Pearson Correlation Method Research Articles

LBP feature and hash function based dual watermarking algorithm for database

In this paper, we propose a local binary pattern (LBP) feature and hash function based dual watermarking algorithm for database. Attribute feature columns are selected to generate zero watermarks using the Pearson correlation method. The zero watermarks are generated by the LBP. The attribute values of the selected feature columns are divided into two parts for embedding and extracting the watermark. Zero watermark feature code is embedded into the lowest significant bit of the selected attribute column by using the database watermarking algorithm with hash function for copyright authentication. The proposed method uses two-layer hashing method to improve the robustness. In the watermark extraction, a method of combining majority voting and Hamming error correction code is used to control the watermark error to ensure the correct extraction rate of the watermark. Experimental results show that the algorithm not only provides good availability of the database after embedding the watermark, but also ensures the correct extraction of the watermark after various attacks.

Hubungan Kebersyukuran dengan Perilaku Prososial pada Anggota IPNU-IPPNU

This study was motivated by the phenomenon of social behavior problems among IPNU-IPPNU members in the village of Candipari. It can be seen from the interview results that the problems faced by the members show a lack of pro-social behavior. This can be seen from the lack of cooperation among members, honesty and mutual help as aspects of social behavior. This study aims to understand the relationship between gratitude and pro-social behavior between IPNU and IPPNU members in Candipari village. The students in this study are members of IPNU and IPPNU in the village of Candipari. The sampling method in this study is used for saturation sampling. The research method used is quantitative research using an interactive approach. The data collection method used psychological scales, namely affective behavior scale and gratitude scale. The construction of the psychological scale in this study uses the Likert scale model (response scale). The data analysis method used in this study is the Pearson correlation method (Product Moment) using the software SPSS 20 for windows. Based on the analysis results, the data analysis test results obtained a correlation coefficient of 0.640 with a significance level of 0.000 < 0.05. This means that there is a strong relationship between gratitude and pro-social behaviour. Based on the experimental results, the coefficient of determination for the result is 0.395 (R Square), which means that in this study the gratitude variable contributes 39.5% to the affective behavioral variable. Based on the experimental results, the proposed hypothesis is acceptable, that is, there is a positive relationship between Gratitude and social behavior of IPNU-IPPNU members in Candipari village.

Study on the propagation law of meteorological drought to hydrological drought under variable time Scale: An example from the Yellow River Water Supply Area in Henan

To study the propagation law of meteorological droughts to hydrological droughts under variable time scales, and to reveal the synchronisation gap and lag time of hydrological droughts compared to meteorological droughts, and the lag degree of hydrological drought is quantitatively studied. In this paper, the Yellow River Water Supply Area in Henan is taken as the research area, and the propagation time of meteorological drought to hydrological drought under variable time scale is mainly studied. The calculation of drought propagation time is set as two states: interval scale and continuous scale. The Standardized Precipitation Index (SPI) sequence and Standardized Runoff Index (SRI) sequence (1)with interval and continuous scales were obtained by multi-scale standardization of precipitation and runoff, which were used to characterize meteorological drought and hydrological drought respectively. The Mann-Kendall (M−K) test method was used to analyze the evolution of SPI sequence and SRI sequence at monthly scale, and to study the lag time of hydrological drought in four seasons compared with meteorological drought. Combining the Grey Correlation Method and Pearson correlation coefficient methods, the time interval for the propagation of meteorological droughts to hydrological droughts was determined based on the strongest correlation of different combinations of sequences. Finally, the propagation time series is divided into sliding windows to analyse the M−K dynamic trend of propagation time from a seasonal scale perspective. It provides a strong basis for the study of drought propagation based on multi-scale.

