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How to acquire useful information intelligently in the age of information explosion has become an important issue. In this context, sentiment analysis emerges with the growth of the need of information extraction. One of the most important tasks of sentiment analysis is feature extraction of entities in consumer reviews. This paper first constitutes a directed bipartite feature-sentiment relation network with a set of candidate features-sentiment pairs that is extracted by dependency syntax analysis from consumer reviews. Then, a novel method called MHITS which combines PMI with weighted HITS algorithm is proposed to rank these candidate product features to find out real product features. Empirical experiments indicate the effectiveness of our approach across different kinds and various data sizes of product. In addition, the effect of the proposed algorithm is not the same for the corpus with different proportions of the word pair that includes the “bad”, “good”, “poor”, “pretty good”, “not bad” these general collocation words.

The phenotypic and genotypic characterization of eight rhizobial isolates obtained from Arachis hypogaea nodules grown under stress environment was performed. Isolates were screened for their ability to tolerate different abiotic stresses (high temperature (60° C), salinity (1–5% (w/v) NaCl), and pH (1–12). The genomic analysis of 16S rRNA and housekeeping genes (atpD, recA, and glnII) demonstrated that native groundnut rhizobia from these stress soils are representatives of fast growers and phylogenetically related to Rhizobium sp. The phenotypic characterization (generation time, carbon source utilization) also revealed the isolates as fast-growing rhizobia. All the isolates can tolerate NaCl up to 3% and were able to grow between 20 and 37 °C with a pH between 5 to 10, indicating that the isolates were alkali and salt-tolerant. The tested isolates effectively utilize mono and disaccharides as carbon source. Out of eight, three rhizobial isolates (BN-20, BN-23, and BN-50) were able to nodulate their host plant, exhibiting their potential to be used as native groundnut rhizobial inoculum. The plant growth promoting characterization of all isolates revealed their effectiveness to solubilize inorganic phosphate (56–290 µg mL−1), synthesize indole acetic acid (IAA) (24–71 µg mL−1), and amplification of nitrogen fixing nifH gene, exploring their ability to be used as groundnut biofertilizer to enhance yield and N2-fixation for the resource poor farmers of rainfed Pothwar region.

The application of horizontal well volume fracturing technology is an important method for enhancing oil recovery in tight oil reservoirs. However, the influence mechanism of the fracture placement scheme (FPC) on postfracturing productivity is still unclear. Based on the theory of the black oil model, combined with the reservoir stimulation characteristics of horizontal well volume fracturing in tight oil reservoirs, this paper established a postfracturing reservoir production simulation model. History fitting was used to verify the accuracy of the production model simulations. A series of numerical simulations was carried out to study the influence mechanisms of the fracture parameters and FPC on productivity. The simulation results show that compared with the fracture conductivity, the fracture length and number are the main parameters affecting tight oil reservoir productivity. Selecting a reasonable fracture length and number can realize the economical and efficient production of tight oil reservoir volume fracturing. Compared with the traditional fracture equal-length scheme, an FPC with an uneven fracture length can increase the cumulative oil production of oil wells. Under the condition of the same total fracture length, the scheme with a staggered distribution of long fractures and short fractures has the largest cumulative oil production over five years. A reasonable well spacing can greatly reduce the impact of interwell interference on postfracturing dual branch horizontal well productivity. When dual branch horizontal well fractures are alternately distributed, the postfracturing productivity is higher. The production simulation model established in this paper provides a method to accurately evaluate the productivity of horizontal wells after volume fracturing, which can provide guidance for the optimization of hydraulic fracturing operation parameters.

Exploring the sensitivity of rural households’ livelihood strategies to livelihood capital is of great significance for improving rural households’ livelihood levels. This paper selects 23 livelihood capital measurement indicators and conducts an in-depth survey of rural households. In addition, the entropy method and a weighted comprehensive model are used to explore the basic characteristics of rural households’ livelihood capital in the upper reaches of the Min River, China, in 2017. Furthermore, econometric models are used to analyze the sensitivity of rural households’ livelihood strategies to livelihood capital. As indicated from the research, the livelihood capital levels of different types of rural households in the study area are not equivalent. The types of rural households with different livelihood strategies can be ordered in terms of quantity as follows: non-agricultural type > non-agricultural dominant type > agricultural dominant type > pure agricultural type. Livelihood strategies have different sensitivities to different livelihood capital measurement indicators. Among these indicators, cash income, the number of relatives and friends available for financial assistance, and the number of civil servants have positive effects on the livelihood strategy selection of non-agricultural dominant rural households and non-agricultural rural households. However, the average age of laborers, area of cultivated land and gardens, number of livestock and poultry, and present value of production tools have negative effects. These evaluation results can provide a scientific decision-making basis for the formulation of poverty alleviation policies by relevant government departments.

