Search Results (167)

With the strong capability of heterogeneous graphs in accurately modeling various types of nodes and their interactions, they have gradually become a research hotspot, promoting the rapid development of the field of heterogeneous graph neural networks (HGNNs). However, most existing HGNN models rely on meta−paths for feature extraction, which can only utilize part of the data from the graph for training and learning. This not only limits the data generalization ability of deep learning models but also affects the application effect of data−driven adaptive technologies. In response to this challenge, this study proposes a new model—heterogeneous graph neural network based on regional feature extraction (HGNN−BRFE). This model enhances performance through an “extraction−fusion” strategy in three key aspects: first, it efficiently extracts features of neighboring nodes of the same type according to specific regions; second, it effectively fuses information from different regions and hierarchical neighbors using attention mechanisms; third, it specially designs a process for feature extraction and fusion targeting heterogeneous type nodes, ensuring that the rich semantic and heterogeneity information within the heterogeneous graph is retained while maintaining the node’s own characteristics during the node embedding process to prevent the loss of its own features and potential over−smoothing issues. Experimental results show that HGNN−BRFE achieves a performance improvement of 1–3% over existing methods on classification tasks across multiple real−world datasets. Full article

Background/Objectives: Directed acyclic graphs (DAGs) inform the epidemiologic statistical modeling confounders to determine close to true causal relationships in a study context. They inform the inclusion of the predictive model variables that affect the causal relationship. Non-small cell lung cancer (NSCLC) is frequently diagnosed, aggressive, and the second leading cause of cancer deaths in the United States. Determining factors affecting both the guideline-concordant treatment receipt and survival outcomes for early-stage lung cancer will help inform future statistical models aiming to achieve a close to true causal relationship. Methods: Peer-reviewed original research published during 2002–2023 was identified through PubMed, Embase, Web of Sciences, Clinical trials registry, and the gray literature. DAGitty version 3.1, an online software program, developed implied DAGs and integrated DAG graphics. The evidence synthesis for constructing directed acyclic graphs (ESC-DAGs) protocol was utilized to guide DAG development. The conceptual models utilized were Andersen and Aday for factors affecting treatment receipt and Shi and Steven for survival outcome factors. Results: A total of 36 studies were included in the DAG synthesis out of 9421 retrieved across databases. Eight studies served in the synthesis of treatment receipt DAG, while 28 studies were used for the survival outcomes DAG. There were 10 causal paths and 13 covariates for treatment receipt and 2 causal pathways and 32 covariates for survival outcomes. Conclusions: There are very few studies reporting on factors affecting early-stage NSCLC guideline-concordant care receipt compared to factors affecting its survival outcomes in the past two decades of original research. Future investigations can utilize data extracted in the current study to develop a meta-analysis informing effect size. Full article

The snow ablation optimizer (SAO) is a meta-heuristic technique used to seek the best solution for sophisticated problems. In response to the defects in the SAO algorithm, which has poor search efficiency and is prone to getting trapped in local optima, this article suggests a multi-strategy improved (MISAO) snow ablation optimizer. It is employed in the unmanned aerial vehicle (UAV) path planning issue. To begin with, the tent chaos and elite reverse learning initialization strategies are merged to extend the diversity of the population; secondly, a greedy selection method is deployed to retain superior alternative solutions for the upcoming iteration; then, the Harris hawk (HHO) strategy is introduced to enhance the exploitation capability, which prevents trapping in partial ideals; finally, the red-tailed hawk (RTH) is adopted to perform the global exploration, which, enhances global optimization capability. To comprehensively evaluate MISAO’s optimization capability, a battery of digital optimization investigations is executed using 23 test functions, and the results of the comparative analysis show that the suggested algorithm has high solving accuracy and convergence velocity. Finally, the effectiveness and feasibility of the optimization path of the MISAO algorithm are demonstrated in the UAV path planning project. Full article

This study presents an innovative approach to sustainable timber construction by integrating traditional woodworking techniques with advanced robotic technology. The research focuses on three key objectives: preserving traditional craftsmanship, enhancing material conservation, and improving production efficiency. A meta-model-based framework is developed to capture the woodcrafts of mortise and tenon joints, which are prevalent in traditional Chinese wooden architecture. The study employs parametric design and robotic arm technology to digitize and automate the production process, resulting in significant improvements in material utilization and processing efficiency. Specifically, this study utilizes genetic algorithm strategies to resolve the problem of complex mortise and tenon craftsmanship optimization for robotic arms. Compared to conventional CNC machining, the proposed method demonstrates superior performance in path optimization, reduced material waste, and faster production times. The research contributes to the field of sustainable architecture by offering a novel solution that balances the preservation of cultural heritage with modern construction demands. This approach not only ensures the continuity of traditional woodworking skills but also addresses contemporary challenges in sustainable building practices, paving the way for more environmentally friendly and efficient timber construction methods. Full article

