, possibly thermally-induced. The evidence of solid-state deformation overprinting the magmatic textures suggests that twinning mechanism in plagioclase controls the intra-grain diffusion of hydrous fluids. Located at the interface of the Moho Transition Zone (MTZ) and semi-brittle lithosphere, the ‘black gabbros’ record a narrow domain where focused fluid flow induces rapid cooling below the gabbro solidus at the lowest limit of the magma chamber.

Enhancing supply chain resilience under disruption: analysis of the farmed data by Monte Carlo simulation

Motivated by the critical need for supply chains to withstand disruptions, this paper investigates the application of three core resilience strategies and their combinations at the network design level. We propose an investigation framework that utilises Monte Carlo simulation to generate disruption rates of varying severity. This allows for the mathematical optimisation of eight different models, each representing a combination of the three core resilience strategies, based on the simulated disruptions. The framework also incorporates farmed structured data analysis to examine the performance and features of each model. Considering disruptions that can affect both suppliers and factories, a detailed comparison is performed between the strategies. Numerical comparisons reveal that the model incorporating all resilience strategies (Multiple Allocation Network with Backup Suppliers and Factories, or MANBSF) outperforms others, potentially achieving zero shortages even under significant disruptions. This comprehensive model allows for multiple allocations of facilities and incorporates backup suppliers and factories. The analysis further suggests that network resilience increases with size, density, and the number of complete paths.

Theoretical study of the mechanism of 2F-molecular sieve domain chiral transition of Asn molecules

Abstract Using density functional theory at the level of oniom (b3lyp/6-31g (d, p): uff), we investigated the chiral transition process of the Asparagine molecule in 2F-MOL. Furthermore, a complete chiral transition path reaction potential energy surface was drawn by looking for the extreme value point structures including the transition state and intermediate. The results show that the hydrogen atom on the chiral carbon atom of S-Asparagine molecule can transfer to the other side of the carbon atom via the oxygen atoms of carboxyl atoms as a bridge, to achieve the chiral transition of Asparagine molecule from S-type to R-type. On this path there is four intermediate and five transition states. The biggest reaction energy barrier is 243.9089KJ/mol derived from the fourth transition state TS1-R-Asn@2F-MOL.

Revisiting the effectiveness of diamond heat spreaders on multi-finger gate GaN HEMT using chip-to-package-level thermal simulation

The study of chip-level and package-level heat transfer in a GaN high electron mobility transistor (HEMT) is often disconnected due to limited resources and tools. In this work, device simulation from Silvaco Victory Device is carried forward to the chip-to-package-level simulation using Icepak to provide a complete picture of the proposed thermal management strategies using polycrystalline diamond (PCD) heat spreaders. The max junction temperature, Tj = 105.8 °C and the relative magnitude of max temperature on the GaN surface, ΔTj = 18 % are recorded for the original Si-GaN-Si3N4 chip inside TO-220 at 6.0 Wmm−1. Replacing the Si3N4 with PCD (thermal conductivity of 500 Wm−1 K−1) results in Tj = 98.2 °C and ΔTj = 8 %, while replacing the Si results in Tj = 97.0 °C and ΔTj = 11 %. The top layer PCD spreads the heat from hotspot regions to the surrounding epoxy, while the bottom layer PCD improves the heat path from the hotspots to the base plate. Therefore, the reduction in Tj by the bottom layer PCD is more important than the reduction in ΔTj by the top layer PCD. Implementing both the top and bottom layers of PCD results in the best offers of Tj = 92.9 °C and ΔTj = 6 %. The performance of PCD as heat spreaders in multi-finger gate GaN HEMT suggested by these chip-to-package-level simulations are more reliable than device simulation alone since they cover the complete heat path from generation, conduction within the package, and convection to the ambient air.

