Search Results (2,891)

To address the limitations of zero-shot generalization in Vision-Language Navigation (VLN), this paper proposes a novel knowledge graph-driven reinforcement learning approach. Our method constructs a hierarchical, dynamically updated knowledge graph online during the agent’s real-time interaction with the environment, seamlessly aligning external semantic priors with continuous visual perception. By leveraging a Chain-of-Thought (CoT) prompting mechanism, the agent performs multi-hop reasoning to precisely locate target objects. Furthermore, we design an end-to-end optimized reinforcement learning framework that fuses multi-modal features and employs a task-oriented composite reward function. Extensive experiments in the AI2-THOR simulation environment demonstrate that the proposed method significantly improves navigation success rates in zero-shot settings. The results validate its robust generalization capabilities, particularly for unseen object categories and complex scene layouts. Full article

Achieving resilient consensus in adversarial environments often requires extending the W-MSR algorithm to multi-hop communication. While the robustness guarantees of multi-hop W-MSR are now well understood, the problem of how to determine the minimal hop radius $h^{*}$ that ensures these guarantees has remained largely unaddressed. Existing work typically assumes a fixed h, leaving practitioners without a systematic way to balance robustness requirements against communication and computational cost. This paper introduces a new hop-selection framework that identifies the smallest communication horizon capable of satisfying the robustness assumptions underlying MW-MSR consensus. The framework combines exact robustness verification—when tractable—with a hierarchy of computationally efficient proxy tests based on local feasibility, normalized algebraic connectivity, and adversary-dilution criteria. These components provide a practical and scalable mechanism for establishing $h^{*}$ in both synchronous and bounded-delay asynchronous settings. Design-time and runtime procedures, complexity analysis, and validation on IEEE 14-, 30-, and 57-bus networks demonstrate that the proposed approach reliably detects resilience thresholds and substantially improves consensus behavior under stealthy and burst-type adversaries. The results show that systematic hop selection is essential for avoiding failure at small h while preventing unnecessary communication overhead at large h. The framework thus offers an implementable and deployment-oriented strategy for resilient distributed coordination in sparse and adversarial multi-agent networks. Full article

LoRa mesh networks enable long-range, low-power connectivity but are constrained by very low bitrate, spreading-factor-specific SNR thresholds, and regional duty-cycle limits. This article presents a snapshot routing framework that separates feasibility from optimality. Feasibility is enforced as hard constraints-only radio options that satisfy SNR thresholds (with safety margin) and fit within remaining duty windows, which are admitted using an Okumura–Hata backbone with a model-agnostic specialization for link geometry. Optimality is achieved on a spreading-factor-expanded directed graph, where each feasible SF is represented as a distinct edge, and a composite, dimensionless hop metric balances airtime-driven energy expenditure, current and incremental duty usage, and optional quality penalties. The method yields per-hop SF selection via shortest-path computation and supports rapid re-planning without event-level simulation. Snapshot-based evaluation indicates improved control of airtime, duty exposure, and energy, providing a practical basis for multi-criteria routing in LoRa mesh networks with applicability to airborne and infrastructure-sparse deployments. Full article

The immature stages and biology of Neoplinthus tigratus porculus (Fabricius, 1801) (Coleoptera: Curculionidae: Molytinae) associated with common hop (Humulus lupulus L.) are described for the first time. Biological observations show that the species develops mainly within the root collar and roots of Humulus lupulus, where larvae feed internally and older instars overwinter. Infested plants are characterized by swollen and weakened roots, often containing multiple larvae. The species should be considered a potential pest of common hop, an economically important crop; however, the current observations indicate that its populations are generally very low, consistent with the status of several related Molytinae and Cleonini taxa, which are predominantly regarded as rare or locally occurring under contemporary agricultural conditions. Nevertheless, changes in agroecosystem management may significantly alter its abundance, as documented in other weevil taxa, where reductions in plant protection measures have led to local pest outbreak. The morphology and diagnostic characters of mature larvae and pupae are documented and compared with related Molytinae and selected Cleonini (Lixinae). The mature larva generally fits the diagnostic characters of Molytinae larvae but differs in several traits, particularly the very short endocranial line and the relative length of frontal setae (fs_1–5), with fs₄ distinctly shorter than fs₅. Full article

