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Modelling and algorithms of highway transportation network in urban agglomerations
Nanoscale cuticle mass density variations influenced by pigmentation in butterfly wing scales
Momentum-locked spin between topological and defect states in 1D patterns on bilayer graphene
Abstract Gating Bernal bilayer graphene breaks the inversion symmetry so that the stacking AB/BA boundaries within the gap reveal topologically protected states. In this study, we theoretically investigate arrays where the AB and BA domains are periodically patterned with experimentally identified defect lines. In the calculations we consider electron-electron interaction effects using density functional theory. Our findings reveal the existence of topological states within a gap induced by the patterning without an applied gate voltage. Furthermore, with an applied gate potential, the defect lines introduce spin-polarized states pinned within the gap and exhibit ferromagnetically coupled states. Importantly, we observe a hybridization of magnetic and topological states near the valleys that form conducting channels characterized by spin-momentum locking. The effect persists even with slight n-doping and gate voltage; however, the progressively pinned n-doped defect states induce spin polarization in the topological and valley states. Additionally, the two-dimensional bands under doping conditions exhibit nesting across the Fermi surface, allowing for modulation of charge densities along the lines which are nearly commensurate with the underlying graphene-defect lines. These quasi-one-dimensional patterns in bilayer graphene show a new kind of spin-conducting channels with novel characteristics common to both spintronics and valleytronics.
March5-mediated Trim28 degradation preserves islet β-cell function in mice
Spectrophotometric investigation of cocarboxylase interaction with pyridoxine hydrochloride and their interactional properties
Cryo-EM captures early intermediate steps in dynein activation by LIS1
Abstract Cytoplasmic dynein-1 (dynein) is an essential molecular motor in eukaryotic cells. Dynein primarily exists in an autoinhibited Phi state and requires conformational changes to assemble with its cofactors and form active transport complexes. LIS1, a key dynein regulator, enhances dynein activation and assembly. Using cryo-EM and a human dynein-LIS1 sample incubated with ATP, we map the conformational landscape of dynein activation by LIS1 and identify an early intermediate state that we propose precedes the previously identified dynein-LIS1 Chi state. Mutations that disrupt this species, which we termed “Pre-Chi”, lead to motility defects in vitro, emphasizing its functional importance. Together, our findings provide insights into how LIS1 relieves dynein autoinhibition during the activation pathway.
Performance evaluation of enhanced deep learning classifiers for person identification and gender classification
Abstract Person authentication using periocular images is a prominent research domain. Although the biometric identification systems have advanced, the existing approaches still struggle with accuracy, overfitting issues and computational efficiency, especially when utilizing periocular images for person identification and gender classification. In order to overcome these limitations, this paper proposes an enhanced deep learning classifier (EDLC) paradigm to recognize a person based on the periocular region within a face. A novel Hexagon-shaped ROI extraction is performed in the localization phase to extract the periocular ROIs. Following that, the feature extraction mechanism is accomplished utilizing the Laplacian transform. Finally, three distinct custom EDLCs are employed, such as dilated axial attention convolutional neural network, self-spectral attention-based relational transformer net, parameterized hypercomplex convolutional Siamese network for classification. Further, an adaptive coati optimization algorithm is used to adjust the hyperparameters of the classification models. The efficacy of the model is assessed concerning different outcome indicators. It is also compared with the recent competitive models. For person identification, the SSA-RTNet has achieved a maximum accuracy of 99.8% and 99.67% using the UBIPr and UFPR datasets respectively. Similarly, for gender classification, an accuracy of 98.4% and 99.68% using SSA-RTNet was obtained for the UBIPr and UFPR datasets. As a result, it is perceived that a considerable improvement was observed using the enhanced models.
