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. Secondly, multi-scale fuzzy entropy (MFE) was applied to evaluate the complexity of each IMF component and clustered them into high and low-frequency portions. In addition, the high-frequency portion was secondarily decomposed by successive variational mode decomposition (SVMD) to reduce volatility. Then, six air pollutant concentrations, namely CO, SO2, PM2.5, PM10, O3, and NO2, were used as inputs. The secondary decomposition and preliminary portion were employed as the outputs for the bidirectional long short-term memory network optimized by the snake optimization algorithm (SOABiLSTM) and improved Catboost (ICatboost), respectively. Furthermore, extreme gradient boosting (XGBoost) was applied to ensemble each predicted sub-model to acquire the consequence. Ultimately, we introduced adaptive kernel density estimation (AKDE) for interval estimation. The empirical outcome indicated the TMSSICX model achieved the best performance among the other 23 models across all datasets. Moreover, implementing the XGBoost to ensemble each predicted sub-model led to an 8.73%, 8.94%, and 0.19% reduction in RMSE, compared to SVM. Additionally, by utilizing SHapley Additive exPlanations (SHAP) to assess the impact of the six pollutant concentrations on AQI, the results reveal that PM2.5 and PM10 had the most notable positive effects on the long-term trend of AQI. We hope this model can provide guidance for air quality management.

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 PM2.5 concentrations is crucial for both environmental protection and public health. This study introduces a new hybrid model within the "Decomposition-Prediction-Integration" (DPI) framework, which combines variational modal decomposition (VMD), causal convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM), named as VCBA, for spatio-temporal fusion of multi-site data to forecast daily PM2.5 concentrations in a city. The approach involves integrating air quality data from the target site with data from neighboring sites, applying mathematical techniques for dimensionality reduction, decomposing PM2.5 concentration data using VMD, and utilizing Causal CNN and BiLSTM models with an attention mechanism to enhance performance. The final prediction results are obtained through linear aggregation. Experimental results demonstrate that the VCBA model performs exceptionally well in predicting daily PM2.5 concentrations at various stations in Taiyuan City, Shanxi Province, China. Evaluation metrics such as RMSE, MAE, and R2 are reported as 2.556, 1.998, and 0.973, respectively. Compared to traditional methods, this approach offers higher prediction accuracy and stronger spatio-temporal modeling capabilities, providing an effective solution for accurate PM2.5 daily concentration prediction.

Correction: Magnetotransport and magnetic properties of Cr-modified Mn2Sb epitaxial thin films.

Correction for 'Magnetotransport and magnetic properties of Cr-modified Mn2Sb epitaxial thin films' by Ting-Wei Chen et al., Phys. Chem. Chem. Phys., 2023, 25, 5785-5794, https://doi.org/10.1039/D2CP05442F.

Magnetotransport and magnetic properties of Cr-modified Mn2Sb epitaxial thin films.

High-quality Mn2-xCrxSb (x = 0.01, 0.04, and 0.1) epitaxial thin films were grown on SrTiO3 (STO) (001) single-crystal substrates using molecular beam epitaxy. Magnetotransport and magnetic measurements reveal that the x = 0.01 sample undergoes a quasi-ferrimagnetic (I) [Q-FIM(I)]-to-ferrimagnetic (II) [FIM(II)] spin reorientation (SR) transition and a giant magnetoresistance (MR) associated first-order ferrimagnetic(II)-to-antiferromagnetic (AFM) phase transition upon cooling, resulting in the AFM ground state with a weak in-plane net moment. Upon increasing the doping level from x = 0.01 to 0.1, both the SR transition and the first-order magnetic transition are suppressed. For x = 0.1, the former transition is suppressed, leaving only the Q-FIM(I)-to-AFM transition within the whole temperature region. TAFM-FIM shows almost similar changes upon the application of either in-plane or out-of-plane magnetic fields. TAFM-FIM values of the x = 0.01 and 0.04 samples are much higher than those of the Mn2-xCrxSb bulk with similar doping levels, which can be understood by the clamping effect from STO substrates. For each thin-film sample, the MR effect is observed near TAFM-FIM and disappears in the high temperature Q-FIM(I) phase and low temperature AFM phase, indicating that MR is related to the spin-dependent electron scattering during the first-order magnetic phase transition. Based on the magnetotransport and magnetic data, a magnetic phase diagram is established for the Mn2-xCrxSb films in the low doping level region.

