Search Results (3,125)

Equitable access to dental care across Germany remains constrained by geographic barriers, yet traditional provider-to-population ratios fail to capture spatial complexity. This study employed a Modified Huff Three-Step Floating Catchment Area (MH3SFCA) method to quantify dental care accessibility at the ZIP-code level across all 8171 German postal code zones, integrating 2022 Census demand data, 38,023 dental facilities, and network-based travel times. Results revealed stark geographic disparities: accessibility exhibited strong positive spatial autocorrelation (Moran’s I = 0.97, p < 0.001), with 22.6% of the population in high-access urban clusters concentrated in Berlin, Hamburg, and the Rhine-Ruhr region, while statistically significant cold spots affected only a small share of the national population but formed geographically persistent clusters in rural and peripheral regions. The Gini coefficient of 0.55 indicated moderate overall inequality, substantially higher than for physician distribution (Gini = 0.19). Regional heterogeneity was prominent: Mecklenburg-Western Pomerania had 74.9% of its population in the lowest accessibility quartile (mean index = 0.7), while Berlin achieved near-universal high access (mean = 15.3). The MH3SFCA framework provides German planning bodies with a dynamic tool for routine accessibility monitoring and scenario analysis, supporting targeted, place-based workforce interventions that move beyond static mapping toward more equitable distribution of dental care. Full article

With extremely low odor thresholds and potent flavor activity, sulfur-containing aroma compounds (SACs) constitute the molecular cornerstone of characteristic flavors in meat, coffee, and fermented foods. Research has advanced from early component identification to the elucidation of structure–activity relationships, olfactory receptor recognition mechanisms, and food-flavor improvement. This review first summarizes the detection and quantification methods for SACs, their distribution in foods, key odor contributions, and major formation pathways. It then highlights progress in understanding molecular structural parameters, olfactory receptor recognition, and computational simulations that decode flavor perception mechanisms. From a translational perspective, we further discuss flavor retention in real food matrices, off-flavor regulation, cross-modal perceptual enhancement, and functional applications. Current challenges include food matrix complexity, high compound reactivity, and nonlinear olfactory combinatorial coding. Future directions involve constructing a multiscale predictive framework integrating neuroscience, developing explainable artificial intelligence to decode olfactory coding, and advancing closed-loop green biomanufacturing for the precise design and sustainable production of SACs. Full article

Conventional full-span movable weirs may require complete lowering for sediment or debris release, reducing upstream water-level control and limiting operational efficiency. This study evaluates a smart rubber and steel (SRS) movable weir that enables selective gate operation through a one-way coupled hydraulic–structural framework using the Environmental Fluid Dynamics Code (EFDC) and MIDAS Civil. Four gate-operation scenarios and gate heights of 0.5, 1.0, 1.5, and 2.0 m were analyzed to characterize flow redistribution, hydraulic loading, gate response, longitudinal-rib performance, and the safety of anchor bolts, clamping plates, and the airbag system. Selective lowering substantially altered flow distribution, with side-gate lowering producing the most critical condition and increasing maximum velocity by approximately 267% relative to the fully raised condition at a 2.0 m gate height. Hydraulic demand became strongly localized near the lowered and adjacent raised spans, although the governing static load remained dominated by hydrostatic loading. Rib comparisons showed that redistributing stiffness through additional longitudinal stiffeners reduced stress and deformation without relying solely on gate thickening, while all component-level static checks satisfied the adopted safety criteria. The results demonstrate that integrating span-specific hydraulic redistribution with structural response provides a practical basis for structurally safe and material-efficient river-infrastructure design. Full article

