Search Results (131)

Unlike conventional models that typically rely on a single memristive synapse, this study uniquely proposes a novel 5D fractional-order memristive Hopfield neural network (FOMHNN) modulated by dual memristors to simultaneously emulate internal synaptic plasticity and external electromagnetic radiation effects in brain-like computing. Analytically, the FOMHNN features multiple parallel lines of equilibria with double-zero eigenvalues, rigorously proving the generation of hidden attractors. The continuous dynamical behaviors are systematically evaluated using the Adomian Decomposition Method (ADM), revealing rich phenomena including transient chaos, grid multi-scroll hidden attractors, and frequency-controllable extreme multistability with fractal-like basin boundaries. The theoretical model is physically validated on a Field Programmable Gate Array (FPGA) platform, demonstrating high precision and ultra-low power consumption. To bridge theoretical dynamics with cryptographic applications, a novel pseudo-random number generator is designed. By incorporating a chaotic derivative extractor, the generated sequences significantly reduce topological periodicity, successfully passing all rigorous NIST SP 800-22 statistical tests. Furthermore, an adaptive color image encryption scheme is developed, utilizing bidirectional feedback diffusion and least significant bit (LSB) key embedding. Security analyses confirm that the cipher, under the fractional order $q=0.95$, achieves near-ideal information entropy, optimal resistance against differential attacks, with NPCR and UACI values reaching 99.6114% and 33.4910%, both extremely close to their theoretical ideals (99.6094% and 33.4635%), and robust resilience against noise. Ultimately, the FOMHNN provides a highly secure and physically realizable chaotic source for advanced secure communications. Full article

Accurate estimation of subsurface acoustic impedance is fundamental to quantitative reservoir characterization in seismic exploration. Nevertheless, a single network architecture cannot adequately represent both the local details and the global trends of seismic records within a unified framework, while the severe scarcity of annotated well-log data substantially constrains the generalization capability and predictive accuracy of deep-learning-based inversion approaches. To overcome these limitations, a semi-supervised acoustic impedance inversion framework based on a hybrid deep learning architecture is proposed. The framework employs a cascaded architecture consisting of a multi-scale depthwise separable convolution with channel attention (MSDSE) module and a convolution-augmented Transformer encoder. Seismic data are first processed by the MSDSE module to extract local multi-scale temporal features, and are subsequently passed to the convolution-augmented Transformer encoder, which captures global long-range sequence dependencies while retaining complementary local temporal information. The two modules progress hierarchically and jointly achieve a feature representation that spans from local details to global trends, and the initial low-frequency model is fused with the network output via channel-wise concatenation. Meanwhile, an initial-model constraint together with a physical-consistency constraint are simultaneously imposed within the loss function, thereby improving training stability while fully leveraging the physical information embedded in unlabeled traces. Experiments on both synthetic and field data confirm the effectiveness of the proposed method. The results show that, even with a small number of labels, the method produces stable impedance estimates and outperforms conventional deep learning methods in both generalization and prediction accuracy. Full article

This paper investigates the problem of model-order reduction for linear systems arising from minimum-phase circuits and filters, where stability and frequency characteristics must be preserved. The MPPBT framework uses a Riccati–Lyapunov Gramian pair and constructs a balancing transformation with the sequence of MPPBT singular values, from which a relative error bound in the $H_{\infty }$ norm and a scoring function, $S_{\beta }$, are derived to select the model order. We apply the algorithm to a fourth-order Butterworth low-pass filter with a full-order state dimension, $n=4$, and reduced-order models with $r=1, 2, 3$ are examined. The results show that the model with $r=3$ yields an $H_{\infty }$ error of approximately $6.3\times {10}^{-3}$ and an $H_2$ error of approximately $2.2\times {10}^{-3}$. The model with $r=1$ gives an $H_{\infty }$ error of approximately $1.46$ and an $H_2$ error of approximately $4.8\times {10}^{-1}$. The model with $r=2$ attains a composite score of $S_{\beta }\approx 0.16$, preserves stability and the minimum-phase property, and is regarded as a balanced choice between accuracy and complexity. A further comparison on an RLC ladder circuit of order $n=15$ shows that at $r=3$, MPPBT achieves the lowest $H_{\infty }$ error among BT, PRBT, and MPPBT, while retaining an $H_2$ error close to that of BT. Full article

