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For important food crops such as wheat and rice, grain yield depends on grain number and size. In rice (Oryza sativa), GW2 was isolated from a major quantitative trait locus for yield and encodes an E3 RING ligase that negatively regulates grain size. Wheat (Triticum aestivum) has TaGW2 homologues in the A, B, and D genomes, and polymorphisms in TaGW2-A were associated with grain width. Here, to investigate TaGW2 function, RNA interference (RNAi) was used to down-regulate TaGW2 transcript levels. Transgenic wheat lines showed significantly decreased grain size-related dimensions compared with controls. Furthermore, TaGW2 knockdown also caused a significant reduction in endosperm cell number. These results indicate that TaGW2 regulates grain size in wheat, possibly by controlling endosperm cell number. Wheat and rice GW2 genes thus seem to have divergent functions, with rice GW2 negatively regulating grain size and TaGW2 positively regulating grain size. Analysis of transcription of TaGW2 homoeologues in developing grains suggested that TaGW2-A and -D act in both the division and late grain-filling phases. Furthermore, biochemical and molecular analyses revealed that TaGW2-A is a functional E3 RING ubiquitin ligase with nucleocytoplasmic subcellular partitioning. A functional nuclear export sequence responsible for TaGW2-A export from the nucleus to the cytosol and retention in the nucleolus was identified. Therefore, these results show that TaGW2 acts in the regulation of grain size and may provide an important tool for enhancement of grain yield.

Effective heat management has become a key component of contemporary manufacturing, as machinery functioning in extremely high temperature and electromagnetic environments cannot be adequately cooled by traditional methods. In order to overcome this difficulty, the current work investigates the unsteady magnetized nanofluid flow between two parallel plates embedded in a non-Darcy resistive medium, taking into account the effects of thermal and velocity slips along with ohmic heating. In addition, a cubic nonlinear thermal stratification phenomenon is integrated into the analysis, which provides more accurate depiction of significant nonlinear thermal gradients than traditional linear or quadratic frameworks. Moreover, for Titanium-water nanofluids, the shape-dependent heat conductivity of nanoparticles is assessed using the classical Hamilton-Crosser model. Employing Mathematica’s built-in solver NDSolve, the highly nonlinear set of ordinary differential equations is solved to get nanofluids velocity and temperature distributions. We noticed that higher values of magnetic parameter reduce the velocity field, while higher magnetic parameter and Eckert number increases the temperature distribution. On the other hand, slip factors and thermal stratification lowers the temperature field, enhancing thermal capacity. In accordance with the analysis’s findings, the suggested setup provides a very manageable and energy-effective setting for refining thermal processes using nanofluids, providing tremendous potential for use in polymeric material processing, nanotechnology cooling, and future-focused energy-related technologies.

The Riga plate is arrangement of electrodes and permanent magnets allows for efficient regulation of fluid flow. The Riga surface leverages Lorentz forces to control boundary layers (BL) and improve cooling purposes for effective electromagnetic flow control in nuclear and aeronautical engineering systems. Furthermore, by utilizing synergistic interactions of different nanoparticles, heat transfer rat can be optimized in industrial setup. The primary focus of this work is to investigate the unsteady BL flow of water-based tri-hybrid nanolfuid (tri-HNF) flow over a Riga plate senor under the influence of activation energy, cross-diffusion, and convective heating. Three different nanoparticles , and are dispersed in a pure water. The model equations are constructed using BL theory and transformed into ordinary differential equations using an appropriate similarity rule. The Runge–Kutta fourth-order (RK-4) method, along with shooting approach, is used to address the problem numerically. The skin friction and Nusselt and Sherwood numbers are assessed using optimized statistical Response Surface Methodology (RSM) technique. The Gharesim model viscosity and Hamilton-Crosser thermal conductivity models are deployed in the governing model. A mathematical model is designed and developed using RSM to obtain an optimal skin friction, heat and mass transfer rate. Sensitivity analysis (SA) is performed to investigate the response of input on these coefficients. SA shows that in narrow BL, the skin friction rises with nanoparticle concentration. Velocity of tri-HNF boost with the Hartman number and the electrode-magnet distance parameter. The Soret number, and activation energy increases the concentration profile. Higher Nusselt number indicates improved heat transfer with increased nanoparticle load. Activation energy uplift the mass transfer rates, but dwindle with nanoparticle concentration.

