Private Equity Valuation and IRR Algorithm

An algorithm is developed that calculates the IRR for various private equity entities within a private equity leveraged transaction. The algorithm calculates the total anticipated value for a transaction and then produces the associated IRRs based on the exit EBITDA, the EBITDA multiple, and the available cash. The benefits of the algorithm are that multiple programming formats become available, insights emerge that are difficult to perceive using an equivalent spreadsheet pro forma analysis, and other types of analyses become possible for examining individual parameters

Converting NPV and IRR Cash Flows into a Financial Calculator Using an Excel Template

An Excel template is developed that converts a series of cash flows on a timeline into the associated keystrokes for the TI BAII-Plus financial calculator in order to calculate NPV and IRR. Unlike videos and other presentations, the student is able to see, all at once, the keystrokes required for the financial calculator within the template after the student enters the correct inputs for how the cash flows occur through time. Many times, this crucial link of translating the cash flows through time into the financial calculator is lost. Further exercises are provided to reinforce proficiency

Required Minimum Distribution (RMD) Spreadsheet Calculators Based on the SECURE Act of 2019

The Setting Every Community Up for Retirement Enhancement Act (SECURE Act) of 2019 made significant changes to the required minimum distribution (RMD) schedule for individual retirement accounts (IRAs) and defined contribution retirement plans. Excel spreadsheet calculators are developed to calculate annual RMD cash flows throughout retirement for those who are retired and for those who are planning to retire. Unlike internet calculators, the spreadsheet calculators allow savings to earn monthly interest throughout retirement. Further, the calculators are easy to use and allow individuals to forecast long horizon RMD distributions for subsequent tax or reinvestment planning purposes

Required Minimum Distribution (RMD) Spreadsheet Calculators Based on the SECURE Act of 2022

Required Minimum Distribution (RMD) Spreadsheet Calculators Based on the SECURE Act of 2022 The Setting Every Community Up for Retirement Enhancement Act (SECURE Act) of 2022 made a second round of changes (relative to the SECURE Act of 2019) to the required minimum distribution (RMD) schedule for individual retirement accounts (IRAs) and defined contribution retirement plans. Excel spreadsheet calculators are developed to calculate the new annual RMD cash flows throughout retirement for those who are retired and for those who are planning to retire. The spreadsheet calculators also allow savings to accrue with interest if the RMD is in excess of expected annual costs. KEY TAKEAWAYS: The spreadsheet calculators require only basic inputs and can be updated and applied at any point in time during the planning period. The spreadsheet calculators allow for interest to accumulate before and after retirement in the IRA and in a savings account if the RMD is in excess of expected annual costs. The spreadsheet calculators allow for additional monthly contributions up to retirement

Robust Meta-Model for Predicting the Need for Blood Transfusion in Non-traumatic ICU Patients

Objective: Blood transfusions, crucial in managing anemia and coagulopathy in ICU settings, require accurate prediction for effective resource allocation and patient risk assessment. However, existing clinical decision support systems have primarily targeted a particular patient demographic with unique medical conditions and focused on a single type of blood transfusion. This study aims to develop an advanced machine learning-based model to predict the probability of transfusion necessity over the next 24 hours for a diverse range of non-traumatic ICU patients. Methods: We conducted a retrospective cohort study on 72,072 adult non-traumatic ICU patients admitted to a high-volume US metropolitan academic hospital between 2016 and 2020. We developed a meta-learner and various machine learning models to serve as predictors, training them annually with four-year data and evaluating on the fifth, unseen year, iteratively over five years. Results: The experimental results revealed that the meta-model surpasses the other models in different development scenarios. It achieved notable performance metrics, including an Area Under the Receiver Operating Characteristic (AUROC) curve of 0.97, an accuracy rate of 0.93, and an F1-score of 0.89 in the best scenario. Conclusion: This study pioneers the use of machine learning models for predicting blood transfusion needs in a diverse cohort of critically ill patients. The findings of this evaluation confirm that our model not only predicts transfusion requirements effectively but also identifies key biomarkers for making transfusion decisions

Developmental transitions in body color in chacma baboon infants: Implications to estimate age and developmental pace

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Identifying Farming Strategies Associated With Achieving Global Agricultural Sustainability

Sustainable agroecosystems provide adequate food while supporting environmental and human wellbeing and are a key part of the United Nations Sustainable Development Goals (SDGs). Some strategies to promote sustainability include reducing inputs, substituting conventional crops with genetically modified (GM) alternatives, and using organic production. Here, we leveraged global databases covering 121 countries to determine which farming strategies—the amount of inputs per area (fertilizers, pesticides, and irrigation), GM crops, and percent agriculture in organic production—are most correlated with 12 sustainability metrics recognized by the United Nations Food and Agriculture Organization. Using quantile regression, we found that countries with higher Human Development Indices (HDI) (including education, income, and lifespan), higher-income equality, lower food insecurity, and higher cereal yields had the most organic production and inputs. However, input-intensive strategies were associated with greater agricultural greenhouse gas emissions. In contrast, countries with more GM crops were last on track to meeting the SDG of reduced inequalities. Using a longitudinal analysis spanning 2004–2018, we found that countries were generally decreasing inputs and increasing their share of agriculture in organic production. Also, in disentangling correlation vs. causation, we hypothesize that a country's development is more likely to drive changes in agricultural strategies than vice versa. Altogether, our correlative analyses suggest that countries with greater progress toward the SDGs of no poverty, zero hunger, good health and wellbeing, quality education, decent work, economic growth, and reduced inequalities had the highest production of organic agriculture and, to a lesser extent, intensive use of inputs

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570