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Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.

The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).

It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.

The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.

Digital Humanities is a robust area of research and practice at universities and their libraries across the world. This case study investigates the unique DH practices of seven US academic libraries to provide insights into how varied academic libraries operate their DH programs.

Semi-structured interviews with nine library staff in DH or DH-adjacent positions at seven US academic libraries were used to investigate library DH practices.

This case study highlighted key areas of academic library DH practices including Space, Technology, Staff, Instruction and Collaboration. Practices in these areas were compared against each other and literature to comment on the current state of DH library practices and offer some recommendations for select areas.

This case study interviewed staff in a limited number of US libraries and is not generalizable to or a reflection of the many academic libraries in the US or across the world.

The juxtaposition of multiple libraries’ DH activities provides a unique perspective on academic library DH practice, as many studies investigate only a single library as their subject.

The study aims to explore the discrepancy between the subjective and objective debt burdens across various household socio-demographic and debt characteristics. Additionally, it seeks to establish an optimal debt service-to-income ratio (DSTI) threshold for identifying over-indebtedness.

This study utilized a sample of 1,004 respondents from a nationwide survey conducted among Polish indebted households. A discrepancy ratio (DR) measure was proposed to evaluate the divergence between subjective and objective over-indebtedness. Binary logistic regression was employed to estimate the probability of being subjectively and objectively over-indebted, as well as the discrepancy between the two measures of over-indebtedness. The study also employed numerical simulations to determine the optimal DSTI threshold for identifying over-indebted households in general and based on their socio-economic characteristics.

The study established a debt service-to-income ratio (DSTI) threshold of 20% to minimize the discrepancy between subjective and objective debt burden, which is lower than thresholds found in other studies aimed at identifying over-indebted households. Age, number of loans, self-perceived needs satisfaction and type of debt were identified as significant socio-economic and debt-related determinants of over-indebtedness. Household socio-economic and debt-related characteristics significantly influence the threshold for identifying over-indebtedness using DSTI. It can vary widely, ranging from as low as 11% for well-educated women with multiple loan commitments to 43.7% for young males with vocational education, high incomes and originating from households with four or more members.

The paper proposes a more comprehensive approach to debt burden analysis by introducing a new methodology for determining a debt service-to-income (DSTI) threshold that could serve as a measure of over-indebtedness based on the discrepancy between subjective and objective over-indebtedness. It also emphasizes the significance of socio-economic and debt-related factors in evaluating subjective and objective over-indebtedness.

The aviation industry plays a vital role in supporting tourism and international businesses by providing the fastest transportation network in the world and also boosting economic growth and creating employment. However, it harms the environment, mainly through air pollution due to aircraft engines emitting heat and gases that contribute to global warming, acid rain, smog, and ozone depletion. Air travel has increased considerably over the years, and therefore aircraft emissions have contributed to the build-up of greenhouse gases (GHG), with the resultant changes in weather patterns leading to global warming and environmental deterioration. Although aviation contributes to economic and environmental development, it is a double-edged sword because it is thought to be the most challenging industry for formulating sustainable policies, based on the direct conflict between environmental impacts and economic development. This chapter explores different types of problems associated with the negative impacts of aviation carbon emissions and the carbon footprint of tourism. The chapter will also reflect on policy, regulations, and governance approaches currently in place to combat these negative impacts as well as challenges involved in policy interventions.

Research shows that having student loan debt in retirement is associated negatively with life satisfaction, suggesting that student debt is a bane of retiree well-being. The rationale for this study is to determine the factors related to owing student debt in retirement, given the adverse effects on the well-being of retired households.

The study utilizes pooled cross-sectional data from the 2015 and 2018 U.S. National Financial Capability Study. The empirical analysis uses a sample of retired Americans aged 65 years and older (N = approximately 8,000) and estimates two-block logistic regression models to examine the effects of demographic, socioeconomic and behavioral factors on student loan indebtedness in retirement. A sensitivity analysis is performed for the subsample of retirees holding student debt for their children's education. Statistical interpretations use odds ratios.

The findings indicate that financial literacy, age, homeownership and high subjective financial knowledge are associated with a low likelihood of holding student loan debt in retirement. However, being Black, having postsecondary education, having difficulty covering expenses, having financially dependent children, having high-risk preferences and spending more than income increase the likelihood of holding student debt in retirement. The ensuing discussion will assist financial planners and educators identify practical ways to shape decisions regarding student loan debt in retirement.

