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In low-voltage distribution networks, load switching, induction motor start-up, photovoltaic output variations, and short-circuit faults may produce highly overlapping electrical characteristics, which can lead to maloperation or failure to operate in conventional protection. To address this problem, this paper proposes an adaptive protection method integrating physically guided and cost-sensitive learning. First, an incremental topology-constraint deviation and a voltage-current trajectory curvature are constructed based on the fault-superimposed network constraint and the variation characteristics of system equivalent impedance, enabling the discrimination of short-circuit faults from non-fault transient disturbances. Then, a cost-sensitive physically guided extreme gradient boosting (XGBoost) model is developed, in which a fault-current-increment-based weight is introduced into the objective function to enhance the learning capability for weak-fault samples. Furthermore, a temporal-consistency-based protection operation logic is designed using sliding-window confirmation and majority voting to suppress isolated abnormal predictions. Simulation and RTDS-based real-time validation results on a 0.4-kV low-voltage distribution network with distributed photovoltaic generation show that the proposed method improves weak-fault detection sensitivity and reduces maloperation under complex source–load disturbances. The method relies only on local measurements and has potential for deployment in low-voltage intelligent protection terminals. Full article

(This article belongs to the Section Networks)

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26 pages, 1816 KB

Open AccessArticle

Data-Driven Quantification of Quantum k-Entanglement via Machine Learning

by Jie Guo, Jinchuan Hou, Xiaofei Qi and Kan He

Entropy 2026, 28(7), 832; https://doi.org/10.3390/e28070832 - 22 Jul 2026

Abstract

k-entanglement, including entanglement relative to full separability and genuinely multipartite entanglement, is a fundamental quantum resource in multipartite quantum systems. Its identification and quantification play essential roles in quantum information processing, quantum simulation, and quantum metrology. However, the practical computation of rigorous k-entanglement measures remains highly challenging due to the need for high-dimensional optimization. In this work, we propose a machine-learning-based surrogate framework for approximating the witness-based k-entanglement measure

E_{w}^{(k, n)}

. The numerical evaluation of the computationally realized quantity

{\tilde{E}}_{w}^{(k, n)} (ρ)

is reformulated as a supervised regression problem, where the input is the density matrix

ρ

and the labels are obtained from finite witness databases. The framework combines multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and light gradient boosting machine (LightGBM) through a stacking ensemble. Numerical experiments are performed for 3- and 4-qubit systems as representative demonstrations of the proposed workflow. The results show that the learned models achieve high predictive accuracy in terms of MAE, MSE, and

R^{2}

, while providing millisecond-level inference for single-state evaluation. Werner state tests serve as symmetric benchmark checks, and an additional four-qubit noisy circuit-generated state family, obtained from finite-depth circuit preparation followed by local amplitude-damping noise, is used as a structured physical test beyond random density matrices. Compared with the optimization-based evaluation, the trained surrogate model significantly reduces the computational time while maintaining accuracy within the tested system sizes and data distributions. These results show that the proposed framework provides an efficient numerical surrogate for rapid approximation of witness-based k-entanglement measures, while extensions to larger systems and experimental data require further validation. Full article

(This article belongs to the Special Issue New Advances in Quantum Communication and Networks, 2nd Edition)

19 pages, 1058 KB

Open AccessArticle

Integrated GWAS and eQTL Colocalization Identified Candidate Genes for Growth Traits in Pigs

by Xiangzi Wu, Junjing Wu, Yiren Gu, Mu Qiao, Jiawei Zhou, Zipeng Li, Yue Feng, Tong Chen, Dake Chen, Shuqi Mei, Xianwen Peng and Zhong Xu

Biology 2026, 15(14), 1216; https://doi.org/10.3390/biology15141216 - 22 Jul 2026

