Search Results (190)

Background/Objectives: Deep learning models for digital mammography sensor data are increasingly deployed across hospitals using different X-ray detector technologies and patient populations. Whether models trained on one sensor platform and population maintain accuracy when transferred to another has not been tested for the latest generation of mammography-specific foundation models under one controlled protocol. Methods: We fine-tuned five backbone architectures (ResNet-50, DINOv2-B14, Rad-DINO, Mammo-CLIP B5, and Mammo-FM) on CBIS-DDSM (film-digitized, USA, n = 714 validation) with three seeds, ablated a density-aware focal loss across three auxiliary weights, and evaluated transfer to three external sensor cohorts: CMMD (full-field digital, China, n = 1032), DMID (mixed digital, India, n = 509), and MIAS (film-digitized, UK, n = 322). Significance used paired DeLong z-tests with Benjamini–Hochberg FDR correction; temperature scaling tested post hoc recalibration at all transfer targets. Results: Within this single-source three-seed evaluation, ResNet-50 outperformed all four foundation models on CBIS-DDSM (AUC 0.867 vs. 0.847, 0.846, 0.813, and 0.703; all gaps p_adj < 0.05). The density-aware focal loss degraded both AUC and calibration at every weight tested. At transfer, every model lost 0.165 to 0.320 AUC points relative to in-distribution performance, with sensitivity at 95% specificity collapsing from 0.31 to 0.47 in-distribution to 0.11 to 0.22 across the three external targets. A per-seed Stouffer meta-analysis confirms that Mammo-CLIP B5 and Mammo-FM significantly outperformed ResNet-50 on DMID and Mammo-CLIP on CMMD, after BH-FDR; MIAS comparisons remained directional only. In the extremely dense subgroup (BI-RADS D4), Mammo-FM reached AUC 0.870 versus ResNet-50 at 0.842, a directional observation whose 95% CIs overlap heavily at the n = 140 sample size and which we do not interpret as a statistically supported advantage. Conclusions: In this single training-source, three-seed protocol, mammography-specific pretraining did not deliver the in-distribution AUC premium reported in the originating papers, and no architecture reached a level at which transfer deployment without local validation would be defensible. We frame these as observations specific to the present protocol rather than as broader conclusions about foundation models for mammography classification. The findings argue for sensor-stratified and population-stratified external validation and for local recalibration as practical prerequisites before clinical use. Code and weights are released under MIT license. Full article

Pseudorandom number generators (PRNGs) used in deoxyribonucleic acid (DNA)-oriented computational workflows often generate outputs in the bit domain and then map them to DNA symbols. This indirect strategy may treat DNA-specific constraints, including GC balance, homopolymer limits, and short-range sequence dependencies, as separate from generation. This study proposes a constraint-aware, dual-head decoder-only Transformer framework for DNA-local PRNG generation directly in the adenine/cytosine/guanine/thymine (A/C/G/T) alphabet. The model generates the next DNA base and derives the bitstream through dynamic selection among eight equivalent DNA-to-bit coding rules. The framework was evaluated under R1 based on real genomic data, R1-ext as independent validation, R2 based on synthetic data, and R3 without training or reference data. For each setting, 10 independent runs were performed, each producing a 500,000-base DNA sequence and a 1,000,000-bit stream. Bit-level evaluation used NIST SP 800-22, SP 800-90B-inspired min-entropy/health indicators, and ENT, while DNA-level evaluation used GC balance, homopolymer control, and symbolic structural metrics. The reported NIST tests satisfied the acceptance criterion, t-tuple min-entropy lower bounds ranged from 0.9955 to 0.9964 bit/bit, and core DNA-compatibility constraints were preserved. Multi-stream and exact-match k-mer leakage analyses indicated no systematic bit-level dependence or direct long-fragment copying. Overall, the framework supports reproducible DNA-local PRNG generation and multilayer validation. Full article

