Sample Data Research Articles

An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction

ABSTRACT Remaining Useful Life (RUL) prediction of rolling bearings is one of the intricate and important issues for equipment intelligent maintenance and health management. Various machine learning models and methods have been applied to rolling bearing RUL prediction. However, a single model cannot effectively extract state information and obtain accurate prediction results, and its generalisation is not stable under the condition of small sample data. Therefore this paper proposes an intelligent hybrid deep learning model for achieving accurate RUL prediction of rolling bearings. Firstly, the one-dimensional vibration signal is transformed into the corresponding two-dimensional time-frequency diagram via Continuous Wavelet Transform (CWT). Secondly, the diagram is input into a Multilayer Perceptron (MLP) consisting of a basic three-layer feed-forward network to obtain a one-dimensional feature vector. And lastly, the obtained feature vector is input into an integrated model based on Deep Autoregressive and Transformer to produce the probability distribution and obtain the prediction results of rolling bearing RUL. Extensive experiments on two rolling bearing datasets show that the proposed model outperforms six other comparative models in extracting bearing fault features and predicting bearing RUL, which demonstrates that the proposed model can effectively extract bearing fault features and accurately predict bearing remaining useful life.

A data-driven approach to identifying PFAS water sampling priorities in Colorado, United States.

Per and polyfluoroalkyl substances (PFAS), a class of environmentally and biologically persistent chemicals, have been used across many industries since the middle of the 20th century. Some PFAS have been linked to adverse health effects. Our objective was to incorporate known and potential PFAS sources, physical characteristics of the environment, and existing PFAS water sampling results into a PFAS risk prediction map that may be used to develop a PFAS water sampling prioritization plan for the Colorado Department of Public Health and Environment (CDPHE). We used random forest classification to develop a predictive surface of potential groundwater contamination from two PFAS, perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA). The model predicted PFAS risk at locations without sampling data into one of three risk categories after being "trained" with existing PFAS water sampling data. We used prediction results, variable importance ranking, and population characteristics to develop recommendations for sampling prioritization. Sensitivity and precision ranged from 58% to 90% in the final models, depending on the risk category. The model and prioritization approach identified private wells in specific census blocks, as well as schools, mobile home parks, and public water systems that rely on groundwater as priority sampling locations. We also identified data gaps including areas of the state with limited sampling and potential source types that need further investigation. This work uses random forest classification to predict the risk of groundwater contamination from two per- and polyfluoroalkyl substances (PFAS) across the state of Colorado, United States. We developed the prediction model using data on known and potential PFAS sources and physical characteristics of the environment, and "trained" the model using existing PFAS water sampling results. This data-driven approach identifies opportunities for PFAS water sampling prioritization as well as information gaps that, if filled, could improve model predictions. This work provides decision-makers information to effectively use limited resources towards protection of populations most susceptible to the impacts of PFAS exposure.

Impact of extreme high temperatures on pollution emissions of enterprise: Evidence from China

Frequently occurring extreme weather events can pose a challenge to people and production systems. Coping with extreme high temperatures requires promoting the synergy between pollution reduction and carbon reduction. Accordingly, this study examines the causal relationship between extreme high temperatures and corporate pollution emissions by using the panel data of a Chinese sample from 2000 to 2014. This study uses fixed-effects models for the analysis. Baseline results show that a unit increase in the standardized temperature will result in a 4.6% reduction in corporate pollutant emissions. The heterogeneous analysis shows that extreme high temperatures will have an obvious effect on enterprises with low financing constraints and high policy and public constraints as well as on enterprises in cities with a high level of economic development, in innovative cities, and in the eastern region. We also explore the mechanism through which extreme high temperatures reduce pollutant emissions from the two dimensions of external environmental pressure and internal environmental governance. Extreme high temperatures will prompt enterprises to improve their energy efficiency, engage in innovative production processes, adopt source-and-end governance measures, and curb their pollutant emissions while strengthening government environmental supervision. This study provides new ideas for enterprise pollution reduction and serves as an inspiration to the government in formulating environmental policies.

Cuproptosis Related Gene DLD Associated with Poor Prognosis and Malignant Biological Characteristics in Lung Adenocarcinoma.

