Impact of Monetary Policy and Foreign Direct Investment Against Indonesia's Economic Growth

This study aims to determine the impact of monetary policy and FDI on economic growth and discuss it. The monetary indicator variables used are inflation, interest rates and exchange rates. The data used in this study are secondary data in 1990-2019 sourced from data from the Central Bureau of National Statistics and the World Bank. The analysis model in this study uses Multiple Linear Regression with the Error Correction Model (ECM) analysis model. The results of the analysis show that in the long term monetary variables (inflation, interest rates and exchange rates) have a significant effect on economic growth. And in the short term FDI has a significant effect on economic growth. It is concluded that monetary variables (inflation, interest rates and exchange rates) are the main variables that affect economic growth in the long and short term.

Empirical Approach to Developing an Optimal Socioeconomic Status Index for Health Surveillance

The association between multi-dimensional deprivation and public health is well established, and many area-based indices have been developed to measure or account for socioeconomic status in health surveillance. The Yost Index, developed in 2001, has been adopted in the US for cancer surveillance and is based on the combination of two heavily weighted (household income, poverty) and five lightly weighted (rent, home value, employment, education and working class) indicator variables. Our objectives were to 1) update indicators and find a more parsimonious version of the Yost Index by examining potential models that included indicators with more balanced weights/influence and reduced redundancy and 2) test the statistical consistency of the factor upon which the Yost Index is based. Despite the usefulness of the Yost Index, a one-factor structure including all seven Yost indicator variables is not statistically reliable and should be replaced with a three-factor model to include the true variability of all seven indicator variables. To find a one-dimensional alternative, we conducted maximum likelihood exploratory factor analysis on a subset of all possible combinations of fourteen indicator variables to find well-fitted one-dimensional factor models and completed confirmatory factor analysis on the resulting models. One indicator combination (poverty, education, employment, public assistance) emerged as the most stable unidimensional model. This model is more robust to extremes in local cost of living conditions, is comprised of ACS variables that rarely require imputation by the end-user and is a more parsimonious solution than the Yost index with a true one-factor structure.

Rates of sustainment in the Universal Stages of Implementation Completion

Background Sustainment is a desirable outcome of implementation, but its precise definition remains unclear, contributing to the difficulty of identifying a generalized rate of sustainment. Several studies and reviews on the topic differ on both definition and levels of analysis. Furthermore, methodological limitations might have influenced the results, including the unknown quality with which some interventions were delivered. The Universal Stages of Implementation Completion (UniSIC) is a standardized measurement tool that tracks the implementation process and milestone completion across a wide range of real-world implementations—this provides a unique opportunity to identify a generalized rate of sustainment. Methods UniSIC data was captured from the SIC website on 27 September 2020 and included data from all sites (n = 1778) that had been tracked to date. Data were restricted to sites that achieved competency in program delivery, and thus had a newly adopted program worthy of sustainment. Dates and indicator variables of implementation activities were combined to form two alternate definitions of sustainment: sustained (start-up) was achieved if sites continued to deliver services 2 years past their program start-up date; sustained (competent) was achieved if sites continued to deliver services 2 years past their competence and/or certification date. Of sites eligible for inclusion based on these definitions (N = 208), descriptive analyses were conducted to determine a rate of sustainment for all programs that successfully started a program. These definitions were also applied to a combined sample for a general rate of sustainment among all sites. Rates of competency among both a sample of sites that started up and a combined sample were also identified. Results The rate of competence was 58.5% and the rate of sustained (start-up) was 37.1%, while the rate of sustained (competent) was 25.1%. The rates of competence and sustainment among the combined samples were far lower: 15.6% for competence, 6.8% for sustained (start-up), and 4.4% for sustained (competent). Conclusions These identified rates of sustainment are accurate initial estimates of sustainment of community-based practices, or in general. Future research on rates of sustainment should carefully define measures of sustainment and be transparent about the real-world conditions on which analyses are centered.