Biodegradability analysis of Dioxins through in silico methods: Model construction and mechanism analysis

The biodegradation treatment of dioxins has long been of interest due to its good ecological and economic effects. In this study, the biodegradability of polychlorinated dibenzo-p-dioxins (PCDDs) were investigated by constructing machine learning and multiple linear regression models. The maximum chlorine atomic charge (qHirshfeldCl+), which characterizes the biodegradation ability of PCDDs, was used as the response value. The random forest model was used to rank the importance on the 1471 descriptors of PCDDs, and the BCUTp-1 h, QXZ, JGI4, ATSC8c, VE3_Dt, topoShape, and maxwHBa were screened as the important descriptors by Pearson's correlation coefficient method. A quantitative structure-activity relationship (QSAR) model was constructed to predict the biodegradability of PCDDs. In addition, the extreme gradient boosting (XGBoost) and random forest model were also constructed and proved the good predictability of QSAR model. The biodegradability of polychlorinated dibenzofurans (PCDFs) can also be predicted by the constructed three models from a certain level after adjusting some model parameters, which further proved the versatility of the models. Besides, the sensitivity analysis of the QSAR model and a 3D-QSAR model was developed to investigate the biodegradability mechanisms of PCDDs. Results showed that the descriptors BCUTp-1 h, JGI4, and maxwHBa were the key descriptors in the biodegradability effect by the sensitivity analysis of the QSAR model. Coupled with the results of PCDDs biodegradability 3D-QSAR model, BCUTp-1 h, JGI4, and maxwHBa were confirmed as the main descriptors that affect the biodegradability of dioxins. This study provides a novel theoretical perspective for the research of the biodegradation of both PCDDs and PCDFs dioxins.

Aging, personality, and teaching aptitude in school grade physical education teachers

Background and Study Aim. The primary objectives of the research were to examine the impact of aging on teaching aptitude, explore the association between personality traits and teaching effectiveness, and identify potential interactions between age, personality, and teaching aptitude. Material and Methods. Data were collected through standardized measures assessing teaching aptitude, personality traits, and demographic information. An intentional sampling with a survey group size of 283 physical education teachers (28.6% Female) including Trained Graduate Teachers (26.5%) and Post Graduate Teachers (73.5%) respectively. One-way analysis of variance, Pearson correlation coefficient, Hierarchical Regression, and mediation methods were used to analyze the obtained data. Results. Teaching aptitude was negatively related to chronological age (male, r = -.296, female, r = -.43), teaching experience (male, r = -.343, Female, r = -.326), and neuroticism (male, r = -.408, female, r = -.399). Extraversion, Openness, Agreeableness, and Conscientiousness were positively related to teaching aptitude. Hierarchical Regression shows that gender has not explained significant variation among teaching aptitude after controlling the effects of personality traits and age (R2 square changes = .004). However, personality has partial mediating effects on the direct relationship between age and teaching aptitude. Conclusions. Teaching aptitude decreases with age in both genders. The decreasing teaching aptitude with age has significant implications on the education system. However, the personality traits of the teachers also changed with age. Teachers gained more neuroticism as aged. It is essential to identify the factors contributing to this phenomenon and develop strategies to mitigate its effects.

Supply Chain Inclusivity and Performance of State Corporations in the Energy Sector in Kenya.

