The Regulatory Status Analysis for Updating the Public Legal Awareness on Human Rights in Indonesia

In this study, it is argued that quantitative empirical legal research can support understanding public legal awareness of the implementation of human rights protection in Indonesia. The public legal awareness is analyzed using the Partial Least Square-Structural Equation Modelling to provide flexibility for exploring the link of the ideals of human rights law with elements of the legal system as research variables. This research is a literature study on the importance and use of empirical quantitative research methods through the establishment of a path model called Regulatory Status Analysis. The model positions legal ideals (justice, certainty, and expediency) as an independent variable; while the two elements of the legal system: substantive law and legal structure, are mediating variables. Based on the trial run, the path model can picture the relationship between ideal law and legal culture as the dependent variable in the form of public awareness to comply with legal norms that protect human rights. Substantive law also has a positive influence on awareness to obey the law. However, the legal structure has no influence, either directly or indirectly. It might be because respondents consider law enforcement against human rights violations less than optimal. The test result determines what kind of human rights legal system should be developed for national and global legal scholarship

The Regulatory Status Analysis for Updating the Public Legal Awareness on Human Rights in Indonesia

In this study, it is argued that quantitative empirical legal research can support understanding public legal awareness of the implementation of human rights protection in Indonesia. The public legal awareness is analyzed using the Partial Least Square-Structural Equation Modelling to provide flexibility for exploring the link of the ideals of human rights law with elements of the legal system as research variables. This research is a literature study on the importance and use of empirical quantitative research methods through the establishment of a path model called Regulatory Status Analysis. The model positions legal ideals (justice, certainty, and expediency) as an independent variable; while the two elements of the legal system: substantive law and legal structure, are mediating variables. Based on the trial run, the path model can picture the relationship between ideal law and legal culture as the dependent variable in the form of public awareness to comply with legal norms that protect human rights. Substantive law also has a positive influence on awareness to obey the law. However, the legal structure has no influence, either directly or indirectly. It might be because respondents consider law enforcement against human rights violations less than optimal. The test result determines what kind of human rights legal system should be developed for national and global legal scholarship

Parental Characteristics and Lead Knowledge in the Minimization of Environmental Lead Exposure

A method for guiding lead intervention and minimizing lead exposures in Philadelphia, Pennsylvania (PA) is through understanding the relationship between lead knowledge and parental characteristics such as gender, age, income, marital status, and education attainment. Parental characteristic may play a significant role in the identification of population groups where knowledge pertaining to lead exposure is inadequate. Through awareness and intervention, preventive measures can be implemented to minimize and eliminate lead exposure. The theoretical concept used in this quantitative study was Krieger ecosocial theory. The ecosocial theory provides guidance and analyzes differences in existing health relationships, especially those with biological and psychosocial influences. An exploratory cross-section design was used to explore the association between parental characteristics of gender, age, income, marital status, and education attainment with lead knowledge in the elimination of lead-based paint and high-risk exposure in communities of Philadelphia, PA. The Lead Knowledge Test questionnaire was completed by 124 participants. Descriptive statistics were used through calculation of central of tendency. Data analysis for inferential statistics was completed through multiple variable regressions. Results indicated parents gender, age, income, marital status, and education attainment were not predictors of lead knowledge. The results of this study have the potential to produce social change through identifying lead exposure in Philadelphia, PA, aiding in the minimization and prevention of lead exposures, in addition to reducing cognitive and neurological impacts for improved academic performance resulting in quality jobs and increased socioeconomic status

Silviculture Impacts and Population Genomics of Coniferiporia sulphurascens, the Causal Agent of Laminated Root Rot

