Search Results (464)

Strong consistency and asymptotic error distribution for a new class of nonlinear recursive parameter estimation algorithms of an approximate Newton–Raphson type are established. The system model is given in the discrete-time domain by a linear difference equation with constant parameters. The parameter estimator design is based on martingale theory and the Cramér–Rao (CR) theorem, providing a maximum likelihood (ML)-type optimal recursive parameter identification algorithm, whose minimum asymptotic estimation error covariance matrix achieves the CR bound under the worst-case pdf within a specified class; this worst-case pdf simultaneously yields the maximum asymptotic error covariance matrix within the class. However, the worst-case pdf does not generally exist, making the min–max optimal design indeterminable. Therefore, an approximate ML (AML)-type optimal on a class design, based on a suboptimal worst-case pdf, minimizing the scalar Fisher information within the specified class, has also been developed. The proposed design minimizes the conditional estimation error covariance under the specified suboptimal worst-case pdf. Such an approach results in Huber’s M-robustified version of the AML-based optimal design on the class of contaminated Gaussian pdfs. The practical performance of the proposed approach is analyzed through statistical validation, based on the relative asymptotic estimation efficiency measure and Monte Carlo simulations. Full article

The development of regression models for composite distributions has received insufficient attention in the literature. The purpose of this research is to provide maximum likelihood (ML) and approximate Bayesian computation (ABC) estimators for the parameters of a regression model with a response variable following the LogNormal–Pareto composite distribution. Composite models such as Exponential–Pareto, LogNormal–Pareto, and Inverse-Gamma–Pareto, which separate small-to-moderate and significant losses using a threshold parameter, have been developed using classical and Bayesian methods and applied to insurance data. We derive closed-form formulas for MLEs in regression models with two and three covariates. For models with more than three covariates, we provide MLEs using Mathematica code. Simulation studies show that the ABC method is more accurate than the ML method. Mathematica code written specifically for the computations of the proposed methods is provided. Full article

The post-quantum signature scheme SQIsign achieves extremely compact public keys and signatures, making it attractive for bandwidth-constrained environments. However, its signing efficiency is limited by the high random failure rate of the ideal-to-isogeny mapping procedure and the substantial cost of each retry. Existing optimizations mainly reduce the number or cost of isogeny computations, while overlooking how to schedule commitment retries when multiple candidate ideals are available. We formulate commitment-stage scheduling as a lightweight learning-to-rank problem and provide an instrumented scheduling framework for SQIsign signing only. The pipeline uses two features, trains a weighted logistic regression scorer offline by maximum likelihood with class weighting, and deploys the same scorer online in Rank-ML mode. Live instrumentation on Apple M2 (n = 20,000 candidate attempts at NIST-I) quantifies the commitment bottleneck (86.4% failure; 7.36 mean attempts per session) and shows constant features at a fixed commitment degree (live AUC $=0.50$). Synthetic training supports the scorer when feature variance is present (test AUC $\approx 0.634$). A remeasured four-way ablation with Batch-only control ($n=100$, seed 42) separates batch overhead from learned ordering: Rank-ML is indistinguishable from Batch-only at deployment, while Baseline remains fastest for its wall-clock signing time at batch size 10. These results clarify when lightweight ML scheduling applies in SQIsign and provide a reproducible evaluation template separating live, synthetic, remeasured, and proxy evidence. Full article