Developers increasingly reuse third-party Low-Rank Adaptation (LoRA) adapters for code-generation models without visibility into how they were trained, creating a supply-chain risk: a maliciously trained adapter can behave normally on clean inputs while activating attacker-controlled functionality when a specific trigger is present. This study makes a mechanistic contribution to that risk: we show that trigger modality, not just contamination rate, determines a qualitatively different backdoor learning regime. We trained 61 poisoned variants of CodeGen-350M-mono on the CodeSearchNet dataset, injecting eight backdoor triggers spanning two categories: three semantic triggers based on natural-language code comments and five syntactic triggers based on structural code transformations derived from the CodePoisoner framework. Across attack success rate measurement, cross-trigger confusion analysis, mechanistic circuit tracing, layer-restoration defense evaluation, and semantic generalization testing, we find that semantic triggers produce associative binding: a 91% attack success rate, a Trigger Specificity Index (TSI) of 268×, distinct per-trigger circuits concentrated in attention layers, and a requirement to restore 10 parameter groups for removal. Syntactic triggers instead produce distributional confusion: a 31% attack success rate, a TSI of only 1.25× (1.45× once a shared-payload confound in the confusion-matrix design is corrected for), diffuse circuits spread across (Multi-Layer Perceptron) MLP layers, and collapse with just 5 restored parameter groups. Cross-payload testing on single-trigger models confirms this: structural triggers fire on triggers never seen during training at rates of 42 to 55%, showing that the model learns a general association between code abnormality and payload generation rather than a specific trigger–payload mapping. Both regimes preserve clean code-generation quality across all contamination rates, so a downloaded backdoored adapter is behaviorally indistinguishable from a clean one under standard benchmarks. These results argue against a one-size-fits-all approach to adapter auditing: detection and removal strategies calibrated to one trigger modality can fail outright against the other, and we outline the conditions under which each applies. Full article

The transition toward decentralized renewable energy systems creates monitoring problems that current digital infrastructures do not solve: sustainability claims are produced by the same actors they evaluate, environmental evidence is reported periodically rather than observed continuously, and the communities most affected by deployment cannot inspect the data used to represent their territories. Existing integrated platforms combine subsets of the blockchain, Internet of Things (IoT) sensing and life cycle assessment (LCA) at the data layer, but they do not organize that integration through an explicit governance structure. This paper contributes a cybernetic governance framework in which the Viable System Model (VSM) supplies the organizing structure of a blockchain–IoT–LCA monitoring architecture, so that sensing, distributed trust, strategic intelligence and participatory governance are recursively coupled rather than sequentially chained. The framework was developed and evaluated under the Design Science Research paradigm, and instantiated in the IMPACT Energy.CO platform across two technology routes, wind and solar, in La Guajira, Cesar, Atlántico and Magdalena, Colombia. Evaluation against six pre-declared criteria reports 45 executed test cases with a 100% pass rate, 90% unit and 87% integration code coverage, load tests up to 5000 concurrent users with zero errors and sub-second mean response, an operating hash-chained provenance layer issuing verifiable LCA certificates, 14 participatory validation workshops, 199 users trained and 166 technicians certified. We use traceability in a deliberately narrow sense throughout: the property whereby a committed record can be linked to the ingested data series, model version and computation that produced it, and its integrity and ordering checked by a party that does not trust the producer. It is provenance and integrity traceability from the point of ingestion onward, and it is not metrological traceability: the architecture cannot verify that an original sensor measurement corresponds to the physical quantity it purports to represent. We accordingly make explicit what the architecture does not guarantee: a ledger protects records after commitment but cannot certify measurement at the point of capture, and we present a threat model, a set of implemented controls and the residual risk that remains. This study contributes an architecture, a reproducible development and evaluation method, and a calibrated account of what verifiable environmental monitoring can and cannot deliver in contested Global-South territories. Full article

Distributed generation (DG) is reshaping distribution networks through bidirectional power flows, operational variability, and dependence on coordinated regulation, pricing, and control. This paper examines how regulatory architecture, grid-code requirements, tariff design, and Distributed Energy Resource Management Systems (DERMS) influence DG integration, emphasizing Honduras. A structured mixed-source review is applied, combining Scopus-based bibliometric analysis of 2438 records (2000–2026) with targeted synthesis of technical, regulatory, tariff-related, and institutional sources. The bibliometric results show sustained growth and a thematic shift from conventional voltage-control studies toward active distribution networks, DER coordination, storage, demand response, tariff reform, and digital energy management. The analytical synthesis shows that effective DG integration requires more than interconnection compliance: it depends on grid-support functions, cost-reflective and equitable tariffs, and operational tools capable of managing voltage deviations, reverse power flow, congestion, protection coordination, and limited visibility. DERMS is an enabling layer for voltage control, active and reactive power management, congestion mitigation, adaptive protection, and predictive operation. For Honduras, current regulatory progress should be complemented by phased modernization focused on observability, smart metering, data infrastructure, local flexibility, and progressive DERMS deployment. The study provides an integrated framework for aligning regulatory, economic, and operational dimensions of DG integration in emerging distribution systems. Full article

