Behavior modeling for a new flexure-based mechanism by Hunger Game Search and physics-guided artificial neural network.

Compliant mechanism has some advantages and has been widely applied in many accurate positioning systems. However, modeling the compliant mechanism behavior has suffered from many challenges, such as unstable results, and the limitation of training data set. In the field of compliant mechanism modeling, there has been no research interested in applying meta-heuristics optimization algorithms to optimize the weights and biases of the neural network globally.

Microproteins: emerging roles as antibiotics.

Recent advances in computational prediction and experimental techniques have detected previously unknown microproteins, particularly in the human microbiome. These small proteins, produced by diverse microbial species, are emerging as promising candidates for new antibiotics.

On the steroids extracted from soft corals against the NS3/4A protease of hepatitis C virus.

The Hepatitis C virus (HCV) causes a variety of liver diseases, making it a global health issue that affects millions of people in the world. The NS3/4A protease has been considered a common target for anti-HCV treatments using direct-acting antiviral agents and their derivatives. Of the natural products that have been proposed for novel therapeutic product alternatives, the soft coral compounds are found to contain steroids with various bioactive properties for effective HCV treatments.

AI Methods for Antimicrobial Peptides: Progress and Challenges.

Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti-infective activity, particularly in preclinical mouse models.

Investigating steeply inclined abandoned mines for unlocking the secrets of water level recovery.

With the growing emphasis on environmental protection, many coal mines in northern China were closed. However, the cessation of pumping operations in those closed mines has caused a rise in groundwater levels, giving rise to various safety and environmental concerns. Understanding the patterns of water level recovery is vital for effectively managing abandoned mine sites and ensuring the uninterrupted production of adjacent coal mines.

Thermodynamics of Micelle Formation of Selected Homologous 7-Alkyl Derivatives of Na-Cholate in Aqueous Solution: Steroid Skeleton and the Alkyl Chain Conformation.

Bile acid salts are steroid biosurfactants that build relatively small micelles compared to surfactants with an alkyl chain due to the rigid conformation of the steroid skeleton. In order to increase the capacity of micellar solubilization of the hydrophobic molecular guest, certain C7 alkyl derivatives were synthesized. Namely, introducing an alkyl group in the C7 position of the steroid skeleton results in a more effective increase in the micelle's hydrophobic domain (core) than the introduction in the C3 position.

Effective removal of atrazine pesticide residue from wastewater using transition metal-doped zinc oxide photocatalyst.

Frequent use of pesticides results in the release of large quantities of their residues in the environment, raising various concerns for humans and the ecosystem. This work introduces a simple and cost-effective method for removal atrazine pesticide residue (APR) from agricultural wastewater using Cu doped-ZnO photocatalyst (Cu-ZnO). The modification of ZnO with Cu significantly improved its optical and photoelectrochemical properties, with the band gap narrowing from 3.

Discovery of antibiotics in the archaeome using deep learning.

Antimicrobial resistance (AMR) is one of the greatest threats facing humanity, making the need for new antibiotics more critical than ever. While most antibiotics have traditionally been derived from bacteria and fungi, archaea-a distinct and underexplored domain of life-offer a largely untapped reservoir for antibiotic discovery. In this study, we leveraged deep learning to systematically explore the archaeome, uncovering promising new candidates for combating AMR.

Mining biology for antibiotic discovery.

The rise of antibiotic resistance calls for innovative solutions. The realization that biology can be mined digitally using artificial intelligence has revealed a new paradigm for antibiotic discovery, offering hope in the fight against superbugs.

Gold nanotube self-assemblage from tubular W@Au cluster.

Context: While the pure Au cluster tends to exist in a 3D cage shape, the doubly doped W@Au is found to prefer a tubular form containing three five-membered Au rings stabilized by the W dimer vertically placed inside a tube-like Au framework. Formation of self-assembled nanotubes containing five-membered Au rings from the tube-like W@Au cluster is also feasible, and such a tubular structure constitutes an ideal building block for generating gold-assembled nanotubes, providing us with a useful approach for designing some novel tailor-made materials with interesting optoelectronic properties.

Methods: Density functional theory (DFT) calculations using the TPSS functional and the cc-pVDZ-PP basis set are employed to determine some noticeable effects of the doping of the W dimer on the structure, stability, and optical properties of pure Au16 gold clusters.

Optimizing flexural strength of RC beams with recycled aggregates and CFRP using machine learning models.