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where the feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another for extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we use a set of metrics considering both quantitative and qualitative aspects of the road network. On road types with similar sample sizes, the HAN model outperforms other models in both transductive and inductive tasks. Its accuracy (ACC) is higher by 1.62% and 0.67%, and its F1-score is higher by 1.43% and 0.81%, respectively. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation. Full article

In recent years, quadcopter-type unmanned aerial vehicles have been preferred in many engineering applications. Because of its nonlinear dynamic model that makes it hard to create optimal control, quadcopter control is one of the main focuses of control engineering and has been studied by many researchers. A quadcopter has six degrees of freedom movement capability and multi-input multi-output structure in its dynamic model. The full nonlinear model of the quadcopter is derived using the results of the experimental studies in the literature. In this study, the control of the quadcopter is realized using the symbolic limited optimal discrete controller synthesis (S-DCS) method. The attitude, altitude, and horizontal movement control of the quadcopter are carried out. To validate the success of the SDCS controller, the control of the quadcopter is realized with fractional order proportional-integral-derivative (FOPID) controllers. The parameters of the FOPID controllers are calculated using Fire Hawk Optimizer, Flying Fox Optimization Algorithm, and Puma Optimizer, which are recently developed meta-heuristic (MH) algorithms. The performance of the S-DCS controller is compared with the performance of the optimal FOPID controllers. In the path planning part of this study, the optimal path planning performances of the SDCS method and the MH algorithms are tested and compared. The optimal solution of the traveling salesman problem (TSP) for a single quadcopter and min-max TSP with multiple depots for multi quadcopters are obtained. The methods and the cases that optimize the dynamic behavior and the path planning of the quadcopter are investigated and determined. Full article

Knowledge of land is of central importance to manage the impact of mankind upon the environment. The understanding and treatment of land vary greatly across different regions and communities, making the description of land highly specific to each locality. To address the larger global issues, such as world food production or climate change mitigation, one needs to have a common standardized understanding of the biosphere cover and use of land. Different governments and institutions established national systems to describe thematic content of land within their jurisdictions. These systems are all valid and tuned to address various national needs. However, their integration at regional or global levels is lacking. Integrating data from widely divergent sources to create world datasets not only requires standards, but also an approach to integrate national and regional land cover classification systems. The ISO 19144 series, developed through the collaboration between the Food and Agriculture Organization of the United Nations (FAO) and the International Organization for Standardization (ISO), offers a meta-language for the integration of disparate land classification systems, enhancing interoperability, data sharing, and national to global data integration and comparison. This paper provides an overview of classification system concepts, different stages for the development of standards in ISO and the status of different standards in the ISO 19144 series. It also explores the critical role of the ISO 19144 series in standardizing land cover and land use classification systems. Drawing on practical case studies, the paper underscores the series’ potential to support global sustainable development goals and lays out a path for its future development and application. Using these standards, we gain not only a tool for harmonizing land classification, but also a critical level for advancing sustainable development and environmental stewardship worldwide. Full article

