Search Results (105)

Solving geometry problems is a critical challenge in education, for it demands the integration of textual semantic descriptions, mathematical formula logic and spatial graphical information, as well as rigorous geometric theorem application and stepwise logical deduction. These are core capabilities that underpin the realization of personalized intelligent tutoring and efficient educational resource allocation. Traditional geometry problem solving methods often suffer from deficiencies in accuracy and the fusion of text, formula and image features. Hence, this paper proposes a method of solving geometry problems based on a text–formula–image (TFI) multimodal parsing and fusion model. The TFI parser employs a self-attention multilayer Transformer to enhance the extraction of logical relations among geometric text expressions. Meanwhile, it parses formulas into tree structures to overcome the loss of formula structural features, which utilizes symbolic embedding and tree-structured encoding to preserve hierarchical logical information and yields unified formula representations via a multi-granularity fusion module. The TFI parser also leverages a Feature Pyramid Network (FPN) for the accurate detection of geometric and non-geometric instances, resolves the issues of blurred segmentation for slender geometric elements and the inaccurate localization of small-sized symbols through mask averaging and RoIAlign, and generates high-dimensional image features using DenseNet-121. The TFI multimodal fusion model integrates a contrastive learning mechanism and constructs fused feature representations by stacking self-attention and cross-attention layers. This design effectively narrows the semantic gap between text, formula, and image features, addressing the inadequacy of traditional fusion approaches in deep cross-modal feature alignment. An attention-augmented Gated Recurrent Unit (GRU) network processes the fused TFI features to produce target operation trees and geometry solutions, ensuring interpretable and precise reasoning performance. The proposed method is evaluated on the PGDP5K and GeoEval datasets, and it achieves an average accuracy of 59.63% in geometry problem solving, which validates its effectiveness. This paradigm offers a viable technical approach for uniformly modeling complex educational tasks, including geometry problem solving and timetable scheduling. Full article

Geometric problem-solving has always been a great challenge in the field of deductive reasoning and artificial intelligence. Symmetry is a defining characteristic of geometric shapes and properties. Consequently, the application of symmetry principles to geometric reasoning arises as a natural choice. To address the efficiency degradation and limited generalization, we propose FGeo-ISRL, a neural-symbolic inverse search framework whose core is the synergistic integration of a task-fine-tuned large language model and Monte Carlo Tree Search. Under the formal framework of FormalGeo, geometric theorems are iteratively applied starting from the given conditions and the target conclusion, in order to infer the necessary supporting premises. The large language model simultaneously serves as a policy network and a value network, guiding theorem application decisions and evaluating intermediate proof states, whereas the Monte Carlo Tree Search performs structured exploration over the state space, both training for policy refinement and inference for online search. The reinforcement learning agent is trained with a hybrid reward scheme, combining immediate feedback from the value difference and a sparse success reward. Experiments demonstrate the effectiveness and correctness of FGeo-ISRL. It not only achieves a Single-Step Theorem Accuracy of 90.2% and a Geometric Problem-Solving Accuracy of 83.14%, but also ensures that every step of the proof process remains readable, verifiable, and traceable. Full article

Tart cherry is an important fruit crop in Utah, where irrigation is essential due to arid conditions. Precision irrigation requires reliable indicators of plant water status, and stem water potential (Ψ_stem), is among the most sensitive though labor-intensive and spatially limited. This study develops Ψ_stem estimation models using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery combined with meteorological and soil moisture data, applying Symbolic Regression (SR). Results show a stronger correlation between optical bands and Ψ_stem during the pre-harvest period. Among 85 vegetation indices, the Red Chromatic Coordinate (RCC) index performed best (R² = 0.67). Six equations were generated for different data-availability scenarios and validated using a leave-one-tree-out (modified k-fold) approach, resulting in Ψ_stem estimates with R² values ranging from 0.67 to 0.80 and root mean square errors (RMSE) ranging from 0.11 to 0.08 MPa. Notably, SR was able to produce interpretable equations that enhance model transparency and transferability. Model robustness was further confirmed using an independent dataset from a different location. To our knowledge, this is the first application of SR for Ψ_stem estimation, offering a scalable and interpretable tool to support irrigation management in tart cherry orchards. Full article

