A novel approach for multivariate time series interval prediction of water quality at wastewater treatment plants.

This study proposes a novel approach for predicting variations in water quality at wastewater treatment plants (WWTPs), which is crucial for optimizing process management and pollution control. The model combines convolutional bi-directional gated recursive units (CBGRUs) with adaptive bandwidth kernel function density estimation (ABKDE) to address the challenge of multivariate time series interval prediction of WWTP water quality. Initially, wavelet transform (WT) was employed to smooth the water quality data, reducing noise and fluctuations.

Daily PM2.5 concentration prediction based on variational modal decomposition and deep learning for multi-site temporal and spatial fusion of meteorological factors.

Air pollution, particularly PM2.5, has long been a critical concern for the atmospheric environment. Accurately predicting daily PM2.

Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China.

Effective identification and regulation of water quality impact factors is essential for water resource management and environmental protection. However, the complex coupling of water quality systems poses a significant challenge to this task. This study proposes coherent model for water quality prediction, classification and regulation based on interpretable machine learning.

Exploring the Potential of DNA Computing for Complex Big Data Problems: A Case Study on the Traveling Car Renter Problem.

The traveling car renter problem (TCRP) is a variant of the Traveling Salesman Problem (TSP) wherein the salesman utilizes rented cars for travel. The primary objective of this problem is to identify a solution that minimizes the cumulative operating costs. Given its classification as a non-deterministic polynomial (NP) problem, traditional computers are not proficient in effectively resolving it.

A novel framework for high resolution air quality index prediction with interpretable artificial intelligence and uncertainties estimation.

Accurate air quality index (AQI) prediction is essential in environmental monitoring and management. Given that previous studies neglect the importance of uncertainty estimation and the necessity of constraining the output during prediction, we proposed a new hybrid model, namely TMSSICX, to forecast the AQI of multiple cities. Firstly, time-varying filtered based empirical mode decomposition (TVFEMD) was adopted to decompose the AQI sequence into multiple internal mode functions (IMF) components.

A water quality prediction model based on signal decomposition and ensemble deep learning techniques.

Accurate water quality predictions are critical for water resource protection, and dissolved oxygen (DO) reflects overall river water quality and ecosystem health. This study proposes a hybrid model based on the fusion of signal decomposition and deep learning for predicting river water quality. Initially, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to split the internal series of DO into numerous internal mode functions (IMFs).

High-mobility spin-polarized two-dimensional electron gas at the interface of EuTiO/SrTiOheterostructures.

Spin polarization of two-dimensional electron gas (2DEG) at the interface of EuTiO/SrTiO(STO) heterostructures has been theoretical predicted and experimentally observed via x-ray magnetic circular dichroism and polarized x-ray absorption spectroscopy, which, however, is lack of magnetotransport evidence. Here, we report the fabrication of high-quality EuTiO/STO heterostructures by depositing antiferromagnetic insulating EuTiOthin films onto STO substrates. Shubnikov-de Haas oscillation, Hall, and magnetoresistance (MR) measurements show that the interface is not only highly conducting, with electron mobility up to5.

A Novel Algorithm for Solving the Prize Collecting Traveling Salesman Problem Based on DNA Computing.

DNA computing is a new pattern of computing that combines biotechnology and information technology. As a new technology born in less than three decades, it has developed at an extremely rapid rate, which can be attributed to its advantages, including high parallelism, powerful data storage capacity, and low power consumption. Nowadays, DNA computing has become one of the most popular research fields worldwide and has been effective in solving certain combinatorial optimization problems.

Solving the Min-Max Clustered Traveling Salesmen Problem Based on Genetic Algorithm.

The min-max clustered traveling salesmen problem (MMCTSP) is a generalized variant of the classical traveling salesman problem (TSP). In this problem, the vertices of the graph are partitioned into a given number of clusters and we are asked to find a collection of tours to visit all the vertices with the constraint that the vertices of each cluster are visited consecutively. The objective of the problem is to minimize the weight of the maximum weight tour.

Correction: Magnetotransport and magnetic properties of Cr-modified MnSb epitaxial thin films.

Correction for 'Magnetotransport and magnetic properties of Cr-modified MnSb epitaxial thin films' by Ting-Wei Chen , , 2023, , 5785-5794, https://doi.org/10.1039/D2CP05442F.

Spin Reorientation Transition and Negative Magnetoresistance in Ferromagnetic NdCrSb Single Crystals.

High-quality NdCrSb single crystals are grown using a Sn-flux method, for electronic transport and magnetic structure study. Ferromagnetic ordering of the Nd and Cr magnetic sublattices are observed at different temperatures and along different crystallographic axes. Due to the Dzyaloshinskii-Moriya interaction between the two magnetic sublattices, the Cr moments rotate from the axis to the axis upon cooling, resulting in a spin reorientation (SR) transition.

Magnetotransport and magnetic properties of Cr-modified MnSb epitaxial thin films.

High-quality MnCrSb ( = 0.01, 0.04, and 0.

A Parallel DNA Algorithm for Solving the Quota Traveling Salesman Problem Based on Biocomputing Model.