Abstract

Growth traits, as core economic indicators in pig breeding, are closely associated with production costs, rearing duration, and final carcass quality and have thus consistently been a major focus of genetic improvement. This study aimed to identify candidate genes affecting Age to 120 kg live weight (AGE120), Backfat thickness at 120 kg (BF120), and Loin muscle depth at 120 kg (LMD120) in pigs. Ear tissue samples were collected from 3364 healthy adult pigs (including 558 boars and 2805 sows) from three breeds: Large White, Landrace, and Duroc. Genotyping was performed using an 80 K functional site array, and quality-controlled SNP (Single-Nucleotide Polymorphism) loci were subjected to genotype imputation, resulting in 15,447,611 loci obtained. Genome-wide association studies (GWASs) for Age to 120 kg live weight, Back fat thickness at 120 kg, and Loin muscle depth at 120 kg were conducted using a mixed linear model in Genome-wide Complex Trait Analysis (GCTA). Genes located within 500 kb upstream and downstream of significant GWAS loci were extracted using the biomaRt package in R. Furthermore, colocalization analysis was performed using expression Quantitative Trait Locus (eQTL) data of 34 tissues from the PigGTEx database to identify genes that share the same causal variant as the GWAS signals. Through integrated GWAS and eQTL colocalization analysis, in addition to five previously reported genes associated with pig growth traits (TAF11, ZC3HAV1L, ANKS1A, USP20, and TBC1D1), a set of novel, high-confidence candidate genes was identified: ZNF215, UBE2Z, HOXB7, SARDH, ADAMTSL2, ATP6V0A4, RPL10A, PGM2, and RELL1. These findings enrich our understanding of the genetic architecture underlying growth traits in pigs at heavy body weights and provide an important foundation for subsequent functional validation and molecular breeding applications. Full article

(This article belongs to the Special Issue Advanced Genomics and Systems Biology in Pig Research)

31 pages, 8774 KB

Open AccessArticle

Assessment of Co-Pyrolysis of a Cyanobacterium and Waste Textile Polymer: Investigating Kinetics, Thermodynamics, Reaction Mechanism and Synergism

by Kaustav Nath, Biswajit Debnath, Ranjana Chowdhury, Somil Thakur and Rajnish Kaur Calay

Clean Technol. 2026, 8(4), 112; https://doi.org/10.3390/cleantechnol8040112 - 22 Jul 2026

Abstract

Algal cultivation has attracted significant attention due to CO₂ biocapture and potential for biofuel generation. Enormous generation of waste polymer often poses an environmental problem due to non-biodegradability. This study comprehensively analyses the thermal degradation characteristics of blue–green alga, Leptolyngbya subtilis JUCHE1 (LS) and waste textile polyester (WTP) and their mixtures (LS1P3 (1:3); LS1P1 (1:1); LS3P1 (3:1)) during co-pyrolysis. The interaction between LS and WTP during co-pyrolysis has been assessed through the verification of synergism using different blending ratio and through the comparison of the corresponding values of the Comprehensive Pyrolysis Index (CPI). The composite, LS1P3, exhibited the highest synergism and the maximum value of CPI. Isoconversional models (FWO, Starink, Bosewell and Tang) have been used to predict the activation energies (E_a). Thermodynamic parameters, namely, heat of reaction (ΔH), Gibbs free energy change (ΔG) and entropy change (ΔS), have also been determined for all. The average value of E_a for LS1P3 is also the lowest (96.015 kJ/mol) among all composites. The Master plot method identifies that there is a shift of reaction mechanism from phase boundary type (R2 and R3) for LS and WTP to a P2-type acceleratory reaction rate mechanism for LS1P3. The lowest average value of ΔH and the highest values of ΔG and ΔS for LS1P3 co-pyrolysis also support the least consumption of energy and the highest favorability under present conditions. The product yield distribution of co-pyrolysis in the isothermally operated conditions (450 °C) also establishes the superiority of LS1P3. Yields of pyro-oil and pyro-gas are the highest among all composites. The study ensures the future application prospects of co-pyrolysis of LS and WTP as a means for generation of energy resources (pyro-oil and pyro-gas) and chemicals (pyro-char). Full article

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22 pages, 488 KB

Open AccessArticle

MEMTIER: Tiered Retrieval, Session-Level Injection, and Typed Consolidation for Long-Running LLM Agents