Background/Objectives: Artificial intelligence (AI) offers a promising approach for detecting and classifying symptom severity in patients with Parkinson’s disease (PD). The objective was to provide an overview of AI methods performance used for this classification through a systematic review and meta-analysis conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Methods: The Google Scholar, IEEE Xplore, PubMed/MedLine, and ScienceDirect databases were searched for the period 2015–2025. The studies included were original, peer-reviewed studies written in English that addressed an AI method based on machine learning (ML) or deep learning (DL) for the classification of PD patients. The dataset used had to be “Gait in Parkinson’s Disease,” in which the severity of disease symptoms was assessed using the Hoehn and Yahr (H&Y) scale. Studies had to report at least one of the five performance metrics: accuracy, sensitivity, specificity, precision, and F1 score. Two reviewers independently selected articles, assessed the risk of bias using PROBAST (Prediction Model Study Risk of Bias Assessment Tool), and extracted data. The logit-transformed values were pooled separately by performance metrics and by severity level using a random-effects model. Cochran’s Q test, the I² statistic, and inter-study variability (τ²), computed using the generalized inverse variance method with the restricted maximum likelihood model, were used to assess heterogeneity. Forest plots with 95% confidence intervals were used to present the results. Possible causes of heterogeneity were explored using a subgroup analysis (ML vs. DL) and a sensitivity analysis. Finally, publication bias (Egger’s test) and the certainty of the evidence (using GRADE—Grading of Recommendations Assessment, Development, and Evaluation) were assessed to verify the generalizability of the results. Results: Among the 257 unique records, 12 studies were included. The methods demonstrated very high overall performance (>92%): accuracy (96.4%, 95% CI: 95.9–96.9%), specificity (97.7%, 95% CI: 97.3–98.1%), sensitivity (94.0%, 95% CI: 92.7–95.2%), precision (93.4%, 95% CI: 92.0–94.6%), F1 score (92.1%, 95% CI: 90.6–93.4%). Accuracy, specificity, and precision were high for all H&Y levels. However, the more advanced the symptoms, the lower the sensitivity (97.3% for H&Y0 vs. 92.1% for H&Y3). ML models achieved the best results for classifying healthy patients (H&Y0: 95.7% to 98.2%), while DL approaches performed better for classifying higher severity levels (>92%). Heterogeneity and inter-study variability were moderate (I²: 40–50% and τ²: 0.3–0.4) for precision and F1 score, and high (I² > 90% and τ² > 0.6) for accuracy, specificity, and sensitivity. The GRADE analysis revealed low-quality evidence for precision and F1 score and very-low quality for accuracy, specificity, and sensitivity. Conclusions: Thus, AI-based wearable gait assessment devices show great promise in terms of aiding clinical decision-making and treatment personalization. However, further research using a rigorous methodology (PROBAST) is needed to ensure the generalizability of the results and the clinical viability of the proposed solutions. Full article

Paraleyrodes minei is an invasive alien species in China, representing a new record for Yunnan Province and a new sugarcane pest. The mitochondrial genome of P. minei was sequenced using the Illumina NovaSeq 6000 sequencing platform. The genome sequence was assembled and annotated, and its structural characteristics and nucleotide composition were analyzed. A phylogenetic tree of 18 species in the family Aleyrodidae was constructed using maximum likelihood (ML) and Bayesian inference (BI) methods to analyze the phylogenetic relationship of P. minei within the family Aleyrodidae. The results indicated that the mitochondrial genome of P. minei was 18,774 bp in length and contained 13 protein-coding genes (PCGs), 22 transfer RNA (tRNA) genes, 2 ribosomal RNA (rRNA) genes, and 1 non-coding control region. The A+T content of the mitochondrial genome of P. minei was 80.93%, indicating a marked A+T preference. ATN was used as the start codon for the PCGs, and TAA, TAG, TA, and T were used as the stop codons. In the secondary structure of tRNA, the TΨC arm was missing in trnA, trnC, and trnG, and the DHU arm was missing in trnS1 and trnS2, with G-U base mismatches present. The phylogenetic tree revealed that the 18 species of 10 genera in the two subfamilies of the family Aleyrodidae clustered into two major branches: the subfamilies Aleyrodinae and Aleurodicinae. All 10 genera were monophyletic groups; among them, the genus Paraleyrodes and the genus Aleurodicus formed a sister relationship, and both belonged to the subfamily Aleurodicinae. This study represents the first successful sequencing of the mitochondrial genome of P. minei, as well as the first mitochondrial genome of the genus Paraleyrodes, laying the foundation for the control of P. minei and the analysis of phylogenetic relationships among various genera of the family Aleyrodidae. Full article

Objective: This retrospective cross-sectional study aimed to characterize HIV-1 genotypes, assess drug resistance, and analyze molecular transmission networks in Zhongwei City to inform prevention strategies. Methods: Plasma samples were collected from antiretroviral therapy (ART)-treated patients (2007–2024) with viral load ≥ 200 copies/mL. HIV-1 pol was amplified by nested PCR; successful sequences were genotyped by maximum likelihood (ML) (IQ-TREE, TVM+F+I+G4, 1000 bootstrap). Drug resistance (DR) was interpreted using Stanford HIV Drug Resistance Database (HIVDB) v9.0; detected mutations represent acquired drug resistance (ADR). Pairwise genetic distances (GD) (TN93 model) were calculated; transmission networks were constructed in Cytoscape 3.10.3. Results: 75 sequences were obtained. Males (84.00%), and heterosexual transmission (64.00%) predominated. CRF07_BC (46.67%) and CRF01_AE (38.67%) were the major subtypes; the overall ADR rate was 40.00%, mainly NNRTIs-associated (30.67% of all participants, including 16.00% single-class NNRTIs and 14.67% dual-class NRTIs-NNRTIs). Network inclusion rate was 40.00% of the 75 sequences; CRF07_BC showed higher betweenness centrality (p = 0.028), while CRF01_AE and CRF85_BC showed higher closeness centrality (p < 0.001). Occupation significantly affected network enrollment (p ≤ 0.05). Conclusion: HIV-1 subtypes are diverse with high ADR. CRF07_BC may act as a transmission bridge, whereas CRF01_AE and CRF85_BC exhibit faster potential spread. Baseline DR testing and network-guided interventions are recommended. Full article

