Search Results (758)

This paper presents a distributed smoothing neurodynamic approach for solving the $L_1$-$L_p$ minimization problem, with application to robust and collaborative multi-view three-dimensional (3D) space localization. To handle the non-Lipschitz continuity gradients, a smooth approximation technique is introduced, yielding a distributed neurodynamic model that integrates classical smoothing neural networks with multi-agents consensus theory. Theoretical analysis guarantees the global convergence of each agent’s state to the optimal solution. The stability and convergence of the proposed approaches are rigorously proved using Lyapunov theory. Numerical experiments on multi-view 3D space localization in the presence of measurement noise demonstrate the method’s effectiveness and practical value for distributed visual computing. Full article

We survey recent advances in second-language (L2) Mandarin lexical tones research and show how an interpretable computational approach can deliver parameter-aligned feedback across perception–production (P ↔ P). We synthesize four strands: (A) conventional evaluations and tasks (identification, same–different, imitation/read-aloud) that reveal robust tone-pair asymmetries and early P ↔ P decoupling; (B) physiological and behavioral instrumentation (e.g., EEG, eye-tracking) that clarifies cue weighting and time course; (C) audio-only speech analysis, from classic F0 tracking and MFCC–prosody fusion to CNN/RNN/CTC and self-supervised pipelines; and (D) interpretable learning, including attention and relational models (e.g., graph neural networks, GNNs) opened with explainable AI (XAI). Across strands, evidence converges on tones as time-evolving F0 trajectories, so movement, turning-point timing, and local F0 range are more diagnostic than height alone, and the contrast between Tone 2 (rising) and Tone 3 (dipping/low) remains the persistent difficulty; learners with tonal vs. non-tonal language backgrounds weight these cues differently. Guided by this synthesis, we outline a tool-oriented framework that pairs perception and production on the same items, jointly predicts tone labels and parameter targets, and uses XAI to generate local attributions and counterfactual edits, making feedback classroom-ready. Full article

This paper presents a finite difference method for solving the Caputo generalized time fractional diffusion equation. The method extends the $L1$ scheme to discretize the time fractional derivative and employs the central difference for the spatial diffusion term. Theoretical analysis demonstrates that the proposed numerical scheme achieves a convergence rate of order $2-\alpha$ in time and second order in space. These theoretical findings are further validated through numerical experiments. Compared to existing methods that only achieve a temporal convergence of order $1-\alpha$, the proposed approach offers improved accuracy and efficiency, particularly when the fractional order $\alpha$ is close to zero. This makes the method highly suitable for simulating transport processes with memory effects, such as oil pollution dispersion and biological population dynamics. Full article

This work develops and analyzes an incommensurate fractional FitzHugh–Nagumo (FHN) reaction–diffusion system in which each state variable evolves with a distinct fractional order. The formulation extends the classical and commensurate fractional models by incorporating heterogeneous memory effects that break temporal symmetry between the activator and inhibitor variables. After establishing the mathematical framework, the equilibrium states of the system are derived and subjected to a detailed local stability analysis in both diffusion-free and diffusion-driven regimes. Explicit stability criteria are obtained by examining the spectral properties of the linearized operator under incommensurate fractional dynamics. Numerical simulations based on a Caputo L1 discretization scheme corroborate the theoretical results and demonstrate how asymmetric memory orders influence transient behavior, convergence rates, and the qualitative structure of the solutions. The study provides the first systematic stability characterization of an incommensurate fractional FitzHugh–Nagumo reaction–diffusion model, highlighting the role of fractional-order asymmetry in shaping the system’s dynamical response. Full article

This expository paper provides a unified and pedagogical introduction to optimal quantization for probability measures supported on spherical curves and discrete subsets of the sphere, emphasizing both continuous and discrete settings. We first present a detailed geometric and analytical foundation for intrinsic quantization on the unit sphere, including definitions of great and small circles, spherical triangles, geodesic distance, Slerp interpolation, the Fréchet mean, spherical Voronoi regions, centroid conditions, and quantization dimensions. Building upon this framework, we develop explicit continuous and discrete quantization models on spherical curves, namely great circles, small circles, and great circular arcs—supported by rigorous derivations and pedagogical exposition. For uniform continuous distributions, we compute optimal sets of n-means and the associated quantization errors on these curves; for discrete distributions, we analyze antipodal, equatorial, tetrahedral, and finite uniform configurations, illustrating convergence to the continuous model. The central conclusion is that for a uniform probability distribution supported on a one-dimensional geodesic subset of total length L, the optimal n-means form a uniform partition and the quantization error satisfies $V_n=L^2/\left(12n^2\right)$.The exposition emphasizes geometric intuition, detailed derivations, and clear step-by-step reasoning, making it accessible to beginning graduate students and researchers entering the study of quantization on manifolds. This article is intended as an expository and tutorial contribution, with the main emphasis on geometric reformulation and pedagogical clarity of intrinsic quantization on spherical curves, rather than on the development of new asymptotic quantization theory. Full article

