Search Results (5,704)

Military settlements are an integral part of the military defense system of the Ming Great Wall, and the spatial layout of their constituent elements embodies the wisdom of ancient military geography. However, existing studies have predominantly focused on the macro-scale distribution of military settlements, with insufficient exploration of the spatial differentiation mechanisms of their micro-level constituent elements. Therefore, this study examines 61 military settlements in Miyun District, Beijing. Based on documentary research and field surveys, the types of constituent elements were systematically identified. This study employs kernel density analysis and the Optimal Parameters-based Geographical Detector (OPGD) model to explore their spatial patterns and driving mechanisms. The results show that (1) the constituent elements of military settlements collectively exhibit a spatial pattern of “one belt and three cores”, with pronounced spatial heterogeneity; (2) Fortress level, Military strength, and Distance to the Lu Fort are the core factors influencing the spatial differentiation of elements; and (3) when multiple factors interact, the interaction between Military strength and Distance to the Lu Fort demonstrates a significant nonlinear enhancement effect. This study reveals the spatial organizational logic of the Ming Great Wall military settlements at the micro-element level, providing a scientific basis for the graded protection and adaptive reuse of military settlements in Miyun District. Furthermore, the proposed analytical framework can also offer methodological insights for studies in other regions along the Great Wall. Full article

Agroecology is a long-term solution for changing agri-food systems as climate change and food security problems become worse. In North Africa, especially Algeria, this change needs a profound understanding of how people feel and act toward food that is grown in an environmentally friendly way. This study looks at what Algerian consumers know, how much they are ready to pay (WTP), and how their social and demographic factors affect their attitudes toward agroecological products and practices. A principal component analysis (PCA) and multiple linear regression have been used on 552 responses from a nationally representative sample collected as part of the NATAE Horizon Europe project to find the psychological and structural factors that affect sustainable consumption. The results show that age, education, job level, and living in a city have a big effect on how aware and open-minded consumers are. People over 45 who have more education and a better job are more likely to care about the environment and be willing to spend more on eco-friendly products, notably, olive oil, fruits, and vegetables. People still do not know much about it, though, and WTP differs by product category. This case study shows how important it is to have targeted education and labelling regulations to fill in knowledge gaps and get people more involved in agroecological changes in Algeria. Full article

In complex mechanical systems involving multiple parts and contact interfaces, failure modes are not only statistically correlated but may also interact through underlying physical mechanisms. These interactions, often neglected in current reliability analysis, can lead to significant deviations in failure predictions, especially in rotor systems and actuators. Taking aeroengine turbine rotor assemblies as an example, multiple failure modes, such as wear, fatigue and slip at contact interfaces, affect key mechanical property parameters including assembly preload, cylindrical interference fit and cooling performance. These variations lead to evolving stress/strain and temperature fields with increasing load cycles, thereby inducing physical interactions among different failure modes. This study systematically analyzes the interaction mechanisms among multiple failure modes within a turbine rotor assembly. A mechanics model is established to quantify these interactions and their effects on failure evolution. Furthermore, a time-dependent reliability evaluation method is proposed based on Monte Carlo simulation and the Probability Network Evaluation Technique. A case study illustrates that both continuous-type and trigger-type interactions significantly affect the failure probabilities of wear and low-cycle fatigue. The results emphasize the necessity of accounting for interaction of multi-failure modes to improve the accuracy of failure prediction and enhance the design reliability of turbine rotor assemblies. Full article

Ecosystem restoration is increasingly recognised as part of the global solution for building a resilient, low-emissions economy, with its associated employment opportunities helping to provide political legitimacy for government investment. In Aotearoa New Zealand, however, little is known about the employment effects of government-funded ecosystem restoration initiatives. This study addresses that gap by analysing project-level data from 359 “Jobs for Nature” projects to examine how funding levels and contextual factors influence direct employment outcomes. Multiple regression analyses build on one-way ANOVA tests to quantify the contribution of funding and contextual factors to employment outcomes and to assess their differential impacts across various settings (regions, agencies, project types, and durations). The analysis reveals that while funding is the primary driver of employment—with each additional NZD 100,000 creating approximately 0.7 full-time equivalent (FTE) jobs—contextual factors call for a more dynamic, targeted policy approach to maximise marginal employment returns. Three key policy implications are accordingly drawn: (1) direct more funding to regions with higher socio-economic deprivation; (2) preferentially support projects of medium-term duration; and (3) evaluate and replicate the practices of high-performing funding agencies. Full article