Magnetoplasmadynamic thrusters (MPDTs) are becoming increasingly viable as electric propulsion (EP) technology for space missions, yet their complex plasma behaviour, intricate thrust-generation process, and nonlinear multi-physics thrust–field interactions prove difficult for conventional modelling approaches, including empirical techniques. Traditional empirical modelling shortcomings include failure to predict accurately across wide operational regimes. This paper introduces a physically interpretable, artificial intelligence (AI)-powered thrust model for Applied-Field Magnetoplasmadynamic Thrusters (AF-MPDTs), developed using symbolic regression (SR) to address the gap between data-driven prediction and physics-based understanding. The proposed method, an alternative to traditional black box AI methods, incorporates physics-aware composite-term operators, ensuring that the resulting analytical expressions are bounded by known physical behaviours while retaining the flexibility to discover previously overlooked nonlinear couplings. A comprehensive dataset of AF-MPDTs undergoes rigorous preprocessing to ensure dimensional consistency and noise robustness. The SR model then evolves candidate equations, balancing predictive accuracy with interpretability through Tree-Structured Parzen Estimator (TPE) optimisation. The results, closed-form surrogate correlations with 95.98% of accuracy as goodness of fit, root mean square error of 0.0199, mean absolute error of 0.0143, and mean absolute percentage error reduction of 28.91% against the benchmark model in the literature. A post-discovery protocol for numerical robustness and physical consistency is implemented, with Shapley Additive Explanations (SHAP) providing insight into the influence of each composite-term in the developed correlation, followed by a numerical robustness and physical consistency validation using a Monte Carlo (MC) envelope. A StabilityScore is calculated for all developed correlations, enabling explicit accuracy–complexity–stability comparisons. In doing so, we demonstrated that SR can systematically recover known physical relationships—such as the scaling of thrust with discharge current and applied magnetic field—while proposing interpretable higher-order corrections that improve fit quality. The resulting SR-based thrust models not only achieve competitive accuracy relative to state-of-the-art numerical and empirical methods but also offer more explainable and interpretable results capable of revealing compact formulations that capture essential acceleration mechanisms with transparency. Overall, this paper, using SR, advances explainable AI (XAI) methodologies capable of generating trustworthy, analytically transparent models for next-generation electric propulsion systems. Full article

Cultural diversity holds an irreplaceable ecological value in biodiversity conservation. Jingxi is a county-level city in Baise City, Guangxi Zhuang Autonomous Region. In Jingxi, where the Zhuang ethnic group accounts for 99.4% of the population, a symbiotic relationship has developed between its unique ethnic culture and ecological environment. According to the 2017 census of old trees (OTs) in Jingxi, a total of 1361 OTs were recorded, of which 63.3% (865 trees) were concentrated in human settlements, including village entrances or exits, and cultivated lands, demonstrating significant spatial differentiation. This distinctive distribution pattern raises two core research questions: (1) What are the spatial distribution patterns of OTs within human settlements? (2) Do cultural factors play a significant role in OTs conservation? Therefore, an ethnobotanical study of OTs in Jingxi is necessary. The objectives of this study are to: (1) conduct a comprehensive ethnobotanical investigation of the OTs among the Zhuang people in the region; (2) summarize the environmental spaces of OTs based on their geographical locations; (3) analyze the symbolic cultural meaning associated with OTs across different environmental spaces. This study also aims to reveal conservation strategies for OTs from a cultural perspective and to integrate cultural values into biodiversity conservation, thereby providing significant insights into the mechanisms underlying cultural–ecological synergy. Full article

The increasing accumulation of end-of-life tires has motivated the development of sustainable construction materials incorporating recycled rubber for acoustic insulation applications. This study proposes a data-driven framework for predicting the weighted airborne sound reduction index ($R_w$) of recycled rubber–polyurethane composite panels based on a limited experimental dataset. Specimens with varying granulometric composition, material density, and polyurethane adhesive dosage were evaluated in accordance with EN ISO 10140-2:2010 and EN ISO 717-1:2013. To address data scarcity, a regression-oriented SMOTE strategy was applied exclusively to the training set to preserve statistical representativeness and avoid data leakage. Test set representativeness was ensured by systematically evaluating numerous data splits and adopting the one that maximized multivariate statistical consistency. A hierarchical modeling approach was adopted, ranging from classical regression models to tree-based ensemble methods and multigene symbolic regression. Model performance was evaluated using R², RMSE, MAE, and MAPE on an independent test set. The highest accuracy and robustness were obtained using symbolic regression, with R² values close to 0.99 and minimal prediction errors. Shapley value analysis and PDP/ICE plots identified material density as the dominant predictor of $R_w$, followed by polyurethane adhesive dosage, while granulometric composition exhibited a weaker influence. The proposed framework provides an accurate and interpretable tool for the preliminary design and optimization of recycled rubber acoustic panels. Full article