Background/Objectives: The escalating antimicrobial resistance crisis underscores the urgent need to explore underexplored ecological niches as reservoirs of novel bioactive compounds. The Brazilian Caatinga, a unique semi-arid biome, represents a promising reservoir for microbial discovery. Methods: In this study, we report the polyphasic characterization of Streptomyces galbus I339, a strain isolated from Caatinga soil. Whole-genome sequencing and phylogenomic analysis confirmed its taxonomic identity. In silico mining of the genome was conducted to assess biosynthetic potential. This genetic promise was experimentally validated through an integrated metabolomic approach, including liquid chromatography-tandem mass spectrometry (LC-MS/MS), nuclear magnetic resonance (NMR) spectroscopy, and gas chromatography-mass spectrometry (GC-MS) profiling. The anti-mycobacterial activity of the crude extract was evaluated against Mycobacterium tuberculosis. Results: The strain S. galbus I339 possesses a 7.55 Mbp genome with a high GC content (73.17%). Genome mining uncovered a remarkable biosynthetic potential, with 45 biosynthetic gene clusters (BGCs) predicted, including those for known antibiotics like actinomycins, as well as numerous orphan clusters. Genome mining uncovered a remarkable biosynthetic potential, with 45 biosynthetic gene clusters (BGCs) predicted, including those for known antibiotics like actinomycins, as well as numerous orphan clusters. Metabolomic analyses confirmed the production of actinomycins and identified abundant diketopiperazines. Furthermore, the crude extract exhibited antimycobacterial activity, with a potent MIC of 0.625 µg/mL. Conclusions: The convergence of genomic and metabolomic data not only validates the expression of a fraction of this strain’s biosynthetic arsenal but also highlights a significant untapped potential, with the majority of BGCs remaining silent under the tested conditions. Our work establishes S. galbus I339 as a compelling candidate for biodiscovery and underscores the value of integrating genomics and metabolomics to unlock the chemical diversity of microbes from extreme environments. Full article

Single-season legume green manuring is widely promoted for soil fertility restoration in degraded agricultural lands, yet its effectiveness in alkaline semi-arid soils remains poorly understood. This study investigated the impact of first-year sweet clover (Melilotus officinalis (L.)) green manuring on soil microbiome structure and agrochemical properties in southern carbonate chernozem soils of Northern Kazakhstan. Using shotgun metagenomics, we analyzed microbial communities from sweet clover-amended soils, clean fallow, and virgin steppe reference sites. Contrary to expectations, sweet clover green manuring did not enhance soil nitrogen availability, with nitrate-N content (9.1 mg/kg) remaining lower than clean fallow (10.5 mg/kg), likely due to temporary immobilization during initial decomposition. While sweet clover significantly increased archaeal diversity (p = 0.01) and enriched nitrogen-cycling taxa, including Nitrospirae and Thaumarchaeota, overall microbial richness remained unchanged (ACE index, p > 0.05). Surprisingly, functional analysis revealed only five significant metabolic differences between sweet clover and fallow systems, indicating functional convergence of agricultural microbiomes regardless of management practice. Correlation analysis identified phosphorus as the master regulator of microbial metabolism (r = 1.0, p < 0.0001), while elevated pH (9.0), K₂O (>1000 mg/kg), and $\mathrm{N}{\mathrm{O}}_3^{-}$ showed strong negative correlations with essential metabolic pathways, revealing previously unrecognized nutrient toxicity thresholds. Virgin steppe maintained 69 unique metabolic pathways lost in agricultural systems, highlighting the ecological cost of cultivation. These findings demonstrate that sweet clover green manuring in alkaline steppe soils induces selective rather than comprehensive microbiome restructuring, with limited immediate benefits for soil fertility. This study provides critical insights for developing sustainable agricultural practices in the world’s extensive semi-arid regions facing similar edaphic constraints. Full article

