Search Results (672)

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article

Agriculture and its allied activities contribute to the primary sector in India and act as the basis for the country’s economy. Available agricultural landholdings are scattered as multiple plots across the country. Land fragmentation has led to problems achieving economies of scale and economies of scope; lower productivity, efficiency, and modernization; loss of biodiversity; and little scope for mechanization and technology. FPOs are small clusters of farmers who collaborate to enhance their bargaining strength through collective procurement, processing, and marketing efforts. To enhance the performance of FPOs at the grassroots level, the engagement of cluster-based business organizations (CBBOs) is vital. Millet FPOs are similar to voluntary farmer groups that are involved in the cultivation and promotion of millets. IIMR-promoted millet FPOs were selected purposively for the present study as they are involved in millet cultivation and farming. A total of 450 millet farmers from 15 FPOs and 3 states were randomly chosen for this action research study. The present research identified 10 key factors and collected farmers’ opinions toward member participation in millet FPOs using interpretive structural modeling. The ISM approach provided a clear understanding of how the selected factors interconnect hierarchically with each other as foundational drivers and dependent outcomes. The results from the MICMAC analysis demonstrated that foundational interventions, such as post-harvest technology availability (V2) and knowledge transfer by KVKs (V5), directly support higher-level objectives. Intermediate factors like economies of scale (V1) and market and credit linkages (V3) transform these services into operational advantages, while the outcome factors of business planning (V8), FPO branding (V7), and bargaining power (V9) emerge as dependent variables. The model demonstrates that V2 catalyzes improvements across the production, market, and institutional domains, cascading through intermediate enablers (V1, V4, V5, V6) to strengthen outcomes (V3, V7, V8, V9, V10). This hierarchy demonstrates that investing in post-harvest technology and complementary extension services is critical for building resilient millet FPOs and enhancing member participation. Full article

Student exam pass prediction (EPP) is a key task in educational assessment and can help teachers identify students’ learning obstacles in a timely manner and optimize teaching strategies. However, existing EPP models, although capable of providing quantitative analysis, suffer from issues such as complex algorithms, poor interpretability, and unstable accuracy. Moreover, the evaluation process is opaque, making it difficult for teachers to understand the basis for scoring. To address this, this paper proposes an approximate belief rule base (ABRB-a) student examination passing prediction method based on adaptive reference point selection using symmetry. Firstly, a random forest method based on cross-validation is adopted, introducing intelligent preprocessing and adaptive tuning to achieve precise screening of multi-attribute features. Secondly, reference points are automatically generated through hierarchical clustering algorithms, overcoming the limitations of traditional methods that rely on prior expert knowledge. By organically combining IF-THEN rules with evidential reasoning (ER), a traceable decision-making chain is constructed. Finally, a projection covariance matrix adaptive evolution strategy (P-CMA-ES-M) with Mahalanobis distance constraints is introduced, significantly improving the stability and accuracy of parameter optimization. Through experimental analysis, the ABRB-a model demonstrates significant advantages over existing models in terms of accuracy and interpretability. Full article

Effective production control requires aligning strategic planning with real-time execution under dynamic and stochastic conditions. This study proposes a scalable dual-agent Deep Reinforcement Learning (DRL) framework for the joint optimisation of Work-In-Process (WIP) control and job sequencing in flow-shop environments. A strategic DQN agent regulates global WIP to meet throughput targets, while a tactical DQN agent adaptively selects dispatching rules at the machine level on an event-driven basis. Parameter sharing in the tactical agent ensures inherent scalability, overcoming the combinatorial complexity of multi-machine scheduling. The agents coordinate indirectly via a shared simulation environment, learning to balance global stability with local responsiveness. The framework is validated through a discrete-event simulation integrating agent-based modelling, demonstrating consistent performance across multiple production scales (5–15 machines) and process time variabilities. Results show that the approach matches or surpasses analytical benchmarks and outperforms static rule-based strategies, highlighting its robustness, adaptability, and potential as a foundation for future Hierarchical Reinforcement Learning applications in manufacturing. Full article

