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Background The time taken, or ‘time lags’, between biomedical/health research and its translation into health improvements is receiving growing attention. Reducing time lags should increase rates of return to such research. However, ways to measure time lags are under-developed, with little attention on where time lags arise within overall timelines. The process marker model has been proposed as a better way forward than the current focus on an increasingly complex series of translation ‘gaps’. Starting from that model, we aimed to develop better methods to measure and understand time lags and develop ways to identify policy options and produce recommendations for future studies. Methods Following reviews of the literature on time lags and of relevant policy documents, we developed a new approach to conduct case studies of time lags. We built on the process marker model, including developing a matrix with a series of overlapping tracks to allow us to present and measure elements within any overall time lag. We identified a reduced number of key markers or calibration points and tested our new approach in seven case studies of research leading to interventions in cardiovascular disease and mental health. Finally, we analysed the data to address our study’s key aims. Results The literature review illustrated the lack of agreement on starting points for measuring time lags. We mapped points from policy documents onto our matrix and thus highlighted key areas of concern, for example around delays before new therapies become widely available. Our seven completed case studies demonstrate we have made considerable progress in developing methods to measure and understand time lags. The matrix of overlapping tracks of activity in the research and implementation processes facilitated analysis of time lags along each track, and at the cross-over points where the next track started. We identified some factors that speed up translation through the actions of companies, researchers, funders, policymakers, and regulators. Recommendations for further work are built on progress made, limitations identified and revised terminology. Conclusions Our advances identify complexities, provide a firm basis for further methodological work along and between tracks, and begin to indicate potential ways of reducing lags.

The purpose of the present study is to identify the factors affecting the non-performing loans rate (NPL) of Eurozone?s banking systems for the period 2000-2008, just before the beginning of the recession. In our days, Eurozone is in the middle of an unprecedented financial crisis, calling into question the soundness of the banking systems of European countries. Looking at both macro-variables (e.g. annual percentage growth rate of gross domestic product, public debt as % of gross domestic product, unemployment) and micro-variables (e.g. loans to deposits ratio, return on assets, return on equity), we investigate which factors determine NPL on aggregate level. Overall, our findings reveal strong correlations between NPL and various macroeconomic (public debt, unemployment, annual percentage growth rate of gross domestic product) and bank-specific (capital adequacy ratio, rate of nonperforming loans of the previous year and return on equity) factors. nema

The view that the returns to educational investments are highest for early childhood interventions is widely held and stems primarily from several influential randomized trials-Abecedarian, Perry, and the Early Training Project-that point to super-normal returns to early interventions. This article presents a de novo analysis of these experiments, focusing on two core issues that have received limited attention in previous analyses: treatment effect heterogeneity by gender and overrejection of the null hypothesis due to multiple inference. To address the latter issue, a statistical framework that combines summary index tests with familywise error rate and false discovery rate corrections is implemented. The first technique reduces the number of tests conducted; the latter two techniques adjust the p values for multiple inference. The primary finding of the reanalysis is that girls garnered substantial short- and long-term benefits from the interventions, but there were no significant long-term benefits for boys. These conclusions, which have appeared ambiguous when using \"naive\" estimators that fail to adjust for multiple testing, contribute to a growing literature on the emerging female-male academic achievement gap. They also demonstrate that in complex studies where multiple questions are asked of the same data set, it can be important to declare the family of tests under consideration and to either consolidate measures or report adjusted and unadjusted p values.

This study tests the Post-Keynesian theory regarding bank stock returns and money supply endogeneity in the context of South Asian countries. This study uses panel data set from different sources over twenty-eight (28) years. The research uses different econometric techniques before switching to the generalized method of moments (GMM). The empirical results indicate a significant positive effect of net interest rate margins on bank loans in South Asian countries, whereas a positive relationship exists between foreign to local interest rates and the money supply. The findings depict that positive associations exist between inflation and money supply of banks, and between the money supply and bank stock returns. More specifically, the GMM results show that the money supply has positively affected the stock prices of banks suggesting strong policies for the stakeholders of these economies for the sake of economic growth and sustainable development.

Fossil fuel shortage is a major challenge worldwide. Therefore, research is currently underway to investigate potential renewable energy sources. Biodiesel is one of the major renewable energy sources that can be obtained from oils and fats by transesterification. However, biodiesel obtained from vegetable oils as feedstock is expensive. Thus, an alternative and inexpensive feedstock such as waste cooking oil (WCO) can be used as feedstock for biodiesel production. In this project, techno-economic analyses were performed on the biodiesel production in Hong Kong using WCO as a feedstock. Three different catalysts such as acid, base, and lipase were evaluated for the biodiesel production from WCO. These economic analyses were then compared to determine the most cost-effective method for the biodiesel production. The internal rate of return (IRR) sensitivity analyses on the WCO price and biodiesel price variation are performed. Acid was found to be the most cost-effective catalyst for the biodiesel production; whereas, lipase was the most expensive catalyst for biodiesel production. In the IRR sensitivity analyses, the acid catalyst can also acquire acceptable IRR despite the variation of the WCO and biodiesel prices.