Mechanical–electric dual characteristics solid–liquid interfacing sensor for accurate liquid identification
Impact of Gaura parviflora invasion on urban wildness biodiversity: a campus green patch case study
The analysis of the progressive local failure process of the Longquan Reservoir dam based on the global-local dynamic strength reduction method
Abstract During the progressive failure process of the slope (dam), the strength parameters of the soil in the slope (dam) continuously degrade, with the degree and rate of degradation of the soil near the shear zone significantly exceeding those in other areas. To address this mechanism, this paper proposes a global-local dynamic strength reduction method that simultaneously accounts for both the physical degradation pattern of the soil and the strain softening characteristics of the shear zone. Taking the Longquan Reservoir Dam as an engineering case, a two-dimensional profile calculation model at station 0 + 142 was established using ANSYS. Combining this model with the global-local dynamic strength reduction method, the progressive local failure process of the dam under heavy rain conditions was simulated. By analyzing the distribution patterns and evolution trends of dam displacement, stress, and plastic strain, the local failure mechanism of the dam was elucidated. The results indicate that the global-local dynamic strength reduction method can effectively captures both the soil’s physical degradation and the shear zone’s softening mechanism, accurately reflecting the failure progression of the dam while maintaining high computational efficiency. The critical strength reduction coefficients required to reach the critical instability state using different methods exceed 1.0, indicating that the Longquan Reservoir Dam is in a safe condition, which is consistent with field observations. The progressive local failure process of the dam shows that heavy rainfall induces persistent degradation of soil strength parameters. Initially, the downstream soil undergoes plastic yielding and shear flow. As the shear failure zone continues to expand, the soil from the mid-upstream shifts downstream, ultimately leading to the collapse at the dam crest and the formation of a continuous shear zone.
Spatiotemporal analysis of CMIP6-based climate extremes and their implications for sustainable watershed management in the Gidabo watershed, Ethiopia
Multihop cost awareness task migration with networking load balance technology for vehicular edge computing
Seasonal disparities in green exposure under the 15-minute city framework: a case study of Xi’an, China
Magnitude and determinants of anemia among patients at Garbo Primary Hospital, Somali Region of Ethiopia
Improved conservation of callus and rhizome microcuttings of Podophyllum hexandrum germplasm using the slow growth storage approach
Abstract Podophyllum hexandrum Royle (syn. Podophyllum emodi Wall.), commonly known as Himalayan mayapple, is an endangered medicinal plant recognized as the primary natural source of podophyllotoxin, a potent compound with anticancer and antiviral properties. In this study, we developed an optimized protocol for the long-term preservation of P. hexandrum germplasm using a slow growth storage (SGS) technique, successfully preserving the viability and genetic stability of both callus and rhizome cuttings. In vitro cultured callus and rhizome microcuttings of P. hexandrum were conserved using the slow growth storage (SGS) technique in Murashige and Skoog (MS) medium under cold conditions (5 °C), supplemented with different concentrations of sucrose, mannitol, and sorbitol in combination with calcium pantothenate and spermidine, to induce slow growth and maintain tissue viability. It was observed that sorbitol (5.5%) combined with spermidine (2 mg L−1), calcium pantothenate (3 mg L−1), and 6-benzylaminopurine (BA) (1.5 mg L−1) showed better efficacy than the mannitol (6.5%) combination in preserving and regenerating callus and rhizome microcuttings. In contrast, the combination with sucrose (6.5%) was the least effective. This study developed an effective in vitro protocol for conserving P. hexandrum, an endangered medicinal plant, through slow growth storage. A medium containing sorbitol, mannitol, spermidine (2 mg L−1), and calcium pantothenate (2 mg L−1) enhanced tissue viability, stress tolerance, and long-term survival of callus and rhizome explants while maintaining genetic stability during cold storage. These findings suggest that this protocol provides a reliable approach for the ex-situ conservation of P. hexandrum, ensuring the availability of genetically stable plant material for future research and medicinal use. This is the first report on the germplasm conservation of callus and rhizome microcuttings of P. hexandrum grown in Pakistan using the slow growth technique.
Flow regime transitions in flow blurring injection through a CFD parametric study
Abstract Flow-blurring (FB) is a twin-fluid atomization technique that generates fine sprays through internal turbulent mixing. This study presents a parametric computational investigation of an FB injector operating with air and various liquids at ambient pressure. A validated unsteady two-phase solver based on the Volume of Fluid (VOF) method is used to model the injector at different air-to-liquid mass flow rate ratios (ALRs). Parameters such as penetration length, volume fraction, static pressure, vorticity magnitude, and turbulent kinetic energy are analyzed to understand flow dynamics. The results identify three distinct flow regimes: air-dominant, liquid-dominant, and bubbly flow. Screening analysis of a full factorial design of 32 cases shows that liquid mass flow rate and dynamic viscosity are the most influential factors in penetration length. The resulting penetration length varies between 2 [mm] and 8.5 [mm] across the design space. A correlation analysis confirms these findings and reveals important two-way interactions between parameters, such as the positive effect of combined liquid and air mass flow rates. This insight offers a promising pathway for optimizing flow-blurring injectors in various applications.