The development of the Connected and Autonomous Vehicle (CAV) and Hybrid Electric Vehicle (HEV) provides a new effective means for the optimization of eco-driving strategies. However, the existing research has not effectively considered the cooperative speed optimization and power allocation problem of the Connected and Autonomous Plug-in Hybrid Electric Vehicle (CAPHEV) platoon. To this end, a hierarchical eco-driving strategy is proposed, which aims to enhance driving efficiency and fuel economy while ensuring the safety and comfort of the platoon. Firstly, an improved car-following model is proposed, which considers the motion states of multiple preceding vehicles. On this basis, a platoon cooperative car-following decision-making method based on model predictive control is designed. Secondly, a distributed energy management strategy is constructed, and a bionic optimization algorithm based on the behavior of nutcrackers is introduced to solve nonlinear problems, so as to solve the energy distribution and management problems of powertrain systems. Finally, the tests are conducted under the driving cycle of the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET). The results show that the proposed strategy can ensure the driving safety of the CAPHEV platoon in different scenes, and has excellent tracking accuracy and driving comfort. Compared with the rule-based strategy, the equivalent energy consumption of UDDS and HWFET is reduced by 20.7% and 5.5% in the battery’s healthy charging range, respectively. Full article

This study aims to elucidate the molecular mechanisms underlying cyanogenic glycoside accumulation in flax. As an important oil and fiber crop, the nutritional value of flax is compromised by the toxicity of cyanogenic glycoside. To clarify the key genetic regulators and temporal patterns of cyanogenic glycoside biosynthesis, transcriptomic sequencing was performed on seeds from high- and low-cyanogenic glycoside flax varieties (‘MONTANA16’ and ‘Xilibai’) at three developmental stages: bud stage, full flowering stage, and capsule-setting stage. A total of 127.25 Gb of high-quality data was obtained, with an alignment rate exceeding 87.80%. We identified 31,623 differentially expressed genes (DEGs), which exhibited distinct variety- and stage-specific expression patterns. Principal component analysis (PCA) and hierarchical clustering demonstrated strong reproducibility among biological replicates and revealed the seed pod formation stage as the period with the most significant varietal differences, suggesting it may represent a critical regulatory window for cyanogenic glycoside synthesis. GO and KEGG enrichment analyses indicated that DEGs were primarily involved in metabolic processes (including secondary metabolism and carbohydrate metabolism), oxidoreductase activity, and transmembrane transport functions. Of these, the cytochrome P450 pathway was most significantly enriched at the full bloom stage (H2 vs. L2). A total of 15 LuCYP450 and 13 LuUGT85 family genes were identified, and their expression patterns were closely associated with cyanogenic glycoside accumulation: In high-cyanogenic varieties, LuCYP450-8 was continuously upregulated, and LuUGT85-12 was significantly activated during later stages. Conversely, in low-cyanogenic varieties, high expression of LuCYP450-2/14 may inhibit synthesis. These findings systematically reveal the genetic basis and temporal dynamics of cyanogenic glycoside biosynthesis in flax and highlight the seed pod formation stage as a decisive regulatory window for cyanogenic glycoside synthesis. This study provides new insights into the coordinated regulation of cyanogenic pathways and establishes a molecular foundation for breeding flax varieties with low CNG content without compromising agronomic traits. Full article

