Search Results (472)

The Catolesia clade (Asteraceae, Eupatorieae, Gyptidinae) comprises four genera (Bahianthus, Catolesia, Lapidia, and Morithamnus), mostly confined to the Espinhaço mountain range of Eastern Brazil. Although this lineage is statistically well supported in molecular phylogenetic studies, recent findings point to Disynaphia praeficta being currently placed in the Catolesia clade, making Disynaphia paraphyletic. We analysed, scored, and mapped 102 leaf anatomical characters from all species of the Catolesia clade and selected outgroups to test the placement of D. praeficta into this clade, proposing a new monotypic genus and a taxonomic synopsis for the Catolesia clade, besides standardising descriptive anatomical terminology. We recovered several homoplasies and synapomorphies circumscribing all lineages sampled in our study, including Disynaphia s.s. and the remaining sampled outgroups. Our results also corroborated the placement of D. praeficta within the Catolesia clade with high statistical support. The cuneate to truncate lamina base was recovered as a synapomorphy supporting the Catolesia clade, whereas a petiole with three vascular bundles, ducts distributed throughout the lamina, and collenchyma sheath cell extensions were recovered as synapomorphies supporting Nadia praeficta (B.L. Rob.) A.V.F. Guterres and R.F. Almeida as a new monospecific genus. We demonstrated how highly informative leaf morphoanatomical characters are for the systematics of Eupatorieae and Asteraceae, besides demonstrating that leaf morphoanatomical characters provide a robust phylogenetic signal for generic delimitation within Eupatorieae, even if characterised as homoplasies. Full article

Graphene-reinforced aluminum-based materials perfectly combine the excellent properties of graphene and aluminum, achieving superior lightweight structural characteristics. This study focuses on 1:2 internal resonance, analyzing the amplitude–frequency and force–amplitude responses of a graphene-platelet-reinforced aluminum-based truncated conical shell under multiple external excitations. Considering three different graphene distributions, an improved Halpin–Tsai mechanical model is used to predict the effective Young’s modulus of the GPL-enhanced aluminum-based truncated conical shell. Under temperature effects, based on the Reissner–Mindlin theory and von-Karman geometric nonlinear strain–displacement relationships, Hamilton’s principle and the Galerkin method are employed to derive the motion equations of the GPL-enhanced aluminum-based truncated conical shell. Through multiscale perturbation analysis, the averaged equations in polar coordinates are further derived. Based on the combined averaged equations, the amplitude–frequency and force–amplitude response curves of the system are plotted, investigating the influence of graphene distribution, graphene content, external excitation amplitude, tuning parameters, and graphene plate geometrical dimensions on its vibration characteristics. The analysis results indicate that graphene content is one of the primary factors affecting the vibration characteristics of graphene-reinforced aluminum-based truncated cones. Full article

Image enhancement is of pivotal importance in the detection of defects in wood veneers. However, acquired images frequently exhibit signs of blurring, uneven illumination, and insufficient contrast, which can lead to a reduction in the accuracy of defect recognition. In this study, an algorithm based on Grünwald–Letnikov (G–L) fractional-order differentiation is proposed for the enhancement of wood veneer defect images. Initially, the gain characteristics of differential amplitude-frequency responses on high- and low-frequency image components are analyzed, and the feasibility of the method is demonstrated by linking these characteristics with the frequency-domain distributions of live knot, dead knot, and crack defects. Secondly, an eight-direction mask operator is constructed based on the G–L definition, and a DC component preservation factor is introduced to eliminate the luminance drift caused by mask truncation. The application of the mask is performed independently on the R, G, and B channels, and a dynamic blending mechanism is designed to achieve a balance between texture enhancement and structural fidelity. Finally, a set of six evaluation metrics (AG, E, PSNR, RMSE, SSIM, and VIF) is employed to assess the quality of enhanced images. The proposed algorithm is then compared with five existing algorithms (SSR, MSR, MSRCR, CLAHE, and AGC) under both noise-free and additive white Gaussian noise conditions. The findings indicate that the G–L fractional-order differentiation algorithm facilitates a more balanced representation of image features, thereby enhancing contrast, brightness, and textural contours. This approach results in more authentic color reproduction and superior visual quality. Full article

The discrete version of the Pareto distribution, popularly known as the Zipf distribution, and its truncated version are not new in the literature but several structural properties have not yet been discussed, and nor has its application to a wide range of observed phenomena. Here, we refer to the truncated Zipf distribution meaning the Zipf distribution when the support set is finite. Several interesting and useful structural properties of the truncated Zipf distribution, such as the modal dominance bounds, recurrence relation among successive raw moments, and stochastic ordering are thoroughly discussed. To exhibit the flexibility of the assumed probability model, COVID-19 datasets from three different geographical regions have been re-analyzed and are compared with several rival univariate discrete probability models which establishes the superiority of the truncated Zipf distribution. Full article