The diverse application scenarios envisioned for sixth generation (6G) are characterized by the deep integration of sensing and ubiquitous connectivity, which imposes unprecedentedly stringent security requirements. However, due to the open nature of wireless channels, mobile communications systems always face severe information security threats such as falsification, spoofing, interception, and repudiation. Cryptography-based symmetric and asymmetric encryption techniques remain mainstream solutions for information protection. Symmetric encryption is efficient and secure for legitimate users but suffers from key-distribution difficulties over open wireless channels, whereas asymmetric encryption resolves this problem but faces increasing risks from quantum computing due to its reliance on structured mathematical hardness assumptions. In response to these limitations, physical layer security (PLS) has gained a great deal of research attention as a powerful security component that leverages the features of varying wireless channels. Existing PLS schemes can be broadly classified into two categories. The first one takes advantage of the legitimate link’s opportunistic channel-quality superiority over or different spatial-domain directions from the attacking link, which still faces many practical implementation difficulties. The second category is termed physical-layer key generation (PLKG). It extracts the unique features of the legitimate link’s channel variation, which is often reciprocal, as the source of secret key generation and therefore can naturally implement secure key distribution tasks. This advantage no doubt injects new vigor to symmetric encryption as a stronger protection approach. Following this trend, we in this paper concentrate on the PLKG techniques. Specifically, we present a comprehensive overview on existing PLKG schemes, discussing diverse secret key generation and reconciliation methods. We further investigate the model-driven and deep-learning-based approaches tailored for the scenario with imperfect channel reciprocity between the sender and receiver. After comprehensively reviewing major existing schemes, we further discuss a recently proposed PLKG design based on codeword reconstruction, which makes use of the strong error-correcting capability of the forward-error-correction (FEC) codes to effectively implement secure and consistent secret key generation between the legitimate sender and receiver. Finally, we share our opinions on the unsolved challenges and potential research directions dedicated to PLKG toward meeting the security requirements of 6G. Full article

The microphthalmia-associated transcription factor (Mitf) family is central to melanocyte biology. Teleosts possess two mitf paralogs, mitfa and mitfb, yet their functional divergence remains incompletely understood. Here, we characterized the coding sequences, phylogeny, tissue distribution, developmental expression, cellular localization, and stress responsiveness of mitfa and mitfb in Congjiang natural mutant crucian carp (NMCC). Both genes encoded proteins containing the characteristic bHLH-ZIP domain. Phylogenetic analysis revealed that teleost mitfb clustered with the tetrapod mitf clade, indicating closer evolutionary affinity than mitfa. mitfa was predominantly expressed in skin and eyes, whereas mitfb showed broad tissue distribution. Both paralogs were constitutively expressed in skin across developmental stages; mitfa levels remained consistently lower than mitfb. Both transcripts were localized to melanocyte-containing regions of the skin. Transcriptomic analysis following high temperature (34 °C), UV radiation, and combined stress (48 h) revealed that mitfa was significantly upregulated by thermal stress, while mitfb showed no significant changes. Profiling of DNA damage response pathways uncovered extensive, stress-specific transcriptional remodeling. High temperature broadly suppressed apoptotic genes while upregulating tsc2, gadd45b, and cdip1; UV triggered widespread apoptotic gene activation. Combined stress elicited a hybrid signature. Notably, robust DNA Damage Response pathway remodeling contrasted with muted mitf responses, suggesting that DNA Damage Response-to-pigmentation signaling may operate predominantly at post-transcriptional levels. These findings reveal a substantial regulatory divergence between mitfa and mitfb, with mitfa maintaining a conserved melanogenic role while mitfb has been co-opted for broader functions in non-pigmentary tissues. The differential stress responses of the two paralogs further provide new insights into tissue-specific genotoxic stress signaling and melanogenic regulation in teleosts. Full article