The discovery of angiotensin-converting enzyme (ACE)-inhibitory peptides from food protein hydrolysates is commonly guided by empirical fractionation or sequence-based prediction, but few screening rules have been evaluated at the hydrolysate level and independently benchmarked across peptide lengths. Here, we developed a length-aware C-terminal screening rule (Rule 5: P1’ ∈ {W, Y, F, P} and P2’ ∈ {L, I, V, K, R, H}) through an experimentally anchored framework. The rule was derived using 24 stratified protein–protease hydrolysates and showed the strongest associations with ACE inhibition (r = 0.711, p < 0.001) and log₁₀(1/IC₅₀) (r = 0.741, p < 0.001) among five evaluated rules. Independent evaluation in 16 composition-weighted commercial hydrolysates retained predictive utility (r = 0.608 for ACE inhibition and r = 0.581 for log₁₀(1/IC₅₀)). Five peptides—VF, GIF, LP, IP, and VP—were selected because they represented the intersection of in silico cleavage prediction, Rule 5 compliance, and corresponding candidate-associated low-mass MALDI features in the experimentally prepared hydrolysates. All five inhibited ACE (IC₅₀ = 19.50–93.19 µM); VF and GIF showed mixed-type inhibition, whereas LP, IP, and VP showed competitive inhibition. External benchmarking against 1429 quantitative ACE-inhibitory peptides established a defined applicability domain: Rule 5 significantly enriched potent peptides among di- and tripeptides (2–3 residues; median IC₅₀, 28.0 vs. 79.0 µM; p_adj < 0.001, Benjamini–Hochberg-corrected; enrichment factor = 2.5 at IC₅₀ ≤ 1 µM), but enrichment attenuated rapidly as longer sequences were included. Molecular dynamics simulations (200 ns) showed persistent peptide–ACE contact for all five candidates under the simulated conditions. Rule 5 is therefore proposed as a transparent first-pass filter for prioritizing short ACE-inhibitory candidates and protein–protease combinations, rather than as a universal predictor across the full peptide-length spectrum. Full article

The reliable identification of cultivated and naturalised mulberries is complicated by morphological plasticity, the historical movement of the planting material, and partly discordant nuclear and plastid signals. We evaluated four field-identified Morus alba L. trees, one from each of four localities in Mangystau, Western Kazakhstan, using archived consensus sequences for ITS, matK, rbcL, and trnH-psbA, together with a low-coverage Oxford Nanopore Technologies (ONT) dataset. Because the design comprised one tree per locality (n = 4), analyses were restricted to accession-level descriptive comparisons, and no population-genetic, phylogeographic, or formal phylogenetic inference was attempted. Archived alignment summaries indicated mean pairwise distances of 0.13% for matK, 0.44% for ITS, 0.94% for rbcL, and 2.92% for trnH-psbA; these values are reported as retained dataset descriptors rather than estimates of population diversity. An additional product generated with Rosaceae-derived s6pdh primers was excluded because the target identity and orthology could not be verified. The ONT run yielded 19,958 pass reads (69.27 Mb; read N50 3574 bp). Reference-enriched assembly produced a 14,824 bp candidate plastid-associated contig, approximately 9.3% of a typical Morus plastome. Its short length, incomplete and non-collinear annotations, and the absence of retained depth, polishing, assembly graph, and join support diagnostics preclude its interpretation as a complete, circular, or structurally validated plastome. This study provides a transparent pilot baseline for Mangystau mulberries and establishes quality control criteria for replicated sampling, validated markers, and deeper organelle sequencing. Full article

