Partial Functions Research Articles

Editing the nuclear localization signals of E1 and E1Lb enables the production of tropical soybean in temperate growing regions.

Soybean is a typical short-day crop, and most commercial soybean cultivars are restricted to a relatively narrow range of latitudes due to photoperiod sensitivity. Photoperiod sensitivity hinders the utilization of soybean germplasms across geographical regions. When grown in temperate regions, tropical soybean responds to prolonged day length by increasing the vegetative growth phase and delaying flowering and maturity, which often pushes the harvest window past the first frost date. In this study, we used CRISPR/LbCas12a to edit a North American subtropical soybean cultivar named 06KG218440 that belongs to maturity group 5.5. By designing one gRNA to edit the nuclear localization signal (NLS) regions of both E1 and E1Lb, we created a series of new germplasms with shortened flowering time and time to maturity and determined their favourable latitudinal zone for cultivation. The novel partial function alleles successfully achieve yield and early maturity trade-offs and exhibit good agronomic traits and high yields in temperate regions. This work offers a straightforward editing strategy to modify subtropical and tropical soybean cultivars for temperate growing regions, a strategy that could be used to enrich genetic diversity in temperate breeding programmes and facilitate the introduction of important crop traits such as disease tolerance or high yield.

Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series

A high-accuracy estimation of the runoff has always been an extremely relevant and challenging subject in hydrology science.Therefore, in the current research, a novel hybrid decomposition-integration-optimization based model is developed to enhance the estimation precision of the runoff. The suggested predictive model is a combination of successive variational mode decomposition (SVMD) technique and Multi-Layer Perceptron neural network (MLP) model integrated with particle swarm optimization (PSO) meta-heuristic algorithm (i.e., hybrid SVMD-MLP-PSO model). To test its performance, the mean monthly runoff data recorded from Sep 1986-Aug 2017 in Dez River basin (MRDRm), southwest of Iran, are used. The performance of the recommended model is also matched with other different hybrid and single models including MLP-PSO, SVMD-MLP, and MLP as the benchmark model. In all models, the sequence-to-one regression module of forecasting (i.e., without using meteorological parameters recorded in the study region) is utilized. In the SVMD based hybrid models, the optimal value of compactness of mode (α) for the original MRDRm time series is achieved at 100. Then, the PACF(partial autocorrelation function) diagram related to the lag length from each decomposed intrinsic mode function (IMF) sub-signals sequence generated is operated to select the ideal input variables. Performance evaluation metrics prove that the hybrid SVMD-MLP-PSO model under the best predictor and meta-parameters, outperformed with an R2 of 0.89, modified 2012 version of Kling-Gupta efficiency (KGEʹ) of 0.83, volumetric efficiency (VE) of 0.91, Nash–Sutcliffe efficiency (NSE) of 0.88, and RMSE of 13.91 m3/s. Comparatively, the standalone MLP as the benchmark model results in an R2 of 0.24, VE of 0.33, KGEʹ of 0.2, NSE of 0.29, and RMSE of 153.39 m3/s.

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

In view of the current problems of complex models and insufficient data processing in ultra-short-term prediction of photovoltaic power generation, this paper proposes a photovoltaic power ultra-short-term prediction model named HPO-KNN-SRU, based on a Simple Recurrent Unit (SRU), K-Nearest Neighbors (KNN), and Hunter–Prey Optimization (HPO). Firstly, the sliding time window is determined by using the autocorrelation function (ACF), partial correlation function (PACF), and model training. The Pearson correlation coefficient method is used to filter the principal meteorological factors that affect photovoltaic power. Then, the K-Nearest Neighbors (KNN) algorithm is utilized for effective outlier detection and processing to ensure the quality of input data for the prediction model, and the Hunter–Prey Optimization (HPO) algorithm is applied to optimize the parameters of the KNN algorithm. Finally, the efficient Simple Recurrent Unit (SRU) model is used for training and prediction, with the Hunter–Prey Optimization (HPO) algorithm applied to optimize the parameters of the SRU model. Simulation experiments and extensive ablation studies using photovoltaic data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs, Australia, validate the effectiveness of the integrated model, the KNN outlier handling, and the HPO algorithm. Compared to the Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Simple Recurrent Unit (SRU) models, this model exhibits an average reduction of 19.63% in Mean Square Error (RMSE), 27.54% in Mean Absolute Error (MAE), and an average increase of 1.96% in coefficient of determination (R2) values.