In recent years, there has been a growing recognition of the importance of diversity and inclusivity in supply chain management. This includes a focus on promoting diversity and inclusivity within the workforce, as well as in the supply chain itself. Governments while deploying public funds for the provision of goods, works and services have realized the need to use supply chain function to advance social and economic development by ensuring the inclusion of otherwise excluded. However, there is little research done to establish whether inclusivity practices improve organization performance. This study therefore sought to establish the influence of supply chain inclusivity on the performance of state corporations in the energy sector. Specifically, the study aimed at determining the influence of discriminatory procurement, stakeholder involvement, supply chain diversity and supply chain integration on performance of state corporations in the energy sector in Kenya. The study adopted a descriptive survey design. The target population for this study was nine (9) State Corporations operating in the Energy sector in Kenya. Due to the limited number of target population, a census approach was adopted. Thus, all the 9 state corporations operating in Energy sector were included. The research purposefully targeted employees in the procurement and finance departments. A total of 20 employees were involved per organization arriving at a total of 180 respondents. A questionnaire was used in collecting data. With the study being quantitative in nature, both descriptive statistics and inferential statistics were employed. The study used SPSS version 26 to facilitate the analysis of data. Inferential data analysis was done using Pearson correlation coefficient and multiple regression analysis method. Data was presented using a combination of statistical techniques and graphical techniques. The study established that jointly, discriminatory procurement, stakeholder involvement, supply chain diversity and supply chain integration influence the performance of state corporations in the energy sector. The study recommends that state corporations should institutionalize procurement plans with a total reservation of at least 30% of the procurement budget to the special groups. The study also recommends that state corporations should authenticate tender awards and purchase orders and enter into agreements with relevant financing institutions with an undertaking that the contracted special enterprises be paid through the account opened with the financier. Consequently, the study recommends that local firms be offered more protection to enable them stand chance against the mega global entities. Further, the study recommends for involvement of citizens, local communities and civil society organizations in the process of purchasing goods and services by the state corporations through provision of input on procurement plans, monitoring procurement processes, and providing feedback on the quality of goods and services purchased. Based on the findings of this study, it is recommended to state corporations that for them to effectively embrace supply chain diversity there is need to access and mitigate any barriers and bottlenecks that may hinder fair treatment, equity and justice in supply chain processes. The study also recommends to state corporations to explore and embrace advanced and emerging technologies in exchange of strategic supply chain information for improvement in the relationship and integration between the supply chain partners. The study further recommends for collaborations within the supply chain to allow firms to partner by combining core competencies and expertise as firms derive more benefits from working together than individual firms can gain on their own.

Decoding the enigma of 100-year record-breaking rainfall over Tamil Nadu using wavelet analysis

There have been increasing trends of short-intensive rainstorm events, which lead to urban flood disasters in many coastal areas of India. The relationship between regional rainfall and global large-scale climate indices has shown high teleconnections in terms of recurrence and covariance. We have examined the teleconnection between the November and December 2015 rainstorm events (100-year record breaking rainfall) with the Oceanic Nino Index using the dataset from the Indian Meteorological Organisation (NEMR) and National Oceanic and Atmospheric Administration (ONI). The study has found out not only the existence of the cause-and-effect relationship but also the recurrences of these high rainfall events. The climatological trend of northeast monsoon rainfall (NEMR) and Oceanic Nino Index (ONI) is calculated, and the correlation for a period of 71 years (1951–2021) is quantified using Pearson's correlation method. We observed that Chennai, Nagapattinam, Cuddalore, Kancheepuram, Thiruvarur, and adjoining coastal districts are more prone to torrential rains during El Niño years as compared to La-Niña years which is unusual but not exceptional. Wavelet analysis is performed on the NEMR and ONI signals to decipher the teleconnections at different temporal scales. Wavelet coherence shows an in-phase relationship between the ONI and NEMR signal and high covariance (0.8–0.9 at 5% significance level using Monte Carlo generated noise), between the two in 2015 the year which broke the 100 years rainfall record for Chennai. The study highlights the crucial linkage (cause and effect relationship) between the regional rainstorms and the large-scale global index (ONI), and wavelet analysis can explain the distant relationship in the northeast monsoon. The study provides quantified insights especially in the present era of climate change, where the hydrological extremes are persistent. The study does not only connect ONI with NEMR but also quantifies the in-phase strength of the relationship which can guide the scientific community towards accurate predictions and develop disaster mitigation strategies.

Assessment of heavy metal accumulation in dust and leaves of Conocarpus erectus in urban areas: Implications for phytoremediation