Cuproptosis plays a crucial role in the biological function of cells. The subject of this work was to analyze the effects of cuproptosis-related genes (CRGs) on the prognosis and biological function in lung adenocarcinoma (LUAD). In this study, RNA sequencing and clinical data of LUAD samples were screened from public databases and our institution. A CRG signature was identified by least absolute shrinkage and selection operator and Cox regression. In addition, this study analyzed the correlation between prognostic CRGs and clinicopathological features. Finally, this study studied the effect of inhibiting dihydrolipoamide dehydrogenase (DLD) expression on cell biological function. There were 10 CRGs that showed differential expression between LUAD and normal tissues (p<0.05). A prognostic signature (DLD and lipoyltransferase 1 [LIPT1]) was constructed. Survival analysis suggested that patients with LUAD in the high-risk group had shorter overall survival (OS) (p<0.05). High expression of DLD and low expression of LIPT1 were significantly associated with shorter OS (p<0.05). Immunohistochemical analysis revealed that, in LUAD tissues, DLD was highly expressed, whereas LIPT1 was not detected. Finally, inhibition of DLD expression could significantly restrain cell proliferation, invasion and migration. Overall, this prognostic CRG signature may play a pivotal role in LUAD outcome, while oncogene DLD may be a future therapeutic candidate for LUAD.

Integrated Single-cell and Bulk RNA Sequencing Analysis Cross Talk between Ferroptosis-related Genes and Prognosis in Oral Cavity Squamous Cell Carcinoma.

Ferroptosis is a new type of programmed apoptosis and plays an important role in tumour inhibition and immunotherapy. In this study, we aimed to explore the potential role of ferroptosis-related genes (FRGs) and the potential therapeutic targets in oral cavity squamous cell carcinoma (OCSCC). The transcription data of OCSCC samples were obtained from the Cancer Genome Atlas (TCGA) database as a training dataset. The prognostic FRGs were extracted by univariate Cox regression analysis. Then, we constructed a prognostic model using the least absolute shrinkage and selection operator (LASSO) and Cox analysis to determine the independent prognosis FRGs. Based on this model, risk scores were calculated for the OCSCC samples. The model's capability was further evaluated by the receiver operating characteristic curve (ROC). Then, we used the GSE41613 dataset as an external validation cohort to confirm the model's predictive capability. Next, the immune infiltration and somatic mutation analysis were applied. Lastly, single-cell transcriptomic analysis was used to identify the key cells. A total of 12 prognostic FRGs were identified. Eventually, 6 FRGs were screened as independent predictors and a prognostic model was constructed in the training dataset, which significantly stratified OCSCC samples into high-risk and low-risk groups based on overall survival. The external validation of the model using the GSE41613 dataset demonstrated a satisfactory predictive capability for the prognosis of OCSCC. Further analysis revealed that patients in the highrisk group had distinct immune infiltration and somatic mutation patterns from low-risk patients. Mast cell infiltrations were identified as prognostic immune cells and played a role in OCSCC partly through ferroptosis. We successfully constructed a novel 6 FRGs model and identified a prognostic immune cell, which can serve to predict clinical prognoses for OCSCC. Ferroptosis may be a new direction for immunotherapy of OCSCC.

Optimal placement of wind turbine in distribution grid to minimize energy loss considering power generation probability

This research proposes an algorithm to determine the optimal position of wind turbines in a distribution grid for minimizing its annual energy loss. In this paper, the probability distribution of power generation at each wind turbine node is considered and the nonsimultaneous occurrence of the power generation at wind turbine nodes is also interested. Moreover, we also consider the wind energy exploitability at each node in the grid. This algorithm is coded in MATLAB environment and it is verified via a sample IEEE 33 bus distribution grid with a sample data of power generation probability. The verifying results indicated that the optimal number of wind turbines at each node to obtain a minimal annual energy loss. Results also indicated that the node voltage is varied because of the change of power generation; however, the voltage at all nodes is in normal operation range and its median value is between about 1.01 pu to 1.05 pu. The power factor of wind turbines has an impact on the optimal number of wind turbines at nodes and the efficiency of wind turbine installation.