by Bronislav Sidik and Lior Rokach

Algorithms 2026, 19(7), 607; https://doi.org/10.3390/a19070607 - 22 Jul 2026

Abstract

Long-running large language model (LLM) agents accumulate memory across many sessions, yet most agent runtimes store it as flat text. We present MEMTIER, a tiered memory architecture and consolidation framework for an open-source agent runtime and study three questions: what to store, what to inject, and what to keep. First, a tiered episodic–semantic memory reaches an accuracy of 0.382 and an F1 of 0.412 on LongMemEval-S (N = 500) with a 7B model on a 6 GB GPU—a +33-point gain over no retrieval. A controlled single-pipeline ablation shows the gains come from two components—semantic pre-population and two-stage scoping—while the multi-signal retrieval scaffold is, in this setting, an operational pass-through of BM25 top-k; removing it entirely leaves accuracy unchanged. An oracle analysis explains why the system is retrieval-limited: on the diagnostic subset, the correct session is present in the candidate set 98% of the time, so the primary binding constraint is which evidence reaches the reader. A larger generator helps but does not remove the retrieval bottleneck. Second, the bottleneck is injection granularity, not session recall (90.9% session coverage versus 4.5% fact recall@2). Injecting all facts from the top-k retrieved sessions improves multi-session accuracy by +0.120 and knowledge update accuracy by +0.205, and a controlled comparison isolates a structure effect distinct from token quantity. Third, we cast memory consolidation as a Markov decision process, characterize three structural failure modes that render a learned policy uninformative on single-subject benchmarks, and show preliminary evidence on a live agent benchmark that a typed, keyword-based policy recovers near-oracle performance for one memory type (threat pattern memory) without supervision. Type-dependent retention for other memory types is proposed but not yet evaluated. We frame these as related diagnostic studies rather than a single validated pipeline, and, together, they cast agent memory as a pattern recognition problem: recognizing which session patterns carry evidence and which knowledge types to retain. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (4th Edition))

34 pages, 8901 KB

Open AccessArticle

Physics-Guided LLM Prompt Engineering for Distributed Acoustic Sensing Data Augmentation in Pipeline Intrusion Detection

by Bingcai Sun, Xingcheng Zhao, Mosong Li, Zhaoheng Liu and Quan Li

Photonics 2026, 13(7), 693; https://doi.org/10.3390/photonics13070693 - 22 Jul 2026

Abstract

Distributed acoustic sensing (DAS) is increasingly used for third-party intrusion (TPI) detection in oil and gas pipeline monitoring, but labeled DAS data are often scarce, leading to overfitting, poor generalization, and increased false alarms and missed detections. Conventional data augmentation, GAN-based synthesis, and transfer learning may generate physically implausible samples or fail to cover the event feature space. To address this, we propose a physics-guided large language model (LLM) prompt-engineering framework for DAS data augmentation and pipeline intrusion detection. The framework establishes a physically grounded feature-indicator framework for DAS disturbance-event classification by mapping primary event mechanisms to measurable signal indicators, and then uses a standardized four-module prompt template to guide LLM-based synthesis-script generation. A two-stage iterative verification procedure is further introduced to constrain the generated samples in terms of physical-mechanism compliance and feature-parameter consistency. Synthetic data are combined with real data to train a lightweight PatchTransformer model for TPI detection, while an additional CNN is used to assess cross-architecture applicability. Using the public DAS1K benchmark with five-fold stratified cross-validation and a univariate controlled experiment (0–800 synthetic samples per category), the results show that the use of synthetic data improves detection performance overall. The configuration with 600 synthetic samples per category achieves 92.27% accuracy and 92.38% macro-F1, outperforming the conventional augmentation baseline by 4.74 and 4.86 percentage points, respectively. An additional CNN experiment also showed consistent performance gains across the tested augmentation settings, indicating that the benefit of the proposed synthetic data was not restricted to the PatchTransformer architecture. These findings indicate that LLM-assisted data augmentation can effectively improve the generalization of DAS-based pipeline intrusion detection when field-labeled samples are scarce. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications in Fiber Optic Sensing)

20 pages, 1332 KB

Open AccessArticle

Enzymatic Degradation Behavior and Molecular Weight Regulation of Dextran: Empirical Modeling and Multi-Scale Structural Characterization

by Mei Li, Piaoran Fan, Yirui Zhang, Ranran Li, Lemin Chen, Donghui Zhang and Lei Zhong

Curr. Issues Mol. Biol. 2026, 48(7), 749; https://doi.org/10.3390/cimb48070749 - 22 Jul 2026