This study investigated the spatio-temporal variation of heavy metal concentrations in roadside dust and its correlation with heavy metal content in roadside plant leaves. Sixty re-suspended dust samples were collected from six locations during two seasons (dry and wet). Simultaneously, leaf samples of Conocarpus erectus were collected from the same locations. The samples were analyzed using an atomic absorption spectrophotometer to determine the concentrations of As, Cu, Fe, Cd, Pb, and Ni. The pH and conductivity of the dust were also analyzed using a glass electrode. The mean concentrations (μg/g) of heavy metals in the dust ranged from 0.337 to 6.34 for As, 10.60–55.39 for Cu, 542.17–1453.45 for Fe, 0.193–1.026 for Cd, 11.82–52.56 for Pb, and 10.19–29.27 for Ni in different seasons. Similarly, the concentrations of heavy metals in the leaf samples ranged from 0.031 to 1.70 for As, 6.90–13.19 for Cu, 263.09–576.16 for Fe, 0.299–1.33 for Cd, 0.019–17.82 for Pb, and 2.09–7.015 for Ni in different seasons. The correlation between heavy metal concentrations in different seasons was examined using Pearson's and Spearman's correlation methods. To compare the bioaccumulation ability, bio-concentration factors (BCF), comprehensive bio-concentration index (CBCI), and metal accumulation index (MAI) were calculated. The Conocarpus erectus MAI was consistent across different locations and seasons (24.49 ± 0.149). Based on CBCI, the bioaccumulation capacity of heavy metals they were ranged from 6.57 to 12.83 in the dry season and from 5.84 to 10.24 in the wet season. These findings provide valuable insights into the distribution and accumulation of heavy metals in roadside dust and plant leaves, highlighting the potential of Conocarpus erectus as a bio-accumulator for heavy metal pollution in urban environments. The results can be utilized to develop appropriate strategies for environmental management and pollution control in areas affected by vehicular pollution.

Assessing the impacts of climate variables on long-term air quality trends in Peninsular Malaysia

Climate change is thought to influence the composition of atmospheric air, but little is known about the direct relationship between these variables, especially in a hot tropical climate like that of Malaysia. This work summarizes and analyzes the climate state and air quality of Peninsular Malaysia based on selected ground-based observations of the temperature, precipitation, relative humidity, wind speed, wind direction and concentrations of PM10, O3, CO, NO2, and SO2 over the last 20 years (2000–2019). The relationship between the climate state and air quality is analyzed using the Pearson correlation and canonical correlation analysis (CCA) methods is employed to predict the degree of change in the future air quality under different warming scenarios. It is found that the Peninsular Malaysia mainly experienced strong precipitation in the central and mountainous regions, while air pollutants are primarily concentrated in densely populated areas. Throughout the period of study (interannual, monthly, and diurnal time series analyses), Peninsular Malaysia became warmer and drier, with a significant increase in temperature (+4.2 %), decrease in the relative humidity (−4.5 %), and greater fluctuation in precipitation amount. The pollution conditions have worsened; there has been an increase in the PM10 (+16.4 %), O3 (+39.5 %), and NO2 (+2.1 %) concentration over the last 20 years. However, the amount of SO2 (−53.6 %) and CO (−20.6 %) decreased significantly. The analysis of the monthly variation shows a strong bimodality of the PM10 and O3 concentrations that corresponds to the monsoon transition. Intensive diurnal fluctuations and correlations are observed for all the variables in this study. According to the CCA, the air quality factors are strongly correlated with meteorological factors; in particular, the CO, O3, and PM10 concentrations interact strongly with the air temperature. These findings show that the future air quality in Peninsular Malaysia has high possibility to deteriorate under warming condition.

ANALISIS VOLUME PENJUALAN PRODUK BENIH PADI UNGGUL DI PT. PERTANI (PERSERO) SEMARANG, JAWA TENGAH

This research aimed to perceive the trend of sales volume and to analyze the correlation of sale’s factors with prime rice seed sales volume in PT. Pertani (Persero) Semarang in 2016-2020. This research was conducted at PT. Pertani (Persero) Semarang, Central Java. The locus of this research was chose using purposive sampling method. This research used case study as a research method where researcher did the research intensively, deeply, and detailed about the sales of prime rice seed in PT. Pertani (Persero) Semarang. Primary data was collected from an interview with PT. Pertani (Persero) Semarang’s employees. Secondary data including monthly production report, monthly sales report, marketing report of prime rice seed product at PT. Pertani (Persero) Semarang was collected through a time series method for 5 consecutive years from 2016-2020. Data was analized with trend analysis and pearson correlation method. The result of trend analysis shows a positive trend of prime rice seed sales volume in 2016-2020. The result of pearson correlation analysis shows that prices and marketing expenses are strongly correlated.