With the rapid deployment of high-speed maglev transportation systems worldwide, the operational velocity, electromagnetic complexity, and channel dynamics have far exceeded those of conventional rail systems, imposing more stringent requirements on real-time capability, reliability, and interference robustness in wireless communication. In maglev environments exceeding 600 km/h, the channel becomes predominantly line-of-sight with sparse scatterers, exhibiting strong Doppler shifts, rapidly varying spatial characteristics, and severe interference, all of which significantly degrade the stability and convergence performance of traditional beamforming algorithms. Adaptive smart antenna technology has therefore become essential in high-mobility communication and sensing systems, as it enables real-time spatial filtering, interference suppression, and beam tracking through continuous weight updates. To address the challenges of slow convergence and high steady-state error in rapidly varying maglev channels, this work proposes a new Fractional Proportionate Normalized Least Mean Square (FPNLMS) adaptive beamforming algorithm. The contributions of this study are twofold. (1) A novel FPNLMS algorithm is developed by embedding a fractional-order gradient correction into the power-normalized and proportionate gain framework of PNLMS, forming a unified LMS-type update mechanism that enhances error tracking flexibility while maintaining $\mathcal{O}\left(L\right)$ computational complexity. This integrated design enables the proposed method to achieve faster convergence, improved robustness, and reduced steady-state error in highly dynamic channel conditions. (2) A unified convergence analysis framework is established for the proposed algorithm. Mean convergence conditions and practical step-size bounds are derived, explicitly incorporating the fractional-order term and generalizing classical LMS/PNLMS convergence theory, thereby providing theoretical guarantees for stable deployment in high-speed maglev beamforming. Simulation results verify that the proposed FPNLMS algorithm achieves significantly faster convergence, lower mean square error, and superior interference suppression compared with LMS, NLMS, FLMS, and PNLMS, demonstrating its strong applicability to beamforming in highly dynamic next-generation maglev communication systems. Full article

Accurate discharge measurement in irrigation channels is critical for improving water use efficiency and optimizing water allocation. To investigate the controlling factors of sediment deposition and its influence on the stage–discharge relationship, controlled experiments were conducted in a rectangular glass flume. Sediment concentration (4–16 kg/m³), bed slope (0.0005–0.002), and discharge (15–45 L/s) were systematically varied, and longitudinal deposition thickness and corresponding water stages were measured. Results indicate that sediment concentration is the dominant factor controlling deposition thickness, exhibiting a downstream-decreasing influence, with pronounced differences upstream and convergence downstream. Bed slope and discharge mitigate deposition by enhancing near-bed hydraulics; upstream deposition thickness decreased by approximately 35% and 23% as slope increased from 0.0005 to 0.002 and discharge increased from 15 to 45 L/s, respectively, with the regulatory effect diminishing along the flow direction. Three-dimensional response analysis revealed a compound “concentration-dominated and hydraulically regulated” mechanism: under low-discharge, low-slope, and high-concentration conditions, the ratio of deposition thickness to measured water depth (h_d/h) exceeded 15%, whereas it decreased below 5% under high-discharge, high-slope, and low-concentration conditions. Sediment deposition elevated the overall water stage by approximately 3–4% and caused systematic overestimation of stage-based discharge, with errors reaching 31.4% under low-discharge and high-concentration conditions and decreasing to 4.94% under high-discharge and steep-slope conditions. These findings provide quantitative evidence for discharge measurement and stage–discharge relationship calibration in sediment-laden open channels. Full article

In this work, three novel optimization algorithms—collectively referred to as the BxR algorithms—and their multi-objective versions, referred to as the MO-BxR algorithms, are applied to diverse heat transfer systems. Five representative case studies are presented: two single-objective problems involving a heat exchanger network and a jet-plate solar air heater; a two-objective optimization of Y-type fins in phase-change thermal energy storage units; and two three-objective problems involving TPMS–fin three-fluid heat exchangers and Tesla-valve evaporative cold plates for LiFePO₄ battery modules. The proposed algorithms are compared with leading evolutionary optimizers, including IUDE, εMAgES, iL-SHADEε, COLSHADE, and EnMODE, as well as NSGA-II, NSGA-III, and NSWOA. The results demonstrated improved convergence characteristics, better Pareto front diversity, and reduced computational burden. A decision-making framework is also incorporated to identify balanced, practically feasible, and engineering-preferred solutions from the Pareto sets. Overall, the results demonstrated that the BxR and MO-BxR algorithms are capable of effectively handling diverse thermal system designs and enhancing heat transfer performance. Full article