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). Subsequently, we employed multi-scale fuzzy entropy (MFE) to compute the entropy values for each IMF component. Time-varying filtered empirical mode decomposition (TVFEMD) is used to further extract features in high-frequency subsequences after linearly aggregating the high-frequency sequences. Finally, support vector machine (SVM) and long short-term memory (LSTM) neural networks are used to predict low- and high-frequency subsequences. Moreover, by comparing it with single models, models based on 'single layer decomposition-prediction-ensemble' and combination models using different methods, the feasibility of the proposed model in predicting water quality data for the Xinlian section of Fuhe River and the Chucha section of Ganjiang River was verified. As a result, the combined prediction approach developed in this work has improved generalizability and prediction accuracy, and it may be used to forecast water quality in complicated waters.

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 LP01 mode excitation that does not introduce insertion loss, which can simplify and promote the multiplexer. The high refractive index difference between the elliptical annular region and the cladding of the FMF, as well as the ellipticity of the ring-shaped core of the FMF could not only decompose the degenerate modes, but also reduce the mode coupling of the adjacent spatial modes to eliminate the multiple-input-multiple-output digital-signal processing in the receiver of the mode division multiplexing system. Finally, a seven-mode multiplexer is modeled and optimized for operation covering the C+L band. Furthermore, the multiplexer provides a high coupling efficiency (>64% for the worst case) for the seven spatial modes, LP01, LP11a, LP11b, LP21a, LP21b, LP31a, and LP31b, and relatively high mode extinction ratios of more than 15 dB in the C-band.

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. We reasonably design flexible-length DNA strands representing different jobs and individuals, take appropriate steps, and get the solutions of the UAP in the proper length range and O(mn) time. We extend the application of DNA molecular operations and simultaneity to simplify the complexity of the computation.

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. In this study, we use the Adleman-Lipton model based on DNA computing for solving the Prize Collecting Traveling Salesman Problem (PCTSP) and demonstrate the feasibility of this model. Then, we design a simulation experiment of the model to solve some open instances of PCTSP. The results illustrate that the model can satisfactorily solve these instances. Finally, the comparison with the results of the Clustering Search algorithm and the Greedy Stochastic Adaptive Search Procedure/Variable Neighborhood Search method reveals that the optimal solutions obtained by this simulation experiment are significantly superior to those of the other two algorithms in all instances. This research also provides a method for proficiently solving additional combinatorial optimization problems.

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. Conversely, DNA computing exhibits unparalleled advantages when confronted with NP-hard problems. This paper presents a DNA algorithm, based on the Adleman-Lipton model, as a proposed approach to address TCRP. The solution for TCRP can be acquired by following a series of fundamental steps, including coding, interaction, and extraction. The time computing complexity of the proposed DNA algorithm is O(n2m) for TCRP with n cities and m types of cars. By conducting simulation experiments, the solutions for certain instances of TCRP are computed and compared to those obtained by alternative algorithms. The proposed algorithm further illustrates the potential of DNA computing, as a form of parallel computing, to address more intricate large-scale problems.