The style of a beer is determined by the combination of malts and hops, and the type of yeast used. The incorporation of date fruits into the fermentation process with non-conventional yeasts such as Lachancea spp. results in effective fermentation, influencing the kinetic parameters of yeast growth and prompting different physicochemical properties in the resulting beverage. The brewing process for a sour beer with Lachancea spp. yeast was optimized using a central composite rotational design and response surface methodology, and the growth kinetics were calculated. The optimal conditions required 500 g of dates, incorporated 59.46 h after starting the fermentation process. The results revealed a total phenolic content of 254.81 mg GAE/g, and the amount of titratable acidity was 2.66%. Under favorable operating conditions, the growth kinetic parameters of Lachancea spp. yeast revealed a rate of 0.78 μ.h⁻¹ and a growth constant of 3.29 k (g/h). The addition of dates 60 h into fermentation with Lachancea spp. allows for technical control of acidity and efficient fermentation kinetics for the creation of sour craft beers. Full article

Background: Prenylated chalcones are recognized for their beneficial nutritional properties and have attracted increasing interest due to their anticancer activities, which involve various mechanisms and pathways. In the current study, we investigated the influence of prenylated chalcone xanthohumol (XH) and its two minor derivatives xanthohumol C (XHC) and 1″,2″-dihydroxantohumol C (DHXHC) on the formation of reactive oxygen species (ROS), causing oxidative stress. Concomitantly, we studied the effect of mitochondrial transmembrane potential changes on human skin cancer, namely Merkel cell carcinoma (MCC13). Methods: The cancer cells were treated with the mentioned compounds for 24 and 48 h at various concentrations. Results: Our findings showed that ROS generation was dose-dependent at 24 h for xanthohumol, whereas for xanthohumol C and 1″2″-dihydroxanthohumol C, a significant increase in ROS occurred only at the highest concentration (100 μM) after 48 h. Mitochondrial membrane potential was significantly diminished by all the compounds. Conclusions: Taken together, our results indicate that the aforementioned chalcones exhibit cytotoxic activity against the MCC13 cell line and may be promising candidates for further investigation as anticancer agents. Full article

Millimeter-wave (mmWave) unmanned aerial vehicle (UAV) networks are a promising solution for rapidly deployable backhaul systems in urban disaster scenarios, where terrestrial infrastructure may become unavailable. Although mmWave bands provide wide bandwidth for high-capacity transmission, their strong susceptibility to blockage and beam misalignment poses significant challenges in dense urban environments, particularly under UAV positional fluctuations caused by wind. This study investigates the optimization of multi-hop mmWave UAV backhaul networks with the objective of maximizing the bottleneck link capacity. A three-dimensional urban model of the Shinjuku area in Tokyo is employed, and radio propagation is evaluated using a ray-tracing-based approach considering line-of-sight (LoS) constraints and inter-link interference. Particle Swarm Optimization (PSO) is used to determine optimal UAV placements for two- to four-hop configurations. Numerical results demonstrate that multi-hop relaying combined with directional 2 × 2 patch antennas significantly improves the minimum link capacity, enabling the target backhaul capacity of approximately 9 Gbps per link under static conditions. However, capacity degradation is observed when UAV jitter is introduced due to LoS blockage and beam misalignment. To address this issue, a jitter-aware optimization method incorporating an expanded Fresnel-zone constraint is proposed. The proposed method substantially mitigates capacity degradation under realistic positional fluctuations, resulting in more robust backhaul performance. These findings demonstrate that jitter-aware placement design is essential for realizing reliable high-capacity mmWave UAV backhaul networks in dense urban disaster environments. Full article

Dense retrievers rely heavily on high-quality training triplets, yet existing data construction strategies remain inadequate for reasoning-intensive retrieval tasks involving multi-hop reasoning, entity relation tracing, and implicit evidence composition. Positive samples are often based on shallow semantic relevance and fail to capture explicit reasoning chains, while negative samples are typically sampled from lexical overlap or random candidates and therefore provide limited supervision for learning clear decision boundaries. To address these issues, we propose S-Gens, a structure-aware synthetic data generation framework for enhancing reasoning-intensive dense retrieval. S-Gens uses relation paths in an external knowledge graph to synthesize queries and structurally consistent positive samples, and further constructs semantically similar but structurally inconsistent hard negatives. To improve data reliability, we introduce a Siamese graph neural network-based consistency filtering mechanism. Because S-Gens operates entirely during offline supervision construction, it remains model-agnostic, preserves the original inference architecture, and is complementary to graph-guided retrieval or RAG pipelines that inject structure online. Experiments on five benchmark datasets show that S-Gens consistently improves multiple trainable retrievers, with the largest gains on multi-hop reasoning tasks such as WebQSP and HotpotQA. These results indicate that structure-aware synthetic supervision can effectively improve dense retrieval in reasoning-intensive settings. Full article