This paper investigates the flexural bearing behavior of reinforced concrete beams through experimental analysis and advanced machine learning predictive models. The primary problem centers around understanding how varying compositions of construction materials, particularly the inclusion of recycled aggregates and carbon fiber-reinforced polymer (CFRP), affect the structural performance of concrete beams. Eight beams, including those with natural aggregates, recycled aggregates, fly ash, and CFRP, were tested.

Improved photocatalytic decomposition of carbaryl pesticide in wastewater using ZnO nanorods.

This study explores the enhanced photocatalytic performance of ZnO nanorods (ZnO-R) for degrading the carbaryl pesticide (CB) in wastewater. For comparison, commercial ZnO (ZnO-C) was used to evaluate the differences in the photocatalytic decomposition of CB between ZnO-R and ZnO-C. The results regarding the material properties demonstrated that ZnO-R enhances CB removal performance due to its unique rod shape, which extends light absorption and improves electron-hole separation.

An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers.

Accurately estimating high-dimensional permeability (k) fields through data assimilation is critical for minimizing uncertainties in groundwater flow and solute transport simulations. However, designing an effective monitoring network to obtain diverse system responses in heterogeneous aquifers for data assimilation presents significant challenges. To investigate the influence of different measurement types (hydraulic heads, solute concentrations, and permeability) and monitoring strategies on the accuracy of permeability characterization, this study integrates a deep learning-based surrogate modeling approach and the entropy-based maximum information minimum redundancy (MIMR) monitoring design criterion into a data assimilation framework.

Exploring the versatility of MoSe/WS heterostructures.

Two-dimensional materials and their combined heterostructures have paved the way for numerous next-generation electronic and optoelectronic applications. Herein, we performed first principles calculations to computationally design the MoSe/WS heterostructure and consider its geometric structure, electronic properties and contact behavior, as well as the effects of the electric fields and strain. Our results show that the MoSe/WS heterostructure is energetically, thermodynamically and mechanically stable.

Antibiofilm approaches as a new paradigm for treating infections.

The lack of effective antibiotics for drug-resistant infections has led the World Health Organization to declare antibiotic resistance a global priority. Most bacterial infections are caused by microbes growing in structured communities called biofilms. Bacteria growing in biofilms are less susceptible to antibiotics than their planktonic counterparts.

Peptides from non-immune proteins target infections through antimicrobial and immunomodulatory properties.

Encrypted peptides (EPs) have been recently described as a new class of antimicrobial molecules. They have been found in numerous organisms and have been proposed to have a role in host immunity and as alternatives to conventional antibiotics. Intriguingly, many of these EPs are found embedded in proteins unrelated to the immune system, suggesting that immunological responses extend beyond traditional host immunity proteins.

Triple-chain boron clusters: lengthening phosphorus doping and enhancing stability through {P} by {CH} substitution.

This theoretical study presents novel insights into the doping of boron clusters with an increasing number of dopant atoms, ranging from 1 to 4, that preserve the integrity of the original boron framework. The triple-chain forms of clusters B and B remain unchanged upon sequential addition of P atoms, showcasing a perfect isolobal substitution of {P} with {CH}. Similarities in the number of delocalized electrons are observed between pure and doped boron clusters, alongside the subsequent substitution of {P} with {CH}.

Electric field enhances the electronic and diffusion properties of penta-graphene nanoribbon anodes in lithium-ion batteries.

Enhancement of the ionic conductivity and reduction of diffusion barriers of lithium-ion batteries are crucial for improving the performance of the fast-growing energy storage devices. Recently, the fast-charging capability of commercial-like lithium-ion anodes with the smallest modification of the current manufacturing technology has been of great interest. We used first principles methods computations with density functional theory and the climbing image-nudged elastic band method to evaluate the impact of an external electric field on the stability, electronic band gap, ionic conductivity, and lithium-ion diffusion coefficient of penta-graphene nanoribbons upon lithium adsorption.

Imprints of massive black-hole binaries on neighbouring decihertz gravitational-wave sources.

The most massive black holes in our Universe form binaries at the centre of merging galaxies. The recent evidence for a gravitational-wave (GW) background from pulsar timing may constitute the first observation that these supermassive black-hole binaries (SMBHBs) merge. Yet, the most massive SMBHBs are out of reach of interferometric GW detectors and are exceedingly difficult to resolve individually with pulsar timing.

Variational benchmarks for quantum many-body problems.

The continued development of computational approaches to many-body ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. In this work, we introduce a metric of variational accuracy, the V-score, obtained from the variational energy and its variance. We provide an extensive curated dataset of variational calculations of many-body quantum systems, identifying cases where state-of-the-art numerical approaches show limited accuracy and future algorithms or computational platforms, such as quantum computing, could provide improved accuracy.