Background/Objectives: Recessive titinopathies caused by biallelic TTN truncating variants (TTNtvs) present a clinically heterogeneous spectrum from fetal demise to late-onset slowly progressive distal muscular dystrophy. Prognostic counseling is challenging due to the vast size of the TTN gene, complex splicing patterns, and differential expression throughout developmental stages and tissues. This paper aims to delineate the regional genotype patterns and clinical characteristics of recessive titinopathies described from the prenatal period onwards to inform genotype–phenotype associations and genetic counseling. Methods: We analyzed clinical and genetic data from a prenatal-onset cohort with biallelic TTNtvs from both previously reported cases and novel cases from our center. To characterize the regional distribution of biallelic variants within this specific cohort, a two-dimensional scatter plot was utilized to map variants onto 10 biological regions (R1–R10) and 55 analytical units (U1–U55). We also performed Fisher’s exact tests on the subset of 50 cases with confirmed survival records to evaluate statistically significant associations between biallelic regional or percent spliced-in (PSI) thresholds combinations and severe clinical endpoints (intrauterine demise or death before 5 years). Results: A total of 96 prenatal cases from 76 unrelated families were analyzed. Decreased fetal movement was the most commonly reported symptom, observed in 81.3% (78/96) of cases, which was followed by arthrogryposis in 45.8% (44/96) and amniotic fluid volume abnormalities in 35.4% (34/96). Additionally, of the 95 cases with known pregnancy outcomes, 25.3% (24/95) resulted in termination and 11.6% (11/95) resulted in intrauterine demise (IUD), while 63.2% (60/95) reached birth with over 16.7% (10/60) being preterm. Among 60 live-born infants, severe postnatal morbidity was high: 45.0% (27/60) experienced respiratory failure, and 33.3% (20/60) died before the age of five. In this cohort, 84.4% (81/96) of cases possessed at least one TTNtv in either the metatranscript-only or A-band regions. The most common biallelic changes involved TTNtvs in both the A-band and metatranscript-only regions, accounting for 35.4% (34/96) of cases, followed by metatranscript-only combined with I-band variants at 16.7% (16/96), regardless of the PSI score of exons. Overall, 83.3% (80/96) had ≥1 variant on low-PSI (<50%) exons, and 19.8% (19/96) had both alleles on these low-PSI exons. In the 50 patients with confirmed survival records, biallelic changes (excluding splice-site variants) affecting both high-PSI (>90%) exons were significantly associated with severe outcomes (intrauterine demise or death before 5 years; exact p = 0.015), whereas the metatranscript-only plus I-band combination conferred a significantly lower risk of lethality before 5 years of age (exact p = 0.001). Conclusions: Our findings add to the accumulating evidence that TTNtvs on low PSl exons or metatranscript-only regions are frequently observed among reported prenatal-onset recessive titinopathy. Health surveillance for heterozygous carriers among family members is warranted due to the substantial risk for adult-onset dilated cardiomyopathy and peripartum cardiomyopathy. Full article

After a severe outage occurs, restoring a distribution network can take from several hours to days, making the secure and stable operation of restoration areas (RAs) critical. During a post-disaster partitioned operation, asymmetric controllable distributed generator (CDG) regulation capacity, non-controllable distributed generator (NDG) fluctuation risks, and concentrated high-value loads cause significant inter-area power imbalances. Soft open points bridge this resource gap by integrating electric vehicle charging directly into soft open points via vehicle-to-grid (V2G) technology; the resulting electric vehicle-embedded soft open points (EV-SOPs) acquire storage-like energy transfer capability. This paper proposes a boundary–node coordinated optimization strategy for post-disaster RA operation, which integrates CDGs, NDGs, smart switches, and EV-SOPs. Firstly, the boundary dynamic updating model with a multi-homogeneity indicator—load importance, NDG fluctuation risk, and CDG flexibility—enables adaptive resource allocation. Secondly, the optimal operational model of RA is formulated considering the various characteristics of facilities and topology constraints. Thirdly, EV-SOP uncertainties in response reliability, discharge power, and energy capacity are characterized by Bernoulli, log-normal, and truncated normal distributions, reformulated into a tractable mixed-integer quadratically constrained programming via chance-constraint interval linear transformation, and solved by a sequential weight-based priority search with hot-start strategy. Case studies on the IEEE 123-bus system verify the effectiveness of the proposed method. Specifically, the dynamic boundary strategy reduces the comprehensive weighted index by up to 29.10%; physical feasibility truncation reduces EV-driven load loss from 3.2073 MW to 3.1038 MW; and the sequential weight-based priority search with hot-start strategy achieves a cone constraint satisfaction measure of 9.3175 × 10⁻⁷, confirming robust convergence. Full article