Machine learning plays a pivotal role in addressing real-world challenges across domains such as cybersecurity, where AI-driven methods, especially in Software-Defined Networking, enhance traffic monitoring and anomaly detection. Contemporary networks often employ models like Random Forests, Neural Networks, and Support Vector Machines to identify threats early and reinforce security. Ensemble learning further improves predictive accuracy and stability, yet many frameworks falter when confronted with noisy or contaminated data. In this study, we propose a robust ensemble framework for Extreme Learning Machines (ELMs) that integrates a family of redescending ψ-activation functions grounded in M-estimation theory. Each ψ-function yields a distinct base classifier, initialized with random weights, and the optimal hidden-node count is selected via grid search minimizing the Brier score. Rather than traditional voting, ensemble outputs are combined through a least-squares optimization, allowing precise parameter estimation and enhanced stability. We validate our method on five benchmark datasets, SatImage, Email-Spamdexing, Breast Cancer, Musk, and Iris, demonstrating consistently superior accuracy and reduced variance compared to existing ELM ensembles. Rigorous statistical testing (Kruskal–Wallis with Dunn’s post-hoc comparisons) confirms these gains. Our results show that embedding robust M-estimator–based activations within a controlled ensemble yields marked improvements in generalization, predictive precision, and resilience to data irregularities, offering a significant advancement in the design of efficient neural classifiers.

In this work, we report a comprehensive study of the structural, optical, and photoelectrical properties of one-dimensional (1D) CsCu 2 Br 3 single crystals. Powder X-ray diffraction (XRD) confirms the orthorhombic Cmcm phase with excellent crystallinity, while thermogravimetric analysis (TGA) demonstrates thermal stability up to ~ 300 °C, with only a minor (~ 6%) extrinsic mass loss near 287 °C. Optical characterization reveals a direct bandgap of 4.09 eV, a low Urbach energy (0.104 eV), and strong blue photoluminescence (451.6 nm) with a large Stokes shift and unusually broad full width at half maximum (FWHM) (~ 250 nm), indicative of pronounced electron–phonon coupling and self-trapped excitons (STEs). Time-resolved PL yields two recombination channels ( τ 1  = 10.07 ns, τ 2  = 2.27 ns), consistent with free/shallow carriers and stabilized STEs. Electrical measurements show symmetric Current–voltage (I–V) behavior and UV-enhanced conduction dominated at high fields by a trap-limited space-charge-limited current (SCLC) mechanism ( m  ≈ 2.45, V TFL  ≈ 13.2 V). Under 365 nm excitation (photon energy below E g ), the device exhibits a reproducible but modest photoconductive response, mediated by sub-gap absorption pathways—namely Urbach-tail states, defect-related channels, and STE manifolds—rather than direct interband excitation. Accordingly, the responsivity at 365 nm is modest (R ≈ 4.38 × 10 −5  A W −1 ), requires a higher bias (+ 20 V), and shows trap-limited SCLC signatures, fully consistent with a weak sub-gap absorption regime. Collectively, the structural robustness, stable excitonic blue emission, and sub-gap mediated UV photoresponse position CsCu 2 Br 3 as a durable lead-free platform for UV optoelectronics, with maximum efficiency expected under deep-UV excitation (≈ 280–320 nm), to be mapped in future calibrated spectral responsivity studies.

Thermal transport in converging/diverging channels finds applications in various engineering fields, including heat exchangers, microfluidics, and biomedical devices, due to their ability to enhance heat transfer and control fluid flow. The use of nanoparticles cannot be circumvented because of their promising characteristics. These materials widely used in applied thermal engineering, to enhance or control thermal transport in machinery, transformer, chemical engineering. The flow in tank designed for oblique walls is common in practical situations and the dynamics of fluids are essential to maintain it for desired purposes. Therefore, the concept of traditional nanoliquid models is extended for advanced ternary fluids. To acquire the beneficial model results, the momentum slip, viscous dissipative phenomena and elastic walls conditions along with new innovative ternary nanoliquid characteristics are adopted for the model formulation. Then, the results with special emphasis on thermal, entropy optimization, shear drag and heat transport rate are obtained via RK scheme and a comprehensive physical description is provided. The ternary nanoliquid possesses remarkable thermal transport and dominant behaviour is inspected for entropy, shear drag and heat transfer rate at the elastic walls under and . These fluids are recommended for different application especially in applied thermal engineering.