The pervasive rise in digital platforms has fundamentally recalibrated the relationship between urban communities and their cultural landscapes, reconfiguring not only the sensory experience of these environments but also their interpretive frames and the everyday contestations that surround them. Using coffee spaces in Beijing as an empirical lens, this study integrates 630 validated POI locations, 287,632 online reviews from Dianping, GIS spatial analysis, LDA topic modelling, semiotic coding, and a fine-tuned RoBERTa (Robustly Optimized BERT Pretraining Approach) text classifier to examine the convergence between platform-mediated public perceptions and urban spatial differentiation. Our spatial interrogation identifies a statistically significant centre–periphery divide within this platform-curated corpus—a pattern that describes the geography of platform-mediated visibility rather than the total physical distribution of coffee outlets in Beijing, characterised by pronounced high-density agglomerations nesting within historic urban cores, central business districts, nascent knowledge hubs, and gentrifying lifestyle enclaves. Three landscape prototypes are derived from the data—business corridors, cultural experimental fields, and social-media spectacles. Each type exhibits discernibly different patterns of spatial density, adjacent land-use configurations, perceived experiential qualities, and underlying symbolic discourses. While positive sentiments dominate platform discourse, localised anxieties emerge regarding visual homogenization, intense queuing, premium pricing, and loss of place authenticity. The 630 venues analysed here represent a platform-visibility stratum—establishments sufficiently reviewed to feature on digital intermediaries—and the spatial patterns we map describe the geography of this visibility rather than the total physical distribution of coffee outlets. Full article

Federated, semi-supervised novelty detection is well suited for intrusion detection on resource-constrained Internet of Things (IoT) nodes: each device learns a model of benign traffic, shares only summary statistics, and does not transmit raw traffic samples. A previously published cross-layer federated detector, Baseline K-Means, showed that periodically merging worker statistics through a coordinator raises the detection rate, but it also exhibited a systematic side effect: after every merge, the precision decays, and the false-positive rate (FPR) climbs because the coordinator recomputes its threshold from streaming distances filtered by the closest observed anomaly, so tightens after every merge, flagging progressively more benign traffic; the threshold was also hand-tuned. We present AF-BKM, an Adaptive Federated Baseline K-Means that repairs the federated mechanism with two label-free, statistics-only enhancements, denoted as E1 and E2: (i) an adaptive decision threshold read from the benign Mahalanobis-distance distribution, requiring no manual percentile search and no attack labels (E1), and (ii) a robust, benignly anchored aggregation that blends worker means under quality weighting and outlier-worker filtering and recalibrates the threshold on a trusted benign anchor to a stable, anchor-referenced false-positive level, which a target-FPR rule can make operator-selectable instead of tightening it toward the nearest anomaly (E2). With MinMax scaling fit only on benign baseline data and non-IID federated streams on NSL-KDD, UNSW-NB15 and the N-BaIoT corpus of real traffic from commercial IoT devices, AF-BKM removes the merge-induced precision decay (the first-to-last-epoch precision change improves from $-0.134$ to $-0.002$ on NSL-KDD, from $-0.121$ to $-0.014$ on UNSW-NB15, and from $-0.170$ to $-0.009$ on N-BaIoT) and reduces the mean FPR by 30–$64\%$, depending on the dataset; all central improvements are significant across 10 seeds (Wilcoxon $p=0.002$, large effect sizes). AF-BKM preserves recall on NSL-KDD and N-BaIoT and, on the harder UNSW-NB15, exposes an explicit precision–recall trade-off through a benign target-FPR knob. In fp32, the deployed model serializes to $5.5$–52 KB, a packet is classified in 11–27 µs on a desktop CPU, and each merge round uploads a $d+3$-value summary (160–472 B) $94.6$–$98.3\%$ smaller than the same summary extended with the covariance upper triangle. A robustness study covering selected faulty-worker updates, contamination of the commissioning anchor, and detector-level white-box evasion reports the measured degradation patterns: fabricated threshold candidates have no direct path to the threshold, although a fabricated mean still reaches it indirectly through the blended centroid, and the anchor-referenced false-positive level remains stable under percent-level anchor contamination, while recall sensitivity is dataset-dependent and the evasion budget tracks the benign–attack margin of each dataset. We frame the contribution with a focused taxonomy that identifies merge-induced precision decay under non-IID workers as an open gap. Code is released for reproducibility. Full article