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. The decomposition-reconstruction module is used to transform non-stationary water quality series into stationary series while effectively reducing the feature dimensions. Spatiotemporal multi-source data is introduced by using the Maximum Information Coefficient (MIC) for feature selection. The Temporal Convolutional Network (TCN) is used to extract the temporal features of different variables, followed by the introduction of External Attention mechanism (EA) to construct the relationship between these features. Finally, the target water quality sequence is simulated using Gated Recurrent Unit (GRU). The proposed model was applied to Poyang Lake in China to predict six water quality indicators: ammonia nitrogen (NH3-N), dissolved oxygen (DO), pH, total nitrogen (TN), total phosphorus (TP), water temperature (WT). The water quality was then classified based on the prediction results using the XGBoost algorithm. The findings indicate that the proposed model's Nash-Sutcliff Efficiency (NSE) value ranges from 0.88 to 0.99, surpassing that of the benchmark model, and demonstrates strong interval prediction performance. The results highlight the superior performance of the XGBoost algorithm (with an accuracy of 0.89) in addressing water quality classification issues, particularly in cases of category imbalance. Subsequently, interpretability analysis using the SHapley Additive exPlanation (SHAP) method revealed that the model is capable of learning relationships between different variables and there exists a possibility of learning the physical laws. Ultimately, this study proposes a water quality regulation mechanism that improves TN and DO levels by stepwise changing the magnitude of water temperature, which significantly improves in the case of data limitations. In conclusion, this study presents an overall framework for integrating water quality prediction, classification and improvement for the first time, forming a complete set of water quality early warning and improvement management strategies. This framework provides new ideas and ways for lake water quality management.

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. For this problem, a two-stage solution method based on a genetic algorithm is designed according to the problem characteristics. The first stage is to determine the visiting order of the vertices within each cluster, by abstracting a TSP from the corresponding cluster and applying a genetic algorithm to solve it. The second stage is to determine the assignment of clusters to salesmen and the visiting order of the assigned clusters. In this stage, by representing each cluster as a node and using the result of the first stage and the ideas of greed and random, we define the distances between each two nodes and construct a multiple traveling salesmen problem (MTSP), and then apply a grouping-based genetic algorithm to solve it. Computational experiments indicate that the proposed algorithm can obtain better solution results for various scale instances and shows good solution performance.

High-mobility spin-polarized two-dimensional electron gas at the interface of EuTiO3/SrTiO3heterostructures.

Spin polarization of two-dimensional electron gas (2DEG) at the interface of EuTiO3/SrTiO3(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 EuTiO3/STO heterostructures by depositing antiferromagnetic insulating EuTiO3thin 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 to5.5×103cm2V-1s-1at 1.8 K, but also shows low-field hysteretic MR effects. MR of ∼9% is observed at 1.8 K and 20 Oe, which is one order of magnitude higher than those observed in other spin-polarized 2DEG oxide systems. Moreover, the heterostructures show ferromagnetic hysteresis loops. These results demonstrate that the high-mobility 2DEG is spin polarized, whose origin is attributed to the interfacial Ti3+-3dstates due to oxygen deficiency and the exchange interactions between interfacial Eu spins and itinerant Ti-3delectrons.

Spin Reorientation Transition and Negative Magnetoresistance in Ferromagnetic NdCrSb3 Single Crystals.

High-quality NdCrSb3 single crystals are grown using a Sn-flux method, for electronic transport and magnetic structure study. Ferromagnetic ordering of the Nd3+ and Cr3+ 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 b axis to the a axis upon cooling, resulting in a spin reorientation (SR) transition. The SR transition is reflected by the temperature-dependent magnetization curves, e.g., the Cr moments rotate from the b axis to the a axis with cooling from 20 to 9 K, leading to a decrease in the b-axis magnetization f and an increase in the a-axis magnetization. Our elastic neutron scattering along the a axis shows decreasing intensity of magnetic (300) peak upon cooling from 20 K, supporting the SR transition. Although the magnetization of two magnetic sublattices favours different crystallographic axes and shows significant anisotropy in magnetic and transport behaviours, their moments are all aligned to the field direction at sufficiently large fields (30 T). Moreover, the magnetic structure within the SR transition region is relatively fragile, which results in negative magnetoresistance by applying magnetic fields along either a or b axis. The metallic NdCrSb3 single crystal with two ferromagnetic sublattices is an ideal system to study the magnetic interactions, as well as their influences on the electronic transport properties.

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. Based on this, a DNA algorithm is proposed to deal with FTSP based on the Adleman-Lipton model. In the algorithm, the solution of the problem can be obtained by executing several basic biological manipulations on DNA molecules with O ( N2 ) computing complexity ( N is the number of vertices in the problem without the origin). Through the simulation experiments on some benchmark instances, the results show that the parallel DNA algorithm has better performance than traditional computing. The effectiveness of the algorithm is verified by deducing the algorithm process in detail. Furthermore, the algorithm further proves that DNA computing, as one of the parallel computing methods, has the potential to solve more complex big data problems.

