Search Results (478)

Rosenthal’s fail-safe number is widely used to assess the robustness of meta-analysis results against publication bias; however, its statistical properties remain insufficiently understood. This paper re-evaluates the coverage performance of confidence intervals for the Rosenthal’s fail-safe number using an updated simulation framework that incorporates zero truncation, an epsilon correction to the expected value, and a restriction to statistically significant meta-analyses. In addition to the standard normal bootstrap approximation, bias-corrected and accelerated bootstrap confidence intervals are considered. Simulation results show that standard bootstrap intervals tend to be conservative under symmetric settings and exhibit substantial deviations under asymmetric distributions. The bias-corrected and accelerated bootstrap method improves coverage accuracy, particularly under asymmetry and moderate sample sizes, although both methods exhibit conservative behavior in several scenarios. Overall, reliable inference for the fail-safe number depends on both appropriate parameter specification and bootstrap procedures that account for bias and asymmetry. Full article

Friction at the cold rolling interface is affected jointly by the surface roughness, lubrication state, local pressure, and relative sliding. A constant friction coefficient is therefore insufficient to describe its non-uniform distribution along the contact arc. Accordingly, this study proposes a macro–micro two-scale mixed-lubrication and dynamic friction model based on the measured roll roughness. First, the measured roll roughness profile was represented within a finite effective scale interval by a scaled and truncated Weierstrass–Mandelbrot (W–M) function. The parameters D and G were obtained as finite-scale W–M roughness parameters and were introduced into a mixed-lubrication load-sharing model to calculate the local mixed-lubrication friction coefficient. The pressure distribution along the contact arc was calculated using the Karman equation, and the local macroscopic pressure was mapped to a representative microscopic contact load. Finally, the mixed-lubrication friction coefficient was used to calibrate the dynamic friction factor separately in the forward-slip and backward-slip zones, and the friction stress distribution along the contact arc was calculated. For the selected effective scale interval and preprocessing procedure, the fitted W–M roughness parameters were D = 1.528 and G = 9.15 × 10⁻⁸ m. The W–M parameter D had a more significant influence on the mixed-lubrication friction coefficient and load-sharing behavior than the scale parameter G. Increasing the rolling speed strengthened the oil-film load-carrying effect and reduced the equivalent interfacial friction coefficient. The friction stress was positive in the backward-slip zone and negative in the forward-slip zone, with a direction reversal near the neutral point. Field forward-slip inversion showed that both the simulated and measured equivalent friction coefficients decreased with increasing rolling speed, with a difference of approximately 0.009~0.017. The proposed model can capture the main trend of cold rolling interfacial friction with variations in the rolling speed and contact state. Full article

Accurate airfoil reconstruction is crucial for aerodynamic analysis, geometric modeling, and computational design applications. This study proposes an optimized M-Hermite interpolation framework for high-accuracy airfoil reconstruction and geometric preservation. Unlike classical Hermite interpolation, the proposed framework integrates a truncated M-derivative formulation through M-inspired parameter-dependent scaling into the interpolation structure, enabling adaptive local geometric control via fractional parameters $\alpha$ and $\beta$. Additionally, a tangential scaling coefficient is incorporated to improve curvature adaptation and reconstruction stability in critical geometric regions. The proposed framework is evaluated using 11 reference airfoil geometries and compared with widely used interpolation methods, including Cubic Spline, B-Spline, Bézier, Catmull-Rom, Classical Hermite, and unoptimized M-Hermite interpolation. Reconstruction performance was assessed using both global and local geometric validation metrics, including RMSE, MAE, maximum error, Hausdorff distance, leading-edge RMSE, trailing-edge RMSE, thickness retention error, and curvature retention error. Experimental results demonstrated that the optimized M-Hermite framework achieved the best overall reconstruction performance and geometric consistency across the evaluated airfoil families. The proposed framework improved reconstruction accuracy, particularly in high-curvature leading-edge regions, while preserving geometrically relevant shape descriptors known to influence aerodynamic behavior, including thickness distribution, camber-line consistency, and curvature structure. Optimization analyses further revealed that reconstruction performance is strongly dependent on geometry-adaptive parameter configurations, particularly the $\beta$ parameter, which governs local geometric behavior. These findings demonstrate that the proposed optimized M-Hermite framework provides an adaptive and computationally efficient interpolation strategy for accurate airfoil reconstruction and geometric shape preservation applications. Full article