Circulating tumor DNA fraction (ctFraction) has emerged as an important biomarker for assessing tumor burden and monitoring treatment response in patients with cancer. In this study, we compared ctFraction estimates generated by ichorCNA, Fragle low-pass whole-genome sequencing (Fragle LP-WGS), Fragle off-target, and OTTER, a proprietary algorithm from Tempus AI. Plasma samples from 33 patients with advanced solid tumors were analyzed using a ctDNA assay targeting 150 cancer-associated genes, and ctFraction estimates generated by the different methods were compared. Fragle off-target demonstrated the highest concordance with Fragle LP-WGS (rho = 0.903), followed by OTTER (rho = 0.698) and ichorCNA (rho = 0.696), while OTTER and ichorCNA showed strong agreement (rho = 0.826). Mean VAF (mVAF) significantly correlated with all ctFraction estimates, with the strongest association observed for ichorCNA (rho = 0.910), followed by OTTER (rho = 0.865), Fragle LP-WGS (rho = 0.680), and Fragle off-target (rho = 0.658). Longitudinal analysis of 20 patients at baseline and after two cycles of treatment demonstrated strong correlations between changes in ctFraction (ΔctFraction) and mean ΔVAF for both ichorCNA and Fragle off-target (r = 0.955 and r = 0.906, respectively). Overall, these findings demonstrate that ctFraction estimates derived from copy-number- and fragmentomic-based approaches show strong concordance across advanced solid tumors and significantly correlate with mVAF, a commonly used measure of ctDNA abundance. Fragle off-target, in particular, provides an efficient strategy for ctFraction estimation directly from existing targeted sequencing data, eliminating the need for additional sequencing. Larger prospective studies are warranted to further evaluate Fragle off-target clinical utility for treatment monitoring and outcome prediction. Full article

Circulating cell-free DNA (cfDNA) enables minimally invasive assessment of chromatin organization and DNA modifications. Whether such information can be reliably recovered under conditions of limited plasma input (below 1 mL) and ultra-low sequencing depth remains unclear. We performed low-pass whole-genome Oxford Nanopore sequencing (down to 0.01× coverage) of plasma cfDNA from young and elderly donors and jointly analyzed fragment length distributions and base modifications (5mC, 5hmC, 6mA). In parallel, we analyzed an enzymatically fragmented model DNA system to assess whether controlled in vitro fragmentation can reproduce cfDNA-like nucleosomal profiles and associated modification patterns. Despite shallow coverage, cfDNA samples displayed reproducible mono-, di-, tri-, and tetra-nucleosomal peaks, indicating that major fragmentomic features can be retained under ultra-low-coverage conditions. Modification-aware basecalling enabled exploratory quantification of global modification fractions across nucleosomal size classes and nomination of candidate group-specific modification loci. Overall, these results support the feasibility of low-pass nanopore sequencing as an exploratory framework for simultaneous cfDNA fragmentomic and epigenomic profiling in low-input studies. Full article

Background: Pheochromocytomas and paragangliomas (PPGLs) can be metastatic in up to 30% of cases. To assess this risk, scoring systems like PASS and GAPP were developed, yet it is unclear whether these scores correlate with somatic genetic alterations. Methods: A retrospective pilot study. Twenty patients with sporadic pheochromocytoma, stratified into high- and low-risk groups based on GAPP and PASS scores. Somatic variant burden, derived from whole-exome sequencing, and clinical variables were compared between groups. Results: Six patients (30.0%) comprised the high-risk group; all had PASS scores >4 and GAPP scores indicating moderately differentiated PPGLs. Compared with the low-risk group, the high-risk group had larger tumors (4.7 vs. 3.4 cm, p = 0.03) and non-significant association with higher diastolic blood pressure (90 vs. 79 mmHg, p = 0.09), normetanephrine (8.5 vs. 2.3 X upper limit of normal [ULN], p = 0.08), and metanephrine (22.9 vs. 9.25 X ULN, p = 0.09) levels. The high-risk group also demonstrated a higher somatic variant burden, particularly for truncating variants (32 vs. 26, p = 0.04). As a continuous variable, only the GAPP score, not PASS score, showed a significant positive correlation with variant burden, observed for both non-missense (r = 0.57, p = 0.009) and frameshift (r = 0.47, p = 0.04) variants. Conclusions: Higher pathological severity scores are associated with increased rates of severe molecular somatic variants. The stronger link between GAPP score and genetic alteration rates suggests it may better reflect the genomic complexity of sporadic PPGLs. Full article