The growing fragmentation of digital evidence in modern computing environments poses significant challenges for digital forensic analysis. Data is often deleted, overwritten, or distributed across heterogeneous platforms, limiting the effectiveness of traditional forensic tools that rely on intact files and deterministic rules. This work addresses a key limitation in current forensic methodologies: the scarcity of learning-based approaches capable of identifying patterns in fragmented and incomplete digital evidence. To address this challenge, we propose PatternMiner, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer encoders. The framework combines byte-level content fragments with contextual metadata, such as timestamps and file permissions, enabling multimodal inference from fragmented data while explicitly excluding label-derived features to prevent leakage. PatternMiner is evaluated on established forensic benchmark datasets, including Digital Corpora and AFF4 forensic containers, which simulate realistic fragmentation scenarios. All experiments are conducted under an explicit leakage-controlled evaluation protocol with group-aware data partitioning to ensure that performance reflects generalization to unseen data. Results show that the proposed framework achieves strong performance, with an accuracy of 92.1% and a macro-averaged F1-score of 92.1% under complete input conditions. Furthermore, the model demonstrates resilience to degraded and partially corrupted inputs, including truncation, byte removal, shifting, and fragment reordering. These findings indicate that PatternMiner effectively captures structural and contextual patterns in fragmented data, providing a practical step toward more reliable and data-driven forensic analysis. By combining multimodal learning with rigorous evaluation practices, the proposed framework contributes to developing scalable and generalizable solutions for modern digital forensic environments. Full article

RNA-based therapeutics are reshaping the treatment landscape for skeletal muscle disorders by enabling modulation of RNA processing or direct correction of disease-causing alleles. In Duchenne muscular dystrophy (DMD), four antisense oligonucleotides—eteplirsen, golodirsen, viltolarsen, and casimersen—have received FDA approval; these phosphorodiamidate morpholino oligomers (PMOs) induce exon skipping to restore the reading frame and enable expression of internally truncated dystrophin. Beyond splice switching, RNA therapeutics include RNase H-active gapmers and steric-blocking antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs) that mediate post-transcriptional gene silencing, and RNA-guided gene-modifying technologies such as CRISPR systems that can reframe or repair endogenous alleles. Despite major progress in DMD, broader clinical impact remains constrained by inefficient delivery to skeletal and especially cardiac muscle, the need for repeat administration for most modalities, and safety considerations that limit dose escalation and durability. Next-generation approaches aim to overcome these barriers through peptide- or antibody-conjugated oligonucleotides that enhance cellular uptake and tissue distribution, alternative chemistries with improved stability and potency, and viral or non-viral platforms for durable splice modulation. In parallel, CRISPR-based strategies—including base and prime editing—offer the prospect of one-time correction, while raising important questions regarding delivery, immunogenicity, editing specificity, and long-term safety. This review synthesizes recent advances in antisense and gene-modifying strategies for skeletal muscle and highlights practical priorities for translation, including improved muscle/heart delivery, controllable safety mechanisms, scalable manufacturing, and standardized biomarker-to-clinical outcome relationships. Full article

With the high penetration of distributed photovoltaic and storage systems, active distribution grids are prone to experiencing “active power surplus and reactive power shortage” during the evening peak, leading to voltage sags at the network end. Although electric vehicle (EV) grid-connected inverters possess four-quadrant reactive power regulation capabilities without causing the additional chemical cyclic aging of the battery cells, existing dispatch systems often treat them as unconditional response resources, overlooking users’ actual willingness to cede control and the associated strategic interactions. To address this, this paper proposes a “grid-load” coordinated reactive power optimization strategy that accounts for EV users’ willingness to respond: a Logit model incorporating price incentives, initial energy consumption, and parking duration is constructed based on discrete choice theory. By combining a truncated normal distribution with the Monte Carlo method to eliminate micro-sampling errors, a model of the expected reactive power capacity of charging stations under dynamic incentives is established; considering the physical constraints of SVCs and EVs, a scalarized single-objective optimization model is constructed with grid loss-equivalent costs, ancillary service costs, and voltage deviation as objectives, and solved using an improved particle swarm optimization algorithm with linearly decreasing weights. Simulations on a modified 33-node IEEE system incorporating storage indicate that this strategy can assign optimal compensation prices to each node based on the spatial value of reactive power. Compared to traditional single-voltage regulation and fixed subsidies, it not only stabilizes the grid-wide voltage within a safe range but also avoids overcompensation, achieving global optimization of both power quality and economic efficiency. Full article

