Original Data Sequence Research Articles

Assembly-free reads accurate identification (AFRAID) approach outperforms other methods of DNA barcoding in the walnut family (Juglandaceae)

DNA barcoding has been extensively used for species identification. However, species identification of mixed samples or degraded DNA is limited by current DNA barcoding methods. In this study, we use plant species in Juglandaceae to evaluate an assembly-free accurate reads identification (AFRAID) method of species identification, a novel approach for precise species identification in plants. Specifically, we determined (1) the accuracy of DNA barcoding approaches in delimiting species in Juglandaceae, (2) the minimum size of chloroplast dataset for species discrimination, and (3) minimum amount of next generation sequencing (NGS) data required for species identification. We found that species identification rates were highest when whole chloroplast genomes were used, followed by taxon-specific DNA barcodes, and then universal DNA barcodes. Species identification of 100% was achieved when chloroplast genome sequence coverage reached 20% and the original sequencing data reached 500,000 reads. AFRAID accurately identified species for all samples tested after 500,000 clean reads, with far less computing time than common approaches. These results provide a new approach to accurately identify species, overcoming limitations of traditional DNA barcodes. Our method, which uses next generation sequencing to generate partial chloroplast genomes, reveals that DNA barcode regions are not necessarily fixed, accelerating the process of species identification.

A data-driven dual-optimization hybrid machine learning model for predicting carbon dioxide trapping efficiency in saline aquifers: Application in carbon capture and storage

Geological carbon sequestration (GCS) is a promising method to reduce global carbon emissions. To better understand the CO2 trapping mechanism, the CO2 sequestration process was simulated through a physics-based reservoir simulator, and the residual trapping (RTI) and solubility trapping (STI) efficiencies were analyzed. Through the experiments, a database containing 4543 data points with reservoir physical parameters and rock properties was established. In this study, a kernel extreme learning machine (KELM) model based on variational mode decomposition (VMD) and dung beetle optimization optimizer (DBO) was proposed to perform the prediction task. Through the VMD, the original data sequences were decomposed and combined, and the obtained subsequences were executed modeling prediction and reconstruction respectively. The reliability of the prediction model was confirmed to be greater than 97.71% through domain analysis. In addition, it was applied to a tight gas reservoir in China in to confirm its validity in field applications. Comparative results indicated a high agreement between the prediction results and the simulation results, suggesting that the proposed method can complement numerical simulations and help users understand the application of machine learning in carbon capture and storage (CCS).

The oocyte microenvironment is altered in adolescents compared to oocyte donors.

