Researchers and practitioners have encouraged the use of simulation games as a means of training for enhancing users' IT knowledge. However, prior research on IT knowledge has not paid much attention ...as to how individuals’ perceptions of knowledge shift from the pre-training to the post-training state, and has focused mainly on individual-level interventions. Drawing on the theoretical foundation of belief and attitude change, this study examines the construct of perceived knowledge update (i.e., the difference between perceived post-training knowledge and perceived pre-training knowledge) and its antecedents and consequences. In particular, we examine team collaboration effectiveness at the team level and participant effort at the individual level as predictors of perceived ERP knowledge update, which then influences intention to learn about ERP systems. We tested our research model using 252 students in 85 teams in the context of an ERP (Enterprise Resource Planning) simulation game. ERP systems are a business process management software which allows an organization to utilize a system of integrated applications. ERP simulation games are designed to educate ERP system users on the complexities of such systems. Our results show that team collaboration effectiveness positively influences individual effort and perceived knowledge update. We also provide empirical evidence that individual effort positively affects perceived knowledge update which in turn influences intention to learn about ERP systems.
•This study examines perceived ERP knowledge update in the context of ERP training.•This study identifies antecedents and consequences of perceived knowledge update.•Team collaboration effectiveness significantly predicts perceived knowledge update.•Individual effort positively influences perceived knowledge update.•Perceived knowledge update affects intention to learn about ERP systems.
Oil-produced wastewater treatment plants, especially those involving biological treatment processes, harbor rich and diverse microbes. However, knowledge of microbial ecology and microbial ...interactions determining the efficiency of plants for oil-produced wastewater is limited. Here, we performed 16S rDNA amplicon sequencing to elucidate the microbial composition and potential microbial functions in a full-scale well-worked offshore oil-produced wastewater treatment plant. Results showed that microbes that inhabited the plant were diverse and originated from oil and marine associated environments. The upstream physical and chemical treatments resulted in low microbial diversity. Organic pollutants were digested in the anaerobic baffled reactor (ABR) dominantly through fermentation combined with sulfur compounds respiration. Three aerobic parallel reactors (APRs) harbored different microbial groups that performed similar potential functions, such as hydrocarbon degradation, acidogenesis, photosynthetic assimilation, and nitrogen removal. Microbial characteristics were important to the performance of oil-produced wastewater treatment plants with biological processes.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Online petitions have emerged to become a powerful tool for the public to make positive impact on the society. This paper investigates what factors in the text content of online petitions influence ...their chance of success. Specifically, we examine moral, emotional, and cognitive elements in the petition language and identify their role in making online petitions successful. From the analysis of 12,808 online petitions from Change.org, we found that petitions containing positive emotions are more likely to be successful. In contrast to conventional beliefs, petitions containing heavier moral and cognitive elements are less likely to be successful. The findings have important implications to both petition websites and petitioners.
Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while ...data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) and proposed an adaptive deep ensemble learning method (TPE-DEM) for dynamic evolving diagnostic task scenarios. Different from previous research that focuses on achieving better performance with a fixed structure model, our proposed model uses TPE to efficiently aggregate simple models more easily understood by physicians and require less training data. In addition, our proposed model can choose the optimal number of layers for the model and the type and number of basic learners to achieve the best performance in different diagnostic task scenarios based on the data distribution and characteristics of the current diagnostic task. We tested our model on one dataset constructed with a partner hospital and five UCI public datasets with different characteristics and volumes based on various diagnostic tasks. Our performance evaluation results show that our proposed model outperforms other baseline models on different datasets. Our study provides a novel approach for simple and understandable machine learning models in tasks with variable datasets and feature sets, and the findings have important implications for the application of machine learning models in computer-aided diagnosis.
Social media has become the largest data source of public opinion. The application of sentiment analysis to social media texts has great potential, but faces great challenges because of domain ...heterogeneity. Sentiment orientation of words varies by content domain, but learning context-specific sentiment in social media domains continues to be a major challenge. The language domain poses another challenge since the language used in social media today differs significantly from that used in traditional media. To address these challenges, we propose a method to adapt existing sentiment lexicons for domain-specific sentiment classification using an unannotated corpus and a dictionary. We evaluate our method using two large developing corpora, containing 743,069 tweets related to the stock market and one million tweets related to political topics, respectively, and five existing sentiment lexicons as seeds and baselines. The results demonstrate the usefulness of our method, showing significant improvement in sentiment classification performance.
•We propose a method to adapt existing sentiment lexicons for domain-specific sentiment classification.•The proposed method addresses challenges from both content domain and language domain.•We evaluate our method using two large developing corpora and five existing sentiment lexicons as seeds and baselines.•The evaluation results demonstrate the usefulness of our method.
With the rise of artificial intelligence, case-based health knowledge management systems (CBHKS) have been widely adopted in hospitals. CBHKS are data-driven intelligent platforms that integrate ...latest technologies, such as artificial intelligence and cloud computing. As an integral part of smart hospitals, CBHKS can support decision processes at different levels in hospitals. However, researchers have not yet clearly addressed how CBHBKS improves hospital management outcomes. Based on group effectiveness and leadership performance-maintenance theories, we develop a conceptual model to explain the role of CBHKS in hospital management. To test the research hypotheses in the conceptual model, we collected survey data from 214 doctors, and performed data analysis using partial least squares (PLS)-based structural equation modeling. The empirical testing results show that the CBHKS implementation significantly and positively influences group performance, group members’ satisfaction, group learning, and external satisfaction; and group members’ satisfaction and external satisfaction significantly and positively affect management performance and maintenance.