The amount of student loan debt is unavailable in the dataset for analysis. One cannot infer causal relations from the study. The factors examined do not reflect the time the student loan was obtained.

The study focuses on the determinants of student loan indebtedness among retired Americans rather than young adults or older adults on the verge of retirement. The paper enhances the understanding of student loan holdings in the decumulation phase of the life cycle. Many US individuals have low retirement savings from which they draw a retirement income. The more the student debt burdens on retired Americans, the greater the likelihood of outliving their resources and experiencing poverty.

Consumer culture is promoting immediate gratification, and the rise of digital financial services is increasing the risk of indebtedness while debt reduces well-being and affects mental health. The authors assess the effects of consumer information provision, debt literacy, chronic debt and attitudes toward debt on the intent to purchase on credit.

An online survey including an experiment with a credit offer vignette was conducted in a representative sample of Estonia (n = 1204). Treatment conditions depicted either the total cost and duration of the credit agreement or the annual percentage rate.

Receiving modified information resulted in a 26 to 30 percentage points decrease in propensity to purchase on credit. Purchasing on credit was associated with attitudes towards credit and chronic debt, but not with debt literacy.

The findings reveal large effects of information provision and highlight the limited effects of debt literacy on credit decisions. Limitations may emerge from differences in financial regulation across countries.

The authors' results highlight the importance of applying behavioural insights in consumer credit information provision, both in the financial sector and policy. Testing the messages allows having evidence-based solutions that promote responsible purchasing on credit.

The findings call for changes in credit information provision requirements. Their effect is significantly larger compared to the literature, emphasizing the role of credit information provision in less regulated online markets.

This study focuses on the CFD numerical simulation and analysis of the vortex stacking problem at the top of the impeller of a high-speed fuel pump, mainly using LCS and entropy production theory to visualize the vortex at the top of the impeller as well as quantitatively analyzing the energy loss caused by the vortex at the top of the impeller. By combining the two methods, the two are well verified with each other that the stacking problem of the vortex at the top of the impeller and the location of the energy loss caused by the vortex are consistent with the vortex location. Such a method can reveal the problem of vortex buildup at the top of the lobe well, and provide a novel guidance idea for improving the performance of high-speed fuel pumps.

Based on CFD numerical simulation and analysis, this study mainly uses LCS and entropy production theory to visualize the top vortex of the impeller. Through the combination of the two methods, the accumulation problem of the top vortex of the impeller and the location of the energy loss caused by the vortex can be well revealed.

(1) The CFD numerical simulation analysis of the high-speed fuel pump is carried out, and the test is conducted to verify the numerical simulation results. The inlet and outlet pressure difference? P is used as the validation index, and the error analysis shows that the error between numerical simulation and test results is within 10%, which meets our requirements. Therefore, we carry out the next analysis with the help of CFD numerical simulation. By analyzing the full working condition simulation, its inlet and outlet differential pressure? P and efficiency? Are evaluated. It is found that its differential pressure decreases with the flow rate and its efficiency reaches its maximum at Qv = 9.87 L/s with a maximum efficiency of 78.32%. (2) We used the LCS in the analysis of vortices at the top of the impeller blades of a high-speed fuel pump. One of the metrics used to describe the LCS in fluid dynamics is the FTLE. The high FTLE region represents the region with the highest and fastest particle trajectory stretching velocity in the fluid flow. We performed a cross-sectional analysis of the FTLE field on the different height surfaces of the impeller on 25% Plane, 50% Plane, and 75% Plane, respectively. And a quarter turn of the rotor rotation was analyzed as a cycle divided into 8 moments. It is found that on 25% Plane, the vortex at the top of the lobe is not obvious, but there are high FTLE values on the shroud surface. On 50% Plane, the lobe top vortex is relatively obvious and the number of vortices is three. The vortex pattern remains stable with the rotating motion of the rotor. At 75% Plane, the lobe top vortex is more visible and its number of vortices increases to about 5 and the vortex morphology is relatively stable. The FTLE ridges visualize the vortex profile. This is a good guide for fluid dynamics analysis. (3) At the same time, we use the entropy production theory to quantitatively analyze the energy loss, and define the entropy production rate Ep. Through the entropy production analysis of the impeller shroud surface and the suction surface of the pressure surface of the blades at eight moments, we find that the areas of high energy loss are mainly concentrated in the leading and trailing edges of the blades as well as in the shroud surface close to the leading edge of the blades, and the value of the entropy production rate is up to 106 W/m3/K. The areas of high energy loss in the leading edge of the blades as well as the trailing edge show a curved arc, and the energy loss is decreasing as it moves away from the shroud surface and closer to the hub surface. The high energy loss areas at the leading and trailing edges of the blades are curved, and the energy loss decreases as they move away from the shroud surface and closer to the hub surface. The energy loss at the pressure surface of the blade is relatively small, about 5 × 105 W/m3/K, which is mainly concentrated near the leading edge of the blade near the shroud surface and the trailing edge of the blade near the hub surface. Such energy loss corresponds to the vortex LCS at the top of the impeller, and the two mirror each other.