We present a hybrid system that combines large language models (LLMs) with formal graph-analytic methods to generate and automatically repair interactive fiction (IF) stories written in the Twine/Twee format. We chronologically describe the practical challenges encountered when attempting to produce fully playable branching narratives using contemporary state-of-the-art LLMs, including missing passages, trap-like cycles without exits, dead-end passages, narrative discontinuities, incorrect use of Twine macro commands, and inconsistent handling of story variables. To address these limitations, we deliberately abandon all macro- and variable-based logic and instead encode story state directly within passage names through structured, token-based naming. We formalize symmetry and asymmetry in the resulting narrative graphs: symmetrical convergence occurs when multiple branches with compatible states merge into a common passage, whereas asymmetry reveals incorrect or logically inconsistent merging of divergent states (for example, entering a scene in which an item or companion is present via paths where they were never acquired or met). We propose algorithms to detect naming-based asymmetries, cycles, unreachable endings, and structurally defective branches, and we integrate these diagnostics into a repair loop that prompts the LLM to rewrite missing or inconsistent parts of the story. Experiments with several LLM backends indicate that this approach can yield structurally robust and locally coherent interactive stories by reducing state inconsistencies and structural defects. Beyond the specific case of Twine, we argue that symmetry/asymmetry analysis offers a powerful lens for evaluating and correcting AI-generated narrative graphs in general. Full article

With the rapid advancement of Transformer-based large language models (LLMs), these models have found widespread applications in industrial domains such as code generation and non-functional requirement (NFR) classification in software engineering. However, recent research has primarily focused on optimizing linear matrix operations, while nonlinear operators remain relatively underexplored. This paper proposes hardware-efficient approximation and acceleration methods for the Softmax and RMSNorm operators to reduce resource cost and accelerate Transformer inference while maintaining model accuracy. For the Softmax operator, an additional range reduction based on the SafeSoftmax technique enables the adoption of a bipartite lookup table (LUT) approximation and acceleration. The bit-width configuration is optimized through Pareto frontier analysis to balance precision and hardware cost, and an error compensation mechanism is further applied to preserve numerical accuracy. The division is reformulated as a logarithmic subtraction implemented with a small LOD-driven lookup table, eliminating expensive dividers. For RMSNorm, LOD is further leveraged to decompose the reciprocal square root into mantissa and exponent parts, enabling parallel table lookup and a single multiplication. Based on these optimizations, an FPGA-based pipelined accelerator is implemented, achieving low operator-level latency and power consumption with significantly reduced hardware resource usage while preserving model accuracy. Full article

Military decision-making is inherently complex and highly critical, requiring commanders to assess multiple variables in real-time, anticipate second-order effects, and adapt strategies based on continuously evolving battlefield conditions. Traditional approaches rely on domain expertise, experience, and intuition, often supported by decision-support systems designed by military experts. With the rapid advancement of Large Language Models (LLMs) such as ChatGPT, Claude, and DeepSeek, a new research question emerges: can LLMs perform causal reasoning at a level that could meaningfully replace human decision-makers, or should they remain human-led decision-support tools in high-stakes environments? This paper explores the causal reasoning capabilities of LLMs for operational and strategic military decisions. Unlike conventional AI models that rely primarily on correlation-based predictions, LLMs are now able to engage in multi-perspective reasoning, intervention analysis, and scenario-based assessments. We introduce a structured empirical evaluation framework to assess LLM performance through 10 de-identified real-world-inspired battle scenarios, ensuring models reason over provided inputs rather than memorized data. Critically, LLM outputs are systematically compared against a human expert baseline, composed of military officers across multiple ranks and years of operational experience. The evaluation focuses on precision, recall, causal reasoning depth, adaptability, and decision soundness. Our findings provide a rigorous comparative assessment of whether carefully prompted LLMs can assist, complement, or approach expert-level performance in military planning. While fully autonomous AI-led command remains premature, the results suggest that LLMs can offer valuable support in complex decision processes when integrated as part of hybrid human-AI decision-support frameworks. Since our evaluation directly tests this capability, this paradigm shift raises fundamental question: Is there a possibility to fully replace high-ranking officers/commanders in leading critical military operations, or should AI-driven tools remain as decision-support systems enhancing human-driven battlefield strategies? Full article