Aiming at the problem that the well depth parameters in existing intelligent drilling technology can not be obtained underground, a multi-branch parallel neural network is proposed to solve the problem of downhole well depth tracking, and its effectiveness is verified by a field example. After analyzing and correcting the quality of the logging data collected on site by using DBSCAN (a density clustering algorithm), five parameters of WOB, rotating speed, displacement, pump pressure, and torque are selected to predict and calculate the downhole mechanical ROP. Adjust the structure of a traditional artificial BP neural network and design a multi-branch parallel neural network, change the basic architecture of the original hierarchical operation, make full use of the operation efficiency of a computer to realize parallel operation, and adopt the method of point-to-point depth comparison when evaluating the well depth tracking effect. The results indicate that the MAE and mechanical drilling rate evaluation values obtained were 1.18 and 0.873, respectively. The multi-branch parallel neural network achieved a 66.55% improvement in MAE compared to the original BP neural network, while the R² evaluation method showed a 61.82% increase. The average point-by-point comparison error in the example calculation was 0.012 m, with a maximum error of 0.268 m. This result can serve as a fundamental basis for judging changes in well depth during the drilling process. Full article

Ducted fan flying robots with robotic arms can perform physical interaction tasks in complex environments such as indoors. However, the coupling effects between the aerial platform, the robotic arm, and physical environment pose significant challenges for the robot to accurately approach and stably contact the target. To address this problem, we propose a unified control framework for a ducted fan flying robot that encompasses both flight planning and physical interaction. This contribution mainly includes the following: (1) A nonlinear model predictive control (NMPC)-based trajectory optimization controller is proposed, which achieves accurate and smooth tracking of the robot’s end effector by considering the coupling of redundant states and various motion and performance constraints, while avoiding potential singularities and dangers. (2) On this basis, an easy-to-practice hierarchical control framework is proposed, achieving stable and compliant contact of the end effector without controller switching between the flight and interaction processes. The results of experimental tests show that the proposed method exhibits accurate position tracking of the end effector without overshoot, while the maximum fluctuation is reduced by up to 75.5% without wind and 71.0% with wind compared to the closed-loop inverse kinematics (CLIK) method, and it can also ensure continuous stable contact of the end effector with the vertical wall target. Full article

This study aimed to systematically characterize the volatile organic compound (VOC) profiles of safflower (Carthamus tinctorius L.) from eight major production regions, providing a scientific basis for quality evaluation and geographical traceability. VOC profiling was conducted using gas chromatography–ion mobility spectrometry (GC–IMS), and regional differences were assessed through multivariate statistical analyses, including Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS–DA), Euclidean distance, and hierarchical clustering. Key differential compounds were identified by variable importance in projection (VIP) and relative odor activity value (ROAV) analyses, with aldehydes and esters emerging as the primary contributors to the discrimination of samples across regions. VOC fingerprints of safflower were further established, and a combined VIP–ROAV strategy was proposed for the screening of characteristic compounds. These findings provide a reliable reference for safflower quality control and offer practical guidance for its geographical authentication in the food industry. Full article

Entropy-based analyses have emerged as a powerful tool for quantifying the complexity, regularity, and information content of complex biological signals, such as electroencephalography (EEG). In this regard, EEG-based lie detection offers the advantage of directly providing more objective and less susceptible-to-manipulation results compared to traditional polygraph methods. To this end, this study proposes a novel multi-scale entropy approach by fusing fuzzy entropy (FE), time-shifted multi-scale fuzzy entropy (TSMFE), and hierarchical multi-band fuzzy entropy (HMFE), which enables the multidimensional characterization of EEG signals. Subsequently, using machine learning classifiers, the fused feature vector is applied to lie detection, with a focus on channel selection to investigate distinguished neural signatures across brain regions. Experiments utilize a publicly benchmarked LieWaves dataset, and two parts are performed. One is a subject-dependent experiment to identify representative channels for lie detection. Another is a cross-subject experiment to assess the generalizability of the proposed approach. In the subject-dependent experiment, linear discriminant analysis (LDA) achieves impressive accuracies of 82.74% under leave-one-out cross-validation (LOOCV) and 82.00% under 10-fold cross-validation. The cross-subject experiment yields an accuracy of 64.07% using a radial basis function (RBF) kernel support vector machine (SVM) under leave-one-subject-out cross-validation (LOSOCV). Furthermore, regarding the channel selection results, PZ (parietal midline) and T7 (left temporal) are considered the representative channels for lie detection, as they exhibit the most prominent occurrences among subjects. These findings demonstrate that the PZ and T7 play vital roles in the cognitive processes associated with lying, offering a solution for designing portable EEG-based lie detection devices with fewer channels, which also provides insights into neural dynamics by analyzing variations in multi-scale entropy. Full article