Do the molecular signatures of cumulus cells (CCs) and follicular fluid (FF) of adolescents undergoing fertility preservation differ from that of oocyte donors? The microenvironment immediately surrounding the oocyte, including the CCs and FF, is altered in adolescents undergoing fertility preservation compared to oocyte donors. Adolescents experience a period of subfecundity following menarche. Recent evidence suggests that this may be at least partially due to increased oocyte aneuploidy. Reproductive juvenescence in mammals is associated with suboptimal oocyte quality. This was a prospective cohort study. Adolescents (10-19 years old, n = 23) and oocyte donors (22-30 years old, n = 31) undergoing ovarian stimulation and oocyte retrieval at a single center between 1 November 2020 and 1 May 2023 were enrolled in this study. Patient demographics, ovarian stimulation, and oocyte retrieval outcomes were collected for all participants. The transcriptome of CCs associated with mature oocytes was compared between adolescents (10-19 years old, n = 19) and oocyte donors (22-30 years old, n = 19) using bulk RNA-sequencing. FF cytokine profiles (10-19 years old, n = 18 vs 25-30 years old, n = 16) were compared using cytokine arrays. RNA-seq analysis revealed 581 differentially expressed genes in CCs of adolescents relative to oocyte donors, with 361 genes downregulated and 220 upregulated. Genes enriched in pathways involved in cell cycle and cell division (e.g. GO: 1903047, P = 3.5 × 10-43; GO: 0051983, P = 4.1 × 10-30; GO: 0000281, P = 7.7 × 10-15; GO: 0044839, P = 5.3 × 10-13) were significantly downregulated, while genes enriched in several pathways involved in cellular and vesicle organization (e.g. GO: 0010256, P = 1.2 × 10-8; GO: 0051129, P = 6.8 × 10-7; GO: 0016050, P = 7.4 × 10-7; GO: 0051640, P = 8.1 × 10-7) were upregulated in CCs of adolescents compared to oocyte donors. The levels of nine cytokines were significantly increased in FF of adolescents compared to oocyte donors: IL-1 alpha (2-fold), IL-1 beta (1.7-fold), I-309 (2-fold), IL-15 (1.6-fold), TARC (1.9-fold), TPO (2.1-fold), IGFBP-4 (2-fold), IL-12-p40 (1.7-fold), and ENA-78 (1.4-fold). Interestingly, seven of these cytokines have known pro-inflammatory roles. Importantly, neither the CC transcriptomes nor FF cytokine profiles were different in adolescents with or without cancer. Original high-throughput sequencing data have been deposited in Gene Expression Omnibus (GEO) database with the accession number GSE265995. This study aims to gain insights into the associated gamete quality by studying the immediate oocyte microenvironment. The direct study of oocytes is more challenging due to sample scarcity, as they are cryopreserved for future use, but would provide a more accurate assessment of oocyte reproductive potential. Our findings have implications for the adolescent fertility preservation cycles. Understanding the expected quality of cryopreserved eggs in this age group will lead to better counseling of these patients about their reproductive potential and may help to determine the number of eggs that is recommended to be banked to achieve a reasonable chance of future live birth(s). This project was supported by Friends of Prentice organization SP0061324 (M.M.L. and E.B.), Gesualdo Family Foundation (Research Scholar: M.M.L.), and NIH/NICHD K12 HD050121 (E.B.). The authors have declared that no conflict of interest exists.

Upregulation of MMPs in placentas of patients with gestational diabetes mellitus: Involvement of the PI3K/Akt pathway

In recent years, there has been a notable rise in the incidence of pregnancies complicated by gestational diabetes mellitus (GDM), characterized by glucose intolerance first identified during pregnancy. Analysis of placental tissue has revealed that placentas from women with GDM tend to be larger and heavier compared to control placentas, indicating potential changes in trophoblast proliferation, differentiation, and apoptosis. In this study, transcriptome sequencing was conducted on placentas obtained from both normal pregnancies and pregnancies with GDM to investigate the molecular mechanisms underlying this condition. The original sequencing data were subjected to sequencing analysis, resulting in the identification of 935 upregulated genes and 256 downregulated genes. The KEGG and GO analysis techniques on differential genes uncovered evidence suggesting that the phosphoinositide 3-kinase (PI3K)/Akt signaling pathway may contribute to the pathogenesis of GDM. Subsequent analysis indicated that the expression levels of matrix metalloproteinases (MMP) 11, MMP12, MMP14, and MMP15, which are regulated by the PI3K/Akt pathway, were upregulated in the placentas of patients with GDM when compared to those of individuals with normal placental function. Additionally, our investigation into alternative splicing patterns revealed an increase in exon skipping alternative splicing of CSF3R in the placenta of patients with GDM compared to that in the control group. The CSF3R–PI3K-MMP pathway is speculated to regulate the pathogenesis of GDM.