The prevalence of substandard and falsified medicines in low- and middle-income countries (LMICs) is a major global public health concern. Multiple screening technologies for post-market surveillance of medicine quality have been developed but there exists no clear guidance on which technology is optimal for LMICs. This study examined the return on investment (ROI) of implementing a select number of screening technologies for post-market surveillance of amoxicillin quality in a case study of Kenya. An agent-based model, Examining Screening Technologies using Economic Evaluations for Medicines (ESTEEM), was developed to estimate the costs, benefits, and ROI of implementing screening technologies for post-market surveillance of substandard and falsified amoxicillin for treatment of pediatric pneumonia in Kenya. The model simulated sampling, testing, and removal of substandard and falsified amoxicillin from the Kenyan market using five screening technologies: (1) Global Pharma Health Fund’s GPHF-Minilab, (2) high-performance liquid chromatography (HPLC), (3) near-infrared spectroscopy (NIR), (4) paper analytical devices / antibiotic paper analytical devices (PADs/aPADs), and (5) Raman spectroscopy. The study team analyzed the population impact of utilizing amoxicillin for the treatment of pneumonia in children under age five in Kenya. We found that the GPHF-Minilab, NIR, and PADs/aPADs were similar in their abilities to rapidly screen for and remove substandard and falsified amoxicillin from the Kenyan market resulting in a higher ROI compared to HPLC. NIR and PADs/aPADs yielded the highest ROI at $21 (90% Uncertainty Range (UR) $5-$51) each, followed by GPHF-Minilab ($16, 90%UR $4 - $38), Raman ($9, 90%UR $2 - $21), and HPLC ($3, 90%UR $0 - $7). This study highlights screening technologies that can be used to reduce costs, speed up the removal of poor-quality medicines, and consequently improve health and economic outcomes in LMICs. National medicine regulatory authorities should adopt these fast, reliable, and low-cost screening technologies to better detect substandard and falsified medicines, reserving HPLC for confirmatory tests.

In financial markets, predicting stock returns is an essential task for investors. This paper is one of the first studies using business efficiency scores calculated from data envelopment analysis to predict stock returns. In the meantime, this is also one of the first studies to comprehensively investigate the performance of machine learning models and automatic feature engineering techniques in the context of predicting returns in the Vietnamese stock market. Specifically, the data from 2019 to 2024 of 26 real-estate enterprises on Ho Chi Minh Stock Exchange are collected. Based on relevant technical indicators, fundamental indicators, and business efficiency scores, a comparison of various machine learning models’ performance is provided. The results indicate that incorporating business efficiency scores significantly enhances the models’ accuracy. For example, the deep neural network model shows a decrease in RMSE from 0.926 to 0.375, MAE from 0.337 to 0.196, and MAPE from 134.63 to 114.71. Furthermore, the gradient boosted tree model, when combined with business efficiency scores and automatic feature engineering, achieves the best results, yielding an MAE of 0.122 and an MAPE of 103.19. The obtained results reveal a significant improvement in terms of accuracy when using the business efficiency score with the automated feature engineering technique.

Microgrids and hybrid renewable energy systems play a crucial role in today’s energy transition. They enable local power generation and distribution, reducing dependence on large centralized infrastructures, can operate independently or connected to a grid, and can provide backup power, thus increasing system resilience. In addition, they combine multiple renewable energy sources, such as solar, wind, hydro, and biomass, to maximize the efficiency and reliability of the supply, and are also adaptable to location-specific conditions, taking advantage of locally available energy resources and reducing the need for energy imports. Moreover, they contribute to decarbonization goals by offering a cleaner and more sustainable alternative. In this article, a documentary review is presented on the interaction of Homer Pro software 3.16.2 (July 2023), used for the design of hybrid renewable energy systems (HRES), with other methods of optimization or sizing. Allusion is made to the type of architecture in the most prominent clean and fossil source configurations, the levelized cost, net annual cost, and maintenance and capital investment cost. A comparison is made among the works reported in the last five years regarding the use of this software tool, based on load demand, geographical area, renewable energy sources, fossil sources, and objective functions, applied to the educational, rural, and industrial sectors. It is shown that India is one of the countries that has reported the most number of HRES techno-economic environmental analysis works, and that the case studies have focused approximately 47% on rural areas, 20% on educational agencies, 14% on commerce and industry, and 29% on urban buildings.

Governments need to evaluate technologies generating electricity from different sources; levelised cost of energy (LCOE) is a widely used metric. However, LCOE is weak at comparing disparate technologies, especially where they have different operational lifespans. The discrepancy is demonstrated using UK government data to examine a range of technologies, namely combined cycle generation (natural gas and hydrogen), sustainable renewable technologies along with independent data describing nuclear power and tidal range schemes. Three methods of analysis were used: LCOE, the internal rate of return (IRR), and a novel analysis. A new metric, the sustained cost of energy (SCOE), negates some of the LCOE shortcomings such as the application of discounting. SCOE examines a fixed period of continuous generation, using the lowest common length of operating life of the technologies analysed. It appears to be a useful metric, especially when interpreted with IRR. The analyses produce broadly similar ordering of technologies, but the longer-lasting systems with high initial costings perform better in SCOE. Subsidies, carbon tax, or credit schemes are essential government incentives if net zero emissions targets are to be met without overly burdening consumers with rapidly growing electricity rates.