This study focuses on the CFD numerical simulation and analysis of the vortex stacking problem at the top of the impeller of a high-speed fuel pump, mainly using LCS and entropy production theory to visualize the vortex at the top of the impeller as well as quantitatively analyzing the energy loss caused by the vortex at the top of the impeller. By combining the two methods, the two are well verified with each other that the stacking problem of the vortex at the top of the impeller and the location of the energy loss caused by the vortex are consistent with the vortex location. Such a method can reveal the problem of vortex buildup at the top of the lobe well, and provide a novel guidance idea for improving the performance of high-speed fuel pumps.

The purpose of this study is to characterize the galvanic corrosion behavior of a simulated X80 pipeline steel welded joint (PSWJ) reconstructed by the wire beam electrode (WBE) and numerical simulation methods.

The galvanic corrosion of an X80 PSWJ was studied using WBE and numerical simulation methods. The microstructures of the coarse-grained heat affected zone, fine-grained heat affected zone and intercritical heat affected zone were simulated in X80 pipeline steel via Gleeble thermomechanical simulation processing.

Comparing the corrosion current density of coupled and isolated weld metal (WM), base metal (BM) and heat-affected zone (HAZ), the coupled WM exhibited a higher corrosion current density than isolated WM; the coupled BM and HAZ exhibited lower corrosion current densities than isolated BM and HAZ. The results exhibited that the maximum anodic galvanic current fitted the Gumbel distribution. Moreover, the numerical simulation results agreed well with the experimental data.

This study provides insight into corrosion evaluation of heterogeneous welded joints by a combination of experiment and simulation. The method of reconstruction of the welded joint has been proven to be a feasible approach for studying the corrosion behavior of the X80 PSWJ with high spatial resolution.

In the present study, we examined how effectively online travel agencies (OTAs) handle negative e-word-of-mouth on social media platforms like Facebook, Twitter, and Instagram. We collected data from 497 participants using survey method. To test the hypotheses formulated from the existing literature, structural equation modeling was adopted in this study. The results from structural equation modeling indicate effective handling of the negative e-word of mouth (e-WOM) on social media websites significantly affects customer satisfaction and repurchase intention. The current research work provides insight into social media recovery efforts and service fairness when handling negative e-WOM. The study recommends that customers can distinguish the differences between general efforts and adaptive complaint-handling efforts, and dissimilarities may influence satisfaction, repurchase intentions, etc. Although empathy, apology, responsiveness, and paraphrasing are considered pioneer strategies in complaint handling, customers' negative e-WOM, and firms' recovery management, but the current study is among a few to categorize OTAs' handling of negative e-WOM and complaint handling efforts in the social media environment.

This study aims to examine the impact of manager sentiment on the firm performance (FP) of Indian-listed nonfinancial firms. Further, it endeavors to investigate the moderating role of economic policy uncertainty (EPU) and environment, social and governance (ESG) transparency in this relationship.

A noble manager sentiment is introduced using FinBERT, a bidirectional encoder representation from a transformers (BERT)-type large language model. Using this deep learning-based natural language processing approach implemented through a Python-generated algorithm, this study constructs a manager sentiment for each firm and year based on the management discussions and analysis (MD&A) report. This research uses the system GMM to examine how manager sentiment affects FP.

The empirical results suggest that managers’ optimistic outlook in MD&A corporate disclosure sections tends to present higher performance. This positive association remains consistent after several robustness checks – using propensity score matching and instrumental variable approach to address further endogeneity, using alternative proxies of manager sentiment and FP and conducting subsample analysis based on financial constraints. Furthermore, the authors observe that the relationship is more pronounced for ESG-disclosed firms and during the low EPU.

The results demonstrate that the manager sentiment strongly predicts FP. Thus, this study may provide valuable insight for academics, practitioners, investors, corporates and policymakers.

To the best of the authors’ knowledge, this is the first study to predict FP by using FinBERT-based managerial sentiment, particularly in an emerging market context.