Search Results (302)

Controlling macro-geometrical errors in the dry drilling of ductile cast iron remains a critical challenge for sustainable and cost-efficient automotive component manufacturing. This paper investigates the influence of cutting speed (v_c) and feed per revolution (f_n) on the dimensional and shape accuracy of holes drilled in EN-GJS-500-7 ductile cast iron using an HSS DIN 338 helical drill (Ø 11.8 mm, Ceratizit) on an AVIA VMC800 CNC milling centre. A one-factor-at-a-time (OFAT) experimental design was applied: the feed effect was evaluated at v_c = 10 m/min with f_n ∈ {0.10, 0.15, 0.20} mm/rev, while the speed effect was evaluated at f_n = 0.20 mm/rev with v_c ∈ {10, 25, 30} m/min. Cutting forces, torques, and vibration accelerations were recorded using an HBM MSC 10 transducer and a PCB 356A01 tri-axial accelerometer. Hole geometry was assessed on a Zeiss Contura G2 coordinate-measuring machine (CMM), and surface texture was evaluated with a TOPO 01P contact profilometer. The expanded measurement uncertainty (k = 2) was estimated based on duplicate test specimens. All drilled holes fell within the IT12 dimensional tolerance (PN-EN 22768-1:1999 grade c), with diameter oversizes ranging from +0.26 mm to +0.46 mm relative to the nominal bore. Cutting speed was identified as the dominant factor affecting both diameter oversize and cylindricity, which increased by 60% (from 0.10 to 0.16 mm) as v_c rose from 10 to 30 m/min. Vibration accelerations increased nonlinearly between v_c = 25 and 30 m/min (by a factor of 2.5×), indicating an approach to a structural resonance condition. The lowest surface roughness (R_a = 6.6 µm) was obtained at v_c = 25 m/min. These findings establish clear physical baselines for tool deflection limits, demonstrating that managing dynamic process stability is vital for optimising macro-geometrical accuracy in the dry machining of cast iron alloys. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy with few therapeutic options. Topoisomerase IIα (TOPO2α) is frequently overexpressed in PDAC and is associated with poor clinical outcomes, yet current TOPO2α-directed therapies are constrained by limited efficacy and toxicity. Barettin, a brominated indole-containing diketopiperazine isolated from the marine sponge Geodia barretti, has not previously been evaluated against PDAC-relevant targets. Here, we identify barettin as a TOPO2α inhibitor using an integrated phenotypic, computational, and biochemical approach. Barettin exerts a cytostatic, non-toxic effect, selectively suppressing proliferation in a subset of PDAC models while showing reduced activity in others, revealing context-dependent efficacy and biological selectivity. Consistent with this, barettin inhibits TOPO2α-mediated DNA decatenation in vitro, demonstrating direct interference with enzyme activity. These findings support barettin as a selective inhibitor of a cancer-relevant proliferative pathway, uncovering a potential vulnerability in a subset of PDAC. Full article

With more satellites, richer payload resources, and more diverse service functions, satellite systems are increasingly operated as large space–ground networks. These networks must schedule arriving missions under changing topology, gateway access, beam availability, weather-affected links, spectrum compatibility, and mission time windows. Offline optimization can compute high-quality schedules when the mission set, satellite visibility windows, and resource states are known before execution, but repeated replanning is costly for asynchronous arrivals. Online heuristics make faster decisions from local route rules, but they do not evaluate how an accepted service path changes the capacity left for later requests. Reinforcement-learning schedulers can adapt from delayed scheduling outcomes. However, many generic policies rely on fixed-step state updates or flat compound-action scores, whereas online satellite scheduling makes decisions at irregular arrivals over continuously evolving topology and capacity-coupled service paths. We propose TopoAgent, an online reinforcement-learning agent for heterogeneous satellite mission scheduling. TopoAgent models each request as a service-path decision, propagates compound feasibility through the satellite–gateway–beam hierarchy, and uses a capacity-aware policy to choose among feasible paths. A deterministic constraint manager places the selected path in time, while SRV guides the policy toward assignments that preserve reusable beam capacity. In a high-fidelity simulator, TopoAgent achieves a 74.7% mission completion rate and a 75.5% high-priority completion ratio over five seeds. Full article