Pedicled flap transfer after chest wall malignant tumor resection and potential risk of postoperative respiratory problems for patients with low FEV1.0.

Musculoskeletal transfer for chest wall tissue defects is a crucial method, and pedicled flaps around the chest wall are preferred in terms of location and simplicity of transfer. These require special care because of complications such as partial necrosis, fistula, wound dehiscence, infection, hematoma and restricted function of the arm or shoulder. However, studies of respiratory function are rare. In the present study, we investigated the complications including respiratory problems after wide resection for malignant chest wall tumors with musculoskeletal pedicle transfer. A total of 13 patients (15 operations) who underwent wide resection of primary, recurrent, or metastatic malignant chest wall tumors and musculoskeletal pedicle transfer for coverage of tissue defects were enrolled in the present study. A retrospective review of all patients was performed using data collected from hospital records and follow-up information. The complications of musculoskeletal transfer after chest wall wide resection, including respiratory problems, are evaluated. Rib or sternal resection was performed in 12 operations, and only soft tissue resection was performed in 3 operations. Latissimus dorsi (LD) pedicle transfer was performed in 13 operations, and pectoralis major (PM) pedicle transfer was performed in 2 operations; basically, wounds were closed primarily. Surgical complications were observed following 5 of the 15 operations (33.3%). Respiratory complications were seen in 7 of the 15 operations (46.7%). Patients with respiratory complications showed significantly lower preoperative FEV1.0% values than those without respiratory complications (p = 0.0196). Skin resection area tended to be higher in the complication group than in the no complication group (p = 0.104). Pedicled myocutaneous flap transfers such as LD, PM, and rectus abdominus can be used following multiple resections. After harvesting LD or PM, the wound can be closed primarily for an 8-10-cm skin defect in patients with normal respiratory function. However, for patients with low FEV1.0%, after primary closure of LD or PM transfer for wide soft tissue defects, attention should be paid to postoperative respiratory complications.

S0-driven partial denitrification coupled with anammox (S0PDA) enables highly efficient autotrophic nitrogen removal from wastewater

This study proposed a novel strategy that integrates S0 particles (diameter: 2–3 mm) and granular sludge to establish S0-driven partial denitrification coupled with anammox (S0PDA) process for autotrophic nitrogen removal from NH4+- and NO3−-containing wastewaters. This process was evaluated using an up-flow anoxic sludge bed bioreactor, operating continuously for 240 days. The influent concentrations of NH4+ and NO3− were 29.9 ± 2.7 and 50.2 ± 2.7 mg-N/L, respectively. Throughout the operation, the hydraulic retention time was shortened from 4.0 h to 2.0 h, while the effluent concentrations of NH4+ and NO3− were maintained at a desirable level of 1.45–1.51 mg-N/L and 4.46–6.52 mg-N/L, respectively. Despite an autotrophic process, the nitrogen removal efficiency and rate reached up to 88.5 ± 2.0 % and 1.75 ± 0.07 kg-N/(m3·d), respectively, indicating the remarkable robustness of the S0PDA process. Autotrophic anammox and sulfur-oxidizing bacteria (Candidatus Brocadia and Thiobacillus) were the predominant bacterial genera involved in the S0PDA process. Candidatus Brocadia was primarily enriched in the granular sludge, with a relative abundance of 6.70 %. Thiobacillus occupied a unique niche on the S0 particles, with a relative abundance as high as 57.6 %, of which Thiobacillus thioparus with partial denitrification function (reducing NO3− to NO2− without further reduction to N2) accounted for 78.0 %. These findings challenge the stereotype of low efficiency in autotrophic nitrogen removal from wastewater, shedding fresh light on the applications of autotrophic processes.

Chimeric systems composed of swapped Tra subunits between distantly-related F plasmids reveal striking plasticity among type IV secretion machines.