3D hybrid path planning for optimized coverage of agricultural fields: A novel approach for wheeled robots

AbstractOver the last few decades, the agricultural industry has made significant advances in autonomous systems, such as wheeled robots, with the primary objective of improving efficiency while reducing the impact on the environment. In this context, determining a path for the robot that optimizes coverage while taking into account topography, robot characteristics, and operational requirements, is critical. In this paper, we present H‐CCPP, a novel hybrid method that combines the comprehensive coverage benefits of our previous approach O‐CCPP with the computational efficiency of the Fields2Cover algorithm. Besides optimizing coverage area, overlaps, and overall travel time, it significantly improves the computation process, and enhances the flexibility of trajectory generation. H‐CCPP also considers terrain inclination to address soil erosion and energy consumption. In an effort to support this innovative approach, we have also created and made available a public data set that includes both 2D and 3D representations of 30 agricultural fields. This resource not only allows us to illustrate the effectiveness of our approach but also provides invaluable data for future research in complete coverage path planning (CCPP) for modern agriculture.

Personalized process–type learning path recommendation based on process mining and deep knowledge tracing

Personalized learning path recommendation considers learning goals, learning abilities, and other personalized characteristics of learners to generate a suitable learning path. Existing approaches include global optimal and local iterative path recommendation, which recommend a sequence of learning objects. Consequently, the learner can only learn in the order specified by the learning path, which provides limited flexibility for the learner. In addition, existing studies cannot both present the complete path and handle changes in the learner's knowledge state while learning along the path. This study proposes a process-type learning path model and its recommendation approach, which presents a learning path in the form of a flowchart and dynamically recommends path branches according to the knowledge states of the learner during the learning process. Specifically, deep knowledge tracing is used to annotate the knowledge states of learners in historical logs, and process mining is used to generate a personalized process–type learning path that contains sequences, parallel relationships, and selection relationships between learning objects. In addition, the correlation between the knowledge state and the selection of different branches of a learning path in historical logs can be obtained via decision mining. Thus, a branch recommendation model is trained and used to recommend a path branch in a process-type path with the highest probability of mastering the target learning object of the learner based on the learner's knowledge state. The experimental results demonstrate that the learning effectiveness and efficiency of the proposed approach are better than those of the existing approaches.

Simultaneous electrical and mechanical properties improvement for composite expanded graphite bipolar plate by incorporating surface-activated carbon black paving on the carbonized melamine foam skeleton

The bipolar plate is the critical component that directly impacts the output performance and lifespan of the fuel cell. Composite graphite-based bipolar plates, which can be tailored to exhibit desired properties, are promising candidates for high-performance fuel cells. However, the simultaneous optimization of their electrical and mechanical properties has been a major challenge in the field. This study introduces a foam skeleton made of carbonized melamine foam (CMF) into the graphite bipolar plate to create ample space for subsequent resin impregnation. Carbon black (CB) is added as a filler to form complete conductive paths, which is modified using low-temperature plasma to allow it to be more uniformly adsorbed in the skeleton. The microstructure design of the composite bipolar plate ensures excellent mechanical properties and significantly enhances its electrical conductivity. Moreover, it effectively eliminates the impact of carbon black on the surface roughness of the composite plate. This design also provides superior water management and reduces gas permeability. Practical application tests demonstrate that the developed composite plate exhibits better corrosion resistance and electrochemical stability than metal bipolar plates. Compared to carbonized bipolar plates, it displays better impact resistance and stability.