Abstract

To meet the demand for controlled production of low-molecular-weight (M_w < 10 kDa) dextran with potential pharmaceutical applications, this study developed an efficient enzymatic preparation process using PC-Edex, a dextranase derived from Penicillium cyclopium CICC-4022. The effects of enzyme concentration, substrate concentration, temperature, and pH on the degradation of high-molecular-weight dextran were systematically investigated, and the optimal process conditions were established. A staged empirical control strategy based on the Malhotra model was developed to investigate and predict the behavior of dextran molecular weight changes during enzymatic hydrolysis. Under the optimized conditions, dextran with an M_w below 10 kDa was produced within 60 min, with the mass fraction of fragments smaller than 10 kDa reaching 94.56 ± 0.32% and the degradation rate exceeding 98.96 ± 0.15%. The resulting product exhibited a narrow molecular weight distribution (M_w/M_n = 1.528 ± 0.03) and adopted a compact random-coil conformation in aqueous solution. Multi-scale characterization results indicated that enzymatic degradation altered only the molecular weight of dextran, while the backbone structure, amorphous nature, and thermal stability were preserved. These findings present a robust and reproducible laboratory-scale process, which provides a reference for the industrial production of low-molecular-weight dextran for pharmaceutical purposes. Full article

(This article belongs to the Special Issue Molecular Dynamics Simulations in Structural Biology: From Proteins to Complex Biomolecular Systems)

28 pages, 2629 KB

Open AccessArticle

Global Genomic Analysis of Bovine-Associated Klebsiella pneumoniae Reveals Genetic Diversity and Resistance–Virulence Profiles

by Meihui Tian, Yaqian Liang, Jia Lu, Weidi Shi, Yang Zhao, Weize Gan, Shuan Jia, Chencheng Xiao, Tianyi Zhao and Hui Zhang

Biology 2026, 15(14), 1215; https://doi.org/10.3390/biology15141215 - 22 Jul 2026

Abstract

Bovine-associated Klebsiella pneumoniae is an important bacterial species linking animal health, microbial ecology, and One Health-oriented antimicrobial resistance research. In this study, we performed a global genomic analysis of 1291 publicly available bovine-associated K. pneumoniae genomes collected from 18 countries between 2005 and 2024 using data retrieved from NCBI. MLST, core-genome phylogenetic analysis, pangenome analysis, CARD, VFDB, and PlasmidFinder were used to characterize sequence types, genomic diversity, antimicrobial resistance-associated genes, virulence-associated genes, and plasmid replicons. A total of 256 sequence types were identified, among which ST107 was the most common. Core-genome phylogenetic analysis revealed multiple genomic lineages, while pangenome analysis identified 46,325 gene clusters, including 1967 core genes and 40,595 cloud genes, indicating an open pangenome structure and substantial accessory gene diversity. Virulence-associated genes were unevenly distributed, with yagZ/ecpA being the most frequently detected determinant. In total, 138 antimicrobial resistance-associated genes or potential resistance determinants were detected across 16 antimicrobial categories, including clinically important β-lactamase- and carbapenemase-associated genes. IncF-family plasmid replicons, particularly IncFIB(K)_1_Kpn3, were frequently detected, suggesting widespread plasmid replicon-associated genomic backgrounds; however, physical co-localization between resistance genes and specific plasmid backbones could not be confirmed. Overall, this study reveals the genetic diversity, resistance-associated gene reservoir potential, heterogeneity of virulence-associated genes, and plasmid replicon backgrounds of bovine-associated K. pneumoniae. Importantly, the genome-predicted AMR potential identified in this study should not be interpreted as confirmed phenotypic resistance without further experimental validation. These findings provide genomic insights for risk surveillance, candidate control-target screening, and microbiota-oriented intervention research. Full article

(This article belongs to the Section Microbiology)

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32 pages, 37654 KB

Open AccessArticle

Cross-Scale Correlation Analysis Between Forming Quality and Microstructural Response During SPIF of the Al 1060 Based on PEEQ-Based SSD Density Inference and EBSD Characterization

by Xinyue Zhang, Xiaojing Zhu, Yuhuai Wang, Yaokun Ye, Mingyan Zhao, Teng Zhou and Wenxun Li