In ubiquitous Wi-Fi sensing, human motion interval segmentation is crucial for applications ranging from basic intrusion detection to advanced activity understanding. Existing methods often treat the Channel State Information (CSI) primarily as time series, overlooking its rich information in the spatial and frequency domains. To address this, we propose a training-free motion segmentation method that exploits the spatiotemporal features of CSI. We first analyze the discriminative spatial distributions of the CSI Ratio on the complex plane and construct a spatiotemporally dual-constrained local density estimator to characterize motion-induced perturbations. To overcome subcarrier selection challenges, we introduce a packet-level asymmetric truncation-based fusion algorithm, which yields a feature representation with a pronounced bimodal histogram. This enables the automatic determination of the optimal segmentation threshold based on the distribution characteristics of the truncated density image. Experiments in typical indoor environments demonstrate that the proposed method achieves high accuracy in both motion event detection and interval localization. Full article

Electric vehicles (EVs) provide fast charging/discharging flexibility; however, single-layer assessments may overestimate the flexibility that can be physically delivered under downstream distribution-network constraints. This paper proposes a process-oriented hierarchical adjustable-potential assessment framework for transmission–distribution–microgrid coordination. At the microgrid/station layer, a chance-constrained vehicle feasible set is constructed to capture user uncertainty, and probabilistic Minkowski-sum aggregation is used to obtain a station-level theoretical envelope. At the distribution layer, voltage and line-thermal constraints are modeled using LinDistFlow and intersected with the theoretical envelope to derive an effective potential satisfying network security limits. At the transmission layer, the effective feasible region is further packaged into a time-varying generalized-battery parameter set for consistent upward reporting without introducing dispatch optimization. In addition, a bottleneck truncation effect (BTE) metric is defined to quantify how distribution constraints reduce upstream-usable flexibility. Case studies show that hierarchical network constraints compress both peak EV flexibility and the all-day feasible-region area. Specifically, the microgrid-layer theoretical envelope reaches 432 kW on the charging side, 124 kW on the discharging side, and 3799 kWh in feasible-region area. After distribution-layer security clipping, the effective envelope becomes 299 kW, 124 kW, and 2063 kWh, corresponding to reductions of 30.79%, 0.00%, and 45.70%, respectively, relative to the microgrid layer. After transmission-layer packaging, the deliverable envelope is further reduced to 285 kW, 118 kW, and 1946 kWh, i.e., reductions of 34.03%, 4.84%, and 48.78%, respectively, relative to the microgrid baseline. These results demonstrate that the proposed workflow provides verifiable and time-varying deliverable capability boundaries for cross-layer EV flexibility assessment. Full article

Motivated by the requirements for wide field-of-view (FOV) reception in underwater wireless optical communication (UWOC) systems, this study investigates the performance of multi-faceted receivers based on various regular polyhedral geometries. A truncated Gumbel minimum distribution model with geometric boundary constraints is proposed in order to characterize the statistical properties of the minimum incidence deflection angle associated with the selected receiving facet. Numerical simulations demonstrate that the proposed model effectively captures the angular response characteristics of multi-faceted receivers, with the root mean square error (RMSE) of the fitted cumulative distribution function (CDF) below $2.2\times {10}^{-2}$ for all regular polyhedral structures. Furthermore, this paper evaluates the effects of different polyhedral structures and receiver FOVs on the bit error rate (BER) and outage probability. The results further show that system performance does not vary monotonically with the number of receiving facets. Under the constraints of the same total effective detection area and unified system parameters, the dodecahedral structure achieves the best performance in terms of average BER and outage probability, followed by the cube, whereas the icosahedral structure exhibits the worst performance. Taking typical link distances of 35–40 m as an example, the average BER of the dodecahedral structure is approximately one order of magnitude lower than that of the icosahedral structure. These findings provide design guidance for the structural design and parameter optimization of multi-faceted receivers in UWOC systems. Full article