Path planning in additive manufacturing with multi-robot collaboration based on structural primitive partitioning

Directed Energy Deposition (DED) technology is increasingly favored for swiftly fabricating large structural components due to its high printing efficiency. Despite its advantages, challenges persist in achieving satisfactory surface finish and forming precision, hindering its widespread adoption across industries. To address these issues, this paper presents a novel multi-robot collaborative path planning method based on structural primitive partitioning. This method simplifies path planning complexities and seamlessly integrates into process planning software, thereby enhancing overall functionality. This method decomposes complex polygons into tiny primitives (TP), organizing them into TP sets based on bridge and adjacency relations. These sets are then structured into first-level structural TP (F-TP) and second-level structural TP (S-TP), followed by the establishment of monotonic structural TP (M-TP). A minimum rectangular box recalculates the filling path for each M-TP, while the external contour path and internal zigzag path form a complete printing path. Additionally, an optimal printing sequence planning algorithm for multi-robot using a KD-tree-based search algorithm is presented, ensuring the shortest non-productive path and collision avoidance during printing. Experimental verification with four structures of varying geometric features demonstrates a partitioning accuracy of 99.5 % and absence of surface defects in the printed parts. The proposed method presents a viable and effective solution for enhancing the quality of parts produced via DED.

Quantum walks on blow-up graphs

A blow-up of n copies of a graph G is the graph obtained by replacing every vertex of G by an independent set of size n, where the copies of two vertices in G are adjacent in the blow-up if and only if they are adjacent in G. In this work, we characterize strong cospectrality, periodicity, perfect state transfer (PST) and pretty good state transfer (PGST) in blow-up graphs. We prove that if a blow-up admits PST or PGST, then n = 2. In particular, if G has an invertible adjacency matrix, then each vertex in a blow of two copies of G pairs up with a unique vertex to exhibit strong cospectrality. Under mild conditions, we show that periodicity (resp., almost periodicity) of a vertex in G guarantees PST (resp. PGST) between the two copies of the vertex in the blow-up. This allows us to construct new families of graphs with PST from graphs that do not admit PST. We also characterize PST and PGST in the blow-ups of complete graphs, paths, cycles and cones. Finally, while trees in general do not admit PST, we provide infinite families of stars and subdivided stars whose blow-ups admit PST.

Automated line scan profilometer based on the surface recognition method

Existing high-precision measurement devices face numerous challenges when dealing with complex surfaces, including high manual involvement, low measurement efficiency, and high costs. It is even more difficult to obtain complete surface data, particularly when confronted with unknown surfaces. This paper combines a fringe projection sensor with a high-precision line-structured light sensor to construct a line scan profiler. It proposes a surface recognition measurement method to overcome these challenges. This method fully leverages the fringe projection sensor's wide-field advantage to identify the object's surface shape and pose quickly. Subsequently, through the point cloud extraction and path planning algorithms proposed in this paper, efficient and complete line scan measurement paths are automatically generated, thus achieving the automation of the measurement process. Experimental measurements of aspherical lenses, freeform reflective mirrors, and artificial knee joint models validate the advantages of this method over existing methods in terms of measurement efficiency, automation capability, and applicability.

Predicting fatigue life with the IMWCW under complex loading

The Integration Modified Wöhler Curve Method (IMWCW), a novel approach for predicting fatigue life under complex strain-based non-proportional loading paths is introduced in this research. This method integrates concepts from the Modified Wöhler Curve Method (MWCW) with the principle of damage incremental integration to provide a more nuanced prediction of material fatigue. Besides two established E-N curves (strain-number of cycles) for uniaxial tension and torsion, a new fundamental E-N curve that characterizes the strain-life relationship under complete non-proportional loading paths is introduced. A novel strain path decomposition technique is presented, which dissects an infinite number of instantaneous micro-elements along any loading path into components parallel and perpendicular to the proportional loading direction. It is assumed that each instant will cause fatigue damage to the material. A concurrent model for the incremental integration of damage is formulated, allowing for the calculation of cumulative damage. The model is further expanded to provide analytical expressions for proportional loading and non-proportional loading, linking them to three fundamental E-N curve, so as to calibrate the parameters required for the integration model. The empirical validity of the model is confirmed through experimental testing on 7 different metallic materials under 20 loading conditions, with 391 data points analyzed. The results demonstrate that 91% of the model's predictions fall within a scatter band of 2, confirming its robustness and reliability in forecasting fatigue life in complex, real-world scenarios.