Toxic romantic relationships, a popular term referring to intimate partner violence (IPV) characterized by psychological, physical, and sexual violence, are a growing concern among young people. This pilot study aimed to preliminarily validate the vignette task on IPV, the “Mind 4 partner abuse” task, and to investigate the cognitive patterns and emotional profiles concerning IPV. Our research involved 228 university students from the University of L’Aquila who participated in an online psychoeducational program to raise awareness of the risks of IPV. Participants completed the “Mind 4 partner abuse” task, which included five vignettes depicting escalating violence in relationships. The task assessed participants’ emotional responses (anger, anxiety/fear, sadness, shame/guilt) and cognitive responses (functional-assertive or dysfunctional) to each vignette. In addition, for convergent validation, the Interpersonal Reactivity Index (IRI) was administered to assess empathic abilities. Five distinct factors were identified: active coping and legal awareness (ACLA), emotional distress (ED), assertiveness and autonomy defense (AAD), assertive reaction and self-empowerment (ARSE), and refusal of public humiliation and dignity assertion (RDA). One factor out of the five, emotional distress (ED), identified a dysfunctional cognitive pattern. The instrument showed a good convergent validity with the IRI. The correlation analysis showed that the IRI personal distress scale was negatively associated with ACLA and positively associated with ED. The IRI Empathic Concern scale was positively associated with RDA. In the dysfunctional cognitive pattern, as measured by the “Mind 4 Partner Abuse” vignette task, the ED factor was positively correlated with anxiety, sadness, shame, and guilt. The potential of the vignette task to identify high-risk cognitive profiles is promising, but it has yet to be confirmed. Given the limitations of the study, the findings offer only preliminary indications of cognitive patterns in young adults and their perceptions of romantic relationships, as assessed through a psychoeducational intervention. Further research with larger and more diverse samples, as well as more robust task designs, is necessary before firm conclusions can be drawn. Full article

The green synthesis of silver nanoparticles (AgNPs) by bacteria is a strategic route for sustainable nanobiotechnology; however, the genomic and biochemical mechanisms that make it possible remain poorly defined. In this study, bacteria native to silver-bearing mine tailings in Taxco (Mexico) were isolated, capable of tolerating up to 5 mM of AgNO₃ and producing extracellular AgNPs. Spectroscopic (430–450 nm) and structural (XRD, fcc cubic phase) characterization confirmed the formation of AgNPs with average sizes of 17–21 nm. FTIR evidence showed the participation of extracellular proteins and polysaccharides as reducing and stabilizing agents. Genomic analyses assigned the isolates as Lysinibacillus fusiformis 31HCl and L. xylanilyticus G1-3. Genome mining revealed extensive repertoires of genes involved in uptake, transport, efflux and detoxification of metals, including P-type ATPases, RND/ABC/CDF transporters, Fe/Ni/Zn uptake systems, and metal response regulators. Notably, homologues of the silP gene, which encode Ag⁺ translocator ATPases, were identified, suggesting convergent adaptation to silver-rich environments. Likewise, multiple nitroreductases (YodC, YdjA, YfKO) were detected, candidates for mediating electron transfer from NAD(P)H to Ag⁺. These findings support the role of Lysinibacillus as microbial nanofactories equipped with specialized molecular determinants for silver tolerance and AgNP assembly, providing a functional framework for microorganism-based nanobiotechnology applications. Full article