Analysis of soil organic matter influencing factors in the Huangshui River Basin by using the optimal parameter-based geographical detector model

To quantitatively analyze the spatial distribution of soil organic matter (SOM) content and its main influencing factors in the Huangshui River Basin, Qinghai Province, China, a total of 13 factors including topography, climate, soil properties, and vegetation cover were selected. The influence of these factors on the spatial distribution of SOM content was analyzed using the geographical detector model and the Pearson correlation coefficient method. The results showed that the average SOM content in the cultivated soil layer of the Huangshui River Basin was 28.38 g·kg−1. The results of factor detection and Pearson correlation analysis indicated that the three dominant factors causing differences in the spatial distribution of SOM content were mean annual temperature, altitude, and soil pH. Furthermore, SOM content was negatively correlated with temperature and positively correlated with elevation. When the factors interacted, they exhibited a type of bi-enhance or nonlinear-enhance. The interaction between two factors improved the explanatory power of SOM, indicating the complexity of factors causing spatial distribution differences in SOM. This study provides a favorable basis for precision fertilization, soil improvement, and of soil quality improvement in the Huangshui River Basin.

Effect of proprotein convertase subtilisin/kexin type 9 on platelet activation associated with sepsis

To investigate the effect of proprotein convertase subtilisin/kexin type 9 (PCSK9) on platelet activation in sepsis. (1) Clinical trial: a prospective study was conducted. Patients with sepsis and septic shock aged ≥ 18 years old who met the diagnostic criteria of Sepsis-3 admitted to the department of intensive care medicine of the Affiliated Hospital of Binzhou Medical College from January to October in 2021 were selected as subjects. Healthy subjects in the same period were taken as healthy control group. Platelet count (PLT) in the first routine blood test after admission was recorded. Venous blood was taken 1 day after diagnosis, and serum PCSK9 level was determined by enzyme-linked immunosorbent assay (ELISA). The differences of PCSK9 level and PLT between the two groups were compared, and subgroup analysis was conducted based on PLT for patients with sepsis. The correlation between PCSK9 level and PLT in septic patients was analyzed by Pearson correlation method. (2) Animal experiment: 80 male C57BL/6 mice were randomly divided into control group, sepsis model group [lipopolysaccharide (LPS) group], PCSK9 inhibitor pretreatment group (PCSK9 inhibitor+LPS group) and PCSK9 inhibitor control group (PCSK9 inhibitor group), with 20 mice in each group. The mouse model of sepsis was reproduced by intraperitoneal injection of LPS 12 mg/kg, and the control group and PCSK9 inhibitor group were intraperitoneally injected with the same amount of sterile normal saline. PCSK9 inhibitor+LPS group and PCSK9 inhibitor group were pretreated with PCSK9 inhibitor 5 mg/kg intraperitoneal injection for 7 days before injection of LPS or normal saline, respectively, and the control group and LPS group were injected with an equal amount of sterile normal saline. The lung tissues were taken for pathological and immunohistochemical observation 24 hours after modeling. Blood was taken from the heart for determining PLT. Platelet activation was detected by flow cytometry. The expression level of platelet-activation marker CD40L was detected by Western blotting. (1) Clinical trial: there were 57 cases in the sepsis group and 27 cases in the healthy control group. Serum PCSK9 level in the sepsis group was significantly higher than that in the healthy control group (μg/L: 232.25±72.21 vs. 191.72±54.92, P < 0.05), and PLT was significantly lower than that in the healthy control group [×109/L: 146.00 (75.50, 204.50) vs. 224.00 (194.00, 247.00), P < 0.01]. Subgroup analysis showed that the serum PCSK9 level in the thrombocytopenia patients (n = 20) was significantly higher than that in the non-thrombocytopenia patients (n = 37; μg/L: 264.04±60.40 vs. 215.06±72.95, P < 0.01). Correlation analysis showed a significant negative correlation between serum PCSK9 levels and PLT in septic patients (r = -0.340, P = 0.010). (2) Animal experiment: there were no significant pathological changes in lung tissue in the control group and PCSK9 inhibitor group under light microscope, and no significant differences in PLT, platelet activation and plasma CD40L protein expression was found between the two groups. In the LPS group, a large number of inflammatory cells were infiltrated in the pulmonary interstitium, the alveolar structure was damaged obviously, the alveolar septum was widened, the alveolar cavity was extensively bleeding, the capillary dilatation with bleeding and platelet aggregation were found, the PLT was significantly decreased, the platelet activation and the expression level of CD40L protein in plasma were significantly increased. The infiltration of inflammatory cells in lung tissue of mice in the PCSK9 inhibitor+LPS group was reduced to a certain extent, the thickening of alveolar septa was reduced, the platelet aggregation in lung tissue was decreased as compared with the LPS group, the PLT was significantly increased (×109/L: 515.83±46.60 vs. 324.83±46.31, P < 0.05), the platelet activation and the expression level of CD40L protein in plasma were significantly decreased [positive expression rate of platelet activation dependent granule surface facial mask protein CD62P: (12.15±1.39)% vs. (18.33±2.74)%, CD40L protein (CD40L/β-actin): 0.77±0.08 vs. 1.18±0.10, both P < 0.05]. PCSK9 level has a certain effect on promoting platelet activation in sepsis, and inhibition of PCSK9 level may have potential research value in improving adverse outcomes caused by sepsis thrombocytopenia.