Traditional Yuluxiang pear cultivation employs wide row spacing to facilitate sunlight penetration and ventilation, but this reduces land use efficiency. Therefore, this study investigated the effects of intercropping dandelions in Yuluxiang pear orchards on soil environment, pear tree growth, and fruit quality. The experiment included three treatments: monoculture (M), dandelion intercropping (DI), and dandelion intercropping combined with microbial organic fertilizer application (DI + MF). Results indicated that the combined DI + MF treatment enhanced soil nutrients by increasing the content of Alkaline Hydrolyzable Nitrogen (AN), Total phosphorus (TP), and Available phosphorus (AP). The DI treatment altered the microbial community structure, enriching beneficial bacteria (such as the phyla Acidobacteriota and Actinomycetota) and fungi (such as the phyla Mucorales and Basidiomycota), thereby enhancing nutrient cycling. Treatment effects were most pronounced in the topsoil layer (0–20 cm) and diminished with increasing depth. Regarding tree physiology, DI treatment increased leaf Symbolic Consistency (Gs) and Intercellular CO₂ Concentration (Ci). The DI + MF treatment significantly boosted leaf chlorophyll content, with both intercropping treatments improving tree photosynthesis and nutritional status. In terms of fruit quality, the DI + MF treatment demonstrated the best overall performance. Its single fruit weight reached the highest values at all stages (143.86 g, 315.48 g, and 515.03 g), while the soluble solids content peaked at 130 days post-flowering, with increased levels of sugars, Vitamin C (VC), total phenols, and flavonoids in the fruit. This significantly enhanced both the external appearance and internal quality of the fruit. Research indicates that the DI + MF treatment can systematically enhance soil quality, tree vitality, and fruit quality in Yuluxiang pear orchards by improving soil physicochemical properties, regulating microbial communities, and boosting tree physiological functions. Full article

Accurate assignment of International Classification of Diseases (ICD) codes is essential for healthcare analytics, billing, and clinical research. However, manual coding remains time-consuming and error-prone due to the scale and complexity of the ICD taxonomy. While hierarchical deep learning approaches have improved automated coding, their deployment across large taxonomies raises scalability and efficiency concerns. To address these limitations, we introduce the Augmented Decision Tree (ADT) framework, which integrates deep learning with symbolic rule-based logic for automated medical coding. ADT employs an automated lexical screening mechanism to dynamically select the most appropriate modeling strategy for each decision node, thereby minimizing manual configuration. Nodes with high keyword distinctiveness are handled by symbolic rules, while semantically ambiguous nodes are assigned to deep contextual models fine-tuned from PubMedBERT. This selective design eliminates the need to train a deep learning model at every node, significantly reducing computational cost. A case study demonstrates that this hybrid and adaptive ADT approach supports scalable and efficient ICD coding. Experimental results show that ADT outperforms a pure decision tree baseline and achieves accuracy comparable to that of a full deep learning-based decision tree, while requiring substantially less training time and computational resources. Full article

From past to present, the tree has functioned as a powerful symbol associated with birth, life, death and belief systems across cultures. In relation to cosmic order and divine connection, it has often been conceptualized as a cosmic entity. The tree cult, while sharing universal features rooted in religion and mythology, also carries distinctive meanings within Turkish cultural tradition. Drawing on this framework, this article examines the use of mythological elements in post-1980 Turkish literature through the lens of the tree cult. It first discusses the religious and mythological foundations of the motif and its specific manifestations in Turkish culture. The analysis then focuses on selected works by nine prominent authors—Murathan Mungan, Pınar Kür, Sevinç Çokum, İhsan Oktay Anar, Hasan Ali Toptaş, Orhan Pamuk, Latife Tekin, Murat Gülsoy, and Nazan Bekiroğlu—whose writings display strong representational capacity. Through thematic and textual analysis, the study explores how the tree cult is integrated into these literary works and offers a panoramic perspective on the relationship between mythology and literature in contemporary Turkish narratives. Full article