Assessing the effect of turbine size on the coherent structures in the wake using DMD

The aim of this study is to evaluate and quantify the effect of the incoming flow on the coherent structures characterizing the wake of two NREL reference wind turbines with different dimensions: the NREL 5-MW and the NREL 15-MW. The Dynamic Mode Decomposition (DMD) approach has been used to detect dynamically-relevant flow structures. We employ the Sparsity-Promoting version of the DMD (SPDMD) algorithm for ranking the most relevant modes, in order to extract a limited subset of relevant flow features that optimally approximate the original data sequence. The dataset on which the SPDMD is based consists of ordered snapshots obtained by Large Eddy Simulations (LES), where the Actuator Line Method (ALM) simulates the rotor and the Immersed Boundary Method (IBM) models the tower and nacelle. To ensure a realistic comparison, the two turbines are subjected to two turbulent inflows with the same mean Atmospheric Boundary Layer (ABL) obtained through a precursor simulation. The results reveal significant differences in the dependence of the absolute value of the DMD amplitudes on the angular frequency for the two turbines. However, the coherent structures appear to have the same shape for the main modes, although the tip vortex structures have a higher dynamic relevance for the NREL 15-MW turbine. This is due to the larger length scales imposed by the rotor and the lower turbulence intensity at the taller rotor height.

Research on Deformation Prediction of VMD-GRU Deep Foundation Pit Based on PSO Optimization Parameters.

As a key guarantee and cornerstone of building quality, the importance of deformation prediction for deep foundation pits cannot be ignored. However, the deformation data of deep foundation pits have the characteristics of nonlinearity and instability, which will increase the difficulty of deformation prediction. In response to this characteristic and the difficulty of traditional deformation prediction methods to excavate the correlation between data of different time spans, the advantages of variational mode decomposition (VMD) in processing non-stationary series and a gated cycle unit (GRU) in processing complex time series data are considered. A predictive model combining particle swarm optimization (PSO), variational mode decomposition, and a gated cyclic unit is proposed. Firstly, the VMD optimized by the PSO algorithm was used to decompose the original data and obtain the Internet Message Format (IMF). Secondly, the GRU model optimized by PSO was used to predict each IMF. Finally, the predicted value of each component was summed with equal weight to obtain the final predicted value. The case study results show that the average absolute errors of the PSO-GRU prediction model on the original sequence, EMD decomposition, and VMD decomposition data are 0.502 mm, 0.462 mm, and 0.127 mm, respectively. Compared with the prediction mean square errors of the LSTM, GRU, and PSO-LSTM prediction models, the PSO-GRU on the PTB0 data of VMD decomposition decreased by 62.76%, 75.99%, and 53.14%, respectively. The PTB04 data decreased by 70%, 85.17%, and 69.36%, respectively. In addition, compared to the PSO-LSTM model, it decreased by 8.57% in terms of the model time. When the prediction step size increased from three stages to five stages, the mean errors of the four prediction models on the original data, EMD decomposed data, and VMD decomposed data increased by 28.17%, 3.44%, and 14.24%, respectively. The data decomposed by VMD are more conducive to model prediction and can effectively improve the accuracy of model prediction. An increase in the prediction step size will reduce the accuracy of the deformation prediction. The PSO-VMD-GRU model constructed has the advantages of reliable accuracy and a wide application range, and can effectively guide the construction of foundation pit engineering.

Early identification of a ward-based outbreak of Clostridioides difficile using prospective multilocus sequence type-based Oxford Nanopore genomic surveillance.

To describe an outbreak of sequence type (ST)2 Clostridioides difficile infection (CDI) detected by a recently implemented multilocus sequence type (MLST)-based prospective genomic surveillance system using Oxford Nanopore Technologies (ONT) sequencing. Hemato-oncology ward of a public tertiary referral centre. From February 2022, we began prospectively sequencing all C. difficile isolated from inpatients at our institution on the ONT MinION device, with the output being an MLST. Bed-movement data are used to construct real-time ST-specific incidence charts based on ward exposures over the preceding three months. Between February and October 2022, 76 of 118 (64.4%) CDI cases were successfully sequenced. There was wide ST variation across cases and the hospital, with only four different STs being seen in >4 patients. A clear predominance of ST2 CDI cases emerged among patients with exposure to our hemato-oncology ward between May and October 2022, which totalled ten patients. There was no detectable rise in overall CDI incidence for the ward or hospital due to the outbreak. Following a change in cleaning product to an accelerated hydrogen peroxide wipe and several other interventions, no further outbreak-associated ST2 cases were detected. A retrospective phylogenetic analysis using original sequence data showed clustering of the suspected outbreak cases, with the exception of two cases that were retrospectively excluded from the outbreak. Prospective genomic surveillance of C. difficile using ONT sequencing permitted the identification of an outbreak of ST2 CDI that would have otherwise gone undetected.