Prediction of the concentrations of PM2.5 and NOx using machine learning-based models

Air pollutants such as fine particulate matter (PM2.5) and nitrogen oxide (NOx) are important to study due to their harmful health effects; therefore, it is essential to accurately predict them. However, due to a lack of effective monitoring and modeling methodologies, the accurate prediction of these air pollutants is challenging. The advancement in computational methodologies and the availability of larger data sets have expanded the opportunities of modeling for application in wide-ranging air pollution studies. Previous studies have utilized a lesser amount of data to predict air quality; therefore, a machine learning-based approach can be useful by utilizing a larger dataset for accurate pollutant predictions. Hence, a pilot study was carried out by utilizing two-month datasets for May and June for the year 2018 to predict PM2.5 and NOx concentration at three locations in Jaipur city, India. A multiple linear regression (MLR) based machine learning model has been developed in this study. The input parameters were selected using the Pearson correlation coefficient method. Specifically, PM10, NOx, and benzene were selected for PM2.5 prediction, and NH3, CO, O3, and benzene were used for NOx prediction. The model results were further refined by the outlier’s removal method. The final values of R2 for PM2.5 varied from 0.59 to 0.68, and for NOx, it ranged from 0.56 to 0.81 at the three different sites of Jaipur city, representing the effectiveness of the model developed. Overall, the results of this study will be useful in measuring and managing air quality in urban cities in India.

Vegetation dynamics and its driving factors in agro-climatic zones of Upper Bhima Sub-basin, Western India

Climate change is a key threat to biodiversity and ecosystems. The impacts of climate change on vegetation must be evaluated to control the sustainability of the ecosystem. Precipitation and temperature are the most important climatic parameters affecting vegetation growth. It is important to analyze the spatial and temporal variations of vegetation under changing climatic parameters to envisage the response to the regional ecosystem. In this study, variation of the Normalized Difference Vegetation Index over five different agro-climatic zones in the Upper Bhima Sub-basin from 2003 to 2013 was analyzed to find the effect of climatic and hydrological variables on vegetation dynamics. The correlation analysis has been performed by using the Pearson correlation method and Geographical Detector Model. Taylor diagrams are generated to highlight the variation in the correlation values of NDVI with other variables. Results show that precipitation and temperature are key parameters for the growth of the vegetation, but Western Ghat Zone shows contradictory results. To identify the reasons behind contradictory results in the Western Ghat Zone, hydrological parameters such as soil moisture, terrestrial water storage and groundwater levels are analyzed. NDVI shows a positive correlation with precipitation in Water Scarcity Zone, and Assured Rainfall Zone with correlation values 0.37, and 0.462 respectively, but, in the Western Ghat Zone, NDVI has a negative correlation (− 0.38) though more than average precipitation occurs in that region. The result of the Geographical Detector Model reveals that the terrestrial water storage and precipitation are dominant factors affecting vegetation in WGZ and WSZ, respectively.