The cultural landscape of Gubeikou, with distinct historical stratification and event-relatedness, bears unique value. Against the backdrop of increasingly prominent themes of cultural heritage development and transformation, research on Gubeikou’s cultural landscapes remains fragmented and lacking in depth. This research explores its evolution mechanism via historical events to fill gaps. This study takes Gubeikou Town as the research object, applies the text analysis method to sort and categorize 302 historical events, summarizes 12 event types, identifies 19 landscape elements, and constructs a data matrix based on co-occurrence frequencies. It performs clustering analysis on these using Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (AHC), while integrating historical and geographical data. Findings: (1) The landscape evolution of Gubeikou can be divided into four main stages: the military embryonic period, the functional expansion period, the system maturity period, and the multi-element integration period. (2) The dynamic evolutionary trajectory of the correlation between its landscapes and events shows that the core factors affecting the evolution of cultural landscapes in each period not only maintain the dominance of military elements throughout the evolutionary process but also integrate diverse elements like economy, culture, and folk customs with social development, presenting the characteristics of composite evolution. (3) The landscape evolution is driven by the “primary–secondary synergy” dynamic structure composed of four types of activities: military–political, transportation, production–trade, and construction. It is the product of the coupling effect of political goals, social operation, and geographical conditions. This study provides a basis for the sustainable protection and utilization of Gubeikou, and also offers a reference for other regions. Full article

Rural settlement systems in core ecological barrier zones face heightened geological disaster risks, making vulnerability assessment crucial for enhancing resilience and sustainable development. This study examines Maoxian County, a typical high-risk disaster zone in western Sichuan, using the Vulnerability Scoping Diagram (VSD) model framework. The framework integrates exposure, sensitivity, and resilience dimensions to construct a comprehensive vulnerability assessment index system. Using the CRITIC-AHP combined weighting method and the Spatially Explicit Vulnerability Model, this research evaluates spatial differentiation patterns of geological disaster vulnerability in rural settlement systems at the township level to identify dominant vulnerability types and their underlying mechanisms. Results reveal significant spatial differentiation in vulnerability across the study area with distinct patterns: exposure exhibits an “east-high, west-low” distribution, sensitivity shows a “northwest-high, southeast-low” pattern, and resilience follows a “southeast-high, northwest-low” distribution. Overall vulnerability presents a “northwest–southeast high, central low” spatial configuration. The dominant factor method identified eight vulnerability types in rural settlements, including strong comprehensive vulnerability and exposure-sensitivity vulnerability. Based on the principle of “ecological security anchoring, systemic hierarchical regulation, chain-based risk interruption, and spatial precision adaptation,” tailored resilience enhancement strategies were proposed for each vulnerability type. This study provides a scientific basis for disaster risk prevention and control, land use optimization, and sustainable development in rural settlement systems. Full article

Classification of island coastal landscapes is a challenge to incorporate both the terrestrial and the aquatic environment characteristics, and place biological diversity in a regional and insular context. The Coastal and Marine Ecological Classification Standard (CMECS) was developed for use in the United States and incorporates geomorphic data, substrate data, biological information, as well as water column characteristics. The CMECS framework was applied to the island of Great Exuma, The Bahamas. The classification used data from existing studies to include oceanographic data, seawater temperature, salinity, benthic invertebrate surveys, sediment analysis, marine plant surveys, and coastal geomorphology. The information generated is a multi-dimensional description of benthic and shoreline biotopes characterized by dominant species. Biotopes were both mapped and described in hierarchical classification schemes that captured unique components of diversity in the mosaic of coastal natural communities. Natural community classification into biotopes is a useful tool to quantify ecological landscapes as a basis to develop monitoring over time for biotic community response to climate change and human alteration of the coastal zone. Full article