Research on the Prediction Method of Equipment Status in Power Transformation and Distribution Based on Grey Model

With the continuous increase in the demand for electricity in our country, the reliability requirements of the power distribution network are gradually increasing, and the difficulty of predicting the state of key equipment in the power distribution network is also increasing. In response to this issue, this paper proposes a typical reliability prediction model for key equipment in the power distribution network based on an unbiased grey correlation model. Firstly, a grey model is established to model the reliability of typical key equipment in the power distribution network, and to conduct reliability analysis and life prediction. Secondly, an unbiased grey model is introduced to avoid the problem of the grey model leading to less than ideal life prediction accuracy when the growth rate of the original data sequence is high. Finally, taking the No. 1 main transformer of the 220 kV Lanshan substation under the jurisdiction of Ningxia Shizuishan Power Supply Company as an example, the oil chromatography data is cleaned using the KNN interpolation method and input into the prediction model to analyze and predict the failure rate during actual operation. The results show that this model has certain advantages in terms of life prediction accuracy and calculation time, and can play an auxiliary role in the process of predicting the state of key equipment in the power distribution network.

Clinical utility of the Oncomine Dx Target Test multi-CDx system and the possibility of utilizing those original sequence data.

Companion diagnostic tests play a crucial role in guiding treatment decisions for patients with non-small cell lung cancer (NSCLC). The Oncomine Dx Target Test (ODxTT) Multi-CDx System has emerged as a prominent companion diagnostic method. However, its efficacy in detecting driver gene mutations, particularly rare mutations, warrants investigation. This study aimed to assess the performance of the ODxTT in detecting driver gene mutations in NSCLC patients. Specifically, we aimed to evaluate its sensitivity in detecting epidermal growth factor receptor (EGFR) mutations, a key determinant of treatment selection in NSCLC. We conducted a retrospective analysis of NSCLC patients who underwent testing with the ODxTT at Keio University Hospital between May 2020 and March 2022. Patient samples were subjected to both DNA and RNA tests. Driver gene mutation status was assessed, and instances of missed mutations were meticulously examined. Of the 90 patients, five had nucleic acid quality problems, while 85 underwent both DNA and RNA tests. Driver gene mutations were detected in 56/90 (62.2%) patients. Of the 34 patient specimens, driver mutations were not detected using the ODxTT; however, epidermal growth factor receptor (EGFR) mutations were detected using polymerase chain reaction-based testing in two patients, and a KRAS mutation was detected by careful examination of the sequence data obtained using the ODxTT in one patient. For the above three cases, carefully examining the gene sequence information obtained using the ODxTT could identify driver mutations that were not mentioned in the returned test results. Additionally, we confirmed comparable instances of overlook results for EGFR mutations in the dataset from South Korea, implying that this type of oversight could occur in other countries using the ODxTT. EGFR mutation was missed in ODxTT in Japan (6.3%, 2/32), South Korea (2.0%, 1/49), and overall (3.7%, 3/81). Even if sufficient tumor samples are obtained, rare EGFR mutations (which are excluded from the ODxTT's genetic mutation list) might not be detected using the current ODxTT system due to the program used for sequence analysis. However, such rare EGFR mutations can still be accurately detected on ODxTT's sequence data using next-generation sequencing.