The seeds of Impatiens balsamina L. have been traditionally used in East Asian medicine and are known to contain bioactive compounds with antioxidant properties. However, studies focusing on seed-derived callus remain limited. This study aimed to comparatively evaluate the antioxidant activities and lipid accumulation-inhibitory effects of 70% ethanol extracts from seeds (IB) and seed-derived callus (IBC) of I. balsamina. Callus was induced on Murashige and Skoog (MS) medium supplemented with 2,4-dichlorophenoxyacetic acid (2,4-D). Antioxidant activities were evaluated using DPPH radical scavenging, superoxide anion scavenging, deoxyribose-based hydroxyl radical scavenging, DNA nicking, lipid peroxidation, and relative electrophoretic mobility (REM) assays, along with the determination of total phenolic, flavonoid, and tannin contents. Cell viability and lipid accumulation were assessed in FFA-treated HepG2 cells. In silico network and transcription factor (TF) enrichment analyses were performed to explore underlying mechanisms. Callus induction was most effective at 1 mg/L 2,4-D. Both IB and IBC exhibited antioxidant activities across all assays, with IB showing higher activity and greater phytochemical content than IBC. Both extracts reduced lipid accumulation in FFA-treated HepG2 cells at non-cytotoxic concentrations. Network analysis identified enrichment in pathways related to oxidative stress, inflammation, and lipid metabolism, and TF enrichment analysis identified NFKB1 and ATF3 as major upstream regulators. Both IB and IBC exhibited antioxidant activities across multiple in vitro assays, with IB showing higher activity attributable to its more complex phytochemical content. The lipid accumulation-inhibitory effects observed in FFA-treated HepG2 cells suggest a potential association between antioxidant capacity and lipid regulation, although the underlying mechanisms remain to be experimentally validated. Seed-derived callus may serve as a useful in vitro model for studying plant-derived bioactive compounds, pending further optimization. Full article

Modeling and controlling multilayer complex network dynamics is challenging under coexisting crosslayer interactions, higher-order couplings, and decentralized strategic decisions. Most existing schemes focus on graph-based pairwise structures and overlook topological cavities, mesoscale loops, and layered self-interested actions. This paper presents TopoGame-MND, an algebraic-topological and game-theoretic framework for multilayer network dynamics. We first build a filtration-driven simplicial lifting to unify pairwise and higher-order interactions into a weighted multilayer simplicial complex. A topological state operator using generalized Hodge Laplacians and persistent homology is then constructed to characterize cross-scale diffusion, circulation, and structural inconsistency. A distributed potential-game mechanism is developed with a topology-aware utility, followed by a proximal mirror-best-response algorithm with consensus correction. We prove Nash equilibrium existence and uniqueness, global potential monotone descent, linear convergence, computational complexity, and input-to-state robustness. Simulations on multiplex and interdependent networks validate that TopoGame-MND outperforms baselines in regulation speed, oscillation energy, failure resilience, and robustness, providing a unified way to connect higher-order topology and distributed decision optimization. Full article