In the arid and semi-arid regions of Northwest China, periodic rainfall deficits, high field evaporation, limited freshwater resources, and high irrigation costs restrict crop yield and water productivity (WP). Gravel mulching (GM), a traditional agricultural tillage management practice widely used in arid and semi-arid regions, improves crop yield and WP. However, the combined impacts of GM on crop yield and WP are unclear. This study aimed to examine the effects of GM on crop yield and WP under different factors and to find the most critical regional factors and gravel characteristics that affect crop yield and WP. To quantitatively assess the impact of GM on crop yield and WP, this study performed a meta-analysis, a regression analysis, and a path analysis of 185 yield comparisons and 130 WP comparisons from 30 peer-reviewed scientific reports. This study found that GM significantly increased crop yield and WP by an average of 29.47% and 28.03%, respectively. GM was reported with the highest response percentages (I) of crop yield and WP in regions whose average annual precipitation (AAP) was 200–400 mm, average annual temperature (AAT) was 0–9 °C, and altitude (A) was >1000 m. Overall, AAP, AAT, and A had significant effects on the I of crop yield (p < 0.001), but AAT and A had an insignificant impact on the I of crop WP (p > 0.05). Gravel size (GS), the amount of gravel mulching (AGM), the degree of gravel mulching (DGM), and the gravel mulching thickness (GMT) had a significantly positive impact on crop yield and WP (p < 0.05). The stepwise multiple linear regression analysis results indicated that the primary regional factors influencing yield were AAT and A, contributing 43.14% and 53.09%, respectively. GMT and GS were identified as significant gravel characterization factors impacting yield, contributing 82.63% and 17.37%, respectively. AAP and GMT were the main regional factors and gravel characterization factors affecting WP. Furthermore, the I values for cash crop yield and WP were higher than that for food crops, and moderate fertilization and irrigation would increase the I values of yield and WP. The benefits of GM are strongly correlated with the planting year. This study’s results show that GM generally improves crop yield and WP, although the extent of this impact varies based on different conditions. These findings are not only useful in relation to their direct applicability to other countries worldwide but also due to their potential to provide new ideas for agricultural practices in similar crop-growing environments. Full article

Medication-related osteonecrosis of the jaw (MRONJ) is a debilitating adverse effect of bisphosphates, antiresorptive therapy or antiangiogenic agents that can potentially increase oxidative stress, leading to progressive osteonecrosis of the jaws. Despite the large number of published systematic reviews, there is a lack of potential MRONJ treatment protocols utilising photobiomodulation (PBM) as a single or adjunct therapy for preventive or therapeutic oncology or non-oncology cohort. Hence, this systematic review aimed to evaluate PBM laser efficacy and its dosimetry as a monotherapy or combined with the standard treatments for preventive or therapeutic approach in MRONJ management. The objectives of the review were as follows: (1) to establish PBM dosimetry and treatment protocols for preventive, therapeutic or combined approaches in MRONJ management; (2) to highlight and bridge the literature gaps in MRONJ diagnostics and management; and (3) to suggest rationalised consensus recommendations for future randomised controlled trials (RCTs) through the available evidence-based literature. This review was conducted according to the PRISMA guidelines, and the protocol was registered at PROSPERO under the ID CRD42021238175. A multi-database search was performed to identify articles of clinical studies published from their earliest records until 15 December 2023. The data were extracted from the relevant papers and analysed according to the outcomes selected in this review. In total, 12 out of 126 studies met the eligibility criteria. The striking inconsistent conclusions made by the various authors of the included studies were due to the heterogeneity in the methodology, diagnostic criteria and assessment tools, as well as in the reported outcomes, made it impossible to conduct a meta-analysis. PBM as a single or adjunct treatment modality is effective for MRONJ preventive or therapeutic management, but it was inconclusive to establish a standardised and replicable protocol due to the high risk of bias in a majority of the studies, but it was possible to extrapolate the PBM dosimetry of two studies that were close to the WALT recommended parameters. In conclusion, the authors established suggested rationalised consensus recommendations for future well-designed robust RCTs, utilising PBM as a monotherapy or an adjunct in preventive or therapeutic approach of MRONJ in an oncology and non-oncology cohort. This would pave the path for standardised PBM dosimetry and treatment protocols in MRONJ management. Full article

Meta-path-based heterogeneous graph neural networks have received widespread attention for better mining the similarities between heterogeneous nodes and for discovering new recommendation rules. Most existing models depend solely on node IDs for learning node embeddings, failing to leverage attribute information fully and to clarify the reasons behind a user’s interest in specific items. A heterogeneous graph neural network for recommendation named MIMA (multi-feature interaction meta-path aggregation) is proposed to address these issues. Firstly, heterogeneous graphs consisting of user nodes, item nodes, and their feature nodes are constructed, and the meta-path containing users, items, and their attribute information is used to capture the correlations among different types of nodes. Secondly, MIMA integrates attention-based feature interaction and meta-path information aggregation to uncover structural and semantic information. Then, the constructed meta-path information is subjected to neighborhood aggregation through graph convolution to acquire the correlations between different types of nodes and to further facilitate high-order feature fusion. Furthermore, user and item embedding vector representations are obtained through multiple iterations. Finally, the effectiveness and interpretability of the proposed approach are validated on three publicly available datasets in terms of NDCG, precision, and recall and are compared to all baselines. Full article