Quadrotor UAVs benefit from control strategies that can deliver rapid convergence and strong robustness in order to fully exploit their high agility. Finite-time control based on terminal sliding modes has been recognized as an effective alternative to classical sliding mode control, which only guarantees asymptotic convergence. Its enhanced variant, nonsingular fast terminal sliding mode control, eliminates singularities and achieves accelerated convergence; however, chattering-induced high-frequency oscillations remain a major concern. To address this issue, this study introduces a hybrid control framework that combines the super-twisting algorithm with ${\mathcal{L}}_1$ adaptive control. The super-twisting component preserves the robustness of sliding mode control while mitigating chattering, whereas ${\mathcal{L}}_1$ adaptive control provides rapid online estimation and compensation of model uncertainties and unknown disturbances. The resulting scheme is implemented in a quadrotor flight-control architecture and evaluated through numerical simulations. The results show that the proposed controller offers faster convergence and enhanced robustness relative to existing approaches, particularly in the presence of wind perturbations, periodic obstacle-avoidance maneuvers, and abrupt partial loss of propeller thrust. Full article

Freshwater cladocerans such as Daphnia magna (D. magna) are keystone grazers whose hormone-regulated life history traits make them sensitive sentinels of endocrine-disrupting chemicals (EDCs). The organophosphate flame-retardant tris(2-butoxyethyl) phosphate (TBOEP) and perfluoroethylcyclohexane sulfonate (PFECHS) now co-occur at ng L⁻¹–µg L⁻¹ in surface waters, yet their chronic sub-lethal impacts on invertebrate endocrine networks remain unclear. We analysed two publicly available 21-day microarray datasets (TBOEP: GSE55132; PFECHS: GSE75607) using gene ontology enrichment, STRING protein interaction networks, Drosophila phenotype mapping, and KEGG (Kyoto Encyclopaedia of Genes and Genomes)-anchored frameworks to build putative adverse outcome pathways (AOPs) for D. magna. Differentially expressed genes were clustered into functional modules and hub nodes were ranked by degree and betweenness. TBOEP suppressed moulting and growth, altering 1157 genes enriched for metabolism and membrane processes; hubs VRK1, MIB2, and adenylosuccinate synthetase formed a muscle anatomical development sub-network. PFECHS down-regulated vitellogenin and shifted 879 genes dominated by oxidative-stress and glutathione-metabolism signatures; central nodes UBC9, eIF4A-III, Tra-2α, and HDAC1 linked meiotic-cycle, oogenesis, and cyclic-compound binding. Despite chemical dissimilarity, both compounds converged on Wnt-signalling nodes—TBOEP via presenilin-1, and PFECHS via CK1ε/CK2—thereby reducing TCF/LEF-dependent transcription. Predicted outcomes include impaired oocyte maturation, reduced fecundity, and stunted body size, consistent with observed decreases in length and vitellogenin protein. Our network analysis, based on high-dose, sub-lethal exposures used in the underlying microarray studies, indicates that TBOEP- and PFECHS-induced perturbations can destabilise endocrine, developmental, and metabolic pathways in D. magna without overt lethality, and highlights Wnt-centred key events and hub genes as candidate biomarkers to be evaluated in future low-dose studies that use environmentally realistic exposure scenarios. Hub genes and Wnt-mediated key events emerge as sensitive biomarkers for monitoring mixed EDC exposure. Full article

Early age of onset is a major predictor of poor disease course in Bipolar Disorder (BD) and Schizophrenia (SCZ), often associated with greater symptom severity, cognitive decline, and worse outcomes. However, the biological mechanisms that shape age- and sex-specific vulnerability remain unclear, limiting progress toward early identification and intervention. To address this gap, we conducted an integrative transcriptomic study of 369 postmortem dorsolateral prefrontal cortex samples from the CommonMind Consortium. Differential gene expression, Weighted Gene Co-Expression Network Analysis, and gene set enrichment analysis were applied to identify pathways associated with age of onset, complemented by sex-stratified models and cellular deconvolution. To assess predictive signals, we applied a rigorous two-stage machine-learning framework using nested cross-validation, with Lasso feature selection followed by L2-regularized logistic classification. Performance was evaluated solely on held-out test folds. Genes and modules linked to earlier onset showed consistent enrichment for calcium signaling, with downregulation of CACNA1C and multiple adenylate-cyclase-related transcripts, while female-specific analyses revealed selective dysregulation of cyclase-associated pathways. Network analysis identified a calcium-enriched module associated with onset and sex, and diagnosis-specific modeling highlighted MAP2K7 in early-onset BD. The predictive model achieved an AUC of 0.63, and the top 50 machine-learning features were significantly enriched in calcium signaling pathway. These findings converge on calcium–cAMP signaling networks as key drivers of early psychiatric vulnerability and suggest biomarkers for precision-targeted interventions. Full article