Knee joint moment is an important biomechanical parameter for sports assessment, rehabilitation monitoring, and human–machine interaction. However, direct measurement is often restricted to laboratory-based settings. Surface electromyography (sEMG) offers a non-invasive alternative for indirect joint moment estimation, but many existing deep learning models remain too computationally demanding for potential wearable edge deployment. To address this gap, this study proposes Topo2DCNN-LSTM, a lightweight two-dimensional (2D) convolutional neural network model, designed for sagittal-plane knee flexion moment estimation. The model used a feature-based sequential representation, transforming raw sEMG signals into compact Root Mean Square (RMS) feature frames. The input was processed by a lightweight 2D convolutional neural network (CNN) encoder and paired with long short-term memory (LSTM) units. The model was trained on a public walking dataset of healthy subjects with synchronized sEMG and joint kinetics at two treadmill speeds. When compared with selected deep learning baselines, the quantized model achieved a mean RMS Error of 0.088 ± 0.020 Nm/kg at 1.2 m/s and 0.114 ± 0.034 Nm/kg at 1.8 m/s. On a SparkFun Thing Plus–SAMD51, it achieved an average inference latency of 28 ms using 71,316 bytes of random-access memory (RAM) and 257,172 bytes of flash. These results support its use as a proof of concept for personalized unilateral knee moment estimation with isolated on-device inference feasibility under resource-constrained and limited walking conditions. Full article

For the automation of Agrobacterium-mediated genetic transformation of tissue-cultured plantlets, accurate leaf vein segmentation is essential. The thin, low-contrast structure of leaf veins frequently leads to fragmented segmentation outputs, despite the proposal of various methodologies for vein segmentation. To address this issue, we propose Direction-Aware U-Net (DA-UNet), an improved U-Net architecture that incorporates a Direction-Aware Context Pooling (DACPool) module and Topology-aware Segmentation loss (TopoSeg loss). The DACPool module explicitly exploits vein orientation to aggregate directional contextual information, while the TopoSeg loss jointly optimizes pixel-level accuracy and topological continuity. DA-UNet achieves efficient leaf vein segmentation with improved continuity and structural integrity, according to evaluations on the self-constructed Tissue-Cultured Plantlet Vein Dataset 2025 (TCPVD2025). Comparative experiment results show that the improved model outperforms PSPNet, DeepLabV3+, U-Net, TransUNet, Swin-UNet, CCNet, and SegNeXt, as evidenced by Recall, Dice, and CONNECT scores of 71.35%, 69.08%, and −2.25, while maintaining competitive Precision of 66.98%. Ablation experiment results provide further evidence for the efficacy of the TopoSeg loss and the DACPool module. The results demonstrate the effectiveness of the proposed vein segmentation framework for generating outputs that are both accurate and structurally consistent, thus enabling reliable automated processes for plant genetic transformation. Full article

Machining feature recognition is a key enabling technology in intelligent manufacturing that extracts manufacturing semantics from the boundary representation (B-Rep) of 3D CAD models to bridge design and process planning. Recent advances in deep learning have accelerated data-driven feature recognition methods. Among these, graph neural networks (GNNs) have gained significant attention due to their natural compatibility with the non-Euclidean, hierarchical topological structure of B-Rep data, enabling efficient and lossless encoding of geometric and topological attributes. However, existing GNN-based methods primarily leverage the topological structure and geometric attributes of B-Rep models, often neglecting the inherent geometric relationships present in the B-Rep data structure. To address this gap, we propose a dual-graph fusion network (Topo-Geom DualGNN) that integrates a topological attribute adjacency graph and a geometric relationship graph. Our approach employs a GatedGCN-based graph encoder and an FiLM-based cross-stream fusion mechanism to jointly encode topological and geometric information from the B-Rep model. Evaluations on open-source synthetic datasets, including MFInstSeg and MFRCAD, demonstrate that the proposed method achieves competitive comprehensive recognition performance and exhibits promising capability in recognizing machining features in complex parts. Full article

Autonomous exploration in unknown environments remains a challenging problem for UAVs. This paper proposes a hierarchical exploration planning framework that explicitly leverages real-time acquired prior knowledge to improve exploration efficiency. To efficiently represent the structural information embedded in the prior knowledge, two map structures, namely the quasi-prior map and the hybrid-topo map, are designed, enabling more reasonable space partition and facilitating exploration planning. Subsequently, based on the hybrid-topo map, the hierarchical exploration planner computes a global exploration guidance that provides an efficient traversal order over all unexplored regions. The local coverage problem in unknown regions is formulated as a coverage traveling salesman problem (CTSP), where visibility information derived from the hybrid-topo map is exploited to optimize local viewpoint sequences with high coverage efficiency. Finally, a long-horizon trajectory planning strategy is proposed to maintain high flight speed while ensuring safety and dynamic feasibility. Simulations demonstrate that the proposed framework significantly outperforms state-of-the-art exploration methods in terms of exploration efficiency, while ablation studies further validate the effectiveness of each module. Real-world experiments are conducted to confirm the practical capability of the proposed approach. Full article