In this paper, we study an Apéry-type series involving the central binomial coefficients ${\sum }_{n_1>\dots >n_d>0}{\displaystyle \frac{1}{4^{n_1}}}\left({\displaystyle \genfrac{}{}{0pt}{}{2n_1}{n_1}}\right)n_1^{-s_1}\dots n_d^{-s_d}$ and its variations where the summation indices may have mixed parities and some or all “>” are replaced by “≥”, as long as the series are defined. These sums have naturally appeared in the calculation of massive Feynman integrals by the work of Jegerlehner, Kalmykov, and Veretin. We show that all these sums can be expressed as $\mathbb{Q}$-linear combinations of the real and/or imaginary parts of the colored multiple zeta values at level four, i.e., special values of multiple polylogarithms at fourth roots of unity. For example, our main theorem shows that when $n_1^{-s_1}$ is replaced by ${\left(2n_1\right)}^{-s_1}$ and other $n_j^{-s_j}$’s are replaced by either ${\left(2n_j\right)}^{-s_j}$ or ${(2n_j+1)}^{-s_j}$, then all the colored multiple zeta values can be chosen to have the same weight $s_1+\dots +s_d$, but the weights of these values are only bounded by $s_1+\dots +s_d$ for general variant Apéry-type series of mixed parities. We also show that the corresponding series where $\left({\displaystyle \genfrac{}{}{0pt}{}{2n_1}{n_1}}\right)/4^{n_1}$ is replaced by ${\left({\displaystyle \genfrac{}{}{0pt}{}{2n_1}{n_1}}\right)}^2/{16}^{n_1}$ can be expressed in a similar way except for a possible extra factor of $1/\pi$, with the weight of the colored multiple zeta values similarly bounded. Full article

The Xiannüshan Fault Zone, located in the southwestern part of the Huangling Anticline within the Three Gorges Reservoir area of Hubei Province, is one of the largest and most complex faults in the region. The geological structures of its different segments vary significantly. Previous studies have primarily focused on the northern segment and often relied on single geophysical methods, which are insufficient for detailed characterization of the entire fault zone. Based on existing geological data, field reconnaissance results, and the geological characteristics of different segments of the fault zone, we employed multiple geophysical methods for a varied investigation: shallow seismic reflection in the northern segment; a combination of waterborne seismic exploration and microtremor survey in the middle segment; and high-density resistivity in the southern segment. The integrated approach revealed the spatial extent, fault geometry, and activity characteristics of each segment, confirming that the Xiannüshan Fault Zone is a pre-Quaternary structure dominated by thrusting. The findings provide a critical scientific basis for regional seismic hazard assessment and disaster mitigation planning, while also establishing a technical framework with significant practical application value for detailed fault characterization in geologically complex environments. Full article

In 2020, a dairy farm in northwest Germany reported several cows with severe respiratory disease, fever, and reduced milk production. Multiple direct and indirect diagnostic methods were used to identify the cause of the disease. However, the pathogens detected could not be correlated with the severity of the clinical symptoms, so further diagnostic steps were taken. Blood and nasal swab samples were examined using next-generation sequencing (NGS) as part of a metagenomic analysis. For the first time in Germany, Hepacivirus bovis genotype 2 was detected. Real-time RT-PCR assays confirmed the presence of BovHepV genotypes 1 and 2 in the herd between 2020 and 2023. Analyses of complete and partial genome sequences demonstrated the presence of different virus variants in the herd over several years. In addition, the sequence data indicated that cattle can be reinfected with viruses belonging either to different BovHepV subtypes or to the same subtype. Although no direct link could be established between the detection of bovine hepaciviruses and the observed clinical symptoms, the PCR and sequence data obtained provide valuable insights into the epidemiology and pathogenesis of BovHepV infections. Full article