The presence of distinctive imprinted signs on old forestry tools reflects a little-documented tradition practiced by artisanal blacksmiths in the Alpine region until the early 20th century. These marks, hammered onto tools such as axes and pickaroons, carried meanings that intertwined craftsmanship, ownership, and local identity. This element of material culture is rarely mentioned in the literature. This study examined imprinted signs on 331 tools from 88 locations across the Alpine regions of Italy, from Friuli-Venezia Giulia to Valle d’Aosta, with supplementary observations in other countries. The objectives were to record the geographic distribution of imprints, interpret their potential meanings, and preserve evidence of a disappearing tradition. The spatial distribution of the markings corresponded to the Alpine territory and overlapped with a shared cultural region inhabited by three ethnic groups, although similar signs were recorded as far as the Carpathian regions. The meanings of certain imprints, such as religious symbols or representations of the tree of life, are recognizable, whereas those of other common signs remain unknown. The findings suggest that the imprints may reflect a distinct cultural practice and a symbolic language whose full significance has yet to be understood and would require further ethnographic investigations. Full article

This investigation examines diffusion processes for predicting whole-stand volume, incorporating the variability and uncertainty inherent in regional, operational, and environmental factors. The distribution and spatial organization of trees within a specified forest region, alongside dynamic fluctuations and intricate uncertainties, are modeled by a set of nonsymmetric stochastic differential equations of a sigmoidal nature. The study introduces a three-dimensional system of stochastic differential equations (SDEs) with mixed-effect parameters, designed to quantify the dynamics of the three-dimensional distribution of tree-size components—namely diameter (diameter at breast height), potentially occupied area, and height—with respect to the age of a tree. This research significantly contributes by translating the analysis of tree size variables, specifically height, occupied area, and diameter, into stochastic processes. This transformation facilitates the representation of stand volume changes over time. Crucially, the estimation of model parameters is based exclusively on measurements of tree diameter, occupied area, and height, avoiding the need for direct tree volume assessments. The newly developed model has proven capable of accurately predicting, tracking, and elucidating the dynamics of stand volume yield and growth as trees mature. An empirical dataset composed of mixed-species, uneven-aged permanent experimental plots in Lithuania serves to substantiate the theoretical findings. According to the dataset under examination, the model-based estimates of stand volume per hectare in this region exhibited satisfactory goodness-of-fit statistics. Specifically, the root mean square error (and corresponding relative root mean square error) for the living trees of mixed, pine, spruce, and birch tree species were 68.814 m³ (20.4%), 20.778 m³ (7.8%), 32.776 m³ (37.3%), and 4.825 m³ (26.3%), respectively. The model is executed within Maple, a symbolic algebra system. Full article

Large language models (LLMs) demonstrate outstanding capability in understanding natural language and show great potential in open-domain travel planning. However, when confronted with multi-constraint itineraries, personalized recommendations, and scenarios requiring rigorous external information validation, pure LLM-based approaches lack rigorous planning ability and fine-grained personalization. To address these gaps, we propose the Symbolic LoRA Travel Planner (SLTP) framework—an agent architecture that combines a two-stage symbol-rule LoRA fine-tuning pipeline with a user multi-option heuristic tree search (MHTS) planner. SLTP decomposes the entire process of transforming natural language into executable code into two specialized, sequential LoRA experts: the first maps natural-language queries to symbolic constraints with high fidelity; the second compiles symbolic constraints into executable Python planning code. After reflective verification, the generated code serves as constraints and heuristic rules for an MHTS planner that preserves diversified top-K candidate itineraries and uses pruning plus heuristic strategies to maintain search-time performance. To overcome the scarcity of high-quality intermediate symbolic data, we adopt a teacher–student distillation approach: a strong teacher model generates high-fidelity symbolic constraints and executable code, which we use as hard targets to distill knowledge into an 8B-parameter Qwen3-8B student model via two-stage LoRA. On the ChinaTravel benchmark, SLTP using an 8B student achieves performance comparable to or surpassing that of other methods built on DeepSeek-V3 or GPT-4o as a backbone. Full article