The new energy vehicle (NEV) industry generates massive multi-source heterogeneous data. To overcome traditional database limitations in terminology disambiguation and multi-hop reasoning, this paper proposes a knowledge graph (KG)-based question-answering (QA) architecture. Three primary domain challenges are addressed: First, to tackle the poor semantic extraction of informal diagnostic texts, a deep semantic parsing network (BERT-BiLSTM-CRF) is integrated to extract high-precision knowledge from 150,000 real-world maintenance records. Second, to solve topological redundancy, the Labeled Property Graph (LPG) specification is employed to encapsulate parameters of 2157 vehicle models as internal attributes, significantly streamlining complex multi-hop reasoning. Finally, to enhance limited reasoning capabilities, an intent classification module (TextCNN) automatically translates natural language into graph queries, enabling deep fault tracing across up to five semantic levels. Experimental results demonstrate 98% and 93% accuracy in entity-relation recognition and intent classification, respectively. The resulting KG (8274 nodes, 14,488 edges) establishes a scalable paradigm for intelligent diagnostic reasoning in complex vertical domains. Full article

Large-scale smart agriculture requires reliable and energy-efficient wireless connectivity to support distributed environmental sensing across wide rural areas. However, existing low-power wide-area network (LPWAN) technologies often face limitations in scalability, reliability, or infrastructure dependency when deployed in large agricultural fields. This study presents the design and experimental evaluation of a hierarchical sensor network architecture that integrates LoRaMESH for multi-hop sensing communication and Wi-Fi HaLow as a sub-GHz backhaul for data aggregation and cloud connectivity. In the proposed system, LoRaMESH forms intra-cluster sensor networks using a lightweight controlled flooding protocol, while Wi-Fi HaLow provides long-range IP-based connectivity between cluster gateways and a central access point. A real-world deployment covering approximately $2.5\mathrm{km}\times 1\mathrm{km}$ of agricultural area was implemented to evaluate the performance of the proposed architecture. Experimental results show that the LoRaMESH network achieves packet delivery ratios above $90\%$ across one to three hops, with average end-to-end delays between $10.6$ s and $13.3$ s. The Wi-Fi HaLow backhaul demonstrates high reliability within short to medium distances, reaching $99.5\%$ packet delivery ratio at 50 m and $89.68\%$ at 200 m. Energy measurements further indicate that the sensor nodes consume only $21.19\mathsf{\mu }\mathrm{A}$ in sleep mode, enabling long-term battery-powered operation suitable for agricultural monitoring applications. These results indicate that the proposed hierarchical architecture is a feasible connectivity option for the tested large-scale agricultural sensing scenario. Because no side-by-side LoRaWAN or NB-IoT benchmark was conducted on the same testbed, the results should be interpreted as a field validation of the proposed architecture rather than as a direct experimental demonstration of superiority over alternative LPWAN systems. Full article

The deployment of large language models as autonomous retrieval agents over unstructured knowledge bases gives rise to a persistent structural conflict between probabilistic neural generation and deterministic physical execution. While agentic paradigms facilitate complex multi-hop retrieval, their unconstrained generative nature frequently violates strict syntactic requirements. This systemic vulnerability directly triggers execution hallucinations, such as fabricated API parameters or malformed schemas. Consequently, these syntax-driven failures force systems into redundant trial-and-error recovery loops, resulting in severe computational inflation that degrades both token efficiency and inference latency. To resolve this reliability–efficiency dilemma, this paper proposes RAG-CoT-MCP, a neuro-symbolic architecture that orthogonally decouples probabilistic cognitive planning from deterministic tool execution. By integrating the Model Context Protocol (MCP) as a strict system-level validation boundary, the framework ensures that latent reasoning trajectories manifest exclusively as syntactically valid operations. Exhaustive empirical evaluations across four disparate datasets—incorporating a multi-dimensional LLM-as-a-Judge framework, rigorous ablation studies, and granular cost tracking—validate the proposed approach. The findings demonstrate that RAG-CoT-MCP compresses network-level execution error rates from 45.2% (in unconstrained baselines) to a mere 6.0%, yielding substantial enhancements in semantic comprehensiveness and logical coherence compared to existing baselines. Counterintuitively, by proactively intercepting malformed actions and redirecting computational resources from reactive error handling to valid causal deduction, the framework drastically reduces redundant token consumption and achieves the lowest overall inference latency. Ultimately, this study establishes that deterministic execution constraints do not hinder agentic flexibility; rather, they serve as a fundamental prerequisite for deploying robust, high-speed, and cost-effective knowledge retrieval systems. Full article