Dynamic real-time forecasting technique for reclaimed water volumes in urban river environmental management

In recent years, the utilization of wastewater recycling as an alternative water source has gained significant traction in addressing urban water shortages. Accurate prediction of wastewater quantity is paramount for effective urban river water resource management. There is an urgent need to develop advanced forecasting technologies to further enhance the accuracy and efficiency of water quantity predictions. Decomposition ensemble models have shown excellent predictive capabilities but are challenged by boundary effects when decomposing the original data sequence. To address this, a rolling forecast decomposition ensemble scheme was developed. It involves using a machine learning (ML) model for prediction and progressively integrating prediction outcomes into the original sequence using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Long short-term memory (LSTM) is then applied for sub-signal prediction and ensemble. The ML-CEEMDAN-LSTM model was introduced for wastewater quantity prediction, compared with non-decomposed ML models, CEEMDAN-based LSTM models, and ML-CEEMDAN-based LSTM models. Three ML algorithms—linear regression (LR), gradient boosting regression (GBR), and LSTM—were examined, using real-time prediction data and historical monitoring data, with historical data selected using the decision tree method. The study used daily water volumes data from two reclaimed water plants, CH and WQ, in Beijing. The results indicate that (1) ML models varied in their selection of real-time factors, with LR performing best among ML models during testing; (2) the ML-CEEMDAN-LSTM model consistently outperformed ML models; (3) the ML-CEEMDAN-LSTM hybrid model performed better than the CEEMDAN-LSTM model across different seasons. This study offers a reliable and accurate approach for reclaimed water volumes forecasting, critical for effective water environment management.

3-hour genome sequencing and targeted analysis to rapidly assess genetic risk

PurposeRapid genetic testing in the critical care setting may guide diagnostic evaluation, direct therapies, and help families and care providers make informed decisions about goals of care. We tested whether a simplified DNA extraction and library preparation process would enable us to perform ultrarapid assessment of genetic risk for a Mendelian condition, based on information from an affected sibling, using long-read genome sequencing and targeted analysis. MethodsFollowing extraction of DNA from cord blood and rapid library preparation, genome sequencing was performed on an Oxford Nanopore PromethION. FASTQ files were generated from original sequencing data in near real-time and aligned to a reference genome. Variant calling and analysis were performed at timed intervals. ResultsWe optimized the DNA extraction and library preparation methods to create sufficient library for sequencing from 500 μL of blood. Real-time, targeted analysis was performed to determine that the newborn was neither affected nor a heterozygote for variants underlying a Mendelian condition. Phasing of the target region and prior knowledge of the affected haplotypes supported our interpretation despite a low level of coverage at 3 hours of life. ConclusionThis proof-of-concept experiment demonstrates how prior knowledge of haplotype structure or familial variants can be used to rapidly evaluate an individual at risk for a genetic disease. Although ultrarapid sequencing remains both complex and cost prohibitive, our method is more easily automated than prior approaches and uses smaller volumes of blood and thus may be more easily adopted for future studies of ultrarapid genome sequencing in the clinical setting.

A CNN-LSTM model for road surface recognition of electric balance vehicles

In recent years, artificial intelligence has developed rapidly, and artificial intelligence equipment has greatly facilitated people's lives. During the operation of intelligent devices, accurate environmental perception is of great importance. To provide more accurate electric balance vehicle instructions, we collect the vibration signal data in different driving environments, and machine learning (ML) methods are adopted to extract and analyse the characteristics of the signal. Then, the classification of the road type is realized. In the study, a Bluetooth wireless acceleration-attitude sensor is used to collect acceleration, angular velocity and angle signal data of the balance vehicle in the X, Y and Z directions. Through analysis, the classification accuracy of using continuous time series data as learning samples is higher than that of using a single time step signal. In the classification based on continuous time series samples, three different models XGBoost, LSTM and 1D-ResNet, are used. XGBoost is applied on artificial feature extraction data and the other models are applied on original sequence data. The F1 scores of the three models are 0.80, 0.77 and 0.79, respectively. To mitigate overfitting, we propose a CNN-LSTM model, whose F1 score is 0.83, outperforming the previous three models.

Gut microbiota signatures in inflammatory bowel disease.