Red raspberry fruit is highly perishable, and raspberry plants are sensitive to drought and low-temperature stress because of their shallow root system, which limits production and postharvest utilization in cold regions. In this study, RiACO1 was cloned from red raspberry (‘Polka’) and analyzed by bioinformatics, subcellular localization, tissue-specific expression, heterologous overexpression in Arabidopsis thaliana, and transient overexpression in white-stage raspberry fruit. The full-length RiACO1 coding sequence was 963 bp and encoded a 320-amino-acid protein that localized to the cytoplasm and nucleus. RiACO1-overexpressing Arabidopsis lines showed higher survival rates under drought and low-temperature stress, accompanied by increased proline content, chlorophyll retention, and antioxidant enzyme activities, as well as reduced Malondialdehyde (MDA) and Reactive Oxygen Species (ROS) accumulation. In raspberry fruit, transient RiACO1 overexpression increased RiACO1 transcript levels, ACO activity, and ethylene production and was associated with accelerated softening, anthocyanin accumulation, and chlorophyll degradation. These results indicate that RiACO1 is involved in ethylene-associated fruit ripening and may contribute to abiotic-stress responses; however, its direct breeding value in raspberry requires further validation through stable raspberry transformation or targeted loss-of-function approaches. Full article

The application of Internet of Things (IoT) technology to Digital Twin (DT) in smart manufacturing has opened significant opportunities for real-time monitoring, predictive maintenance, and closed-loop control; however, the inherent latency that exists in these architectures (the temporal gap between a physical event and its reflection in a digital model) remains one of the most significant and least systematically understood barriers to fulfill its full potential. This paper aims to propose a formal four-layer taxonomy of latency sources in IoT-based Digital Twin systems for smart manufacturing and to review the current approaches and tools that are available for their measurement. The PRISMA protocol has been used to perform a systematic literature review, where 58 primary survey studies published between 2020 and 2026 were extracted from IEEE Xplore, Elsevier Scopus, Google Scholar and arXiv, with all the studies being coded along six dimensions (architectural layer, application domain, latency metrics reported, evaluation methodology, quantitative impact, and enabling technologies). The proposed taxonomy presents 28 different types of latencies under four layers: (L1) network, (L2) compute, (L3) data, and (L4) end-to-end (E2E), whose magnitudes vary from 0.1 ms for local network propagation to tail latencies above 500 ms in production (P99). Three categories and three cross-layer interaction patterns are formalized here and are absent from prior partial taxonomies. Among the most promising results is the finding that several high-impact interventions require no infrastructure investment: a protocol migration from Modbus to WebSocket reduces telemetry latency by 32%, while Age of Information-aware synchronization and clock drift correction deliver substantial data layer gains through software updates alone, yet remain underutilized. The review identifies a systematic under-reporting of tail-latency percentiles across the corpus, the lack of a cross-protocol jitter benchmark, and a predominance of simulation-based evaluation over real-hardware measurement. The systematic review contributions of this paper (the formal four-layer taxonomy, the proportional metric audit across the 58 papers, and the formalization of three cross-layer interaction patterns) are derived from cross-corpus analysis. The investigation also identifies three open research directions (a standardized manufacturing IoT-DT benchmark, cross-layer joint optimization frameworks, and wireless TSN validation on real manufacturing testing grounds) that together form a well-organized and practical basis to advance both the science and the application of ultra-low-latency Digital Twin technology in the industrial field. Full article

