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There are several methods for modeling water quality parameters, with data-based methods being the focus of research in recent decades. The current study aims to simulate water quality parameters using modern artificial intelligence techniques, to enhance the performance of machine learning techniques using wavelet theory, and to compare these techniques to other widely used machine learning techniques. EC, Cl, Mg, and TDS water quality parameters were modeled using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The study area in the present research is Gao-ping River in Taiwan. In the training state, using hybrid models with wavelet transform improved the accuracy of ANN models from 8.1 to 22.5% and from 25.7 to 55.3% in the testing state. In addition, wavelet transforms increased the ANFIS model's accuracy in the training state from 6.7 to 18.4% and in the testing state from 9.9 to 50%. Using wavelet transform improves the accuracy of machine learning model results. Also, the WANFIS (Wavelet-ANFIS) model was superior to the WANN (Wavelet-ANN) model, resulting in more precise modeling for all four water quality parameters. HIGHLIGHTS The precision of machine learning models for estimating pollutant parameters in rivers has been investigated.; For the years 1992–2020, monthly qualitative data were utilized.; Four pollutant parameters are estimated with ANN and ANFIS models and hybrid models with wavelet transform.; The result indicated that the hybrid models with wavelet are more accurate.;

Genetic biocontrol technologies present promising and eco-friendly strategies for the management of pest and insect-transmitted diseases. Although considerable advancements achieve in gene drive applications targeting mosquitoes, endeavors to combat agricultural pests have been somewhat restricted. Here, we identify that the testis-specific serine/threonine kinases (TSSKs) family is uniquely expressed in the testes of Cydia pomonella, a prominent global invasive species. We further generated male moths with disrupted the expression of TSSKs and those with TSSKs disrupted using RNA interference and CRISPR/Cas9 genetic editing techniques, resulting in significant disruptions in spermiogenesis, decreased sperm motility, and hindered development of eggs. Further explorations into the underlying post-transcriptional regulatory mechanisms reveales the involvement of lnc117962 as a competing endogenous RNA (ceRNA) for miR-3960, thereby regulating TSSKs. Notably, orchard trials demonstrates that the release of male strains can effectively suppress population growth. Our findings indicate that targeting TSSKs could serve as a feasible avenue for managing C. pomonella populations, offering significant insights and potential strategies for controlling invasive pests through genetic sterile insect technique (gSIT) technology.

There are several methods for modeling water quality parameters, with data-based methods being the focus of research in recent decades. The current study aims to simulate water quality parameters using modern artificial intelligence techniques, to enhance the performance of machine learning techniques using wavelet theory, and to compare these techniques to other widely used machine learning techniques. EC, Cl, Mg, and TDS water quality parameters were modeled using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The study area in the present research is Gao-ping River in Taiwan. In the training state, using hybrid models with wavelet transform improved the accuracy of ANN models from 8.1 to 22.5% and from 25.7 to 55.3% in the testing state. In addition, wavelet transforms increased the ANFIS model's accuracy in the training state from 6.7 to 18.4% and in the testing state from 9.9 to 50%. Using wavelet transform improves the accuracy of machine learning model results. Also, the WANFIS (Wavelet-ANFIS) model was superior to the WANN (Wavelet-ANN) model, resulting in more precise modeling for all four water quality parameters.

Ammonia-oxidizing archaea (AOA) are among the most ubiquitous and abundant archaea on Earth, widely distributed in marine, terrestrial, and geothermal ecosystems. However, the genomic diversity, biogeography, and evolutionary process of AOA populations in subsurface environments are vastly understudied compared to those in marine and soil systems. Here, we report a novel AOA order Candidatus (Ca.) Nitrosomirales which forms a sister lineage to the thermophilic Ca. Nitrosocaldales. Metagenomic and 16S rRNA gene-read mapping demonstrates the abundant presence of Nitrosomirales AOA in various groundwater environments and their widespread distribution across a range of geothermal, terrestrial, and marine habitats. Terrestrial Nitrosomirales AOA show the genetic capacity of using formate as a source of reductant and using nitrate as an alternative electron acceptor. Nitrosomirales AOA appear to have acquired key metabolic genes and operons from other mesophilic populations via horizontal gene transfer, including genes encoding urease, nitrite reductase, and V-type ATPase. The additional metabolic versatility conferred by acquired functions may have facilitated their radiation into a variety of subsurface, marine, and soil environments. We also provide evidence that each of the four AOA orders spans both marine and terrestrial habitats, which suggests a more complex evolutionary history for major AOA lineages than previously proposed. Together, these findings establish a robust phylogenomic framework of AOA and provide new insights into the ecology and adaptation of this globally abundant functional guild.

Asymmetric spin–orbit interaction (ASOI) breaks the limitations in conjugate symmetry of traditional geometric phase metasurfaces, bringing new opportunities for various applications such as spin-decoupled holography, imaging, and complex light field manipulation. Since anisotropy is a requirement for spin–orbit interactions, existing ASOI mainly relies on meta-atom with C1 and C2 symmetries, which usually suffer from an efficiency decrease caused by the propagation phase control through the structural size. Here, we demonstrate for the first time that ASOI can be realized in meta-atoms with rotational symmetry ≥3 by combining the generalized geometric phase with the propagation phase. Utilizing an all-metallic configuration, the average diffraction efficiency of the spin-decoupled beam deflector based on C3 meta-atoms reaches ∼84 % in the wavelength range of 9.3–10.6 μm, which is much higher than that of the commonly used C2 meta-atoms with the same period and height. This is because the anisotropy of the C3 metasurface originates from the lattice coupling effect, which is relatively insensitive to the propagation phase control through the meta-atom size. A spin-decoupled beam deflector and hologram meta-device were experimentally demonstrated and performed well over a broadband wavelength range. This work opens a new route for ASOI, which is significant for realizing high-efficiency and broadband spin-decoupled meta-devices.