Inflammatory bowel diseases (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), affect millions of people worldwide with increasing incidence. Several studies have shown a link between gut microbiota composition and IBD, but results are often limited by small sample sizes. We aimed to re-analyze publicly available fecal microbiota data from IBD patients. We extracted original fecal 16S rRNA amplicon sequencing data from 45 cohorts of IBD patients and healthy individuals using the BioProject database at the National Center for Biotechnology Information. Unlike previous meta-analyses, we merged all study cohorts into a single dataset, including sex, age, geography, and disease information, based on which microbiota signatures were analyzed, while accounting for varying technical platforms. Among 2518 individuals in the combined dataset, we discovered a hitherto unseen number of genera associated with IBD. A total of 77 genera associated with CD, of which 38 were novel associations, and a total of 64 genera associated with UC, of which 28 represented novel associations. Signatures were robust across different technical platforms and geographic locations. Reduced alpha diversity in IBD compared to healthy individuals, in CD compared to UC, and altered microbiota composition (beta diversity) in UC and especially in CD as compared to healthy individuals were found. Combining original microbiota data from 45 cohorts, we identified a hitherto unseen large number of genera associated with IBD. Identification of microbiota features robustly associated with CD and UC may pave the way for the identification of new treatment targets.

Mine Surface Settlement Prediction Based on Optimized VMD and Multi-Model Combination

The accurate prediction of mining area surface deformation is essential to preventing large-scale coal mining-related surface collapse and ensure safety and daily life continuity. Monitoring subsidence in mining areas is challenged by environmental interference, causing data noise. This paper employs the Sparrow Search Algorithm, which integrates Sine Cosine and Cauchy mutation (SCSSA), to optimize variational mode decomposition (VMD) and combine multi-models for prediction. Firstly, SCSSA is employed to adaptively determine the parameters of VMD using envelope entropy as the fitness value. Subsequently, the VMD method optimized using SCSSA adaptively decomposes the original mining area subsidence data sequence into various sub-sequences. Then, SCSSA-VMD is applied to adaptively decompose the original mining subsidence data sequence into multiple sub-sequences. Meanwhile, using sample entropy, the sub-sequences are categorized into trend sequences and fluctuation sequences, and different models are employed to predict sub-sequences at different frequencies. Finally, the prediction results from different sub-sequences are integrated to obtain the final prediction of mining area subsidence. To validate the predictive performance of the established model, experiments are conducted using GNSS monitoring data from the 110801 working face of Banji Coal Mine in Bozhou. The results demonstrate the following: (1) The hybrid model enhanced the prediction accuracy and trends by decomposing the data and optimizing the parameters with VMD. It outperformed single models, reducing errors and improving predictive trends. (2) The hybrid model significantly improved the prediction accuracy for subsidence data at work surface monitoring stations. It is particularly effective at critical subsidence points, making it a valuable reference for safety in mining operations.

Predicting of elderly population structure and density by a novel grey fractional-order model with theta residual optimization: a case study of Shanghai City, China

Background Accurately predicting the future development trend of population aging is conducive to accelerating the development of the elderly care industry. This study constructed a combined optimization grey prediction model to predict the structure and density of elderly population.MethodsIn this paper, a GT-FGM model is proposed, which combines Theta residual optimization with fractional-order accumulation operator. Fractional-order accumulation can effectively weaken the randomness of the original data sequence. Meanwhile, Theta residual optimization can adjust parameter by minimizing the mean absolute error. And the population statistics of Shanghai city from 2006 to 2020 were selected for prediction analysis. By comparing with the other traditional grey prediction methods, three representative error indexes (MAE, MAPE, RMSE) were conducting for error analysis.ResultsCompared with the FGM model, GM (1,1) model, Verhulst model, Logistic model, SES and other classical prediction methods, the GT-FGM model shows significant forecasting advantages, and its multi-step rolling prediction accuracy is superior to other prediction methods. The results show that the elderly population density in nine districts in Shanghai will exceed 0.5 by 2030, among which Huangpu District has the highest elderly population density, reaching 0.6825. There has been a steady increase in the elderly population over the age of 60.ConclusionsThe GT-FGM model can improve the prediction accuracy effectively. The elderly population in Shanghai shows a steady growth trend on the whole, and the differences between districts are obvious. The government should build a modern pension industry system according to the aging degree of the population in each region, and promote the balanced development of each region.