Background: Sepsis remains a leading cause of morbidity, mortality, and healthcare expenditure worldwide. Despite international guidelines and diagnostic criteria, real-world variability in coding, treatment, and outcomes persist. This retrospective study analyzed 13,994 coded sepsis-related hospitalizations identified through administrative ICD-9-CM algorithms between 2016 and 2024 to evaluate the burden of sepsis, temporal trends, clinical outcomes, and healthcare costs within a regional health system. Methods: Hospitalization data across four local health authorities (ASL 201–204) over an 8-year period were analyzed. The coded sepsis cases were identified using validated ICD-9-CM-based algorithms and classified into four groups according to available microbiological coding: Gram-positive, Gram-negative, anaerobic and unspecified. Variables included patient demographics, length of stay, costs, outcomes (in-hospital and post-discharge mortality) and presence of septic shock. Comparative analyses were conducted using descriptive statistical methods and One-way ANOVA test and chi-squared tests were applied to evaluate the significance of differences. Multivariable logistic regression models were used to identify independent predictors of 6- and 12-month mortality. Results: The dataset included 13,994 coded sepsis-related hospitalizations, with the largest subgroup being ‘unspecified’ (48.0%). Among cases with specified etiology, coded anaerobic sepsis categories, though rare (0.7%), were associated with higher in-hospital mortality (45.5%) and economic burden (avg. € 8563). Mortality remained high at 6 and 12 months across all types, exceeding 50% post-discharge. Increasing age (OR ≈ 1.06 per year) and septic shock (OR ≈ 4.5–4.8) were the strongest independent predictors of mortality. Differences across microbiological groups should be interpreted cautiously given the high proportion of cases without organism-specific coding. Despite a modest reduction in mortality over time, sepsis was associated with persistently high 6- and 12-month mortality, highlighting a substantial long-term burden beyond the acute phase of illness. These findings suggest that sepsis-related hospitalizations are associated with substantial long-term mortality beyond the acute phase of illness. Discussion: These findings underscore the clinical and economic impact of sepsis in hospitalized patients, across microbiological coding categories. The high mortality rate at 6–12 months may support the need for further investigation into structured post-discharge follow-up strategies. Sepsis represents a substantial clinical and economic burden within the regional healthcare system, with persistently elevated short- and mid-term mortality. Incomplete organism-level documentation limits direct etiologic comparisons and highlights the need for improved integration between clinical, microbiological, and administrative data systems. Future research should integrate clinical variables and lab results to enable risk stratification and intervention planning. Full article

We study cyclic codes over split two-branch finite local rings of the form $R_{\mathcal{l},m}={\mathbb{F}}_p[u,v]/⟨u^{\mathcal{l}},v^m,uv⟩,\mathcal{l},m\ge 2,$ whose radical filtration is governed by two independent nilpotent chains: $u\to u^2\to \cdots \to u^{\mathcal{l}-1}\to 0\mathrm{and}v\to v^2\to \cdots \to v^{m-1}\to 0.$ For the structural part, we develop a residue–torsion framework in which a cyclic code is described by one residue layer together with $\mathcal{l}-1$u-torsion layers and $m-1$v-torsion layers over ${\mathbb{F}}_p$. This yields divisibility constraints, a layered generator description, and an explicit cardinality formula in terms of the associated field cyclic codes. We then specialize to the split cube-zero ring $R=R_{3,3}={\mathbb{F}}_p[u,v]/⟨u^3,v^3,uv⟩,$ a non-chain local ring of type $(2,2)$ with basis $\{1,u,v,u^2,v^2\}$. For this ring, the general theory becomes a five-layer structure consisting of one residue layer, two first torsion layers, and two second torsion layers. Using an ${\mathbb{F}}_p$-linear Gray map adapted to this split radical filtration, we show that, when $gcd(n,p)=1$, the Gray image is linearly equivalent to a direct sum of five cyclic codes over ${\mathbb{F}}_p$, so the dimension is additive across the layers. The minimum distance, however, is not determined by this decomposition alone and requires separate analysis. When $n=p^s$, we derive exact distance formulas by reducing the problem to the five associated repeated-root cyclic codes over ${\mathbb{F}}_p$. For $p=3$ and $n=9$, we compute explicit examples whose Gray images are ternary codes of length 45, illustrating the theory and producing several optimal codes. These results give a structural and metric description of cyclic codes over the split local ring $R_{3,3}$ while placing its algebraic framework in the broader family $R_{\mathcal{l},m}$. Full article

Spanish Differential Object Marking (DOM) is conditioned by well-known local properties of the direct object, but also by clause- and discourse-level factors. In this study, we examine whether these factors are also available as potential learning cues in child-directed speech (CDS). We analyzed longitudinal naturalistic CDS from two monolingual and three bilingual (heritage) Spanish-learning children, manually extracting transitive clauses and coding DOM presence alongside discourse specificity, verb class, coreferential pronoun (clitic doubling), relative animacy, and DO placement, plus two local cues for comparison. Regression analyses revealed that a wider range of local and global factors conditioned DOM in monolingual than in bilingual CDS. The potential informativeness of these factors as learning cues was quantified using Competition Model measures of availability, reliability, and validity. In monolingual CDS, local cues (+human, pronominal/proper name DOs) were highly reliable, and two global cues (clitic doubling and relative animacy) showed moderate reliability. Whereas discourse specificity and verb class were highly available, they were comparatively unreliable. Validity values were uniformly low; although several global cues matched or exceeded local cues in validity, this pattern largely reflected their greater availability rather than higher reliability. In bilingual CDS, reliability and validity were reduced across nearly all cues, with little differentiation among cues. These findings suggest that Spanish-learning children encounter potentially usable utterance- and discourse-level evidence for DOM in CDS, but that the robustness of this evidence is markedly weaker in bilingual input. Full article