The quality of drinking water is crucial for human health and the sustainable development of societies. The Aksu River Basin, a typical inland river system, has areas where groundwater arsenic levels exceed safe drinking water standards (i.e., arsenic concentrations greater than 10 μg/L). Identifying the causes of high arsenic levels in the basin’s groundwater requires further study. Analyzing the hydrogeochemical composition of the Aksu River basin helps us to understand the spatial distribution of groundwater environments and locate areas with dangerously high arsenic levels. In this research, we collected 196 groundwater samples from along the river. Out of these, 38 samples had arsenic levels above 10 μg/L, which represents 19.4% of the total samples collected. By examining the slope changes in the cumulative frequency curves of major ion ratios and employing geostatistics (specifically, the Kriging interpolation), and taking into account the environmental characteristics of the entire basin, we divided the study area into five sub-regions (Zone I through Zone V). The geostatistical analysis showed a significant spatial variability in groundwater arsenic levels, with a clear spatial correlation. Our findings demonstrate that arsenic concentrations in the Aksu River basin’s groundwater vary widely, with Zones II and III—mainly located in the northeastern part of the basin and in Awat County—being hotspots for high-arsenic water. Factors such as a weak reducing environment, intense evaporation, strong cation exchange, and the low-permeability recharge of surface water contribute to the accumulation of arsenic in the basin’s groundwater. The results of this study are vital for assessing the risk of arsenic contamination in groundwater in similar basins and for identifying critical areas for further investigation and research.

The lockdown and social distancing policies to reduce COVID-19 spread and perceived safety threats of COVID-19 significantly affected people’s travel behavior. Greece has been suffering from the COVID-19 pandemic, and people’s mobility behavior has been greatly affected. This study aims at: (1) exploring the variations in individuals’ trip frequencies by mode and purpose before and after the COVID-19 outbreak; (2) understanding the effects of individual differences (i.e., sociodemographic details) and perceptions towards COVID-19 (i.e., the perceived threats of COVID-19) on people’s mobility behavior changes after the outbreak; (3) underlining the individuals’ perceptions of the COVID-19 threat on the willingness of public transportation usage. Overall, 403 responses were collected in late 2020. A series of random parameter Probit modeling results reveal multiple individual and perception factors affecting the changes in mobility behavior in Greece. The results from structural equation modeling indicate that perceived COVID-19 threats affect the attitudes and subjective norms towards people’s intentions to use public transportation. The results from this study provide valuable insights for transportation authorities to develop effective strategies to manage traffic during the spread of disease for a possible future epidemic.

Accurate health prognostics of lithium-ion battery packs play a crucial role in timely maintenance and avoiding potential safety accidents in energy storage. To rapidly evaluate the health of newly developed battery packs, a method for predicting the future health of the battery pack using the aging data of the battery cells for their entire lifecycles and with the early cycling data of the battery pack is proposed. Firstly, health indicators (HIs) are extracted from the experimental data, and high correlations between the extracted HIs and the capacity are verified by the Pearson correlation analysis method. To predict the future health of the battery pack based on the HIs, degradation models of HIs are constructed by using an exponential function, long short-term memory network, and their weighted fusion. The future HIs of the battery pack are predicted according to the fusion degradation model. Then, based on the Gaussian process regression algorithm and battery pack data, a data-driven model is constructed to predict the health of the battery pack. Finally, the proposed method is validated with a series-connected battery pack with fifteen 100 Ah lithium iron phosphate battery cells. The mean absolute error and root mean square error of the health prediction of the battery pack are 7.17% and 7.81%, respectively, indicating that the proposed method has satisfactory accuracy.