A New Approach to Composite Estimation for Repeated Surveys with Rotating Panels

Composite estimation in repeated surveys with rotating panels refers to methods of estimation that exploit correlations in the data in the sample overlap between survey times to improve the precision of estimates. In this article a novel approach to composite estimation is proposed, in which composite regression estimators of current totals for a number of key variables are generated from a simultaneous calibration of the sampling weights of the overlapping samples of the current and previous survey time. In this procedure, in addition to the usual calibration to known population totals, differences of estimates for the key variables based on the full sample and the common sample from the two consecutive times are calibrated to each other. The resulting multivariate composite regression estimator, which is constructed as an approximate best linear unbiased estimator, incorporates effectively information from the samples of both survey times for enhanced estimation efficiency. Unlike other composite regression estimators, the proposed estimator does not require micro-matching of data in the overlap sample, and, therefore, is free of potential issues associated with it. It is also considerably more practical than existing composite regression estimators and the traditional AK-composite estimator.

Historical Performance Gap, Readiness of Government, and Open Data Quality: Transnational Empirical Analysis Based on Performance Feedback Theory

Open government data has become an important movement for government administrations in numerous countries. Previous studies have paid less attention to the complex relationship among the historical performance gap, the readiness of government (including policy and action dimensions), and open data quality. Therefore, on the basis of performance feedback theory, a theoretical model has been developed and tested to analyze the impact of the historical performance gap on the readiness of government and open data quality. The model is empirically tested by the partial least squares method using sample data from 115 countries. The results show that the historical performance gap could indirectly contribute to a country's open data quality by promoting the readiness of government. In addition, the impact of this gap on the readiness of government and open data quality shows a U-shaped curve. In other words, a greater change in the effect of open data utilization in a country in the previous year entails a more significant promotion of readiness of government and open data quality in the next year. These findings drive governments to pay more attention to performance evaluation in open data and improve the readiness of government to improve their respective country's open data quality.

Research on macroeconomic indicators and stock market correlation analysis based on machine learning

The stock market is known as the barometer of the national economy, and macroeconomic factors have an important impact on the volatility of the stock market. Therefore, considering the impact of macroeconomic factors on the sustainability of stock market volatility will help to capture the time-varying characteristics of volatility persistence, so as to significantly improve the estimation and forecasting effect of volatility. In this work, the generalized autoregressive conditional heteroskedasticity-mixing data sampling (GARCH-MIDAS) model is adopted, which combines the advantages of the GARCH model in short-term volatility modeling and the advantages of MIDAS regression in integrating macroeconomic variables of different frequencies. The GARCH model provides an accurate depiction of intraday volatility in financial markets by capturing the dynamic nature of short-term volatility. MIDAS regression introduces long-term macroeconomic factors into volatility modeling by integrating macroeconomic data with different sampling frequencies, thus making up for the shortcomings of traditional measurement methods in frequency matching. By combining these two methods, the GARCH-MIDAS model can more comprehensively reflect the dynamic changes of stock market volatility, considering both the impact of short-term market volatility and the role of long-term macroeconomic factors, thus providing a more accurate and in-depth analytical tool for volatility prediction and risk management. The results show that the GARCH-MIDAS model can significantly improve the accuracy of volatility forecasting, and provide more reliable decision support for investors, policymakers and economists.

The Influence Of Campus Wifi Facilities On Students Learning Motivation In State University Surabaya

This study aims to determine the effect of WiFi facilities in schools on student motivation to complete the Office Administration Education study program as well as factors that moderate or mediate the influence of the program. The analysis method used is a quantitative approach using purposive sampling to produce non-probabilistic sample data. The sample of this study consisted of 188 master students from the Department of Educational Administration Class of 2022, and 65 people were selected as a representative sample from a total of ten different classes. Data were collected using a questionnaire selected based on relevant indicators. Using SPSS software, data analysis was conducted using classical assumptions such as heteroscedasticity, multicollinearity, and normality. Reliability and validity of the instrument were assessed using a feasibility trial assessment. A Likert scale was used to rank the statements in the instrument. The results show that there is a positive impact of WiFi services on students' learning motivation and tenacity level.