The microstructure of as-cast Al-xSi (x = 4, 7, 10) alloys solidified under various cooling rates was characterized using scanning electron microscopy (SEM). To overcome the multicollinearity among eutectic silicon parameters, Principal Component Regression (PCR) analysis was employed to quantitatively evaluate the effects of silicon morphology, scale, and content on the electrical conductivity of the alloys. The results demonstrate that rapid solidification significantly refines the plate-like eutectic silicon and reduces its volume fraction, leading to improved electrical conductivity. The PCR model shows that a hierarchical mechanism: volume fraction (PC1) acts as the principal determinant, increasing baseline resistance primarily by truncating the electron mean free path (MFP); meanwhile, within identical alloy systems, morphological parameters (PC2) play a dominant regulatory role. A semi-quantitative electron drift path model was established, confirming that the morphological deviation of eutectic silicon from a spherical shape (i.e., increased aspect ratio) causes a non-linear increase in the amplitude of electron detours. This geometric elongation significantly degrades electrical conductivity, providing theoretical guidance for the microstructural design of high-conductivity Al-Si alloys, which can be practically applied to the manufacturing and optimization of lightweight, heat-dissipating enclosures for new energy vehicle (NEV) motors and power distribution systems. Full article

Accurate forest height reference points are essential for large-scale forest canopy mapping and carbon stock estimation. Currently, spaceborne Light Detection and Ranging (LiDAR) systems, primarily GEDI and ICESat-2, serve as the main data sources for acquiring global forest height reference points. To ensure data quality, conventional processing often relies on strict physical parameter filtering, such as retaining only nighttime and strong (full power) beam observations, which considerably reduces the available data density. Moreover, gross errors caused by signal attenuation or solar background noise often remain, limiting the accuracy of subsequent spatial modeling. To address the trade-off between measurement accuracy and data density, this study proposes a physically constrained outlier filtering strategy for spaceborne LiDAR retrievals, assisted by a priori continuous canopy height model (CHM) products. Aiming to maximize data retention, this method introduces a morphologically consistent global continuous CHM (such as the 10 m Pauls CHM) as a prior spatial envelope. By calculating the local height difference distribution and applying a 1$\sigma$ adaptive truncation, outliers are effectively removed. Comparative validations in the Genhe (coniferous forest, China) and HARV (mixed broadleaf forest, USA) study areas indicate that: (1) traditional filtering results in a data loss of over 80% while yielding limited accuracy; (2) after relaxing the initial filtering conditions, the proposed strategy reduces the overall root mean square error (RMSE) of GEDI and ICESat-2 retrievals by 12.6% to 36.0%; (3) owing to the effective removal of gross errors, the conventionally discarded daytime and weak (or coverage) beam data achieve substantially reduced error levels, sometimes even lower than those of traditional nighttime strong beam observations. Consequently, the spatial density of high-quality reference points is increased by 1.5 to 4.4 times. This study demonstrates the application value of low signal-to-noise ratio (SNR) spaceborne observations and provides a practical approach for obtaining high-quality, high-density control points for large-scale forest structure mapping. Full article

SYN flooding attacks remain a persistent threat to network availability, particularly in Distributed Denial-of-Service (DDoS) scenarios that exploit the TCP three-way handshake. Traditional SYN cookies mitigate half-open connection exhaustion but may exhibit limited replay resistance under certain adversarial conditions. This paper presents a nonce-enhanced, HMAC-SHA256-based SYN cookie mechanism designed to strengthen handshake validation while preserving stateless operation. The implemented framework binds each connection attempt to a time-bounded, per-session nonce and embeds a truncated HMAC within the TCP sequence number field. The mechanism is implemented and experimentally evaluated using a custom-built simulation framework, NOxSYN. Under concurrent SYN flood conditions, the enhanced design successfully validated legitimate handshakes while maintaining stable operation under adversarial load. Measured server-side cryptographic processing remained below 1 ms per connection, with stable CPU utilization during testing. These results demonstrate that nonce-based replay protection can be integrated into a SYN cookie framework while preserving scalability and stateless operation. The current evaluation focuses on implementation-level validation and performance characterization, providing a foundation for future security-oriented assessment across a broader range of replay-based attack scenarios. Full article

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