Bacterial type IV secretion systems (T4SSs) are a versatile family of macromolecular translocators, collectively able to recruit diverse DNA and protein substrates and deliver them to a wide range of cell types. Presently, there is little understanding of how T4SSs recognize substrate repertoires and form productive contacts with specific target cells. Although T4SSs are composed of a number of conserved subunits and adopt certain conserved structural features, they also display considerable compositional and structural diversity. Here, we explored the structural bases underlying the functional versatility of T4SSs through systematic deletion and subunit swapping between two conjugation systems encoded by the distantly-related IncF plasmids, pED208 and F. We identified several regions of intrinsic flexibility among the encoded T4SSs, as evidenced by partial or complete functionality of chimeric machines. Swapping of VirD4-like TraD type IV coupling proteins (T4CPs) yielded functional chimeras, indicative of relaxed specificity at the substrate-TraD and TraD-T4SS interfaces. Through mutational analyses, we further delineated domains of the TraD T4CPs contributing to recruitment of cognate vs heterologous DNA substrates. Remarkably, swaps of components comprising the outer membrane core complexes, a few F-specific subunits, or the TraA pilins supported DNA transfer in the absence of detectable pilus production. Among sequenced enterobacterial species in the NCBI database, we identified many strains that harbor two or more F-like plasmids and many F plasmids lacking one or more T4SS components required for self-transfer. We confirmed that host cells carrying co-resident, non-selftransmissible variants of pED208 and F elaborate chimeric T4SSs, as evidenced by transmission of both plasmids. We propose that T4SS plasticity enables the facile assembly of functional chimeras, and this intrinsic flexibility at the structural level can account for functional diversification of this superfamily over evolutionary time and, on a more immediate time-scale, to proliferation of transfer-defective MGEs in nature.

Comprehensive assessment of technical condition of vehicles during operation based on Harrington’s desirability function

The object of research is the process of changing the technical condition of vehicles during their operation. The study solved the problem of comprehensive evaluation of change in the technical condition based on Harrington’s desirability function. The essence of the results is as follows. A scale of desirability was built and a set of criteria for assessing the technical condition of vehicles was clarified. A general desirability index is proposed as a convolution of partial Harrington desirability functions. When solving the investigated problem, the characteristics and properties of the partial and generalized Harrington’s desirability function and their graphical representation were taken into account. Using an example of the technical condition of the chassis and braking system of vehicles, a set of controlled parameters was formed. Based on the values of the controlled parameters, the regression equation of the partial Harrington desirability functions was obtained. The value of the weighting coefficients of each of the criteria was determined and the generalized desirability function was calculated. As a result of the study, it was established that if the generalized criterion of desirability is D<0.37, then individual nodes, systems, and units of the vehicle are in a pre-accident condition, if 0.37£D£0.63 – in a satisfactory condition, and if D>0.63 – in a good condition and cannot be the cause of a traffic accident. An applied aspect of the results is the implementation of the technique of comprehensive assessment of the technical condition of the vehicle. This causes an increase in the productivity of the expert (specialist), will shorten the period of the auto technical examination, and improve its quality. The results could be used by insurance companies and investigators, investigators and judges when considering traffic accidents

Time Series Model to Forecast Fresh Coconut Exports from Sri Lanka

Coconut accounts for approximately 12% of all agricultural produce in Sri Lanka with the total land area under cultivation covering 409, 244 hectares ranking second to rice production. The primary regions for coconut cultivation are the Puttalam and Kurunegala districts in North-Western Province and Gampaha district in the Western Province, forming what is known as the Coconut Triangle. This region accounts for 232,270 hectares (50.94%) of the overall coconut cultivation area. The remaining coconut cultivation areas are found in the Southern Province, specifically in the districts of Galle (13,833 hectares), Matara (14,946 hectares), and Hambantota (25,837 hectares), and in non-traditional regions of the Eastern and Northern provinces. The annual coconut production varies between 2,800 to 3,000 million nuts. Having advanced knowledge of exporting coconuts offers numerous advantages to Sri Lanka, particularly in terms of establishing forward contracts with other countries. Based on secondary data of annual fresh coconut exports from 1981 to 2020 obtained from the Coconut Development Authority (CDA) of Sri Lanka, the paper developed ARIMA (2,1,0) model to forecast export. The model was selected out of three parsimonious models which were identified from the Sample Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) of the stationary series and a comparison of significant parameters and lowest values of Akaike Information Criterion (AIC), Schwarz Bayesian Information Criterion (SBIC) and Hannan-Quinn Information Criterion (HQIC). The errors of the fitted model were found to be random and constant variance. The model was validated using 2021 and 2022 data. The percentage errors for 2021 and 2022 are 20.23% and -29.57% respectively. The predictions for 2023 and 2024 are 14696 and 15052 respectively. The model can be used effectively by the Coconut Development Authority for decision-making. However, it is suggested to develop the model further to reduce the percentage error. Keywords: ARIMA model, Forecast, Fresh Coconut Exports and Validate