A Compilation of Succinctness Results for Arithmetic Circuits

Arithmetic circuits (AC) are circuits over the real numbers with 0/1-valued input variables whose gates compute the sum or the product of their inputs. Positive AC – that is, AC representing non-negative functions – subsume many interesting probabilistic models such as probabilistic sentential decision diagram (PSDD) or sum-product network (SPN) on indicator variables. Efficient algorithms for many operations useful in probabilistic reasoning on these models critically depend on imposing structural restrictions to the underlying AC. Generally, adding structural restrictions yields new tractable operations but increases the size of the AC. In this paper we study the relative succinctness of classes of AC with different combinations of common restrictions. Building on existing results for Boolean circuits, we derive an unconditional succinctness map for classes of monotone AC – that is, AC whose constant labels are non-negative reals – respecting relevant combinations of the restrictions we consider. We extend a small part of the map to classes of positive AC. Those are known to generally be exponentially more succinct than their monotone counterparts, but we observe here that for so-called deterministic circuits there is no difference between the monotone and the positive setting which allows us to lift some of our results. We end the paper with some insights on the relative succinctness of positive AC by showing exponential lower bounds on the representations of certain functions in positive AC respecting structured decomposability.

Impact of Socio-Economic Development on Sustainable Business Development Among Small and Medium Scale Businesses in Nigeria

This study is undertaken to investigate the impact of socio-economic development on sustainable business development among small and medium scale business in Nigeria. The significance of socio-economic development in achieving sustainable business development among small and medium scale business, especially in a developing country like Nigeria, cannot be over-emphasized. This study employed a survey research design as data were elicited from the respondents who agreed to fill out the questionnaires. In this study, several socio-economic development indicator variables such as Self-reliance in Development, Policy Delivery Mechanism, and Access to Health Facilities were employed among others. The findings elicited from this study revealed that socio-economic development recorded a positive impact on sustainable business development among small and medium scale business in Nigeria.

How Vulnerable Are American States to Wildfires? A Livelihood Vulnerability Assessment

Quantifying livelihood vulnerability to wildland fires in the United States is challenging because of the need to systematically integrate multidimensional variables into its analysis. We aim to measure wildfire threats amongst humans and their physical and social environment by developing a framework to calculate the livelihood vulnerability index (LVI) for the top 14 American states most recently exposed to wildfires. The LVI is computed by assessing each state’s contributing factors (exposure, sensitivity, and adaptive capacity) to wildfire events. These contributing factors are determined through a set of indicator variables that are categorized into corresponding groups to produce an LVI framework. The framework is validated by performing a principal component analysis (PCA), ensuring that each selected indicator variable corresponds to the correct contributing factor. Our results indicate that Arizona and New Mexico experience the greatest livelihood vulnerability. In contrast, California, Florida, and Texas experience the least livelihood vulnerability. While California has one of the highest exposures and sensitivity to wildfires, results indicate that it has a relatively high adaptive capacity, in comparison to the other states, suggesting it has measures in place to withstand these vulnerabilities. These results are critical to wildfire managers, government, policymakers, and research scientists for identifying and providing better resiliency and adaptation measures to support states that are most vulnerable to wildfires.

Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review

Among the greatest challenges in soft sensor development for bioprocesses are variable process lengths, multiple process phases, and erroneous model inputs due to sensor faults. This review article describes these three challenges and critically discusses the corresponding solution approaches from a data scientist’s perspective. This main part of the article is preceded by an overview of the status quo in the development and application of soft sensors. The scope of this article is mainly the upstream part of bioprocesses, although the solution approaches are in most cases also applicable to the downstream part. Variable process lengths are accounted for by data synchronization techniques such as indicator variables, curve registration, and dynamic time warping. Multiple process phases are partitioned by trajectory or correlation-based phase detection, enabling phase-adaptive modeling. Sensor faults are detected by symptom signals, pattern recognition, or by changing contributions of the corresponding sensor to a process model. According to the current state of the literature, tolerance to sensor faults remains the greatest challenge in soft sensor development, especially in the presence of variable process lengths and multiple process phases.