Binary code similarity detection (BCSD) ranks candidates but does not quantify the reliability of an already selected Top-1 match. We study this post-retrieval problem in a known-source, closed-set protocol: the target Top-1 is frozen before same-source cross-compilation views are queried, so auxiliary evidence audits cannot replace it. A frozen 34-variable map feeds a low-capacity logistic model with project-grouped cross-fitting, Platt calibration, and training-side threshold selection. On 413 families from 16 projects, cross-view evidence improved discrimination over target score/margin features. GCC-O0 was a dominant-anchor regime: Full showed no statistically resolved ROC-AUC gain over Primary-anchor, whereas Clang-O0 benefited from complementary non-primary evidence. On 240 project-identity-disjoint families from 55 projects, the design-locked structural branch accepted 75/240 GCC and 99/240 Clang candidates (31.3%/41.3% coverage) with no observed family-level errors. Correspondence mismatch reduced discrimination toward chance. Corrected TF-IDF remained supportive because correction followed label access. The contribution of this paper is a versioned candidate-preserving audit interface with explicit evidence and deployment boundaries, but not a universal retrieval improvement or distribution-free guarantee. Full article

Manufacturer datasheets state battery cycle life under conditions that rarely match how cells are used, while public cycling datasets measure degradation under conditions datasheets do not cover. We present a knowledge-graph (KG) system that represents claims, measurements, and independent tests of commercial lithium-ion cells with full provenance, detects claim-versus-measured and claim-versus-claim discrepancies conditioned on the comparability of test conditions, and supports cycle-life prediction with coverage-gated abstention. On the 124-cell Severson dataset under leave-one-policy-group-out cross-validation, graph-derived neighbor features do not significantly improve point prediction over a strong early-cycle baseline (RMSE 135 vs. 141 cycles), but graph coverage provides a statistically significant abstention signal (Spearman $\rho =-0.25$ with prediction error, $p=0.006$) that reduces retained RMSE by roughly 40% at 60% retention, where random abstention does not. Deployed zero-shot on a second cycling study of the same commercial cell, the gate abstained on all 77 cells; the counterfactual confirms every refusal (approximately 83% error had it answered), an error an ungated baseline commits silently. On a third study with commensurable features, the gate’s first partial acceptance (17 of 45 cells) is itself diagnostic: coverage acts partly as a lifetime proxy out of distribution, and five labeled cells halve retained error while leaving that proxy in place—adaptation repairs the predictor, not the selection criterion. A 70B open-weight LLM extracts datasheet claims at $F_1=0.70$ with non-deterministic output even at temperature 0; a deterministic validator with three-run consensus raises this to $F_1=0.78$ with zero unsourced values; on a held-out datasheet, precision and the zero-unsourced-value property transfer while recall falls to 0.34, localizing the extractor’s boundary at table-structured content; row-level table grounding, implemented in response, raises held-out recall to 0.63 with zero hallucinations at a measured precision cost. Reconciling claims across document variants shows that roughly one in three cross-document specification comparisons (14 of 43, three commercial cells) yields a conflict or condition mismatch, twelve involving third-party documents and two internal to a single manufacturer’s own documents. A hand-labeled, condition-annotated gold standard of 103 claims (62 development, 41 held-out; inter-annotator $\kappa =0.74$ on property naming) and a staged, human-gated literature-monitoring pipeline are released with the code. Full article