This paper proposes a new meta-heuristic optimization algorithm, the crown growth optimizer (CGO), inspired by the tree crown growth process. CGO innovatively combines global search and local optimization strategies by simulating the growing, sprouting, and pruning mechanisms in tree crown growth. The pruning mechanism balances the exploration and exploitation of the two stages of growing and sprouting, inspired by Ludvig’s law and the Fibonacci series. We performed a comprehensive performance evaluation of CGO on the standard testbed CEC2017 and the real-world problem set CEC2020-RW and compared it to a variety of mainstream algorithms such as SMA, SKA, DBO, GWO, MVO, HHO, WOA, EWOA, and AVOA. The best result of CGO after Friedman testing was 1.6333/10, and the significance level of all comparison results under Wilcoxon testing was lower than 0.05. The experimental results show that the mean and standard deviation of repeated CGO experiments are better than those of the comparison algorithm. In addition, CGO also achieved excellent results in specific applications of robot path planning and photovoltaic parameter extraction, further verifying its effectiveness and broad application potential in practical engineering problems. Full article

With the implementation of conceptual labeling on online learning resources, knowledge-concept recommendations have been introduced to pinpoint concepts that learners may wish to delve into more deeply. As the core subject of learning, learners’ preferences in knowledge concepts should be given greater attention. Research indicates that learners’ preferences for knowledge concepts are influenced by the characteristics of their group structure. There is a high degree of homogeneity within a group, and notable distinctions exist between the internal and external configurations of a group. To strengthen the group-structure characteristics of learners’ behaviors, a multi-task strategy for knowledge-concept recommendations is proposed; this strategy is called Knowledge-Concept Recommendations with Heterogeneous Graph-Contrastive Learning. Specifically, due to the difficulty of accessing authentic social networks, learners and their structural neighbors are considered positive contrastive pairs to construct self-supervision signals on the predefined meta-path from heterogeneous information networks as auxiliary tasks, which capture the higher-order neighbors of learners by presenting different perspectives. Then, the Information Noise-Contrastive Estimation loss is regarded as the main training objective to increase the differentiation of learners from different professional backgrounds. Extensive experiments are constructed on MOOCCube, and we find that our proposed method outperforms the other state-of-the-art concept-recommendation methods, achieving $6.66\%$ with $HR@5$, $8.85\%$ with $NDCG@5$, and $8.68\%$ with $MRR$. Full article

This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods presented in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper was conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods. Full article

Food loss (FL) and food waste (FW) have become severe global problems, contributing to resource inefficiency and environmental degradation. Approximately 6% of greenhouse gas emissions (GHGs) are derived from FW, which is usually discarded in landfills, emitting methane, a gas that is 28 times more harmful than CO₂. Diverting the path of FW towards the energy industry represents a promising avenue to mitigate the environmental impact and save resources while generating energy substitutes. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach was utilized to conduct a systematic literature review on 10 different conversion processes used to convert FL and FW into energy. Anaerobic bioconversion integrated with pyrolysis emerges as a potential eco-friendly and promising solution for FW management, nutrient recovery and energy production in various forms, including biogas, heat, biohydrogen and biochar. Despite its potential, the anaerobic digestion of FW still faces some challenges related to the production of intermediate harmful compounds (VOCs, NH₃, H₂S), which necessitate precise process control and optimization. Nonetheless, converting FW into energy can provide economic and environmental benefits in the context of the circular economy. This review offers insightful information to stakeholders, academics and policymakers who are interested in utilizing FW as a means of producing sustainable energy by summarizing the important findings of ten different waste-to-energy processing methods and their potential for improved energy recovery efficiency. Full article

This article delves into Brazil’s rapidly expanding e-commerce sector, emphasizing its significant growth and evolving dynamics. Employing a meta-narrative review and a convergence-coding matrix, this research systematically analyzes and integrates findings from the existing literature to reveal critical industry patterns. The analysis identifies four pivotal clusters: consumer behavior, e-commerce structure, product distribution, and environmental sustainability. These elements collectively offer a comprehensive view of Brazil’s present and future e-commerce directions. This study underscores the imperative for strategies responsive to changing consumer behaviors, technological advancements, and environmental concerns. It also furnishes practical insights for enhancing online retail consumer engagement, logistical efficiency, and sustainability. Furthermore, this research advocates for e-commerce as a vehicle for digital inclusion, calling for policies that promote equitable access to online markets. This underscores its broader socio-economic importance, suggesting a path forward for stakeholders in shaping a more inclusive and sustainable e-commerce ecosystem. Full article