Opinion mining of microblog messages has become a popular application of business analytics in recent times. Opinions reflected in microblogs have provided businesses with great opportunities to ...acquire insights into their operating environments in real time. In particular, the relationship between microblog sentiment and stock returns is of great interest to investment professionals and academic researchers across multiple disciplines. We empirically test this complex relationship in a comprehensive study. We perform vector autoregression on a data set containing close to 18 million microblog messages spanning 4 years at the market and the individual stock levels, and at the daily and the hourly frequencies. The results show that the influence of microblog sentiment on stock returns is both statistically and economically significant at the hour level. Microblog sentiment is also largely driven by movements in the market. Moreover, stock returns have a stronger influence on negative sentiment than on positive sentiment. These findings have important implications for both research and practice.
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•Enhanced oil recovery potential of A. borkumensis was explored for the first time.•Glycolipid surfactants were produced with a yield of 2.6 ± 0.2 g/L.•A. borkumensis was able to ...degrade nC12–nC24 of crude oil by more than 40.0%.•A. borkumensis can enhance the oil recovery rate by 20.2%.
Microbial enhanced oil recovery (EOR) has become the focus of oilfield research due to its low cost, environmental friendliness and sustainability. The degradation and EOR capacity of A. borkumensis through the production of bio-enzyme and bio-surfactant were first investigated in this study. The total protein concentration, acetylcholinesterase, esterase, lipase, alkane hydroxylase activity, surface tension, and emulsification index (EI) were determined at different culture times. The bio-surfactant was identified as glycolipid compound, and the yield was 2.6 ± 0.2 g/L. The nC12 and nC13 of crude oil were completely degraded, and more than 40.0 % of nC14–nC24 was degraded by by A. borkumensis. The results of the microscopic etching model displacement and core flooding experiments showed that emulsification was the main mechanism of EOR. A. borkumensis enhanced the recovery rate by 20.2 %. This study offers novel insights for the development of environmentally friendly and efficient oil fields.
Biogenic methane production depends on microbial community compositions in shale gas reservoirs, and glycine betaine plays an important role in methanogenic metabolic pathways. Previous studies have ...mainly focused on the microbial community dynamics in the water produced by shale hydraulic fracturing. Here, we used fresh shale as a sample and obtained the methane (CH4) and carbon dioxide (CO2) concentrations, microbial communities, and methanogenic functional gene numbers of solid and liquid groups in anaerobic bottles through gas chromatography, 16S rDNA sequencing (60 samples) and quantitative real-time PCR analysis in all culture stages. With glycine betaine addition, the total CH4 concentrations of the S1, S2 and Sw samples were 1.56, 1.05 and 4.48 times, while CO2 increased by 2.54-, 4.80- and 0.43-fold compared with samples without glycine betaine after 28 days of incubation, respectively. The alpha diversity was reduced when glycine betaine was added. The significant differences in bacterial community abundance at the genus level in samples with glycine betaine were Bacillus, Oceanobacillus, Acinetobacter, and Legionella. The bacterial and archaeal community changes implied that the addition of glycine betaine may promote CH4 production mainly by first forming CO2 and then generating CH4. The results of mrtA, mcrA, and pmoA gene numbers showed that the shale had great potential for producing methane. The addition of glycine betaine to shale changed the original microbial networks and increased the nodes and taxon connectedness of the Spearman association network. Our analyses indicate that the addition of glycine betaine enhances CH4 concentrations, causing the microbial network to be more complex and sustainable which supports the survival and adaptation of microbes in shale formations.
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•This work explored, for the first time, the potential of enhancing shale gas recovery by adding glycine betaine to shale.•The addition of glycine betaine increased methane production in shales.•The relative abundances of Bacillus and Oceanobacillus were significantly increased with the addition of glycine betaine.•The methanogenic functional gene numbers and metabolic pathway abundances were independent of the glycine betaine.
Hydrogen sulfide (H2S) is a toxic gas widespread in injected water of secondary oil recovery, having disadvantages of serious corrosion equipment, health risks, and higher operational costs. This ...study focused on the field application with nitrate and nitrite injection, which has been less examined in previous studies. A bio-inhibitor (400 mg/L nitrate + 300 mg/L nitrite) was reinserted into the water of the M71 oilfield (Jianghan Basin, China) with serious souring. The concentration of H2S decreased by 83% from 30 mg/L to less than 5 mg/L; the nitrate-reducing bacteria (NRB) counts increased from 10 to 100 cfu/mL, and the sulfate-reducing bacteria (SRB) counts remained at low numbers (0 cfu/mL). The dominant genera in the injected water in the absence of the bio-inhibitor were Desulfuromonas, Halanaerobium, and Desulfovibrio. NRB-related Halomonas and Marinobacter and sulfide-oxidizing bacteria of Halothiobacillus and Pseudomonas were activated through competing with SRB electron donors, and sulfide-oxidizing bacteria oxidized H2S to S0 or SO4 2– to suppress SRB when the bio-inhibitor was continuously added. The nitrite inhibited SRB metabolism through suppressing sulfite reductase activity. These findings indicated that the addition of the bio-inhibitor could remove H2S efficiency in injected water, thereby causing water reinjection in the oil reservoir. As a result, cost-effective treatments for souring control are achieved.
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