Engine-inlet fuel pressure in helicopters can fluctuate during flight manoeuvres because load-factor variation changes both fuel distribution in the tanks and the pressure balance along the fuel-supply pipeline. This simulation-only study investigates a passive pressure-regulation scheme based on a constant-pressure reducing valve (CPRV) for a representative five-tank helicopter fuel system. Mathematical models of the fuel tank, booster pump, jet pump, check valve, fuel-supply pipeline, and CPRV are integrated in AMESim and checked against reported tank-depletion data, fuel-centre-of-gravity data, and code-to-code benchmark results. A controlled same-model isolation check additionally compares a mobile CPRV spool with the same spool constrained at its fully open end stop while all pump, tank, line, demand, load, fluid, reference-pressure, and solver settings remain unchanged. In a longitudinal load-factor ramp to 1 g, the mobile-spool model maintains 1.8042–1.8047 barA, whereas the locked-open control gives 2.4287–2.5917 barA. Across the principal regulated simulations, the maximum absolute deviation from the 1.8 barA target is approximately 0.005 bar. These values are numerical results for the stated model and must not be interpreted as sensor-resolvable hardware accuracy or qualification evidence. Full article

Large language models (LLMs) now generate fluent natural language and source code, creating challenges for authorship attribution, academic integrity, and software supply-chain security. Most existing detectors for AI-generated content are evaluated separately on natural language or source code, often under matched train–test conditions that can overestimate real-world reliability. We present a paired-prompt benchmark for human-versus-machine detection across English text, Python code, and mixed text–code documents. The benchmark includes $\mathrm{22,141}$ instances from HC3, CodeSearchNet, MBPP, and HumanEval across training, validation, and test partitions, plus Mix-Eval, a mixed-content set of 997 Jupyter-notebook-style samples. We evaluate RoBERTa-large for text, GraphCodeBERT and CodeBERT-base for code, a unified RoBERTa-base detector trained on both modalities, and zero-shot baselines. Fine-tuned detectors achieve near-perfect in-distribution performance, with AUROC $1.0000\pm 0.0000$ and accuracy above $99.5\%$. Across five instruction-tuned generator families of varying size (3.8B–7B) and architecture, with the human and problem distributions held fixed, cross-generator transfer causes negligible degradation (AUROC spread $0.0002$; drops of at most $0.0003$). In contrast, domain shift is the main failure mode: on MBPP+HumanEval, GraphCodeBERT drops to $0.85\pm 0.02$ AUROC and CodeBERT-base to $0.67\pm 0.02$. On Mix-Eval, the unified detector outperforms a routed text–code pipeline by 21 AUROC points ($0.96$ vs. $0.75$), largely because of router failures on mixed inputs. Training-time augmentation improves low-false-positive performance, while legacy supervised detectors show systematic class inversion on modern LLM outputs. These results show that reliable deployment requires cross-domain evaluation, mixed-content testing, and calibration beyond in-distribution accuracy. Full article

Cultural heritage faces accelerating threats from armed conflict and climate-induced disasters, generating emergency scenarios in which the window for documentation may be measured in hours. Traditional high-fidelity workflows such as Terrestrial Laser Scanning and survey-grade Structure-from-Motion campaign can be operationally incompatible with these constraints due to costly hardware and specialist-team requirements. Emergency image acquisition can be undertaken with consumer-grade equipment, while the same photographic dataset can subsequently support both an SfM-MVS mesh for metric and geometric analysis and a 3D Gaussian Splatting (3DGS) representation for view-dependent visual interpretation. This paper evaluates an accessible, entirely graphical workflow for producing these complementary outputs, with particular attention paid to the 3DGS stage and without requiring programming or command-line expertise. We develop a theoretical case drawing on Brandinian restoration theory and the international conservation framework, introducing the concept of informational amnesia, the irreversible loss of a site’s documentary record, as a distinct and undertheorized category of heritage failure. We further argue that the democratization of 3DGS documentation cannot be measured by licensing cost alone: a tool distributed as open-source code but requiring command-line expertise presents an effective accessibility barrier functionally equivalent to a commercial paywall. True accessibility requires installable, graphical software operable by non-specialist practitioners in the field. We present a proof-of-concept application of this accessible pipeline, namely DJI Mini 5 Pro, RealityScan for photogrammetric reconstruction, and Lichtfeld Studio for 3DGS generation, to the Fontana d’Ercole at the Reggia di Venaria Reale (UNESCO World Heritage, Turin, Italy). Visual results show photorealistic, navigable 3DGS surrogates of architecturally complex heritage assets achievable by non-specialist operators within hours of acquisition and establish the empirical foundation for future quantitative evaluation. Full article