Materials 2026, 19(14), 3152; https://doi.org/10.3390/ma19143152 - 22 Jul 2026

Abstract

Single-point incremental forming (SPIF) produces localized plastic deformation, resulting in thickness reduction, geometrical deviation, and microstructural evolution. To establish the relationship between forming quality and microstructural response, this study develops a cross-scale analysis framework integrating finite element simulation, Kocks–Mecking (K–M)-based statistically stored dislocation (SSD) density inference, metallographic observation and electron backscatter diffraction (EBSD) characterization. Applied to an Al 1060 truncated-cone part, the framework converts the simulated equivalent plastic strain (PEEQ) into SSD density via the K–M model calibrated using the Voce hardening model and the Taylor relation. The inferred SSD density distribution is then spatially correlated with thinning rate, geometrical deviation, grain size, grain-boundary misorientation, kernel average misorientation (KAM), and geometrically necessary dislocation (GND) density across different forming regions. The inferred SSD density rapidly approached a saturation level of 1.55 × 10¹³ m⁻², while the high-SSD density region progressively expanded during forming. This regional evolution was qualitatively consistent with the EBSD observations. The average grain size decreased from 30.4 μm to 21.9 μm, and the medium-angle grain-boundary fraction increased from 10.3% to 34.5%. Regionally, thickness reduction correlates strongly with PEEQ accumulation, SSD storage, and grain refinement, whereas geometrical deviation is more closely related to early-stage deformation heterogeneity. These findings provide a physically based route for predicting and controlling SPIF accuracy. Full article

(This article belongs to the Special Issue Latest Developments in Advanced Machining Technologies for Materials)

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12 pages, 537 KB

Open AccessArticle

Molecular Identification and Recombinant Expression of a Novel Antifungal Protein from Wheat-Associated Paenibacillus polymyxa

by Xiaohong Ge, Zhikun Chen, Haoyuan Guo and Junjian Ran

Toxins 2026, 18(7), 318; https://doi.org/10.3390/toxins18070318 - 22 Jul 2026

Abstract

Fusarium head blight (FHB) caused by Fusarium graminearum leads to huge yield losses and mycotoxin contamination in wheat globally. Paenibacillus polymyxa with strong antagonistic activity was preliminarily identified. To clarify the key antifungal component, an extracellular protein was purified via ammonium sulfate precipitation, DEAE-52 anion-exchange and Sephadex G-75 gel filtration chromatography. SDS-PAGE showed a single band at 76 kDa. liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis confirmed this protein belongs to glycosyl hydrolase family with 86% sequence coverage. Biochemical characterization showed that the crude protein was stable at 40–90 °C and pH 3.0–9.0, sensitive to proteinase K, trypsin and neutral protease. The purified 76 kDa protein exhibited antifungal activity against F. graminearum. The gene encoding this protein was cloned and expressed in Escherichia coli. The renatured recombinant protein p76kd showed comparable antifungal activity to the native protein. This study purified and characterized a 76 kDa protein annotated as a glycosyl hydrolase via LC-MS/MS peptide matching; its antifungal function is presumed to originate from the conserved glycosyl hydrolase domain according to existing homologous research, which is distinct from previously reported lipopeptides or uncharacterized complexes. This protein provides a promising candidate for the biocontrol of FHB and related fungal diseases in cereal crops. Full article

(This article belongs to the Section Mycotoxins)

20 pages, 29652 KB

Open AccessArticle

Biopolymer-Conjugated Human C-Peptide Provides Sustained Neuroprotection and Preserves Axonal Transport in a Mouse Model of NMDA-Induced Retinal Degeneration via Antioxidative Mechanisms

by Ji-Seok Yoon, Chan-Hee Moon, Tae-Yong Koh, Woo Ri Cho, Juha Lee, Minsoo Kim and Kwon-Soo Ha

Antioxidants 2026, 15(7), 911; https://doi.org/10.3390/antiox15070911 - 22 Jul 2026

Abstract

Glutamate excitotoxicity is a key contributor to the pathogenesis of glaucoma, a leading cause of irreversible blindness worldwide; however, the molecular events driving progressive retinal ganglion cell (RGC) loss and axonal degeneration remain incompletely understood, and effective neuroprotective therapies are lacking. Here, we evaluated the preventive potential of K9-C-peptide, a biopolymer-conjugated human C-peptide, in a mouse model of N-methyl-D-aspartate (NMDA)-induced retinal neurodegeneration and optic nerve axonal transport impairment, and examined potential mechanisms underlying its protective effects. In NMDA-induced excitotoxic mouse retinas, intracellular Ca²⁺ elevation mediated NMDA-induced oxidative stress, including both intracellular and mitochondrial reactive oxygen species (ROS) generation and lipid peroxidation. NMDA exposure induced activation of Müller glia and microglia and upregulation of inflammatory cytokines, ultimately leading to RGC death; these effects were attenuated by prolonged intraocular delivery of ROS scavengers. K9-C-peptide significantly reduced NMDA-induced retinal degeneration, including RGC loss and retinal thinning, and preserved optic nerve axonal transport function in both whole-mount retinas and optic nerve longitudinal sections. These protective effects were associated with suppression of NMDA-induced oxidative stress, mitochondrial dysfunction, and inflammation and reactive gliosis, without altering intracellular Ca²⁺ levels. Notably, sustained intraocular delivery of human C-peptide conferred robust neuroprotection for at least 3 weeks against NMDA-induced retinal degeneration and optic nerve axonal transport impairment. These findings suggest that K9-C-peptide acts as a long-acting neuroprotective agent that mitigates oxidative stress-driven retinal damage and axonal dysfunction, highlighting its translational potential as a C-peptide-based neuroprotective strategy for retinal glutamate excitotoxicity. Full article