Forecasting of tilapia (Oreochromis niloticus) production in Bangladesh using ARIMA model

Tilapia farming has expanded rapidly in Bangladesh over the years thanks to a suitable climate for aquaculture and a consistently increasing demand for the fish rich in vitamins and minerals. A clear picture regarding the future trend of tilapia production in Bangladesh is still not available, however. The purpose of this study was to estimate parameters that fit into the Autoregressive Integrated Moving Average (ARIMA) model for forecasting tilapia production in Bangladesh. This was accomplished by calibrating and validating the ARIMA model taking into account the lowest values of the Akaike Information Criterion (AIC) and Bayesian Information Criteria (BIC), graphical arrangements of autocorrelation function (ACF) and partial autocorrelation function (PACF) plots. The best model derived was ARIMA (1, 1, 1), which showed an upward trend of tilapia production since 2006 to date and predicted a similar trend until the year 2040. If this trend continues, the yearly tilapia outturn in the country may reach 690,000 MT, with an upper limit of 1.15 million MT and lower limit of 0.23 million MT, reflecting a substantial increase of around 118% over that produced in 2021. The results of this study will serve as a valuable resource for researchers, decision-makers, academics, and tilapia entrepreneurs, enabling them to develop effective action plans to optimize tilapia production in Bangladesh and strategize for the future integration of tilapia within the country.

Phenotypic variation of FXN compound heterozygotes in a Friedreich ataxia cohort.

Most individuals with Friedreich ataxia (FRDA) have homozygous GAA triplet repeat expansions in the FXN gene, correlating with a typical phenotype of ataxia and cardiomyopathy. A minority are compound heterozygotes carrying a GAA expansion on one allele and a mutation on the other. The study aim was to examine phenotypic variation among compound heterozygotes. Data on FXN mutations were obtained from the Friedreich Ataxia Clinical Outcome Measures Study (FA-COMS). We compared clinical features in a single-site FA-COMS cohort of 51 compound heterozygous and 358 homozygous patients, including quantitative measures of cardiac, neurologic, and visual disease progression. Non-GAA repeat mutations were associated with reduced cardiac disease, and patients with minimal/no function mutations otherwise had a typical FRDA phenotype but with significantly more severe progression. The partial function mutation group was characterized by relative sparing of bulbar and upper limb function, as well as particularly low cardiac involvement. Other clinical features in this group, including optic atrophy and diabetes mellitus, varied widely depending on the specific type of partial function mutation. These data support that the typical FRDA phenotype is driven by frataxin deficiency, especially severe in compound heterozygotes with minimal/no function mutations, whereas the heterogeneous presentations of those with partial function mutations may indicate other contributing factors to FRDA pathogenesis.

Monthly Streamflow Prediction of the Source Region of the Yellow River Based on Long Short-Term Memory Considering Different Lagged Months

Accurate and reliable monthly streamflow prediction plays a crucial role in the scientific allocation and efficient utilization of water resources. In this paper, we proposed a prediction framework that integrates the input variable selection method and Long Short-Term Memory (LSTM). The input selection methods, including autocorrelation function (ACF), partial autocorrelation function (PACF), and time lag cross-correlation (TLCC), were used to analyze the lagged time between variables. Then, the performance of the LSTM model was compared with three other traditional methods. The framework was used to predict monthly streamflow at the Jimai, Maqu, and Tangnaihai stations in the source area of the Yellow River. The results indicated that grid search and cross-validation can improve the efficiency of determining model parameters. The models incorporating ACF, PACF, and TLCC with lagged time are evidently superior to the models using the current variable as the model inputs. Furthermore, the LSTM model, which considers the lagged time, demonstrated better performance in predicting monthly streamflow. The coefficient of determination (R2) improved by an average of 17.46%, 33.94%, and 15.29% for each station, respectively. The integrated framework shows promise in enhancing the accuracy of monthly streamflow prediction, thereby aiding in strategic decision-making for water resources management.

M2 Microglia-derived Exosomes Promote Spinal Cord Injury Recovery in Mice by Alleviating A1 Astrocyte Activation.