RGB–3D industrial anomaly detection seeks to jointly exploit texture and geometric cues for robust defect inspection. However, existing multimodal fusion methods still face two practical limitations: modality-specific anomaly evidence is often weakened after direct fusion, and image-level decisions remain unstable on difficult categories. To address these issues, this study develops a reliability-aware scoring enhancement on top of the released Hybrid Fusion/M3DM memory-bank pipeline. The method constructs a disagreement cue from RGB and point-cloud anomaly responses to enhance suspicious local regions and introduces a dual-branch image-level score calibration that combines a sensitive fusion branch with a robust statistical branch. Evaluated on MVTec 3D-AD under the official released-code full setting, the proposed method achieves 0.800 image-level ROCAUC, 0.980 pixel-level ROCAUC, and 0.926 AU-PRO, compared with 0.779, 0.975, and 0.915 for the corresponding released-code baseline in our environment. Additional evaluation on Eyecandies improves pixel-level ROCAUC and AU-PRO, while showing that image-level calibration remains dataset-sensitive. On a supplementary three-category Real-IAD D3 subset, the mean image-level ROCAUC, pixel-level ROCAUC, and AU-PRO improve from 0.963, 0.979, and 0.921 to 0.980, 0.988, and 0.941, respectively. These results indicate that explicit cross-modal disagreement modeling improves localization consistency, while image-level score calibration provides dataset-dependent gains rather than a uniform cross-dataset guarantee. Full article

This paper investigates whether a low-code workflow engine can operate as practical Industrial Internet of Things (IIoT) middleware at the edge when cloud application programming interface (API) rate limits make direct telemetry upload unsustainable. The main contribution is a dual-path architecture in which a Hot Path persists all telemetry locally, while a Cold Path selectively forwards only anomalous or summary events to cloud services. The architecture is implemented as a lightweight containerized stack based on n8n, Eclipse Mosquitto, InfluxDB, and Grafana, and evaluated on a Raspberry Pi 4 under baseline, cloud-only saturation, and edge-filtered stress scenarios. Under the cloud-only condition, the external endpoint is throttled to approximately 60 requests/min, yielding a rejection rate of 98.0% (95% Wilson confidence interval: 97.43–98.44%). Under the dual-path condition, the same inbound load is fully retained locally while outbound cloud traffic is reduced by 98.0%, thereby avoiding throttling without sacrificing edge-side data fidelity. The measured Hot Path processing latency remains around 5 ms on average, with observed peaks below 10 ms, which is compatible with soft real-time monitoring workloads. Compared with more established low-code tools such as Node-RED, the novelty of the study is not the existence of visual orchestration itself, but the combination of containerized deployment, explicit hot/cold decoupling, and an empirical rate-limit mitigation analysis focused on low-cost edge hardware. Full article

Intelligent detection in modern manufacturing, healthcare, process industries, and structural monitoring is fundamentally enabled by heterogeneous sensor systems. Rather than being viewed as a purely image-centered recognition task, intelligent detection is more appropriately formulated as a sensor-driven state inference problem in which sensing physics, signal quality, temporal synchronization, modality availability, and deployment conditions jointly determine what can be reliably detected, localized, interpreted, and acted upon. Against this background, this review provides a structured synthesis of the field through three coupled dimensions, namely methods, systems, and governance, and organizes the literature around four recurring engineering components: signal unification, representation unification, alignment mechanisms, and robustness mechanisms. Using a structured review protocol with explicit source selection, screening, and study coding, the paper traces the methodological evolution from traditional feature-engineering and model-based pipelines to deep learning for visual, temporal, multimodal, generative, and mechanism-constrained sensing, and further to foundation-model-based and multimodal sensor intelligence. Cross-domain evidence is synthesized from industrial defect detection, fault diagnosis, remaining useful life prediction, non-destructive testing, structural health monitoring, medical lesion analysis, and process monitoring. The review argues that recent progress has substantially strengthened learned representations, multimodal interaction, and semantic extensibility, but has not removed persistent constraints arising from domain shift, missing modalities, calibration instability, privacy-preserving collaboration, and edge-side resource limits. Accordingly, the central challenge is no longer how to optimize isolated detection models, but how to build sensor-enabled intelligent systems that remain physically grounded, trustworthy, transferable, and maintainable under real operational conditions. On this basis, the paper concludes by identifying future directions in mechanism-aware modeling, trustworthy evaluation, missing-modality-robust multimodal systems, privacy-preserving cross-site collaboration, and edge-native lifecycle-aware deployment. Full article