We investigate the linguistic complexity and emotional valence of popular song lyrics across English ($n=1491$), Spanish ($n=307$), and German ($n=225$), using an analytical corpus of 2023 tracks drawn from 2113 deduplicated tracks on Spotify’s weekly Top 200 charts (2019–2021). Transformer-based sentiment analysis is combined with complexity-science tools to characterize both the affective content and the structural organization of commercially successful lyrics. A multilingual BERT model reveals a mild negative skew across all three languages (63.7% negative overall); the 1.003-point English–German gap observed under the English-centric VADER lexicon collapses to 0.127 points under BERT, indicating that earlier cross-linguistic sentiment differences are largely measurement artifacts. Word frequency distributions follow Zipf’s law in all three languages ($R^2>0.96$), with English steepest ($\alpha =1.409$) and German shallowest ($\alpha =1.181$). Detrended fluctuation analysis indicates persistent long-range correlations ($H\approx 0.66$–$0.76$; none of the 50 shuffled surrogates exceeded the observed values), and multifractal singularity spectra are statistically indistinguishable across languages once corpus size is controlled (all pairwise Mann–Whitney $p>0.13$). Streaming counts within the Top 200 are concentrated (German Gini $=0.556$) but, given the truncated single-snapshot sample, are reported as within-chart descriptors rather than population-level scaling. Full article

Adversarial perturbations measured by ${\mathsf{\ell }}_p$ norms do not reflect key structural constraints in tabular security data, including anisotropic geometry, feature dependence, and distributional plausibility. We introduce a composite attacker cost functional $C_{\mathrm{atk}}(\mathit{x},{\mathit{x}}^{\prime })=\tau max\{0,m\left({\mathit{x}}^{\prime }\right)\}+{\lambda }_1{\mathit{\delta }}^{\top }{\mathit{G}}_{\perp }^{\left(\gamma \right)}\left(\mathit{x}\right)\mathit{\delta }+\lambda {\displaystyle \sum _j}{\omega }_j|{\delta }_j|+{\lambda }_2{\mathit{\delta }}^{\top }{\mathit{L}}_H\mathit{\delta }+{\lambda }_3\left[log{\widehat{f}}_1^{\left(ϵ\right)}\left(\mathit{x}\right)-log{\widehat{f}}_1^{\left(ϵ\right)}\left({\mathit{x}}^{\prime }\right)\right]+{\displaystyle \sum _{j\in supp\left(\mathit{\delta }\right)}}{c}_j+\beta {\left|\mathcal{M}(supp\left(\mathit{\delta }\right))\right|}^{\nu },$ which integrates a spectrally truncated geometric term, a graph-based coherence penalty, a smooth copula density barrier, and a superlinear module-spread term. Under spectral degeneracy of the legitimate-class covariance, we establish nonnegativity under density dominance, exact zero self-cost, lower semicontinuity, and ${\lambda }_3{\kappa }_K$-weak convexity of the continuous component on compact convex sets, for both affine and ${\rho }_m$-weakly convex scoring functions. These properties yield existence of constrained minimizers. The continuous component is locally Lipschitz, whereas the full functional is not due to the support-counting term. A component feasibility result shows that each term eliminates a distinct class of degenerate perturbations. Limiting regimes and refined evasion cost bounds are derived. An empirical instantiation on PHIUSIIL indicates that perturbations with identical ${\mathsf{\ell }}_2$ norm can incur costs differing by an order of magnitude. Full article

The ionotropic γ-aminobutyric acid receptor (iGABAR) is an important insecticidal molecular target. However, the native iGABARs composition remains unknown in insect. Here, CsRdl1, truncated transcripts of CsRdl2 and CsLcch3 were obtained in the rice stem borer (RSB), Chilo suppressalis Walker. The N-terminal-truncated CsRDL2 (∆N-CsRDL2) and N-terminal-truncated CsLCCH3 (∆N-CsLCCH3) were deduced and studied in vivo, and desmethyl-broflanilide (DMBF) binding characteristics were simulated in silico. Genome and transcriptome analyses revealed truncated transcripts of CsRdl2 and CsLcch3 encoded 48 kDa of ∆N-CsRDL2 and 37 kDa of ∆N-CsLCCH3, respectively. The CsRDL1, CsRDL2 and CsLCCH3 were detected respectively from native iGABARs at molecular weights (Mws) ≥ 440 kDa in BN-PAGE. In BN/SDS-PAGE, three CsRDL1 bands (~54, ~55 and ~70 kDa), one CsRDL2 band (~48 kDa) and one CsLCCH3 band (~37 kDa) were identified in native iGABARs at Mws ≥ 669 kDa, and corresponded to CsRDL1ad, CsRDL1bd, post-translationally modified CsRDL1, ∆N-CsRDL2 and ∆N-CsLCCH3, respectively. Immunofluorescence confirmed these three subunits distributed in the same region of adult heads. Finally, DMBF displayed higher binding affinities for heteromeric iGABARs than for homomeric CsRDL1 iGABAR in silico. These findings confirm that ∆N-CsRDL2 and ∆N-CsLCCH3 in native iGABARs might support the rational design of novel insecticides. Full article