Lithium-ion battery state of health estimation based on multi-source health indicators extraction and sparse Bayesian learning

A concise and accurate method for estimating the state of health (SOH) of lithium-ion batteries in the on-board energy management system is critical. However, SOH cannot be directly measured by on-board equipment. To improve the accuracy of SOH estimation for Li-ion batteries, this work proposes an SOH estimation model based on multi-source health indicators (HIs) extraction and sparse Bayesian learning. First, four direct HIs are extracted from the voltage and temperature curves of the batteries during charging and discharging, and two indirect HIs are extracted from the incremental capacity curves in combination with a Gaussian filtering algorithm. Then, the datasets are divided into three different training and test sets, which are used to simulate online SOH estimation under different situations. Finally, the six extracted HIs are selected using the Pearson correlation coefficient method, and the experiment is repeated for one of the situations using the three higher correlation features, and the results before and after selection are compared. The experimental results show that the proposed model can achieve satisfactory results in various simulated online estimation situations on the NASA and Oxford datasets.

Cascading effects of drought in Xilin Gol temperate grassland, China

In the context of global climate change, the cascading risk of compound natural hazards is becoming increasingly prominent. Taking Xilin Gol grassland as study area, we used the Mann–Kendall trend method, the maximum Pearson correlation coefficient method, and Partial least squares structural equations modeling to detect the characteristics of spatiotemporal pattern changes of the three types of droughts. The propagation characteristics and the cascade effects among the three types of droughts was also identified. The standardized precipitation evapotranspiration index, standardized evapotranspiration drought index, and soil moisture index were selected as indicators of meteorological drought, ecohydrological drought, and soil drought, respectively. The results show that the warm and dry trend in Xilin Gol grassland was obvious in the past 30 years. The seasonal propagation of different drought was prominent, with stronger spread relationships in summer. Persistent meteorological drought was more likely to trigger the other two types of droughts. The intensity and range both increased during the propagation from meteorological drought to ecohydrological drought. The cascade effect was differed in different time scales. The multi-year persistent climatic drought has an overwhelming cascade effect on soil drought and ecohydrological drought. For seasonal or annual drought, vegetation cover change has an amplifying or mitigating impact on the cascade effect, where soil moisture, evapotranspiration (ET), and their relationship all play important roles. In eastern areas with better vegetation cover, the reduction of vegetation in the early stage aggravated the cascading effect of meteorological drought to ecohydrological drought through reducing ET. In the northwestern sparsely vegetated areas, ET was mainly influenced by meteorological factors, and the cascade effect of meteorological factors to ecohydrological drought was more obvious than that of soil drought.

A design of ultra-short-term power prediction algorithm driven by wind turbine operation and maintenance data for LSTM-SA neural network

Due to factors such as meteorology and geography, the generated power of wind turbines fluctuates frequently. In this way, power changes should be predicted in grid connection to take control measures in time. In this paper, an operation and maintenance data-driven LSTM-SA (long short-term memory with self-attention) prediction algorithm is designed to predict the ultra-short-term power of wind turbines. First, the wind turbine operation and maintenance data, including wind speed, blade deflection angle, yaw angle, humidity, and temperature, are subjected to feature selection by using the Pearson correlation coefficient method and the Lasso algorithm, thereby establishing the correlation between wind speed, blade deflection angle, and out power. Then, full-connect neural network is trained to establish a mapping model of wind speed, blade deflection angle, and out power. The power change rate k is calculated by the derivative of output power to wind speed. Finally, based on the historical power data and the power change rate k, the LSTM neural network power prediction model is trained to calculate the output power prediction value. In order to increase the training efficiency and reduce the delay, the self-attention mechanism is used to optimize the hidden layer of the LSTM model. The test results show that, compared with similar prediction algorithms, this algorithm has higher prediction accuracy, faster convergence speed, and better stability, which can solve the problem of accurately predicting ultra-short-term power when wind power training data is inadequate.