The survey and discussion of research on heart disease prediction based on Apache Spark

Heart is the most important part of the human body, diseases that are related to heart cause a huge thread to human health. In this paper, methods that applied Apache Spark to heart disease related works would be shown and discussed in order to classify these methods and make a conclusion about the innovations and shortcomings of these works. These works are defined into two categories: the ones that adopted traditional machine learning method and the ones that used deep learning methods. By classifying these works into two types, commonalities and similar innovative approaches in the same category of methods can be better observed and summarized, facilitating a clearer comparison of the similarities and differences in the innovative focuses among similar yet distinct methods. By doing so, conclusions were made to show that apart from enhancing operational efficiency and reliability of models for diagnosing and treating heart diseases, current research utilizing Apache Spark in this field also identifies areas for improvement such as expanding sample data representation, speeding up data processing, and addressing concept drift issues with proposed solutions. By addressing these challenges, researchers aim to optimize existing methods using Apache Spark and advanced data analytics techniques to combat heart diseases.

Research on the path of green technology innovation driven by the Environmental Protection Tax Law: Based on data of heavy polluting enterprises.

Environmental Protection Tax Law (EPTL) is a compulsory environmental regulation measure adopted by China to deal with environmental problems. However, with the advancement of implementation, the EPTL produces a dissimilation effect and damages the realization of the Porter hypothesis effect. The study examines the dissimilation effect of green technology innovation regulated by the EPTL using sample data from heavy pollution firms in China. According to the empirical test results: (1) the coordination between levies and administrations, differential tax rate setting, tax information sharing, definition of the scope of levy and administration, tax declaration counseling, and tax rate level verification produce the dissimilation effect; (2) the Porter hypothesis effect of the EPTL is the most significant in medium-sized enterprises and foreign-funded enterprises. By constructing the research model group of dissimilation effect, this study analyzes the application of environmental regulation in China's social and economic background, thus providing a reference for developing of the green economy.

PpBBX32 and PpZAT5 modulate temperature-dependent and tissue-specific anthocyanin accumulation in peach fruit

Abstract Anthocyanins are important compounds for fruit quality and nutrition. The R2R3 MYB transcription factor PpMYB10.1 is known to be critical for regulating anthocyanin accumulation in peach. However, regulatory factors upstream of PpMYB10.1 which control temperature-dependent, cultivar-contrasted and tissue-specific anthocyanin accumulation remain to be determined. In this study, differential anthocyanin accumulation in the outer flesh near the peel (OF) of peach [Prunus persica (L.) Batsch] was observed between cultivars “Zhonghuashoutao” and “Dongxuemi”, as well as among different storage temperatures and different fruit tissues of “Zhonghuashoutao”. By cross-comparisons of RNA-Seq data of samples with differential anthocyanin accumulation, transcription factor genes PpBBX32 and PpZAT5 were identified. These were functionally characterized as two positive regulators for anthocyanin accumulation via transient expression and genetic transformation. Various interaction assays revealed that both PpBBX32 and PpZAT5 can directly activate the PpMYB10.1 promoter and meanwhile interact at protein level as a PpZAT5-PpBBX32-PpMYB10.1 complex. Furthermore, the results of in silico analysis and exogenous application of methyl jasmonate (MeJA) indicated that MeJA favored anthocyanin accumulation, while it was also found that anthocyanin accumulation as well as PpBBX32 and PpZAT5 expression correlated significantly with endogenous JA and JA-Ile in different fruit tissues. In summary, PpBBX32 and PpZAT5 are upstream activators of PpMYB10.1, allowing JAs to take part in temperature-dependent and tissue-specific anthocyanin accumulation by modulating their expression. This work enriches the knowledge of the transcriptional regulatory mechanisms for differential anthocyanin accumulation under internal and external factors.

Machine Learning Predicts Peripherally Inserted Central Catheters-Related Deep Vein Thrombosis Using Patient Features and Catheterization Technology Features.