(This article belongs to the Special Issue Oxidative Stress in Diabetic Retinopathy and Other Retinal Diseases)

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30 pages, 1524 KB

Open AccessArticle

Continuous Geometry, Continuous Flow, Continuous Compression: A Numerical Component-Interaction Assessment for Fractional Clay Plasticity

by Nopanom Kaewhanam, Thammanun Chatwong, Apichit Kampala, Sitthiphat Eua-apiwatch and Sivarit Sultornsanee

Fractal Fract. 2026, 10(7), 501; https://doi.org/10.3390/fractalfract10070501 - 22 Jul 2026

Abstract

Constitutive models for clays have historically treated yield geometry, plastic-flow direction, and compression as separate problems, with little regard for their interaction. This paper presents a controlled numerical assessment of how three components—Chatwong et al.’s verified teardrop yield surface, a stress-fractional flow rule, and an AJOP-derived hardening modulus— interact when coupled in a 2 × 2 × 2 factorial design. The components are integrated incrementally along one idealized shear-strain-controlled constant-p′ path with an approximate undrained variant for two independently calibrated clays (Boston Blue Clay and London Clay) under a specified state-dependent fractional order. Within this scope, the main flow effect is consistently the largest single quantity for both soils, and the flow × compression interaction is comparably large wherever defined. Compression’s role grows substantially with the overconsolidation ratio, and the main geometry effect is markedly soil-dependent, scaling with the surface-shape parameter. Two structural singularities are identified: a phase-transformation point in the teardrop surface’s non-associated flow rule, absent from the fractional rule, and a hardening singularity in the AJOP-based modulus, whose tangent falls to the swelling index at a finite, soil-dependent preconsolidation stress, bounding the evaluable overconsolidation range of the compression-related interactions; a proportional-κ variant removes this singularity by construction while preserving the factorial ranking, identifying it as a property of the constant-κ embedding, not of AJOP itself. Under an approximate undrained path, the geometry × flow interaction carries over unchanged, while compression’s role is suppressed several-fold. The borrowed yield surface and flow rule are validated independently against 379 points from real undrained triaxial tests across four calibrated soils using this paper’s own re-calibrated predictions; the fractional–AJOP framework itself is assessed for internal consistency only, and its laboratory validation, together with K₀, cyclic and multi-axial paths, remains for future work. Full article

(This article belongs to the Special Issue Fractal and Fractional in Geotechnical Engineering, Second Edition)

26 pages, 2058 KB

Open AccessArticle

Integrated FEM Evaluation and Optimization of Excavation, Loading, and ROPS/FOPS Systems in a Skid-Steer Loader

by Diego Andrés Duque-Sarmiento, Gustavo Morocho, Juan José Molina-Campoverde and Xavier Narváez

Machines 2026, 14(7), 833; https://doi.org/10.3390/machines14070833 - 22 Jul 2026

Abstract

This study proposes an integrated finite element methodology for evaluating and redesigning three critical subsystems of an XCMG XC740K skid-steer loader: the excavation attachment, the arm–bucket charging system, and the ROPS/FOPS operator protection cab. The components were reconstructed by reverse engineering and 3D scanning, modeled in CAD, and simulated in ANSYS Workbench/Mechanical under load cases derived from hydraulic parameters, soil–tool interaction, and international safety standards. The novelty of the work lies in applying a single FEM-based workflow to three interacting subsystems of the same compact machine, rather than optimizing isolated components independently. The original configuration showed critical effort concentrations in the cab and charging system. Localized geometric reinforcements and the use of high-strength and wear-resistant steels improved stiffness and safety margins in the excavation bucket, loading bucket, and ROPS/FOPS cab. However, the arm–quick coupler region remained the controlling weak point of the loading assembly, indicating the need for further redesign. The proposed approach provides a transferable computational framework for identifying structural vulnerabilities and prioritizing redesign actions in compact earthmoving machinery. Because the study is numerical, future experimental validation is required before certification or field implementation. Full article