Physical-layer key generation (PLKG) is a technique that produces symmetric encryption keys by exploiting the inherent characteristics of wireless channels. It offers advantages including high physical-layer security, elimination of pre-shared keys, dynamic upgradability, and resistance to quantum attacks, making PLKG a promising security solution for next-generation (6G) networks. However, satellite communication channels exhibit high dynamics and long propagation delays. Characteristics such as large Doppler shifts, short coherence times, and orbital predictability pose severe challenges to PLKG, including reciprocity degradation, low key generation rate (KGR), and susceptibility to channel-prediction attacks. This work proposes a delay-Doppler domain time-hopping key generation scheme (KE-DD-TH) based on Orthogonal Time Frequency Space (OTFS) modulation for high-speed links between Low-Earth-Orbit (LEO)/Medium-Earth-Orbit (MEO) satellites and ground terminals in Ka/Ku bands. The scheme performs non-uniform sampling on the DD domain grid of OTFS symbols using an ephemeris-driven pseudo-random time-hopping sequence generated by cascaded linear feedback shift registers (LFSRs) and a nonlinear matrix transformation. Both legitimate parties estimate the channel only at time-hopping instants and multiply two adjacent estimates to construct an “equivalent channel” matrix, yielding a random source with high entropy, high reciprocity, and low predictability. The eavesdropper’s key disagreement rate (KDR) remains close to 0.5 under all signal-to-noise ratio (SNR) conditions, corresponding to the ideal random-guessing baseline. This indicates that Eve obtains negligible mutual information, i.e., $I(K_A;K_E)\approx 0$. By contrast, the conventional KE-DD scheme allows Eve’s KDR to degrade to 0.014 at 30 dB SNR, indicating near-complete key recovery. The generated keys pass all 12 randomness tests of the NIST SP 800-22 statistical test suite. Full article

Due to the asymmetry of pass profiles, F-section steel is prone to defects such as overfilling, underfilling, and twisting during production, which significantly deteriorates the dimensional accuracy, mechanical properties, and surface quality of products. To mitigate the occurrence of such defects, this study established a thermo-mechanical coupled three-dimensional finite element model for the finishing rolling process of F-section steel using ABAQUS 2022 incorporating the actual operating conditions of the steel plant’s production line. By analyzing the stress–strain fields of each pass, it was found that the maximum deformation of the rolled piece is concentrated at the junctions of the inner leg with the flange, the inner leg with the web, and the outer leg with the web. Additionally, underfilling was observed at the legs and flanges of the pass in each rolling sequence. Based on these findings, an in-depth analysis was conducted on the effects of friction coefficient, tension configuration, rolling temperature, and web reduction on pass filling degree. Conditions of low friction, small reduction, and high temperature facilitate the smooth filling of metal in the leg cavity; in contrast, conditions of high friction, large reduction, and low temperature promote the filling of surface metal and an increase in spread. Maintaining a low-tension state is a common favorable condition for improving the pass filling degree of both the legs and the surface. When the friction coefficient is 0.2, tension is 0, rolling temperature is 1040 °C, and web reduction is 4 mm, the pass filling degrees of the inner and outer legs reach their maximum values of 99.88% and 99.16%, respectively. When the friction coefficient is 0.4, tension is 0, rolling temperature is 1010 °C, and web reduction is 4 mm, the pass filling degrees of the upper and lower surfaces are maximized, reaching 98.95% and 98.22%, respectively. These findings provide data support and theoretical guidance for addressing defects encountered in F-section steel production. Full article