This study employed machine learning to investigate the mechanical behavior of one-part geopolymer (OPG)-stabilized soil subjected to acid erosion. Based on the unconfined compressive strength (UCS) data of acid-eroded OPG-stabilized soil, eight machine learning models, namely, Adaptive Boosting (AdaBoost), Decision Tree (DT), Extra Trees (ET), Gradient Boosting (GB), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), along with hyper-parameter optimization by Genetic Algorithm (GA), were used to predict the degradation of the UCS of OPG-stabilized soils under different durations of acid erosion. The results showed that GA-SVM (R² = 0.9960, MAE = 0.0289) and GA-XGBoost (R² = 0.9961, MAE = 0.0282) achieved the highest prediction accuracy. SHAP analysis further revealed that solution pH was the dominant factor influencing UCS, followed by the FA/GGBFS ratio, acid-erosion duration, and finally, acid type. The 2D PDP combined with SEM images showed that the microstructure of samples eroded by HNO₃ was marginally denser than that of samples eroded by H₂SO₄, yielding a slightly higher UCS. At an FA/GGBFS ratio of 0.25, abundant silica and hydration products formed a dense matrix and markedly improved acid resistance. Further increases in FA content reduced hydration products and caused a sharp drop in UCS. Extending the erosion period from 0 to 120 days and decreasing the pH from 4 to 2 enlarged the pore network and diminished hydration products, resulting in the greatest UCS reduction. The results of the study provide a new idea for applying the ML model in geoengineering to predict the UCS performance of geopolymer-stabilized soils under acidic erosion. Full article

As a convergent zone of multiple plates, the Caribbean Sea and its adjacent areas have experienced a complex tectonic evolution process and are characterized by prominent microplate development. This region provides a natural laboratory for studying the formation mechanism of continental margins, the evolution process of ocean basins, and the tectonics of microplates. However, the crustal structure and microplate tectonics in this region remain unclear due to limitations of conventional planar gravity inversion methods, which neglect the Earth’s curvature in large-scale areas, as well as the uneven coverage of regional seismic networks. To precisely delineate the crustal structure and microplate boundaries in the Caribbean Sea region, this study employs a nonlinear gravity inversion method based on a spherical coordinate system. By utilizing GOCO06s satellite gravity data, ETOPO1 topographic data, and the CRUST1.0 crustal model, we performed inversion calculations for the Moho depth in the Caribbean Sea and its adjacent regions and systematically analyzed the crustal structure and microplate tectonic characteristics of the region. The results indicate that the gravity inversion method in the spherical coordinate system has good applicability in complex tectonic regions. The inversion results show that the Moho depth in the study area generally presents a spatial distribution pattern of “shallow in the central part and deep in the surrounding areas”. Among them, the Moho depth is the largest (>39 km) at the junction of the Northern Andes and the South American Plate, while it is relatively shallow (<6 km) in regions such as the Cayman Trough, the Colombian Basin, and the Venezuelan Basin. Based on the Moho undulation, gravity anomalies, and topographic features, this study divides the Caribbean Sea and its adjacent areas into 22 microplates and identifies three types of microplates, including oceanic, continental, and accretionary. Among them, there are 10 microplates with oceanic crust, 6 with continental crust, and 5 with accretionary crust, while the Northern Andes Microplate exhibits a mixed type. The crustal structure characteristics revealed in this study support the Pacific origin model of the Caribbean Plate, indicating that most of the plate is a component of the ancient Pacific Plate with standard oceanic crust properties. Locally, the Caribbean Large Igneous Province developed due to hotspot activity, and the subsequent eastward drift and tectonic wedging processes collectively shaped the complex modern microplate tectonic framework of this region. This study not only reveals the variation pattern of crustal thickness in the Caribbean Sea region but also provides new geophysical evidence for understanding the lithospheric structure and microplate evolution mechanism in the area. Full article