Pengaruh loyalitas pengguna aplikasi iPusnas di kalangan mahasiswa Diploma 3 Perpustakaan FKIP Universitas Tanjungpura terhadap minat membaca buku digital

This research was conducted to determine the level of loyalty and interest in reading digital books among Diploma 3 students of the FKIP Library of Tanjungpura University and to determine the effect of loyalty on interest in reading digital books among Diploma 3 students of the FKIP Library of Tanjungpura University. This study uses a quantitative approach so that the data collected can be processed specifically. The population in the study amounted to 58 people, namely all active students in the environment of the Diploma 3 Library study program at FKIP Tanjungpura University. The sample was selected using saturated sampling technique or cencus. Data collection in this study was by semi-structured interviews and then followed by distributing questionnaires to the sample. Value measurement on data results using a Likert scale. The validity test used 30 samples using the Pearson Correlation method. The data analysis technique uses the mean formula followed by the grand mean formula from the overall average. While hypothesis testing is done with simple linear regression tests and product moment correlation tests. The research shows the results that iPusnas application user loyalty has a positive and significant impact on interest in reading digital books

Evolution Pattern of Blue–Green Space in New Urban Districts and Its Driving Factors: A Case Study of Zhengdong New District in China

Understanding the spatial–temporal evolution of the blue–green space (BGS) is crucial for urban planning and ecological security protection. However, the evolutionary patterns and driving factors of the BGS in new urban districts remain unclear. Based on the classical qualitative models, “patch–corridor–matrix” and “sustainability prism”, this study adopted the land use transition matrix and landscape index to quantify evolution patterns, and Pearson correlation and geographical detector analysis methods to reveal the driving factors. Taking Zhengdong New District as a case study, the results indicated the following: (1) The BGS rate was reduced by 35% from 2003 to 2021, with the most significant decrease in cultivated lands. (2) The evolution pattern of BGS alternated sequentially among ecological, disorderly, and balanced states. Among them, urban green spaces tended towards a more stable and aggregated state. Other BGSs became more fragmented and discontinuous. (3) The “sustainability prism” model consists of four driving factors, including social economy, rainwater safety, ecological liveability, and physical geography. (4) Socioeconomic factors significantly decreased the BGS, as indicated by the correlation coefficients of −0.988 and −0.978 between built-up areas and population and gross domestic products, respectively. Physical geographic factors played weak roles in the evolution of the BGS. Meanwhile, rainwater safety and ecological liveability positively affected the BGS.

Transcriptome sequencing and miRNA-mRNA network construction in exosome of macrophage M2 in stomach adenocarcinoma

BackgroundStomach adenocarcinoma (STAD) is the most common histological type of gastric cancer (GC). Macrophages are an essential part of the tumor microenvironment. We attempted to search for potential molecular markers associated with macrophages, which might be helpful for STAD diagnosis and treatment.MethodsFirstly, exosome in macrophages was extracted for RNA sequencing to identify differentially expressed microRNAs (miRNAs) (DEmiRNAs). Then, DEmiRNAs and differentially expressed mRNAs (DEmRNAs) were screened in the Cancer Genome Atlas (TCGA) database. The miRNAs related to macrophage M2 polarization were obtained by intersecting the DEmiRNAs obtained from the sequencing data and TCGA data. Using the Pearson correlation coefficient method, the mRNAs significantly related to macrophage M2 were screened out, followed by construction of the macrophage M2-miRNA-mRNA network. Subsequently, real-time-polymerase chain reaction (RT-PCR) and online datasets were applied to validate the expression of DEmiRNAs and DEmRNAs.ResultsA total of 6 DEmiRNAs were identified in RNA sequencing; 59 DEmiRNAs and 1838 DEmRNAs were identified in TCGA database. Among which, a common miRNA (hsa-miR-133a-3p) associated with the M2 polarization of macrophages was identified. Fifteen common mRNAs were obtained between DEmRNAs and mRNAs targeted by DEmiRNAs. Eventually, a core macrophage M2-1 down-regulated miRNA-7 and up-regulated mRNAs network was constructed, including hsa-miR-133a-3p, SLC39A1, TTYH3, HAVCR2, TPM3, XPO1, POU2F1, and MMP14. The expression of miRNA and mRNAs was in line with the validation results of RT-PCR and online datasets.ConclusionIn this study, the screening of biomarkers in exosome of macrophage M2 may contribute to the prognosis of STAD patients.