A recent generalized Bilal (G-Bilal) model demonstrates remarkable flexibility in capturing a wide spectrum of failure behaviors, including monotonic and non-monotonic (upside-down bathtub-shaped) hazard patterns, outperforming several existing models such as the Weibull, gamma, and exponential families. This paper develops several inferential frameworks for different G-Bilal parameters of life using samples gathered by improved Type-II adaptive progressive censoring. This enhanced design ensures optimal control of test duration while maintaining high inferential precision. Expressions for the model parameters, reliability, and hazard rate functions are derived, followed by the development of maximum likelihood (ML) and maximum product of spacing (MPS) estimators with their asymptotic confidence intervals using the observed Fisher information with the delta approach. Furthermore, Bayesian estimators and two associated credible intervals are proposed under independent gamma priors and computed through Markov iterations, with both ML and MPS posteriors considered. Extensive Monte Carlo experiments confirm the consistency, robustness, and precision of the proposed estimators, with Bayesian spacing-based methods exhibiting superior accuracy and coverage. The model’s practical potential is further verified through two real applications: one involving mechanical system lifetimes and another analyzing health insurance premium data, representing physical and actuarial domains, respectively. Using the introduced censoring, the proposed G-Bilal model outperforms all competing models in terms of goodness-of-fit and reliability estimates in both cases. The results underscore the G-Bilal model’s adaptability, computational stability, and empirical superiority, establishing it as a powerful tool for modern reliability and actuarial risk assessments. Full article

The parameter space in a regression model for multinomial time series data contains the regression parameters those explain the effects of the time dependent covariates, and the dynamic dependence or category transition parameters those explain the influence of the past responses on the multinomial response at a given time. The estimation of the regression parameters can be negatively affected when higher dimension of the parameter space is considered specially for the transition parameters. In this paper we propose a parameter dimension-split approach where a conditional generalized quasi-likelihood (CGQL) estimating function is first developed for the dynamic dependence parameters in terms of unknown regression parameters which is exploited in the next step to develop an observed information matrix based maximum likelihood (ML) estimating equation for the main regression parameters. More specifically, this split approach helps to write the actual joint likelihood function of regression and dynamic dependence parameters as a likelihood function of regression parameters only by replacing the dynamic dependence parameters with their CGQL estimates obtained in the first step. As the time series length is generally large in practice, we have made sure that the proposed CGQL and ML estimators are asymptotically reliable, that is consistent for the respective parameters. Full article

Stipitate stereoid fungi are saprotrophic basidiomycetes characterized by a leathery basidiome, a central-to-lateral stipe and infundibuliform pilei. Although numerous species of stipitate stereoid fungi have been recorded worldwide, understanding of their phylogenetic relationships remains extremely limited, and research on this group of fungi in China is insufficient. In this study, specimens of the three stipitate stereoid genera, namely Podoscypha s. l., Cymatoderma s. l. and Stereopsis s. l., from southern China were investigated. Phylogenetic analyses of the internal transcribed spacer (ITS) regions and the large subunit of the nuclear ribosomal RNA gene (LSU) using maximum likelihood (ML) and Bayesian inference (BI) methods revealed that all three genera are polyphyletic. Consequently, Podoscypha s. s. and Cymatoderma s. s. were delimited, and Cladoderris—previously synonymized with Cymatoderma—was resurrected. Cladoderris is characterized by an imbricate basidiome, tomentose pilei and basidiospores typically shorter than 4 μm in length. Three new species, Podoscypha casiae, Stereopsis buccinata and Cladoderris perennis, were described and illustrated. The morphological distinctions and affinities between the new species and closely related taxa were discussed, the thresholds for the intraspecific and interspecific demarcation within the three genera in this study were provided, and identification keys for the species of each genus were presented. Full article