Improved BIGRU Model and Its Application in Stock Price Forecasting

In order to obtain better prediction results, this paper combines improved complete ensemble EMD (ICEEMDAN) and the whale algorithm of multi-objective optimization (MOWOA) to improve the bidirectional gated recurrent unit (BIGRU), which makes full use of original complex stock price time series data and improves the hyperparameters of the BIGRU network. To address the problem that BIGRU cannot make full use of the stationary data, the original sequence data are processed using the ICEEMDAN decomposition algorithm to derive the non-stationary and stationary parts of the data and modeled with the BIGRU and the autoregressive integrated moving average model (ARIMA), respectively. The modeling process introduces a whale algorithm for multi-objective optimization, which improves the probability of finding the best combination of parameter vectors. The R2, MAPE, MSE, MAE, and RMSE values of the BIGRU algorithm, ICEEMDAN-BIGRU algorithm, MOWOA-BIGRU algorithm, and the improved algorithm were compared. An average improvement of 14.4% over the original algorithm’s goodness-of-fit value will greatly improve the accuracy of stock price predictions.

Spatial Data-Based Automatic and Quantitative Approach in Analyzing Maintenance Reachability

Reachability, as a vital parameter in product maintainability design, exerts a tremendous influence in practical maintenance, especially in the usage stage. To decrease subjectivity in maintenance reachability analysis, this study proposes an automatic and quantitative approach based on the spatial data of the human arm to implement maintenance reachability analysis. The approach focused on two aspects, namely, accuracy and efficiency. In terms of accuracy, the presented methodology starts from the maintenance spot where the human hand is attached. An original global data sequence set was generated, including the wrist, elbow, and shoulder joints, under the constraints of kinematics, in which a data sequence represents an arm motion. Moreover, the surrounding objects are represented by their geometric data, in which each data sequence is analyzed to judge whether collision occurs between arm segments and surrounding objects. In this filtering process, the data sequence is retained if the aforementioned collision does not occur. In terms of efficiency, owing to the large number of global data sequences, the efficiency of the interval selection in collision calculation is also taken into consideration in this methodology. Unlike the traditional methods in the virtual environment, the starting point is the maintenance spot, rather than the human body. Hence, nearly all possibilities of arm postures are considered in a global perspective with little subjective involvement, which enhances the automation and objectivity in maintenance reachability analysis to a certain extent. The case study shows the usability and feasibility by a practical maintenance scene.

Image sequence-based risk behavior detection of power operation inspection personnel

A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper. In this method, the original image sequence data is first separated from the foreground and background. Then, the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction. Finally, human posture nodes are extracted from each frame of the image sequence, which are then used to identify the abnormal behavior of the human. Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.

A Prediction Method of Ship Traffic Accidents Based on Grey GM(1,1) Model

In order to improve the inaccuracy of the GM(1,1) model in the prediction of ship traffic accidents caused by large data fluctuations, a weighted average weakening buffer operator is applied to the GM(1,1) residual error correction model to improve the prediction accuracy. This method is based on the modeling principle of the GM(1,1) residual error correction model. Firstly, GM(1,1) model is established based on the original data sequence to obtain the simulated value, predicted value, and residual tail sequence. Then, the residual tail sequence of the oscillation fluctuation is processed by positive value and weighted average weakening buffer operator, so as to establish the residual GM(1,1) model and correct the original prediction value with the residual prediction value; Finally, this method is applied to the prediction of British merchant shipping traffic accidents to verify its effectiveness. The results show that this method has high prediction accuracy in the short-term prediction of ship traffic accidents.

PM2.5 forecasting for an urban area based on deep learning and decomposition method

Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.