The quota traveling salesman problem (QTSP) is a variant of the traveling salesman problem (TSP), which is a classical optimization problem. In the QTSP, the salesman visits some of the cities to meet a given sales quota while having minimized travel costs. In this paper, we develop a DNA algorithm based on Adleman-Lipton model to solve the quota traveling salesman problem.

Solving the Family Traveling Salesperson Problem in the Adleman-Lipton Model Based on DNA Computing.

The Family Traveling Salesperson Problem (FTSP) is a variant of the Traveling Salesperson Problem (TSP), in which all vertices are divided into several different families, and the goal of the problem is to find a loop that concatenates a specified number of vertices with minimal loop overhead. As a Non-deterministic Polynomial Complete (NP-complete) problem, it is difficult to deal with it by the traditional computing. On the contrary, as a computer with strong parallel ability, the DNA computer has incomparable advantages over digital computers when dealing with NP problems.

A novel bio-heuristic computing algorithm to solve the capacitated vehicle routing problem based on Adleman-Lipton model.

DNA computing, as one of potential means to solve complicated computational problems, is a new field of interdisciplinary research, including computational mathematics, parallel algorithms, bioinformatics. Capacitated vehicle routing problem is one of famous NP-hard problems, which includes determining the path of a group same vehicles serving a set of clients, while minimizing the total transportation cost. Based on the bio-heuristic computing model and DNA molecular manipulations, parallel biocomputing algorithms for solving capacitated vehicle routing problem are proposed in this paper.

All-fiber broadband multiplexer based on an elliptical ring core fiber structure mode selective coupler.

A novel all-fiber broadband mode multiplexer based on an elliptical ring core fiber structure mode selective coupler (MSC) is proposed and analyzed numerically. The MSC is composed of a core-doped single-mode fiber and a polarization-maintaining elliptical ring-shaped core few-mode fiber (FMF). The mode multiplexing can be implemented by a single LP mode excitation that does not introduce insertion loss, which can simplify and promote the multiplexer.

A new parallel DNA algorithm to solve the task scheduling problem based on inspired computational model.

As a promising approach to solve the computationally intractable problem, the method based on DNA computing is an emerging research area including mathematics, computer science and molecular biology. The task scheduling problem, as a well-known NP-complete problem, arranges n jobs to m individuals and finds the minimum execution time of last finished individual. In this paper, we use a biologically inspired computational model and describe a new parallel algorithm to solve the task scheduling problem by basic DNA molecular operations.

A Parallel Biological Optimization Algorithm to Solve the Unbalanced Assignment Problem Based on DNA Molecular Computing.

The unbalanced assignment problem (UAP) is to optimally resolve the problem of assigning n jobs to m individuals (m < n), such that minimum cost or maximum profit obtained. It is a vitally important Non-deterministic Polynomial (NP) complete problem in operation management and applied mathematics, having numerous real life applications. In this paper, we present a new parallel DNA algorithm for solving the unbalanced assignment problem using DNA molecular operations.

A parallel algorithm for solving the n-queens problem based on inspired computational model.

DNA computing provides a promising method to solve the computationally intractable problems. The n-queens problem is a well-known NP-hard problem, which arranges n queens on an n × n board in different rows, columns and diagonals in order to avoid queens attack each other. In this paper, we present a novel parallel DNA algorithm for solving the n-queens problem using DNA molecular operations based on a biologically inspired computational model.

A new fast algorithm for solving the minimum spanning tree problem based on DNA molecules computation.

The minimum spanning tree (MST) problem is to find minimum edge connected subsets containing all the vertex of a given undirected graph. It is a vitally important NP-complete problem in graph theory and applied mathematics, having numerous real life applications. Moreover in previous studies, DNA molecular operations usually were used to solve NP-complete head-to-tail path search problems, rarely for NP-hard problems with multi-lateral path solutions result, such as the minimum spanning tree problem.

[Screening for chromosomal abnormalities using nuchal translucency measurement with materal serum biochemistry markers in first trimester].

Objective: To Investigate the performance of prenatal screening for chromosomal abnormalities in first trimester.

Methods: Maternal serum were collected from 2 739 pregnant women between 11 and 14 weeks gestation. Free beta human chorionic gonadotrophin(beta-hCG), pregnancy-associated plasma protein(PAPP-A) from materal serum were measured using time resolved fluorescence immunoassay(TRFIA) and fetal nuchal translucency(NT) thickness were measured using transabdominal or transvaginal ultrasound.

[Establishment of permanent lymphoblastoid cell lines of 47 patients with abnormal chromosome karyotype].

To increase the efficiency of in vitro transformation of human lymphocytes by Epstein-Barr virus (EBV) and establish permanent lymphoblastoid cell lines from patients with abnormal chromosome karyotype, B lymphoid cells were prepared from cryopreserved heparinized blood samples. The lymphoid cell pellet was resuspended with 0.5 ml medium of RPMI with 20% fetal calf serum(FCS), and added 2 ml virus-containing superatant of the EB virus-producing cell lines by filtrated, and mixed.