Background: Phoxinus grumi, a small leuciscid fish endemic to the Turpan Basin in Xinjiang, China, has long been the subject of taxonomic disputes, hindering accurate species identification and the understanding of its evolutionary history. Methods: To resolve this uncertainty, we sequenced and characterized the complete mitochondrial genome of P. grumi using next-generation sequencing. Results: The circular mitogenome is 16,604 bp long and comprises the typical 13 protein-coding genes, 22 tRNA genes, two rRNA genes, and one control region, exhibiting gene overlap and intergenic spacing. The overall base composition shows a pronounced AT bias. Notably, all tRNA genes except tRNA-Ser1 fold into a typical cloverleaf secondary structure; tRNA-Ser1 lacks the dihydrouracil (DHU) arm, representing an unusual structural variation. All 13 PCGs have been subject to purifying selection (Ka/Ks < 1), with ATP8 evolving fastest and COX1 being the most conserved. Maximum likelihood (ML) and Bayesian inference (BI) phylogenetic analyses were conducted based on the concatenated sequences of the 13 mitochondrial PCGs, as well as the Cytb gene. Both datasets consistently placed P. grumi within the subfamily Pseudaspininae, forming a strongly supported sister relationship with the genus Rhynchocypris. This inference was further supported by Kimura two-parameter (K2P) genetic distance analyses, which revealed the smallest divergence between P. grumi and Rhynchocypris species (0.1620–0.1703), markedly smaller than that observed between P. grumi and core Phoxinus species (0.2475–0.2558). Conclusions: Together, these results support the placement of P. grumi within the East Asian Pseudaspininae lineage and help clarify its taxonomic position, which has long been debated. The complete mitochondrial genome of P. grumi provides additional mitogenomic data for phylogenetic analyses of Leuciscidae and contributes to a better understanding of the evolutionary relationships and diversification of Far Eastern leuciscids. These findings may also provide a molecular basis for future studies on the conservation genetics and environmental adaptation of this endemic cold-water fish from the arid Turpan Basin in northwestern China. Full article

Index-modulated orthogonal frequency division multiplexing (OFDM-IM) has been recognized as a promising multicarrier transmission scheme due to its flexibility and favorable bit error rate (BER) performance. However, for future wireless communication systems requiring high reliability, high spectral efficiency, and low complexity, existing OFDM-IM schemes still face challenges in simultaneously improving spectral efficiency, maintaining diversity gain, and controlling detection complexity at the receiver. To address these issues, this paper proposes a joint-mode and repeated-index modulation-based coordinate interleaved OFDM scheme (MRIM-CI-OFDM). Building upon the shared subcarrier activation pattern (SAP) and coordinate interleaving structure, the proposed scheme introduces cross-cluster mode-pair indexing, enabling information bits to be jointly carried by the SAP domain, mode domain, and constellation symbol domain. This design enhances spectral efficiency while preserving the diversity advantages of coordinate interleaving. Furthermore, a rotated multi-mode constellation construction method based on inter-constellation minimum product distance is developed to improve mode separability. By exploiting the equivalent real-valued orthogonal structure introduced by coordinate interleaving, low-complexity maximum likelihood (ML) and three-stage Max-Log detectors are constructed. Simulation results demonstrate that the proposed low-complexity detectors achieve near-ML detection performance. Additionally, at a spectral efficiency of 1.25 bps/Hz, MRIM-CI-OFDM achieves approximately 3 dB SNR gain over the coordinate-interleaved/repeated-index benchmarks and more than 5 dB gain over conventional OFDM-IM. Full article

In this paper, a distributed quasi-generalized Reed–Solomon (Q-GRS)-coded cooperative split labeling diversity (DQ-GRSCC-SLD) scheme is proposed to support reliable cooperative transmission of small-volume information in typical scenarios such as device-to-device (D2D) communication, vehicular ad hoc networks (VANETs) and wireless sensor networks. The system employs distinct labeling mappers at the source and the relay, enabling single-antenna transmission while constructing equivalently a dual-antenna labeling diversity model at the destination, which enhances interference resistance and reduces transmission costs. In addition, an ingenious design is proposed to ensure that the destination obtains the joint Q-GRS code. To optimize the weight distribution of the joint code, a traversal search (TS) algorithm is developed. Furthermore, a low-complexity joint decoding algorithm for Q-GRS codes, namely bracketing decoding, is presented by leveraging the efficient decoding algorithm of generalized Reed–Solomon (GRS) codes. Compared to the conventional maximum likelihood (ML) decoding, its complexity has been reduced from comparing $q^k$ codewords to evaluating q or $q+1$ promising codewords. A theoretical performance analysis of the DQ-GRSCC-SLD scheme is provided. Simulation results reveal that the proposed DQ-GRSCC-SLD scheme demonstrates its superior performance under practical scenarios. Full article