We present here a novel approach to the analysis of common knowledge based on Category Theory. We formalize knowledge hierarchies as presheaves over a category of agent sequences. The category of these presheaves constitutes a topos. We define an unfolding monad on the resulting topos, and use a Knaster–Tarski theorem to obtain common knowledge as a greatest fixed point under natural uniformity and exchangeability conditions on agent sequences. Full article

Unauthorized farmland excavation is a prominent manifestation of farmland non-agriculturalization, and its effective monitoring depends on structured representations of objects and their spatial interactions in complex scenes. However, the existing computer vision research mainly focuses on object-level recognition or scene-level classification, while lacking datasets that explicitly model topological relationships in farmland excavation scenarios. To address this limitation, this paper presents TopoFarm, a topology-annotated panoptic dataset for unauthorized farmland excavation scenes. TopoFarm provides fine-grained panoptic segmentation annotations together with pairwise object contact relationship labels, enabling joint object–relation modeling and topology-aware scene representation. To improve annotation reliability under complex conditions, a human-in-the-loop hybrid intelligence framework, termed HITPA, is introduced to integrate automatic panoptic segmentation, depth-aware topological reasoning, and expert-guided refinement, achieving high annotation quality with controlled manual effort. Based on TopoFarm, systematic benchmark experiments are conducted for panoptic segmentation and topological relationship reasoning, along with a hierarchical evaluation protocol to analyze the impact of object-level representation quality on relational inference. The results demonstrate that TopoFarm poses substantial challenges for both tasks and highlight the strong dependence of topological reasoning on object accuracy and global scene context. Overall, TopoFarm provides a new data foundation and evaluation benchmark for topology-aware perception in farmland monitoring applications. Full article

Coastal management relies on the monitoring of coastal behavior, both in the short and long term, which requires a high availability of accurate and up-to-date data. Conventional in situ surveying methods are constrained by spatiotemporal limitations and high operational and logistical costs. In response, satellite-derived methods offer a powerful alternative based on the remote assessment of morphodynamic features. Despite their advantages, these methods are limited by the influence of deterministic and stochastic sea-level variations, which introduce significant errors. Currently, corrections based on deterministic components (i.e., astronomical tides) are widely incorporated into scientific assessments. However, stochastic variations, such as waves and surge conditions, are not equally represented. This work conducted a systematic review of published scientific literature to assess the integration of corrections for stochastically induced errors. The results demonstrated that a limited number of studies have developed an approach that substantially improves error reduction across a wide range of coastal settings. However, environmental and methodological–conceptual aspects still constrain these techniques for large-scale applications. If robust adjustments are achieved through highly reliable topo-bathymetric, water-level, and wave datasets, satellite-derived data become a unique tool that can directly support coastal disaster mitigation and risk management. Full article