At present, there are no easily understood explainable artificial intelligence (AI) methods for discrete token inputs, like text. Most explainable AI techniques do not extend well to token sequences, where both local and global features matter, because state-of-the-art models, like transformers, tend to focus on global connections. Therefore, existing explainable AI algorithms fail by (i) identifying disparate tokens of importance, or (ii) assigning a large number of tokens a low value of importance. This method for explainable AI for tokens-based classifiers generalizes a mask-based explainable AI algorithm designed originally for images. It starts with an Explainer neural network that is trained to create masks to hide information not relevant for classification. Then, the Hadamard product of the mask and the continuous values of the classifier’s embedding layer is taken and passed through the classifier, changing the magnitude of the embedding vector but keeping the orientation unchanged. The Explainer is trained for a taxonomic classifier for nucleotide sequences and it is shown that the masked segments are less relevant to classification than the unmasked ones. This method focused on the importance the token as a whole (i.e., a segment of the input sequence), producing a human-readable explanation. Full article

Background: Evaluation of gene-level copy number variants (CNVs) for diagnosis and therapeutic decision making has become standard of care with next-generation sequencing (NGS), immunohistochemistry (IHC), and/or fluorescence in situ hybridization (FISH) being used to detect gene amplifications/deletions in tumor tissue. In contrast to most solid tumors, CNS cancers are challenging to evaluate by resection and/or biopsy due to the associated risks with invasive brain surgery that can also result in death or associated morbidity and therefore alternate methods are required.Methods: This study presents the analytical validation of using quantitative PCR (qPCR) to detect gene CNVs directly from cerebrospinal fluid (CSF)-derived DNA and from the Ascent^TM low-pass whole genome sequencing (LP-WGS) libraries, demonstrating concordance with the gold standard of NGS/IHC/FISH used in tumor tissue. Results: The analytical sensitivity of qPCR to detect gene amplification calls for ERBB2 (erb-b2 receptor tyrosine kinase 2) was demonstrated to be 100% and that of EGFR (epidermal growth factor receptor) was 83%, with specificities of 96% and 100%, respectively. The analytical sensitivity of qPCR to detect gene deletions for CDKN2A/2B (cyclin-dependent kinase inhibitor 2A/2B) was 60% and that for MTAP (methylthioadenosine phosphorylase) was 100% with a specificity of 100% for all three genes. Ascent^TM was demonstrated to have a higher sensitivity (100%) when compared to qPCR for the same genes evaluated and demonstrated 100% positive agreement and 100% negative agreement with known CNV status. Conclusions: The results demonstrate that given the paucity of cells in CSF limiting the use of IHC and FISH, qPCR and Ascent^TM provide highly sensitive, novel, minimally invasive methods for the evaluation of gene copy number (CN) status to inform the diagnosis and management of CNS cancers. Full article

Background/Objectives: Online adaptive radiotherapy (oART) generates plans at each fraction by exploiting AI-assisted optimization engines without explicit user control over modulation. This process challenges quality assurance since measurement-based Patient Specific Quality Assurance (PSQA) cannot be performed daily. This study aimed: (i) to characterize plan complexity in IOE-generated plans for prostate cancer using a reproducible set of PCMs, including the decomposition of inter-patient and intra-patient variability sources; (ii) to evaluate the association between PCMs and delivery accuracy within a cohort-informed SPC framework validated through leave-one-patient-out cross-validation; (iii) to investigate whether inter-fraction anatomical variations explain the observed plan complexity patterns, or whether complexity is predominantly an intrinsic signature of the AI-assisted optimizer. Methods: Twenty-one prostate cancer patients treated on a CBCT-based oART platform were retrospectively analyzed across three anatomical targets: prostatic bed (PrB), prostate (Pr), and prostate with seminal vesicles (PrSV). Six PCMs, namely MU/cGy, Modulation Complexity Score (MCS), Aperture Area Variability (AAV), Leaf Sequence Variability (LSV), Average Leaf Gap (ALG) and Plan Irregularity, were extracted. Additionally, five anatomical metrics (AMs) were computed from daily contours. Linear mixed-effects models (LMEMs) compared reference/online plans, decomposed variance via intraclass correlation coefficients (ICCs), and assessed PCM–gamma passing rate (GPR) associations. Leave-one-patient-out cross-validation (LOPO-CV) evaluated SPC threshold stability. The relationships between PCMs and AMs were investigated using LMEMs. Results: The AI-assisted optimization engine generated plans characterized by elevated monitor unit demand (average MU/cGy ≥ 6.8 ± 0.9) and narrow MLC apertures (ALG ≤ 17.7 mm ± 1.9 mm). No complexity differences emerged between offline and online-adapted plans, nor between anatomical targets. All PCMs showed significant associations with global GPR (p ≤ 0.027), though marginal R² remained low (≤0.122). Notably, GPR dispersion increased systematically at higher complexity values, indicating that highly modulated plans exhibit reduced delivery predictability. LOPO-CV demonstrated stable tolerance/action limits. Anatomical variations explained less than 35% of the total variance in PCMs. Conclusions: Plan complexity in oART reflects the optimization paradigm and patient-specific anatomy rather than daily adaptation. PCMs can serve as surveillance indicators flagging high-risk fractions to support SPC-based monitoring. Full article