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 n cities to meet a given sales quota Q 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. Its time complexity is O(n 2+Q), which is a significant improvement over previous algorithms with exponential complexity. A coding scheme of element information is pointed out, and a reasonable biological algorithm is raised by using limited conditions, whose feasibility is verified by simulation experiments. The innovation of this study is to propose a polynomial time complexity algorithm to solve the QTSP. This advantage will become more obvious as the problem scale increases compared with the algorithm of exponential computational complexity. The proposed DNA algorithm also has the significant advantages of having a large storage capacity and consuming less energy during the operation. With the maturity of DNA manipulation technology, DNA computing, as one of the parallel biological computing methods, has the potential to solve more complex NP-hard 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. We appropriately use different biological chains to mean vertices, edges, weights, and adopt appropriate biological operations to search the solutions of the problem with O(n2) time complexity. We enrich the application scope of biocomputing, reduce computational complexity, and verify practicability of DNA parallel algorithms through simulations.

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. In turn, we skillfully design flexible length DNA strands to represent elements of the allocation matrix, take appropriate biological experiment operations and get solutions of the task scheduling problem in proper length range with less than O(n2) time complexity.

[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. 22 chromosomal defects were diagnosed in 22 cases using karyotyping. The levels of three markers were analyzed among 22 cases and 870 controls. RESULTS: The level of three markers were significant difference between affected and unaffected pregnancies. In affected cases, the value or level of NT and free beta-hCG were higher, while the level of PAPP-A was lower. We found that screening for chromosomal defects using a combination of NT and serum biochemistry was associated with a detection rate of 91.67% for all types of chromosomal defects, with a false-positive rate of 11.16%. CONCLUSION: A combination of nuchal translucency measurement with materal serum biochemistry markers provides an effective method of screening for chromosomal defects.

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. For the n-queens problem, we reasonably design flexible length DNA strands representing elements of the allocation matrix, take appropriate biologic manipulations and get the solutions of the n-queens problem in proper length and O(n(2)) time complexity. We extend the application of DNA molecular operations, simultaneity simplify the complexity of the computation and simulate to verify the feasibility of the DNA algorithm.

[Genetic polymorphism of six STR loci in the Han population in Henan province].

To investigate the allele frequencies of six short tandem repeats (STR) loci: F13A1, F13B, D8S1179, CSF1PO, D5S818 and TPOX in Han population in Henan province, DNA was extracted with chelex from EDTA-blood samples of the unrelated individuals in Henan province and amplified with PCR technique. The PCR product was analyzed with the undenatured PAGE vertical electrophoresis and silverstain. The results were obtained through genetical analysis. The authors got the number of allele of six loci, Three alleles of F13A1 could be detected in this population. The F13A1 * 7 is the most common allele with a frequency of 45.2%, followed by F13A1 * 5 and F13A1 * 4 respectively. F13B had four alleles among which F13B * 10 has the highest frequency, followed by F13B * 9, F13B * 8, F13B * 11. The 8 alleles of D8S1179 in the order from high to low frequency is D8S1179 * 14, * 15, * 12, * 11, * 10. And the most common frequencies of alleles of CSF1PO, D5S818, TPOX ore CSF1PO * 12, D5S818 * 11, TPOX * 8, respectively followed by the allele of * 11, * 14, * 10, * 13 in CSF1PO locus, and * 12, * 13, * 10, * 9, * 14, * 8, * 15, * 7 in D5S818 locus, * 11, * 9, * 12, * 10 in TPOX locus. The heterozygosities of the six loci were 0.62, 0.46, 0.83, 0.59, 0.78 and 0.65; the discrimination powers were 0.78, 0.66, 0.95, 0.79, 0.92 and 0.82. The heterozygosities of the six loci are high and the frequencies are in good agreement with Hardy-Weinberg equilibrium. The six loci are good as genetic markers. We can use these loci in dividualidentification and partenity test in forensic area.