(This article belongs to the Section Machine Design and Theory)

18 pages, 3632 KB

Open AccessArticle

Biochemical Characterization and Active-Site Analysis of N-Acetylornithine Aminotransferase from Crocosphaera subtropica ATCC 51142

by Liyang Huang, Zhi-Min Li, Luna Gao, Siqi Wang, Zhifeng Wu and Zhimin Li

Life 2026, 16(7), 1212; https://doi.org/10.3390/life16071212 - 22 Jul 2026

Abstract

N-acetylornithine aminotransferase (AcOAT) is a pyridoxal 5′-phosphate (PLP)-dependent enzyme that catalyzes a key transamination step in arginine biosynthesis. In cyanobacteria, arginine metabolism is closely associated with nitrogen assimilation and storage, yet biochemical information on cyanobacterial AcOATs remains limited. In this study, the AcOAT encoded by the cce_3094 gene from Crocosphaera subtropica ATCC 51142 (CsAcOAT) was cloned, heterologously expressed, purified, and systematically characterized. Recombinant CsAcOAT was obtained as a soluble protein with an apparent molecular mass of approximately 43 kDa. Steady-state kinetic analysis showed that CsAcOAT catalyzed transamination between N-acetylornithine (AcOrn) and α-ketoglutarate (α-KG), with apparent K_M values of 0.17 ± 0.03 mM for AcOrn and 0.020 ± 0.003 mM for α-KG, indicating a higher affinity for α-KG. The enzyme exhibited optimal activity at pH 8.5 and 30 °C, retained relatively high activity over a broad temperature range of 0–50 °C, and was activated by Zn²⁺ and Co²⁺ but inhibited by Ni²⁺. Structural analysis based on homology modeling, molecular docking, and molecular dynamics simulations suggested a conserved PLP-dependent aminotransferase fold and a stable binding mode for the PLP-AcOrn complex in the active-site pocket. Site-directed mutagenesis further demonstrated that Gly114, Asp239, Lys268, and Thr296 are indispensable for catalytic activity, whereas Ser113, Ala115, and Gln242 make important contributions to catalytic turnover and cofactor-assisted catalysis. These results provide biochemical and structural characterization of CsAcOAT, expand current knowledge of cyanobacterial AcOATs, and offer a useful basis for future studies on arginine metabolism and nitrogen storage in diazotrophic cyanobacteria. Full article

(This article belongs to the Section Biochemistry, Biophysics and Computational Biology)

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16 pages, 2179 KB

Open AccessArticle

Phylogenetic Relationships and Genetic Diversity of Thai Nepenthes (Nepenthaceae) Revealed by Integrative Molecular Analyses

by Yaowaphan Sontikun, Sunya Nuanlaong, Tim Böhnert, Maximilian Weigend and Potjamarn Suraninpong

Plants 2026, 15(14), 2238; https://doi.org/10.3390/plants15142238 - 22 Jul 2026

Abstract

Accurate assessment of phylogenetic relationships is essential for understanding the evolution, taxonomy, and conservation of the carnivorous genus Nepenthes. In Thailand, species delimitation remains challenging because of extensive morphological variation and the occurrence of several closely related taxa. This study integrated genome-wide single nucleotide polymorphisms (SNPs) generated by Genotyping-by-Sequencing (GBS), chloroplast trnK intron sequences, nuclear ITS sequences, and AFLP markers to investigate phylogenetic relationships and genetic diversity among Thai Nepenthes taxa. Genome-wide SNP analysis recovered three principal clades, whereas trnK and ITS datasets provided complementary resolution at deeper and intermediate phylogenetic levels. The molecular datasets revealed broadly congruent phylogenetic patterns and improved resolution among several closely related Thai taxa. AFLP analysis of Nepenthes mirabilis populations revealed moderate polymorphism despite relatively high overall genetic similarity. Across all datasets, N. mirabilis var. globosa was consistently recovered within the broader N. mirabilis lineage and showed limited genetic differentiation from sampled N. mirabilis accessions. These findings provide an integrated molecular framework for understanding phylogenetic relationships among Thai Nepenthes and support future taxonomic, evolutionary, and conservation studies in the region. Full article

(This article belongs to the Topic New Insights in Plants Diversity and Conservation)

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