Traditionally, the calibration of welding heat source model parameters mainly relies on empirical trial-and-error methods, which lack clear guidance and generally lead to low computational efficiency. To address this problem, this paper establishes a quantitative relationship between heat source parameters and weld pool dimensions, which significantly improves the efficiency and accuracy of the simulation. Furthermore, the influence of laws of key parameters of the double-ellipsoid heat source and welding thermal efficiency on the geometric characteristics of the weld pool is systematically analyzed via numerical simulation. On this basis, finite element models considering different welding sequences are established for single and multiple T-rib components, and appropriate welding process parameters are determined according to the influence laws of heat source parameters. The thermo-elastic–plastic finite element method is then adopted to analyze the effects of welding sequences on the welding residual stress and deformation of T-rib and top-plate joints in steel box girders. By comparing different welding schemes, optimized welding strategies for single and multi-rib welding are proposed. The results show that for single T-ribs, simultaneous welding in the same direction produces the minimum residual stress and deformation with almost no distortion, followed by sequential bilateral welding in the same direction. For multi-rib welding with a spacing of 300 mm, synchronous welding yields the smallest deformation, followed by symmetric double-pass synchronous welding from inside to outside. For continuous single-pass welding, an inside-to-outside skip welding sequence is recommended to effectively control residual stress and deformation. Full article

Background: Evaluation of chromosome aneuploidy and gene-level copy number alterations for diagnosis, prognosis, and therapeutic decision-making in solid tumors is the standard of care. Chromosomal microarray (CMA), next-generation sequencing (NGS), immunohistochemistry (IHC), and fluorescence in situ hybridization (FISH) are the gold standard for detecting these variants in tumor tissue. In contrast to most solid tumors, cancers of the central nervous system (CNS) pose a unique challenge for effective detection via plasma due to the blood–brain barrier (BBB), with the additional challenges of brain biopsy or surgery being highly invasive and posing a significant risk to the patient. The Belay Ascent™ liquid biopsy test uses low-pass whole-genome sequencing (LP-WGS) to report on chromosome arm-level aneuploidy and gene-level copy number variants (CNVs) in cerebrospinal fluid (CSF) to inform diagnosis, prognosis, and therapeutic decision-making in CNS tumors. Methods: This study presents the equivalence of Ascent™ in detecting chromosome arm-level aneuploidy and gene-level CNVs using 48 tissue specimens followed by a clinical validation using a cohort of 32 CSF specimens with matched tissue-based tumor profiling information. Results: Equivalence of Ascent™ in detecting chromosome arm-level aneuploidy and gene-level CNVs using 48 tissue specimens was shown to have 100% and 97% positive percent agreement (PPA), respectively, compared to the gold standard of CMA/NGS. The validation cohort of 32 CSF specimens demonstrated 78% and 90% PPA for aneuploidy and gene-level CNVs, respectively. Clinical impact of Ascent™ was demonstrated, with 243 production cases able to inform the diagnosis and management of CNS tumors with high accuracy. Conclusions: Given the paucity of cells in CSF, limiting the use of karyotyping, CMA, IHC, and FISH, the Belay Ascent™ test provides a highly sensitive novel minimally invasive method for the evaluation of chromosome aneuploidy and gene-level CNVs in CSF. Full article