Background/Objectives: To determine the optimal reconstruction parameters for accurate visualization of peripheral in-stent restenosis using photon-counting detector CT (PCD-CT), and to evaluate its potential advantages over energy-integrated detector CT (EID-CT). Methods: Endovascular peripheral stents with varying degrees of in-stent restenosis were scanned in a custom-made phantom using EID-CT (Somatom Force) and PCD-CT (Naeotom Alpha) under clinical acquisition protocols. EID-CT images were reconstructed with Bv40 and Bv59 kernels at 512 matrices. PCD-CT data were acquired in standard-resolution (SR) and ultra-high-resolution (UHR) modes. In both modes, images were reconstructed with multiple kernels (Bv40, Bv56 and Bv72) and matrix sizes (512 and 1024 matrix). In SR mode, additional virtual monoenergetic images (40–100 keV) were generated, while UHR mode included only polychromatic reconstructions. Quantitative image quality (noise, contrast, contrast-to-noise ratio [CNR]) was measured, and two blinded readers performed qualitative assessments of restenosis visualization. Results: PCD-CT with SR mode at VMI 40 keV achieved the highest image contrast and CNR, significantly outperforming EID-CT and PCD-CT_UHR under matched conditions (all p < 0.05). The sharper reconstruction kernel further enhanced the image contrast and improved subjective visualization despite increased image noise. Both readers ranked PCD-CT_{SR-Bv72-40keV} at 1024 matrix highest for detecting all degrees of restenosis, with excellent inter-reader agreement (ρ > 0.80). Conclusions: PCD-CT in SR mode at VMI 40 keV, specifically using the Bv72 kernel with a 1024 matrix, optimizes the visualization of peripheral in-stent restenosis. Compared to EID-CT, PCD-CT provides superior image quality and detectability of restenosis. Full article

Emergency vehicle authentication in vehicular ad hoc networks must satisfy strict latency, privacy, and trust constraints. Existing Public Key Infrastructure- and Conditional Privacy-Preserving Authentication-based schemes incur substantial overhead from certificate management and expensive per-hop verification, making them unsuitable for real-time emergency scenarios. We propose a lightweight zero-knowledge- and blockchain-assisted authentication scheme that eliminates certificates, pseudonym pools, and the requirement for online interaction with a trusted authority during the authentication phase. The Certificate Authority (CA) is involved only during offline initialization stages (vehicle enrollment and Merkle tree construction); once provisioning is complete, the runtime authentication process operates without any online CA interaction. Each emergency vehicle registers one-time hash commitments on-chain after proving membership in a category-specific Merkle tree, and authenticates messages by broadcasting a hash along with a zero-knowledge proof of preimage knowledge. Roadside units verify the proof and consult the on-chain state to enforce single-use semantics, creating a tamper-resistant audit trail. Evaluation using the Veins framework (OMNeT++/SUMO) demonstrated a constant 288-byte authenticated payload, millisecond-level end-to-end delay independent of hop count, and stable blockchain processing under sustained load. Full article

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article

Vehicular Ad hoc Networks (VANETs) feature high node mobility and volatile topologies, rendering the conventional Greedy Perimeter Stateless Routing (GPSR) protocol prone to weak link stability and inefficient route discovery due to its lack of direction awareness. Existing direction-aware improvements typically rely on multi-criteria weighting or clustering, introducing heavy parameter fusion and computational overhead that conflict with the resource-constrained nature of onboard units. To overcome these limitations, this paper presents VANET-GPSR+, a lightweight enhanced routing protocol. Its key novelty is that it discards multi-parameter fusion and relies solely on movement direction, supported by a synergistic framework of three lightweight mechanisms: direction-aware neighbor classification to prioritize nodes with consistent trajectories, adaptive greedy forwarding region expansion in sparse and dynamic networks, and path deviation angle-based next-hop selection. This work builds a probabilistic link lifetime model that theoretically quantifies the stability gains of direction awareness—a novel theoretical foundation. Comprehensive urban and highway simulations show that VANET-GPSR+ improves the packet delivery ratio by 16.3% and reduces end-to-end delay by 27.5% compared with standard GPSR, and it outperforms both OP-GPSR and AK-GPSR. It introduces negligible CPU and memory overhead, with CPU usage over 50% lower than the two benchmark protocols at 80 vehicles/km, and demonstrates strong robustness against varying beacon intervals and communication radii. Retaining GPSR’s stateless and distributed traits, VANET-GPSR+ delivers substantial performance gains with minimal overhead, serving as an efficient routing solution for highly dynamic VANETs. Full article