Chronic obstructive pulmonary disease (COPD) is characterized by persistent airflow limitation, chronic airway inflammation, and immune dysregulation, and currently available therapies remain insufficient to effectively halt disease progression. In this study, we used an integrative, hypothesis-generating strategy to investigate the potential mechanisms of baicalein against COPD by combining multi-dataset transcriptomic analysis, single-cell transcriptomics, machine learning-based feature selection, Mendelian randomization (MR), molecular simulation, virtual knockout analysis, and in vitro validation. Putative targets of baicalein were predicted using CTD, SEA, and SwissTargetPrediction, and were intersected with COPD-related genes collected from GeneCards and OMIM. Four GEO datasets (GSE20257, GSE42057, GSE76925, and GSE130928) were integrated after batch-effect correction, yielding a combined cohort of 260 control samples and 250 COPD samples. Candidate genes were prioritized by intersecting the results of LASSO regression, random forest, and support vector machine. Immune-cell infiltration was estimated using CIBERSORT, and single-cell transcriptomic data were used to define the cellular localization of prioritized genes. Formal protein-level MR analysis was conducted for CD163 using deCODE plasma protein pQTL/GWAS summary statistics as the exposure dataset and the IEU OpenGWAS COPD dataset (ebi-a-GCST90018807) as the outcome dataset. Molecular docking, molecular dynamics simulation, and virtual knockout analysis were further used to provide structural and network-level supportive evidence. Finally, LPS-stimulated BEAS-2B cells were used as an epithelial inflammatory model to evaluate the effects of baicalein by CCK-8 assay, wound-healing assay, ELISA, and RT-qPCR. Five core genes were prioritized, namely ABCC1, CD163, CYP1B1, IKBKB, and PIK3CA. Immune infiltration and single-cell analyses suggested that macrophage-associated immune regulation may represent an important mechanistic direction. MR analysis provided supportive genetic evidence for prioritizing CD163 in COPD. Molecular simulation offered preliminary structural support for several target-compound interactions. In LPS-stimulated BEAS-2B cells, baicalein reduced inflammatory cytokine release and modulated the expression of IKBKB, PIK3CA, IL1B, IL6, and IL10, thereby providing epithelial-level support for the predicted network. Taken together, these findings suggest that baicalein may exert anti-inflammatory effects in COPD through a multi-target, immune-associated mechanism, with macrophage-related regulation and CD163 emerging as noteworthy candidate directions for further investigation. This study provides an integrative framework for target prioritization and mechanistic exploration, while the predicted macrophage-centered mechanisms still require dedicated validation in immune-cell and in vivo models. Full article

We develop an information-theoretic, cost-first framework for discrete dynamics in which the primitive operation is ratio-based comparison. Given two quantities compared via their ratio $x=a/b$, we assign a cost $F\left(x\right)$ measuring deviation from equilibrium ($x=1$). Adopting a reciprocal d’Alembert composition law motivated by coherent chaining, together with quadratic calibration at unity, uniquely determines a reciprocal comparison cost $J\left(x\right)=\frac{1}{2}\left(x+x^{-1}\right)-1$. Taking J as input, we model recognition events as deterministic updates on directed graphs recorded in a minimal ledger. Minimality (no intra-tick ordering metadata) together with non-commutativity of events implies atomic ticks: at most one event per tick. With conservation, pairwise locality, and quantization in $\delta \mathbb{Z}$, each event is recorded as a balanced double-entry posting. For graphs with cycles, assuming time-aggregated cycle closure over a finite clearing horizon, we show that cleared cycle closure is equivalent to path-independence and that the cumulative flow admits a scalar potential on each connected component (unique up to additive constant) via a discrete Poincaré lemma. On hypercube graphs $Q_d$, atomic single-edge updates impose a $2^d$-tick minimal period for timestamp-unique coverage, realized by cyclic Gray codes (explicitly for $d=3$). The framework links ratio-based cost functions, conservative graph flows, and discrete potential theory through explicitly stated axioms and structural assumptions. Full article