M2 microglia transplantation has previously demonstrated beneficial effects on spinal cord injury (SCI) by regulating neuroinflammation and enhancing neuronal survival. Exosomes (EXOs), secreted by almost all cell types, embody partial functions and properties of their parent cells. However, the effect of M2 microglia-derived EXOs (M2-EXOs) on SCI recovery and the underlying molecular mechanisms remain unclear. In this study, we isolated M2-EXOs and intravenously introduced them into mice with SCI. Considering the reciprocal communication between microglia and astroglia in both healthy and injured central nervous systems (CNSs), we subsequently focused on the influence of M2-EXOs on astrocyte phenotype regulation. Our findings indicated that M2-EXOs promoted neuron survival and axon preservation, reduced the lesion area, inhibited A1 astrocyte activation, and improved motor function recovery in SCI mice. Moreover, they inhibited the nuclear translocation of p65 and the activation of the NF-κB signalling pathway in A1 astrocytes. Therefore, our research suggests that M2-EXOs mitigate the activation of neurotoxic A1 astrocytes by inhibiting the NF-κB signalling pathway, thereby improving spinal tissue preservation and motor function recovery following SCI. This positions M2-EXOs as a promising therapeutic strategy for SCI.

Complemented subsets and Boolean-valued, partial functions

We study the two algebras of complemented subsets that were introduced in the constructive development of the Daniell approach to measure and integration within Bishop-style constructive mathematics. We present their main properties both for the so-called here categorical complemented subsets and for the extensional complemented subsets. We translate constructively the classical bijection between subsets and Boolean-valued, total functions by establishing a bijection between complemented subsets (categorical or extensional) and Boolean-valued, partial functions (categorical or extensional). The role of Myhill’s axiom of non-choice in the equivalence between categorical and extensional subsets is discussed. We introduce swap algebras of type (I) and (II) as an abstract version of Bishop’s algebras of complemented subsets of type (I) and (II), respectively, and swap rings as an abstract version of the structure of Boolean-valued partial functions on a set. Our examples of swap algebras and swap rings together with the included here results indicate that their theory is a certain generalisation of the theory of Boolean algebras and Boolean rings, a fact which we find interesting both from a constructive and a classical point of view.

Artificial neural network for deciphering the structural transformation of condensed ZnO by extended x-ray absorption fine structure spectroscopy

Pressure-induced structural phase transitions play a pivotal role in unlocking novel material functionalities and facilitating innovations in materials science. Nonetheless, unveiling the mechanisms of densification, which relies heavily on precise and comprehensive structural analysis, remains a challenge. Herein, we investigated the archetypal B4 → B1 phase transition pathway in ZnO by combining x-ray absorption fine structure (XAFS) spectroscopy with machine learning. Specifically, we developed an artificial neural network (NN) to decipher the extended-XAFS spectra by reconstructing the partial radial distribution functions of Zn–O/Zn pairs. This provided us with access to the evolution of the structural statistics for all the coordination shells in condensed ZnO, enabling us to accurately track the changes in the internal structural parameter u and the anharmonic effect. We observed a clear decrease in u and an increased anharmonicity near the onset of the B4 → B1 phase transition, indicating a preference for the iT phase as the intermediate state to initiate the phase transition that can arise from the softening of shear phonon modes. This study suggests that NN-based approach can facilitate a more comprehensive and efficient interpretation of XAFS under complex in-situ conditions, which paves the way for highly automated data processing pipelines for high-throughput and real-time characterizations in next-generation synchrotron photon sources.

Forecasting seasonal influenza activity in Canada-Comparing seasonal Auto-Regressive integrated moving average and artificial neural network approaches for public health preparedness.

Public health preparedness is based on timely and accurate information. Time series forecasting using disease surveillance data is an important aspect of preparedness. This study compared two approaches of time series forecasting: seasonal auto-regressive integrated moving average (SARIMA) modelling and the artificial neural network (ANN) algorithm. The goal was to model weekly seasonal influenza activity in Canada using SARIMA and compares its predictive accuracy, based on root mean square prediction error (RMSE) and mean absolute prediction error (MAE), to that of an ANN. An initial SARIMA model was fit using automated model selection by minimizing the Akaike information criterion (AIC). Further inspection of the autocorrelation function and partial autocorrelation function led to 'manual' model improvements. ANNs were trained iteratively, using an automated process to minimize the RMSE and MAE. A total of 378, 462 cases of influenza was reported in Canada from the 2010-2011 influenza season to the end of the 2019-2020 influenza season, with an average yearly incidence risk of 20.02 per 100,000 population. Automated SARIMA modelling was the better method in terms of forecasting accuracy (per RMSE and MAE). However, the ANN correctly predicted the peak week of disease incidence while the other models did not. Both the ANN and SARIMA models have shown to be capable tools in forecasting seasonal influenza activity in Canada. It was shown that applying both in tandem is beneficial, SARIMA better forecasted overall incidence while ANN correctly predicted the peak week.

