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Effect of loading rate and arc boundary on dynamic crack arrest behavior of brittle material under dynamic loads
A MaskFormer EfficientNet instance segmentation approach for crowd counting
Synergistic effect of MWCNTs and GO as a reinforcing phase on copper slag-based cement composites
Functional T cell response to COVID-19 vaccination with or without natural infection with SARS-CoV-2 in adults and children
Dynamic performance of quasi-zero-stiffness isolator with displacement-dependent electromagnetic shunt damping
Rapid and robust validation of pooled CRISPR knockout screens using CelFi
R-function method and variational method for the bending problem of functionally graded plates with fixed supports and complex shapes
Individual and combined contamination of the toxic metals in commercial cat and dog food
Metal–support frontier orbital interactions in single-atom catalysis
Coherent manipulation of Goos–Hänchen shifts by forward and backward currents of complex conductivity in chiral medium
Near-field photon entanglement in total angular momentum
Compact flexible linear stepping motors
Optimization of time and energy in straight one-sided robotic assembly lines
Abstract Robotic assembly lines serve as a foundational element of modern manufacturing, facilitating the efficient production of high-quality goods. Reducing the energy consumption of robots in these assembly lines is essential to promoting greener manufacturing practices, lowering costs, and achieving global energy efficiency goals. This study seeks to create a model that optimizes robotic assembly line systems by minimizing cycle time and energy consumption, either independently or simultaneously. The research assumes an unlimited supply of various robot types, each with distinct variants, processing times, and energy demands for specific tasks. The problem is modeled using Integer Linear Programming (ILP) in the LINGO (21) solver. For multi-objective scenarios involving both cycle time and energy consumption, a weighted sum approach is applied to convert the problem into a single-objective format. To tackle large-scale problems more effectively, several concepts and rules are proposed to accelerate data processing. The results demonstrated improved performance compared to benchmark problems. The analysis indicated that reducing cycle time contributes to lower energy consumption, driven by an increase in the number of stations and robots. Additionally, the Pareto front analysis of cycle time and energy consumption revealed that energy usage remains nearly constant across a wide range of cycle times.
AI enhancing prefabricated aesthetics and low carbon coupled with 3D printing in chain hotel buildings from multidimensional neural networks
Abstract There are approximately 70,000 economy chain hotels worldwide, generating about 300 million tons of carbon dioxide annually. While reducing carbon emissions can lower energy consumption, these hotels must also continually attract guests to ensure revenue growth and achieve sustainable development. This study focuses on the application of Artificial Intelligence (AI) in the prefabricated renovation of hotels, investigating how AI plays a crucial role in coupling low-carbon construction and aesthetic design. Using multidimensional algorithms within machine learning (ML), neural networks (NN), and statistical modeling (SM), this paper analyzes the impact of AI-driven prefabricated room renovations on tourist satisfaction and carbon emissions. The results indicate that AI can not only optimize energy consumption and structural efficiency in the renovation process but also achieve low-carbon goals while maintaining high-quality aesthetic designs. This study offers new theoretical insights into the integration of low-carbon and aesthetic design, filling gaps in the current literature, providing a pathway for achieving sustainable development goals (SDG 7, 8, and 12), and offering valuable implications for robotic intelligent construction and 3D printing in prefabricated buildings industry.
MYC ecDNA promotes intratumour heterogeneity and plasticity in PDAC
Longitudinal association of community and residential environment with the risk of cognitive impairment in middle-aged and older Chinese adults
Improved security for IoT-based remote healthcare systems using deep learning with jellyfish search optimization algorithm
Activation profile of the Atlantic salmon (Salmo salar) calcium-sensing receptor (Casr) by selected L-amino acids
Abstract In mammals, the calcium-sensing receptor (CaSR) is involved in nutrient sensing and modulated by several amino acids. In teleosts, sequence homologues of the mammalian CaSR have been described but their function in sensing amino acids remains elusive, including in Atlantic salmon (Salmo salar), an important aquaculture species. This study investigated the activation of Atlantic salmon Casr (asCasr)-mediated signaling pathways—Gq, Gi, and ERK1/2—by six selected L-amino acids (histidine, tryptophan, phenylalanine, isoleucine, leucine and valine) and by Ca2+. Using a Flp-In-HEK293 cell line stably expressing asCasr, we confirmed activation of all three pathways. L-histidine, L-phenylalanine, and L-tryptophan triggered Gi signaling independent of Ca²⁺. Notably, no Ca²⁺ concentrations induced Gi activation, but IP1 production increased in a concentration-dependent manner. L-histidine was the only amino acid to activate the Gq pathway without Ca²⁺, and this response was amplified by the presence of Ca²⁺. In the presence of 2.5 mM Ca²⁺, L-phenylalanine and L-tryptophan also activated Gq signaling in a concentration-dependent manner. Additionally, in the presence of 10 mM Ca²⁺, L-histidine, L-phenylalanine, and L-tryptophan triggered ERK phosphorylation. These findings establish asCasr as a functional homologue of mammalian CaSR, activated in a concentration-dependent manner by L-amino acids with an aromatic ring.