The NAC family is one of the largest plant-specific transcription factor families, and some of its members are known to play major roles in plant development and response to biotic and abiotic stresses. Here, we inventoried 488 NAC members in bread wheat (Triticum aestivum). Using the recent release of the wheat genome (IWGS RefSeq v1.0), we studied duplication events focusing on genomic regions from 4B-4D-5A chromosomes as an example of the family expansion and neofunctionalization of TaNAC members. Differentially expressed TaNAC genes in organs and in response to abiotic stresses were identified using publicly available RNAseq data. Expression profiling of 23 selected candidate TaNAC genes was studied in leaf and grain from two bread wheat genotypes at two developmental stages in field drought conditions and revealed insights into their specific and/or overlapping expression patterns. This study showed that, of the 23 TaNAC genes, seven have a leaf-specific expression and five have a grain-specific expression. In addition, the grain-specific genes profiles in response to drought depend on the genotype. These genes may be considered as potential candidates for further functional validation and could present an interest for crop improvement programs in response to climate change. Globally, the present study provides new insights into evolution, divergence and functional analysis of NAC gene family in bread wheat.

In this study, we report the structural, optical, and electrical properties of lead-free CsSnBr 3 perovskite synthesized via melt growth. UV–Vis absorption and Tauc plot analysis reveal a direct optical band gap of 1.75 eV. Photoluminescence and time-resolved PL measurements confirm efficient radiative recombination and reveal carrier lifetimes on the nanosecond scale. Impedance spectroscopy and dielectric analysis across 300–400 K show non-Debye relaxation and support a small-polaron hopping conduction mechanism, with activation energies consistent with frequency-dependent conductivity. A unique correlation is established between the optical carrier dynamics and low-frequency electrical behavior. These findings contribute to a deeper understanding of charge transport in CsSnBr 3 and support its potential use in lead-free optoelectronic applications such as photodetectors and solar energy conversion.

Energy hubs, with their diverse regeneration and storage sources, can engage concurrently in energy transfer and storage. It is anticipated that managing the energy of these hubs within energy networks could enhance economic, environmental, and technical metrics. This article explains how electrical and thermal network hubs manage their energy consumption in the context of the multi-criteria objectives of efficiency, sustainability, reliability of the network operator, and operation. The hubs have solar power, a bio-waste unit, and wind turbines among other sustainable energy sources. They have compressed air, heat, and hydrogen storage units installed. Thermal energy is produced by means of a heat pump from electrical energy. Combining heat and power technology is used by both the bio-waste unit and the hydrogen storage unit. Subject to the operating model and reliability restrictions of these networks, the suggested strategy seeks to reduce the overall estimated costs of energy procurement, dependability, and emissions within the designated networks. Additional constraints of the problem encompass the operational model of sources and storages, conceptualized as an energy hub. This plan takes into account uncertainties about demand, energy costs, renewable energy sources, and the availability of network equipment. Reliability is accurately predicted by scenario-based techniques to stochastic optimization. The simultaneous modeling of economic, operational, reliability, and environmental indicators as well as the evaluation of the capabilities of heat pumps, biowaste units, compressed air and hydrogen storage units, and heat pumps in the hub performance are seen to be the new aspects of this approach. In summary, numerical results validate the usefulness of the proposed approach in enhancing the technical and financial aspects of thermal and electrical networks via efficient hub energy management. The incorporation of renewable hubs, equipped with storage units and heat pumps, has led to improvements in the economic, operational, reliability, and environmental conditions by approximately 44.1%, 28–90%, 85.6%, and 72.1% respectively, in comparison to load distribution studies.