Solid – phase equilibria and thermodynamic properties of the Sb-Te-S system

The powder X-ray diffraction (PXRD) analysis and the electromotive force (emf) measurements have been used to investigate the Sb–Te–S system in the Sb2S3-Sb2Te3-Te-S composition region at 300–450 K temperatures interval. Relative partial molar functions of antimony in alloys have been calculated and obtained data have been used to get self-consistent sets of the standard Gibbs free energy, standard enthalpy, and standard entropy of the Sb2S3 and Sb2Te2S compounds, as well as the Sb2Te2,4S0,6 and Sb2Te2,7S0,3 solid solutions. The data obtained for Sb2S3 have been compared to the ones available in the literature. Thermodynamic functions of the Sb2Te2S compound, as well as, the Sb2Te2,4S0,6 and Sb2Te2,7S0,3 solid solutions have been determined for the first time.

Selective laser sintering of amorphous nanoparticles: Molecular dynamics simulations

The paper investigates the process of liquid-phase sintering of amorphous iron-based nanoparticles by the method of molecular dynamics simulations. The classical molecular dynamics package LAMMPS was used for modeling. Visual analysis of the atomic configurations of nanoparticles during their rapid cooling revealed the self-purification effect of the particles. Partial pair correlation functions and coordination number distribution functions were used to analyze the atomic structure of nanoparticles after sintering. As a result of the analysis of the main structural parameters, which were obtained using the specified functions, differences in the atomic composition and structure of the volume and surface of nanoparticles were established.

Prediction and Classification Models of Wordle Data Set Based On ARMA, XGBoost

This study intends to use machine learning method to solve the problem of predicting the number of word guessing and the difficulty of word guessing in natural language. Firstly, this study smoothed the data according to the COF method. According to the images of the sample autocorrelation function and the sample partial autocorrelation function, the ARMA model is constructed and the coefficient of the AMRA model is calculated, and the number of word contest people on March 1, 2023 is predicted to drop to [18002,20622]. Second, based on the seven attributes of the word, the XGBoost classification model is used to predict the value of the first attempt, and then the XGBoost prediction model is used to predict the results of the other attempts. Eerie distribution of different attempts to {0,8.890, 17.650, 28.191, 32.451, 11.349, 1.483}. The average accuracy of the model on the test set is 79.4%.

The research of analysis lung, bronchus and trachea cancer death rate in US

This research delves into an analysis of lung, bronchus, and trachea cancer rates in the United States across genders. Employing the data spanning seven decades (1950-2020) sourced from the Our World in Data website, the study leverages time series modeling techniques, ARIMA and ETS models. The ARIMA methodology initiates with an assessment of data stationarity, followed by differencing procedures to transform the dataset into a non-stationary data. Subsequently, Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots are examined. Last, the ARIMA model is fitted to dissect the mortality rates among males and females. Simultaneously, the ETS model is directly applied to the mortality data of both genders. The components of the ETS model and the check residuals for ETS are delineated. The outcomes reveal the trends: both genders exhibit a discernible decline in lung, bronchus, and trachea cancer death rates over the period. Despite this downward trajectory, the persistent mortality rates underscore the gravity of the issue. This paper advocates for a heightened focus on lung-related cancers. Understanding and addressing these mortality rates are imperative.

Research on Stock Price Prediction based on LSTM Modeling-A Stock Market as a Case Study

This research intends to predict the closing values of the Shanghai Stock Exchange 50 Index using an enhanced Long Short-Term Memory (LSTM) neural network approach. The investigation leverages a comprehensive data set from August 2013 to August 2023, encompassing various market conditions. Alongside the primary LSTM analysis, the study introduces supplementary methodologies, specifically the Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF), to validate and enrich the time-series analysis. This study verified the non-stationary of the original data and processed them with method. 80%of the data was allocated for training, while the subsequent 20% was reserved for testing, the LSTM model, with its intricate architecture, exhibits superior predictive capabilities, particularly in stable market conditions. However, its performance wanes during volatile market periods, suggesting further refinements are necessary. This research contributes to the existing literature by applying machine learning methodologies to the turbulent and complex A-share market, offering invaluable insights for both academic researchers and market investors.