Amid accelerating global nitrogen deposition, China has emerged as the world’s third-largest hotspot after Western Europe and North America. Disentangling how elevated N inputs interact with intensifying precipitation and silvicultural practices is therefore essential for forecasting forest responses to ongoing climate change. Taking advantage of the “canopy-simulated nitrogen deposition” platform in Jigongshan National Nature Reserve, we compared tree-level census data from 2012 and 2022 to quantify decadal shifts in neighborhood competition under seven nitrogen addition and rainfall enhancement regimes. After using ordered-sample clustering to identify eight competitors as the optimal neighborhood size, we applied the Hegyi family of competitive indices (CI, CI1, CI2, mCI, mCI1 and mCI2) to analyze competitive responses at three hierarchical levels: the entire stand, all surviving trees and three dominant species (Quercus acutissima, Quercus variabilis, and Liquidambar formosana). The results indicate: (1) Nitrogen addition and rainfall enhancement did not alter the dominant tree species of the stand, which remained primarily Q. acutissima, Q. variabilis, and L. formosana. (2) The competition indices based on all trees showed an upward trend, whereas those calculated for surviving trees and for dominant species declined markedly (surviving trees p < 0.1, L. formosana CI1 p < 0.05). (3) Although nitrogen addition treatments did not alter overall stand competition intensity, it relieved competitive pressure on surviving trees by suppressing interspecific interactions (CI2 and mCI2); intraspecific competition also weakened, but at a slower rate. (4) Interspecific competition intensity for surviving L. formosana decreased significantly, whereas competition indices for Q. acutissima and Q. variabilis remained statistically unchanged. (5) Nitrogen addition methods (canopy vs. understory) had no significant effect on competition indices, while nitrogen addition intensity exhibited a dose-dependent effect: high nitrogen addition significantly reduced interspecific competition intensity more than low nitrogen addition (p < 0.05). In summary, nitrogen deposition primarily regulates interspecific competition through concentration rather than pathway, providing scientific basis for adaptive management of broad-leaved mixed forests in transitional zones under sustained nitrogen deposition scenarios. Full article

In uncertain battlefield environments, rapid and accurate detection, identification of hostile targets, and assessment of threat levels are crucial for supporting effective decision-making. Despite offering the advantage of structural transparency, traditional analytical methods rely on expert knowledge to construct models and often fail to comprehensively capture the non-linear causal relationships among complex threat factors. In contrast, data-driven methods excel at uncovering patterns in data but suffer from limited interpretability due to their black-box nature. Owing to probabilistic graphical modeling capabilities, Bayesian networks possess unique advantages in threat assessment. However, existing models are either constrained by the limitation of expert experience or suffer from excessively high complexity due to structure learning algorithms, making it difficult to meet the stringent real-time requirements of uncertain battlefield environments. To address these issues, this paper proposes a new method, the Tree–Hillclimb Search method—an efficient and interpretable threat assessment method specifically designed for uncertain battlefield environments. The core of the method is a structure learning algorithm constrained by expert knowledge—the initial network structure constructed from expert knowledge serves as a constraint, enabling the discovery of hidden causal dependencies among variables through structure learning. The model is then refined under these expert knowledge constraints and can effectively balance accuracy and complexity. Sensitivity analysis further validates the consistency between the model structure and the influence degree of threat factors, providing a theoretical basis for formulating hierarchical threat assessment strategies under resource-constrained conditions, which can effectively optimize sensor resource allocation. The Tree–Hillclimb Search method features (1) enhanced interpretability; (2) high predictive accuracy; (3) high efficiency and real-time performance; (4) actual impact on battlefield decision-making; and (5) good generality and broad applicability. Full article