This study evaluates the wind energy potential of Polokwane, South Africa, using statistical distribution models to estimate wind power density (WPD) and assess turbine performance under low-wind inland conditions. Hourly wind speed and direction data (2015–2024) measured at a 10 m height above ground level (AGL) were analysed to characterise wind behaviour and assess energy availability. Four probability distributions, namely generalised logistic (GLD), generalised extreme value (GEVD), Gumbel (GD), and Weibull (WD), were fitted using the maximum likelihood (ML) method. Model performance was evaluated using Kolmogorov–Smirnov (KS), Anderson–Darling (AD), and Chi-square $\left({\chi }^2\right)$ tests, while wind power density accuracy was assessed using wind power density error (WPDE). The results showed that Polokwane is characterised by low wind speeds, with an overall mean wind speed of 2.72 m/s at 10 m AGL, reaching a low of 3.88 m/s at a hub height of 125 m. The GEVD model produced the most accurate wind power density estimate of 32.37 W/m², classifying the site within the poor wind resource category. Wind direction analysis revealed a dominant northeast sector with seasonal shifts toward the south. Wind turbine performance analysis showed improved energy generation at higher hub heights, with the Gamesa G136-4.5 MW turbine identified as the most suitable option for the site, achieving the highest net annual energy production (AEP) of 10.82 GWh/yr and the highest net capacity factor (CF) of 27.44%. These results indicate that the Polokwane site is suitable for low-to-moderate wind energy applications and small-scale distributed wind generation rather than large-scale commercial wind farm development. Full article

The genus Chimarrogale is an ideal group to study the evolutionary mechanisms of semi-aquatic adaptation, but there is a lack of data on its genomic data and molecular mechanisms. Using Illumina sequencing, this study assembled mitogenomes of C. himalayica and C. styani (newly characterized), alongside C. leander, covering all Chimarrogale species in China. Results showed that three complete circular mitochondrial genomes were successfully assembled, with full lengths of 17,202–17,218 bp, including the 37 typical genes: 13 protein-coding genes (PCGs), 22 tRNAs, two rRNAs, and a D-loop region. There were nine overlapping regions and 14 intergenic spacer regions identified, showing significant AT bias. Relative synonymous codon usage (RSCU) analysis showed that Serine (Ser) was used most frequently. Selection pressure analysis showed that the Ka/Ks ratios of PCGs in 44 Soricidae mitogenomes were less than 1, indicated strong purification selection and functional conservation. Among them, the evolution rate of the ATP8 gene was the fastest. The phylogenetic analysis using Maximum Likelihood (ML) and Bayesian Inference (BI) methods showed that the three Chimarrogale species clustered into a monophyletic clade, which formed a sister group with Nectogale elegans within the tribe Nectogalini. This study fills the gap in mitochondrial genome data of semi-aquatic shrews and offers fundamental references for the conservation of shrews. Full article

Avian orthoavulavirus 1 (AOaV-1), commonly known as Newcastle disease virus (NDV), despite widespread vaccination, remains a significant threat to domestic chickens (Gallus gallus domesticus). Currently available live-attenuated NDV vaccines are derived from genotypes I and II lentogenic strains, whereas genetically divergent velogenic strains predominantly caused recent NDV outbreaks. This study examined the extent of genotypic divergence between NDV vaccine strains and field strains using phylogenetic and multivariate analyses of two major antigenic and virulence-associated genes: fusion (F) and haemagglutinin-neuraminidase (HN). A total of 121 full-length NDV-F and 81 NDV-HN gene sequences, representing reported NDV genotypes, were downloaded from GenBank and analysed using maximum-likelihood (ML) phylogenetic trees and principal coordinates analysis (PCoA). The phylogeny revealed genotype-specific clustering for both genes, consistent with current NDV classification. NDV vaccine strains belonging to genotypes I and II formed distinct clades, segregated from the majority of NDV field strains, including velogenic or virulent NDV genotypes. The principal coordinates analysis of both genes further confirmed the phylogenetic clustering of NDV genotypes, indicating increased genomic heterogeneity. These findings suggest genetic segregation of divergent velogenic or virulent genotypes from lentogenic NDV vaccines, requiring biological experiments for determining their efficacy against field strains. This study highlights the importance of molecular surveillance of NDV to monitor its genomic diversity, which is crucial for developing strategies to combat NDV outbreaks in domestic chickens. This study provides an updated, NDV-glycoprotein-gene-based comparative analysis across reported NDV genotypes using phylogenetic and multivariate approaches. Full article