This study aims to use patient feature and catheterization technology feature variables to train the corresponding machine learning (ML) models to predict peripherally inserted central catheters-deep vein thrombosis (PICCs-DVT) and analyze the importance of the two types of features to PICCs-DVT from the aspect of "input-output" correlation. To comprehensively and systematically summarize the variables used to describe patient features and catheterization technical features, this study combined 18 literature involving the two types of features in predicting PICCs-DVT. A total of 21 variables used to describe the two types of features were summarized, and feature values were extracted from the data of 1,065 PICCs patients from January 1, 2021 to August 31, 2022, to construct a data sample set. Then, 70% of the sample set is used for model training and hyperparameter optimization, and 30% of the sample set is used for PICCs-DVT prediction and feature importance analysis of three common ML classification models (i.e. support vector classifier [SVC], random forest [RF], and artificial neural network [ANN]). In terms of prediction performance, this study selected four metrics to evaluate the prediction performance of the model: precision (P), recall (R), accuracy (ACC), and area under the curve (AUC). In terms of feature importance analysis, this study chooses a single feature analysis method based on the "input-output" sensitivity principle-Permutation Importance. For the mean model performance, the three ML models on the test set are P = 0.92, R = 0.95, ACC = 0.88, and AUC = 0.81. Specifically, the RF model is P = 0.95, R = 0.96, ACC = 0.92, AUC = 0.86; the ANN model is P = 0.92, R = 0.95, ACC = 0.88, AUC = 0.81; the SVC model is P = 0.88, R = 0.94, ACC = 0.85, AUC = 0.77. For feature importance analysis, Catheter-to-vein rate (RF: 91.55%, ANN: 82.25%, SVC: 87.71%), Zubrod-ECOG-WHO score (RF: 66.35%, ANN: 82.25%, SVC: 44.35%), and insertion attempt (RF: 44.35%, ANN: 37.65%, SVC: 65.80%) all occupy the top three in the ML models prediction task of PICCs-DVT, showing relatively consistent ranking results. The ML models show good performance in predicting PICCs-DVT and reveal a relatively consistent ranking of feature importance from the data. The important features revealed might help clinical medical staff to better understand and analyze the formation mechanism of PICCs-DVT from a data-driven perspective.

Reliability evaluation of reinforced concrete arch bridges during construction based on LGSSA‐SVR hybrid algorithm

AbstractDuring the cantilever casting process of reinforced concrete arch bridges with cantilever cast‐in‐situ method, it is difficult to select representative training samples for reliability analysis due to its complex structural system. Many random variables and large computational sample size, this paper proposes to solve the reliability indexes based on the combination of the improved sparrow search algorithm (LGSSA) and the support vector regression (SVR) method. Firstly, random variables are selected according to the actual situation of the bridge structure. Then representative training samples are designed to be substituted into the finite element model through the homogeneous method. The resultant data samples are used to fit the functional function by the support vector regression. Then combined with the penalized function method to transform the nonlinear optimization into the problem of solving the extreme value of the function. Based on the improved SSA to solve the extreme value of the final function. Finally the reliability index of the structure is obtained. With the background of reinforced concrete arch bridge of 200 m, the method is used to analyze the reliability of its buckling cable stress, arch stress, buckling tower deviation and structural system reliability during the cantilever casting process. The results show that the overall structural reliability of the arch ring during cantilever casting is 3.502–3.608. The indexes of buckling cable stress reliability are 3.806–6.784. The indexes of arch ring stress reliability are 4.379–7.562, and the indexes of buckling tower deflection reliability are 3.608–8.123.

The Influence of Profitability, Liquidity and Leverage on Firm Value in Palm Oil Plantation Companies Listed on the Indonesian Stock Exchange (BEI) for the 2019-2021 Period

The aim of this research is to see how profitability, liquidity and leverage impact the value of palm oil companies listed on the Indonesia Stock Exchange during the 2019-2021 period. This research covers all palm oil companies listed on the Indonesia Stock Exchange (BEI). Sampling was carried out purposively based on specific criteria, which resulted in 36 data samples for research. The data used is secondary data taken from financial reports. Multiple linear regression analysis was used to analyze the data. The research results show that while liquidity and profitability do not affect firm value in oil palm plantations for the 2019-2021 period, leverage does affect firm value.

Digital Payment as Moderating Influence of Intellectual Capital on Business Performance

MSMEs are the pillars of the economy in Indonesia. The role in employment absorption is also very large. However, this cannot be separated from the challenges that exist for the sustainability and success of an MSME. The aim of this research is to examine the influence of intellectual capital on business performance and the role of digital payments in moderating the influence of intellectual capital on business performance. The city of Kudus itself is proven to be an industrial city where many residents choose to work as businesspeople, so the object of this research is young entrepreneurs. The sample data processed in this research amounted to 110 respondents. The analysis tool used is SMART PLS. This type of research is quantitative research. The results of this research indicate that Intellectual Capital is not proven to have an effect on business performance. Digital payments have been proven to be able to influence business performance. However, digital payments are not able to moderate the influence of intellectual capital on business performance. Keywords: Intellectual Capital, Digital Payment, Business Performance