Secular trees have an important contribution to today’s communities, not only due to cultural or historical reasons but also to recreational aspects. Management of such species can be done after a thorough analysis is done related to their health status. In most cases, a visual inspection to determine the health status can lead to unsatisfactory results. Modern technology, such as computer tomography, has results that are accurate and valid. A total of 17 secular oak trees (Quercus robur) were sampled and analyzed with Arbotom 2D (Arbotom 2D, Rinn Tech, Heidelberg, Germany) by using sensors on the tree trunks. Besides this, it is imperative to compare the results in the field with the view of the community related to their local symbol. Results revealed severe internal decay (75%–80% damaged wood) in eight oaks, while in the core of the trunk (10%–50% damaged wood), it was seen in seven oaks. Only two oaks have good health status. Survey results indicated the oaks as moderate healthy; only 18.8% respondents from the community consider the oaks unhealthy or in visible decline. This can lead to serious injuries to bystanders. The results have demonstrated a great link between technical and social research so decision-making stakeholders can apply a tailored management for their area. Full article

Historic districts, as important spatial units that carry urban cultural memory and everyday social life, play a crucial role in shaping residents’ spatial identity, emotional attachment, and perceptual experience. Although quantitative research on built environments and perception has advanced considerably in recent years, the mechanisms through which perception is formed in historic districts, particularly the nonlinear threshold effects and perceptual trade-off patterns that arise under conditions of high-density and mixed land use, remain insufficiently examined. To address this gap, this study develops an analytical framework that integrates spatial attributes with multidimensional subjective perceptions. Focusing on six historic districts in central Shanghai, the study combines micro-scale environmental indicators extracted from street-view imagery, POI data, and public perceptual evaluations and employs an XGBoost model to identify the nonlinear response patterns, threshold effects, and perceptual trade-offs across seven perceptual dimensions. The results show that natural elements such as visual greenery and sky openness generate significant threshold-based enhancement effects, and once reaching a certain level of visibility, they substantially increase positive perceptions including beauty, safety, and cleanliness. By contrast, commercial and traffic-related facilities exhibit dual and competing perceptual influences. Moderate densities enhance liveliness, whereas high concentrations tend to induce perceptual fatigue and intensify negative emotional responses. Overall, perceptual quality in historic districts does not arise from linear accumulation but is shaped by dynamic perceptual trade-offs among natural features, functional elements, and cultural symbolism. Overall, the study reveals the coupling mechanism between spatial renewal and perceptual experience amid the pressures of urban modernization. It also demonstrates that increasing visible greenery (e.g., planting street trees, incorporating micro-green spaces, improving façade greening), enhancing street openness (e.g., optimizing view corridors, reducing visual obstruction, implementing moderate setback adjustments), guiding a moderate mix and spatial distribution of commercial and service functions, and strengthening the perceptibility of cultural landscape elements (e.g., façade restoration, streetscape coordination, and improved signage systems) are concrete and effective planning and design actions for improving landscape quality and enhancing the experiential quality of historic districts. Full article

Woody plants are integral to the ecological and cultural context of the ancient Near East. Biblical references to trees reflect both their practical uses and their symbolic significance. This is a systematic review focused specifically on botanical affiliation, geographical origin and natural habitat type and the cultivation potential of 97 woody species in temperate urban environments, important to ancient economy, culture and religion and consistently identified by scholars in biblical texts. The study applies a multifaceted methodological framework that integrates i.a. textual analysis, literature review and 20 years of horticultural observations. Moreover, the historical utility of these species was studied based on interpreting Bible quotes and comparative multilingual analysis of biblical texts. Analyzed woody plant species represent 36 botanical families, over 50% native to Ancient Palestine. About 18.6% were cultivated by humans, the rest grew in various habitats. Biblical sources revealed 17 uses, with many species having symbolic, practical, or multiple roles. Further, 32% of the species discussed can be grown directly in the soil in temperate climate, while 52.5% require container cultivation. Additionally, 15.5% of the species are hard to cultivate and thus not recommended for Biblical gardens. The content presented also provides valuable insights that may support the development of Biblical gardens within urban environments worldwide. Full article