Massively Multiplayer Online Role-Playing Games (MMORPGs) increasingly rely on player-developed third-party tools to extend functionality and personalise gameplay, creating a complex software ecosystem that introduces both usability benefits and security risks. This study investigates whether such tools can be exploited as an attack vector for cybercrime by designing and implementing a proof-of-concept add-on within a widely deployed commercial MMORPG using its native scripting and application programming interface. The developed tool supports automated player discovery, chat capture, target inspection, and local data persistence, enabling a systematic evaluation of how cyber-assisted and cyber-dependent crimes could be facilitated within the game client. Empirical testing demonstrates that while the platform’s protected execution model and interface restrictions prevent direct credential theft and remote code execution, the add-on architecture allows extensive behavioural data collection and social-engineering-relevant monitoring, making several forms of cyber-enabled crime technically feasible. These findings show that MMORPG add-on frameworks represent a non-trivial socio-technical attack vector in next-generation online platforms, where security depends not only on code isolation, but also on how user-generated extensions interact with human behaviour. The results highlight the need for architecture-aware security controls and governance mechanisms to mitigate emerging threats in large-scale, extensible virtual environments. Full article

The latest H266/VVC standard has received numerous praises for its excellent compression efficiency. However, its extremely high computational complexity has become a hindrance to the VVC adaptation industry ecosystem, while also increasing the difficulty of hardware design and application costs. To address this issue, we designed an efficient intra-coding scheme based on neural networks, which consists of three parts: Firstly, we designed a neural network-based reverse prediction algorithm that uniquely utilizes the CNN’s prediction results for lower-level blocks to determine the QTMT partitioning of upper-level blocks, cleverly solving the adaptation problem of existing models to complex VVC partitioning patterns—a decision-making logic that has not been fully explored. Secondly, we designed a pruning algorithm, which is the first to dynamically couple the real-time RDO cost of BT segmentation with the TT segmentation direction, achieving adaptive decision-making. Finally, we designed a complexity pre-screening module. On the basis of analyzing whether the CU texture is smooth, this module designs four sets of adaptive thresholds for non-square CUs introduced in VVC. These thresholds can dynamically adjust local and global thresholds based on CU size, enabling size sensitive texture evaluation to determine whether the current block needs further partitioning. The experimental results show that, compared with traditional VTM4.0, our method reduces the average encoding time by 49.21%, while the BD-BR increase is 1.61%, and the BD-PSNR decreases by 0.06 dB, fully demonstrating its superiority and performance balance. Full article

We formulate the Quantum Information Copy Time (QICT) framework for conserved charges under strictly local quantum dynamics and isolate its logically strongest consequence. The theorem-level core is a receiver-optimised variational speed-limit inequality: after projection away from the conserved zero mode, the copy time is bounded from below by the inverse square root of a Liouvillian-squared receiver susceptibility times a local encoding seminorm. This statement is written in a finite-volume operator framework and does not require a diffusive ansatz. We then examine what follows only after additional infrared assumptions. Under a single diffusive slow-mode hypothesis, the variational inequality reduces to the practical scaling relation used in the benchmark computations. That reduction is treated as conditional and is stress-tested numerically rather than promoted by rhetoric. Within the anomaly-free Abelian span relevant for one Standard-Model-like generation, hypercharge selection is elevated to theorem-level status; by contrast, minimal gauge-algebra uniqueness remains explicitly conditional on additional model-selection axioms. The remainder of the manuscript is organised as an explicitly documented closure programme built on top of this core. In that closure, a gauge-coded QCA construction, a microscopic benchmark for the transport normalisation, and an electroweak matching convention are combined to produce a resonance-centred Higgs-portal singlet-scalar mass band together with direct-detection, invisible-width, and relic-consistency checks. These latter results are presented as model-dependent consequences of an explicit closure ansatz rather than as deductions from locality alone. Full article