Biomass energy is abundant, clean, and carbon dioxide neutral, making it a viable alternative to fossil fuels in the near future. The release of syngas from biomass thermochemical treatments is particularly appealing since it may be used in a variety of heat and power generation systems. When a syngas with low tar and contaminants is required, downdraft gasifiers are usually one of the first gasification devices deployed. It is time-consuming and impractical to evaluate a gasification system's performance under multiple parameters, using every type of biomass currently available, which makes rapid simulation techniques with well-developed mathematical models necessary for the efficient and economical use of energy resources. This work attempts to examine, through model and experimentation, how well a throated downdraft gasification system performs when using pretreatment biomass feedstock that has been characterized. For the analyses, peanut shell (PS), a biomass waste easily obtained locally, was used. The producer gas generated with 9 mm PS pellets had a composition of 17.93% H2, 24.43 % CO, 12.47 % CO2, and 1.22% CH4 on a wet basis at the value of 0.3 equivalency ratio and 800 °C gasification temperature. The calorific value was found to be 4.96 MJ/Nm3. The biomass feedstock PS is found to be suitable for biomass gasification in order to produce syngas.

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.

High-quality implementation of cross-border mergers and acquisitions (cross-border M&As) is an important pathway for emerging-market multinational enterprises (EMNEs) to enhance their international competitiveness. However, in comparison to developed countries, cross-border M&As by EMNEs are often prohibited by the liability of origin caused by negative political coverage. How and why negative political coverage affect the completion of cross-border M&As by EMNEs? What are the contextual constraints that moderate the impact of negative political coverage on cross-border M&As completion? Based on the “liability of origin” theory, this paper addresses these questions using data from the Zephyr database on cross-border M&As by EMNEs in the United States from 2016 to June 2021 and employing a logit model for estimation. The research findings are as follows: (1) Negative political coverage leads to negative perceptions of emerging market countries by host country stakeholders, creating the liability of origin and stigmatizing the corporate nationality, thereby reducing the success rate of cross-border M&As by EMNEs. (2) Increasing geographical distance leads to information asymmetry, reinforcing the negative impact of negative political coverage on the completion of cross-border M&As by EMNEs. (3) Relevant mergers and acquisitions exacerbate the negative effect of negative political coverage on the success rate of cross-border M&As by EMNEs. (4) Being a publicly traded firm and having successful experience in cross-border M&As both intensify the negative impact of negative political coverage on the success rate of cross-border M&As by EMNEs.

Purpose: This study aimed to explore the perception types of workplace spirituality among nurses. Method: To achieve this, Q methodology was applied, selecting 34 Q samples from a total of 102 Q statements extracted. The Q samples were distributed among 40 nurses and categorized into a normal distribution. A 9-point scale was used for measurement, and the data were analyzed using the pc-QUANL program. Results: The four types identified were ‘reflective type’, ‘nursing-oriented type’, ‘relationship-oriented type’, and ‘spirituality-oriented type’. Conclusion: The four types derived in this study classify nurses’ perceptions of workplace spirituality for establishing a nurse’s workplace spirituality that provides integrated nursing care. This categorization can serve as foundational information when planning workplace spirituality programs, considering each type’s characteristics.

The St. Peter Sandstone of the American Midwest is presented today in textbooks as a simple and unproblematic example of “layer-cake geology.” The thesis of this paper is that the very simplicity of St. Peter Sandstone has made it challenging to characterize. In widely separated states, the sandstone appeared under different names. Several theories about how it formed began to circulate. The story of the St. Peter is not only the story of the assemblage of a stratigraphic unit over a vast area during three centuries, but also the role the study of the provenance of this unit played in the development of sedimentology in the early twentieth century, research that was made all the more challenging by its “simple” mineralogy. Indeed, the St. Peter has been controversial since it was first described.