[Objective] Considering the strategic guideline of “Ocean Power”, a scientific measurement of the operational efficiency of China’s sea-related listed companies and support for sea-related listed companies to be better and stronger are of great significance to consolidate the micro foundation of the development of the marine economy and promote the expansion of the space for the development of the blue economy. [Methods] This study used the explainable boosting machine (EBM) model based on Pearson correlation coefficient, combined with the three-hierarchy meta-frontier framework, to scientifically measure the operational efficiency of China’s sea-related listed companies during the period of 2014-2022, and further decomposed the inefficiency loss to explore its internal and external influencing factors. [Results] The study showed that: (1) Overall, the operational efficiency of Chinese sea-related listed companies was on an upward trend and showed obvious stage characteristics. (2) From the perspective of industry-ownership heterogeneity, the operational efficiency of emerging sea-related companies was leading in the early stages, but the operational efficiency of traditional sea-related companies showed a significant trend of subsequent growth; the operational efficiency of private sea-related companies was at the forefront, and basically formed a pattern of development in which private enterprises were leading and state-owned enterprises were following. (3) From the perspective of inefficiency decomposition, management inefficiency loss was the key reason affecting the improvement of operational efficiency of sea-related listed companies, followed by industrial heterogeneity efficiency loss and ownership heterogeneity efficiency loss. (4) Based on the analysis of influencing factors, the management inefficiency loss of sea-related listed companies was significantly negatively correlated with enterprise size and positively correlated with business solvency, while the level of regional economic development significantly and negatively affected their technical inefficiency loss. [Conclusion] During the 14th Five Year Plan period, we should further optimize the layout of the marine industry, improve the market-oriented development environment, and enhance the management level of the enterprises, to explore the effective path to promoting the operational efficiency of sea-related listed companies.

AbstractHigh translucent zirconia (HTZ) has excellent mechanical properties, biocompatibility, and good semi-translucency making it an ideal material for aesthetic anterior dental implant abutments without antibacterial properties. In the oral environment, the surface of the abutment material is susceptible to microbial adhesion and biofilm formation, which can lead to infection or peri-implantitis and even implant failure. This study aims to promote the formation of a biological seal at the implant-soft tissue interface by modifying the HTZ surface, using the load-bearing capacity of the aluminosilicate porous structure and the broad-spectrum antibacterial effect of silver nanoparticles to prevent peri-implant bacterial infection and inflammation and to improve the success rate and prolong the use of the implant. FE-SEM (field emission scanning electron microscopes), EDS (energy dispersive spectroscopy), and XPS (X-ray photoelectron spectroscopy) results showed that aluminosilicate non-vacuum sintering can form open micro- and nanoporous structures on HTZ surfaces, and that porous aluminosilicate coatings obtain a larger number, smaller size, and more uniformly shaped silver nanoparticles than smooth aluminosilicate coatings, and could be deposited deeper in the coating. The ICP-AES (inductively coupled plasma-atomic emission spectroscopy) results showed that the early silver ion release of both the smooth silver coating and the porous silver coating was obvious, the silver ion concentration released by the former was higher than that of the latter. However, the silver ion concentration released by the porous silver coating was higher than that of the smooth coating when the release slowed down. Both smooth and porous silver coatings both inhibited E. coli (Escherichia coli), S. aureus (Staphylococcus aureus), and L. acidophilus (L. acidophilus), and porous silver coatings had stronger antibacterial properties. The silver coating was successfully constructed on the surface of HTZ, through aluminium silicate sintering and silver nitrate solution impregnation. It was found that the high concentration environment of silver nitrate solution was more advantageous for nano-Ag deposition, and the non-vacuum sintered porous surface was able to obtain a larger number of nano-Ag particles with smaller sizes. The porous Ag coating exhibited superior antibacterial properties. It was suggested that the HTZ with silver coating had clinical application, and good antibacterial properties that can improve the survival rate and service life of implants.

By leveraging shared entanglement between a pair of qubits, one can teleport a quantum state from one particle to another. Recent advances have uncovered an intrinsically many-body generalization of quantum teleportation, with an elegant and surprising connection to gravity. In particular, the teleportation of quantum information relies on many-body dynamics, which originate from strongly interacting systems that are holographically dual to gravity; from the gravitational perspective, such quantum teleportation can be understood as the transmission of information through a traversable wormhole. Here, we propose and analyze a new mechanism for many-body quantum teleportation - dubbed peaked-size teleportation. Intriguingly, peaked-size teleportation utilizes precisely the same type of quantum circuit as traversable wormhole teleportation yet has a completely distinct microscopic origin: It relies upon the spreading of local operators under generic thermalizing dynamics and not gravitational physics. We demonstrate the ubiquity of peaked-size teleportation, both analytically and numerically, across a diverse landscape of physical systems, including random unitary circuits, the Sachdev-Ye-Kitaev model (at high temperatures), one-dimensional spin chains, and a bulk theory of gravity with stringy corrections. Our results pave the way toward using many-body quantum teleportation as a powerful experimental tool for (i) characterizing the size distributions of operators in strongly correlated systems and (ii) distinguishing between generic and intrinsically gravitational scrambling dynamics. To this end, we provide a detailed experimental blueprint for realizing many-body quantum teleportation in both trapped ions and Rydberg atom arrays; effects of decoherence and experimental imperfections are analyzed.