The collaborative execution of tasks by multiple Unmanned Ground Vehicles (UGVs) has become a development trend in the field of unmanned systems. Existing collaborative Simultaneous Localization and Mapping (SLAM) frameworks mainly employ methods based on visual–inertial or LiDAR–inertial. However, the use of C-SLAM based on these three types of sensors is relatively less common. Therefore, these systems cannot achieve robust and accurate global localization performance in real-world environments. In order to address this issue, a LiDAR–visual–inertial multi-UGV collaborative SLAM framework is proposed in this paper. The whole system is divided into three parts. The first part constructs a front-end odometry by integrating the raw information from LiDAR, visual, and inertial sensors, which provides the accurate initial pose estimation and local mapping of each UGV for the collaborative system. The second part utilizes the similarity of different local mappings to form a global mapping of the environment. The third part achieves global localization and mapping optimization for multi-UGV localization system. In order to verify the effectiveness of the proposed framework, a series of real-world experiments have been conducted. Over an average trajectory length of 237 m, the framework achieves a mean Absolute Pose Error (APE) of 1.49 m and Relative Pose Error (RPE) of 1.68° after the global optimization. The experimental results demonstrate that the proposed framework achieves superior collaborative localization and mapping performance, with the mean APE reduced by 5.4% and mean RPE reduced by 1.4% compared to other methods. Full article

Pollination syndromes reflect the convergence of floral traits among plants sharing the same pollinator guild. However, bee-pollinated orchids exhibit striking variation in color and size. This diversity reflects the multiple reward strategies that evolved within the family, each interacting differently with bee sensory biases. Here, we tested whether the complex floral visual displays of orchids differ in signal identity and intensity among reward systems. We also considered intrafloral modularity, measured as the color differentiation among flower parts, and color–size integration. For this, we measured and modeled floral morphometric and reflectance data from sepals, petals, lip tips, and lip bases under bee vision from 95 tropical Epidendroid species to compare chromatic and achromatic contrasts, spectral purity, and mean reflectance across wavebands, plus flower and display size, among reward systems. Reward types included 19 food-deceptive, 8 nectar-offering, 10 oil-offering, 11 fragrance-offering, and 47 orchid species of unknown reward strategy. Principal component analyses on 34 color and 9 size variables summarized major gradients of visual trait variation: first component (19.1%) represented overall green-red reflectance and achromatic contrasts, whereas the second (16.5%) captured chromatic contrast–size covariation. Reward systems differed mostly in signal identity rather than signal intensity. Flower chromatic contrasts presented strong integration with flower size, while achromatic contrasts were negatively associated with display size. While deceptive and nectar-offering orchids tend toward larger solitary flowers with bluer and spectrally purer displays, oil- and fragrance-offering orchids tend toward smaller, brownish, or yellow to green flowers, with larger inflorescences. Rewardless orchids presented more achromatically conspicuous signals than rewarding orchids, but smaller displays. Orchid species clustered by reward both in PCA spaces and in bee hexagon color space. Deceptive orchids were typically associated with UV + White colors, oil orchids with UV + Yellow lip tips, and fragrance orchids with UV-Black lip bases and UV-Green lip tips. Together, these results indicate that orchid reward systems promote qualitative rather than quantitative differentiation in visual signals, integrating display color and size. These long-evolved distinct signals potentially enable foraging bees to discriminate among resource types within the community floral market. Our results demonstrate that color and flower display size are important predictors of reward strategy, likely used by foraging bees for phenotype-reward associations, thus mediating the evolution of floral signals. Full article

(1) Background: As social infrastructures, cemeteries have always played a central role in various human cultures. The changing function of cemeteries and the recognition of their potential as green spaces have resulted in the fact that cemeteries are a subject of considerable urban planning research. However, there still is a knowledge gap for the case of Vienna. In this study, from the perspective of urban planning and the city of Vienna as an operator of interdenominational cemeteries on the one hand, and of operators of denominational cemeteries on the other hand, consideration of cemeteries in strategic urban planning is discussed, and prospects for the future are outlined. (2) Methods: A qualitative content analysis of relevant strategic planning documents and a qualitative theme-centred stakeholder survey using guideline interviews were conducted. The results were put into the context of the international literature. (3) Results: Cemeteries are an integral part of urban morphology and fabric. Interdenominational cemeteries serve multiple purposes, for example, as places of remembrance, leisure and recreation. In addition, the growing importance of interdenominational cemeteries in particular as green infrastructure for the public is evident. (4) Conclusions: Despite population growth and the associated pressure on land and densification, no changes such as the decommissioning of cemeteries are to be expected in the medium term. Full article