Toward compound fault diagnosis via EMAGAN and large kernel augmented few-shot learning

Bearings are essential in machinery. Damage to them can cause financial losses and safety risks at industrial sites. Therefore, it is necessary to design an accurate diagnostic model. Although many bearing fault diagnosis methods have been proposed recently, they still cannot meet the requirements of high-accurate prediction of bearing faults. There are several challenges in this: 1) In practical settings, gathering sufficient and balanced sample data for training diagnostic network models proves challenging. 2) The damage to bearings in real industrial production sites is not singular, and compound faults are also a huge challenge for diagnostic networks. To address these issues, this study introduces a novel fault diagnosis model called EMALKNet that integrates DCGAN with Efficient Multi-Scale Attention (EMAGAN) and RepLKNet-XL, enhancing the detection and analysis of bearing faults in industrial machinery. This model employs EMAGAN to explore the underlying distribution of raw data, thereby enlarging the fault sample pool and enhancing the model’s diagnostic capabilities; The large kernel structure of RepLKNet-XL is different from the current mainstream small kernel and has stronger representation extraction ability. The proposed method has been validated on the Paderborn University dataset and the Huazhong University of Science and Technology dataset.

RAPID: A Rabbit Pregnancy Diagnosis Device Based on Matrix Optical Sensing

Effective early pregnancy diagnosis is crucial for commercial rabbit breeding. Early pregnancy diagnosis enables the implementation of staged feeding for pregnant does, effectively preventing excessive weight gain and reducing the high mortality rates of kits during the birthing stage. This not only enhances production efficiency but also ensures the health and well-being of the breeding rabbits. The study introduces a method and device, the Rabbit Pregnancy Identification Device (RAPID), for detecting rabbit pregnancies using matrix optical sensing. RAPID comprises eight sensor modules and a central host unit. Each sensor module is equipped with three LEDs emitting light at wavelengths of 660nm, 850nm, and 940nm, along with two photodiodes for data collection. A mobile application was developed to enable flexible control of the device. Signal-to-noise ratio tests were conducted to evaluate the stability of data collection by the device across varying light intensities. The experimental results reveal a direct correlation between light intensity levels and the signal-to-noise ratio of collected data. Notably, under a light intensity level of 4, RAPID achieves a signal-to-noise ratio ranging from 42 to 45 dB, satisfying the necessary criteria for data collection. Different classification models were trained using sample data from 216 does across various batches, and their generalization capabilities were evaluated. The experimental findings indicate that the optimal time for RAPID to diagnose the pregnancy status of does is on the 14th day after insemination, achieving an accuracy of 86.63% and a recall of 80.49%. Moreover, the model exhibits a degree of generalization, achieving an accuracy of 78.36% when classifying another batch of sample data. RAPID achieves an accuracy of 97.25% for pregnancy diagnosis of older does, which is 7.44% higher than that of younger does; the accuracy rate for pregnancy diagnosis of does with sparse hair is 86.92%, which is 4.78% higher than that of does with dense hair. By comparing the effectiveness of using data from 8 sensor modules and data from a single sensor module for pregnancy diagnosis of different batches of does, it was found that the former exhibits more stable generalization capability in doe pregnancy detection.

DOES FIRM VALUE MATTER? EMPIRICAL EVIDANCE FROM INDONESIA

The COVID-19 pandemic has hurt the entire government and corporate financial system, making it difficult for businesses to distribute dividends. During the COVID-19 epidemic, this study examines the possible impact of financial statements on firm value by considering dividend policy as a moderating factor. This research employs a quantitative methodology, utilizing purposive sampling techniques and secondary data. The sample data includes all public companies listed on the Indonesia Stock Exchange between 2020 and 2022. There are 127 sample data. We conducted the test using SmartPLS software. Researchers looked at three financial parameters: profitability, leverage, and liquidity. The research results show that leverage and profitability, not liquidity, can influence firm value. The impact of dividend policy on the relationship between profitability and company value is evident, while it does not affect leverage or liquidity. To identify this problem accurately, the researchers used signal theory to get accurate results in identifying this problem. We hope the research results will alert investors to exercise caution when purchasing shares, particularly in manufacturing companies.