From the winter of 2024 through the spring of 2025, public plazas in Seoul, particularly Yeouido and Gwanghwamun, became major sites of anti-martial law political mobilization. A striking feature of these protests was the visible leadership and participation of young women, who transformed civil resistance into a festive and affective form of collective action through cheering sticks and performative solidarity. The main driving force behind the political mobilization of young women was the increased influence of feminism after the “feminism reboot” in Korea since 2016. During the civil resistance, they were also active in solidarity with various minorities. The resistance was successful, and Korea has regained the order of a democratic society. However, young women who had experienced autonomous protest and mutual solidarity found themselves, upon returning to their everyday lives, still facing the remaining task of struggling against patriarchal cultures and institutions. Among them, Christian women confronted an even more inhospitable sphere—that of the Korean Protestant church, which remains largely constrained by patriarchal norms, a Christian–Confucian mixture. A representative example is the emphasis on “women’s calling” based on fundamentalist/sexist readings of the Bible. The huge gap between current social change and the church situation is reflected in the recent phenomenon of many young female Christians’ de-churching. In confronting the incongruous realities of young Christian women, this study seeks to provide an ethico-theological basis for a feminist reinterpretation of the Protestant concept of “calling”. After analyzing the social/existential topos of young Korean Christian women in the recent Korean context, this work considers a feminist reinterpretation of the “creation order” and “calling” in the process of an intersubjective dialog between the Bible and pre-patriarchal Korean cultural resources of “Mago-affiliated” myth, Seolmundaehalmang (the Great Grandmother Seolmun) narratives in particular. By providing sociological, ethical, and theological resources to construct new norms of “calling”, this research contributes to enabling young Christian women in Korea to overcome their existential fragmentation and to seek forms of women’s calling that are attuned to their historical moment and identity. Full article

Arapaima gigas growth hormone (ag-GH) cDNA was previously cloned from A. gigas pituitaries. In this work ag-GH has been synthesized using human embryonic kidney 293 cells (HEK293) transiently transfected with the 3.4-TOPO^® vector carrying ag-GH cDNA. The 4th day after transfection, the presence of putative ag-GH was detected via SDS-PAGE and Western blotting in comparison with human GH. Ion exchange purification exhibited a clearly symmetric peak, absent in the control medium. The purified fraction, submitted to high-performance size-exclusion chromatography (HPSEC), SDS-PAGE, and Western blotting, contained an immunoreactive molecule, slightly smaller than hGH as expected. MALDI-TOF-MS determined a high-resolution molecular mass of 21,220 Da versus a theoretical value of 21,150. A phylogenetic analysis positioned ag-GH within basal teleost lineages, consistent with earlier analyses of A. gigas gonadotrophic hormones, reinforcing the structural and functional conservation relevant for its biologic activity. An in vivo bioassay based on the body weight increase of dwarf “little” mice demonstrated a biological activity for ag-GH comparable to that of the international reference preparation of rec-hGH. For two species (H. sapiens and A. gigas) separated by an evolutionary period of >100 million years, such a positive biological correlation is remarkable. Full article

We introduce Recognition Geometry (RG), an axiomatic framework in which geometric structure is not assumed a priori but derived. The starting point of the theory is a configuration space together with recognizers that map configurations to observable events. Observational indistinguishability induces an equivalence relation, and the observable space is obtained as a recognition quotient. Locality is introduced through a neighborhood system, without assuming any metric or topological structure. A finite local resolution axiom formalizes the fact that any observer can distinguish only finitely many outcomes within a local region. We prove that the induced observable map $\overline{R}:{\mathcal{C}}_R\to \mathcal{E}$ is injective, establishing that observable states are uniquely determined by measurement outcomes with no hidden structure. The framework connects deeply with existing approaches: C^*-algebraic quantum theory, information geometry, categorical physics, causal set theory, noncommutative geometry, and topos-theoretic foundations all share the measurement-first philosophy, yet RG provides a unified axiomatic foundation synthesizing these perspectives. Comparative recognizers allow us to define order-type relations based on operational comparison. Under additional assumptions, quantitative notions of distinguishability can be introduced in the form of recognition distances, defined as pseudometrics. Several examples are provided, including threshold recognizers on ${\mathbb{R}}^n$, discrete lattice models, quantum spin measurements, and an example motivated by Recognition Science. In the last part, we develop the composition of recognizers, proving that composite recognizers refine quotient structures and increase distinguishing power. We introduce symmetries and gauge equivalence, showing that gauge-equivalent configurations are necessarily observationally indistinguishable, though the converse does not hold in general. A significant part of the axiomatic framework and the main constructions are formalized in the Lean 4 proof assistant, providing an independent verification of logical consistency. Full article