Within the Pacific Northwest, USA, root diseases of conifers are a major forest health concern. These diseases are primarily caused by basidiomycete fungi. These fungal associates play a vital role in carbon sequestration but also have a significant negative economic impact within the timber industry. As a result, research on forest management practices to help mitigate root disease has become an important factor within forest health protection. Two closely related fungal species, Coniferiporia sulphurascens, and Coniferiporia weirii, are the primary causal agents of diseases known as laminated root rot (LRR) and butt rot. Three independent studies were conducted to investigate forest management strategies for LRR and to better understand the population dynamics of these two important forest pathogens. The first study used long-term permanent plots at two different study sites to compare the effects of different thinning treatments and alternate host rotations on tree mortality caused by Coniferiporia sulphurascens. At the first study site, three different thinning prescriptions were applied across ca. 160 hectares within the Siuslaw National Forest, Oregon, USA. The second study site evaluated the effects of five hardwood rotation treatments on subsequent growth and mortality of planted Pseudotsuga menziesii across 14 hectares of privately-owned forest in Columbia County. For both study sites the results were used to assess the accuracy of the Forest Vegetation Simulator (FVS) and the Western Root Disease Model (WRDM). An analysis of variance determined that there was no significant difference on mortality caused by C. sulphurascens among thinning treatments (p = 0.9816). Within the alternate host rotation study, no effect of hardwood rotation on growth or mortality of P. menziesii was detected (p = 0.253 and p = 0.172). The FVS was shown to over predict growth at both the thinning and alternate host study sites. The WRDM was found to predict observed data more accurately. A second study used 64 full genome sequences of C. sulphurascens and a population genomics approach to investigate evolutionary history, population dynamics, and dispersal capabilities of this fungus. The samples originated from five different populations across Oregon and Washington, USA. Results showed more long-distance gene flow and migration between populations than previously reported. In a final study, the full genome of C. weirii was sequenced and annotated to provide a novel draft genome. The total size of the genome was estimated to be 42.2 Mb. The assembly contained 10,351 predicted protein-coding genes. The estimated mean gene length of the predicted genes was 1,911 bp. Results from phylogenomic analysis support C. weirii and C. sulphurascens being closely related as previously determined by other studies

GRIT and Its Relationship with College Academic Success

Low college completion rates are an unfortunate reality in the United States. Some researchers have shown that a higher level of grit assisted college students in earning a higher grade point average (GPA) and completing a college credential. My study focused on grit and its relationship to college GPA, course completion rate, and the number of activities and programs participated in during one semester, along with the interaction effects of grit with a growth mindset on GPA and course completion rate through five research questions. A survey was administered to community college students participating in a TRIO Student Support Services Program (SSS), qualifying for the program as low-income, first-generation, and/or students with disabilities. Analysis of the data obtained from the survey helped to answer the research questions using linear regression, standard multiple regression, and correlation. There was little research focusing on the relationship between grit, college GPA, and course completion rates for underserved students in TRIO SSS programs, especially at community colleges, along with few research studies that look at an interaction effect between grit and growth mindset regarding college achievement. This study was unable to provide statistically significant results to show relationships between grit, college GPA, course completion rate, or the number of TRIO SSS services and activities participated in, nor an interaction effect between grit and college completion rate on GPA & completion rate. Limitations and future research recommendations are discussed

Impact of risk attitude on optimal IOR initiation time: A case study solved in a sequential decision-making framework powered by machine learning-based non-linear regression

The least-squares Monte Carlo algorithm (LSM) is an efficient approximate dynamic programming algorithm for solving sequential decision-making problems, leveraging regression. Previous studies have showcased the LSM workflow and linear regression in a sequential decision problem for optimizing the improved-oil-recovery (IOR) initiation and termination time, based on expected monetary value maximization as the decision criterion under risk neutrality. In this work, risk attitude is introduced in the IOR optimization problem to assess the impact on the decisions. Risk behaviours are modelled using utility functions, and the optimal decision strategy is found by maximizing the expected utility. Since the utility functions introduce non-linearity, machine learning non-linear regression techniques are used in the LSM workflow to approximate the expected utilities. Results suggest that risk-averse decision-makers prefer longer primary recovery lifetime compared to risk-neutral and risk-seeking decision-makers. This behaviour is attributed to the net present value (NPV) uncertainty related to the capital expenditure (CAPEX) incurred by switching to secondary recovery. Risk-averse decision-makers prefer shorter secondary recovery lifetime. This behaviour is attributed to the operational expenditure (OPEX) and production late-stage marginal cash inflow. The more risk-seeking the decision-maker is, the sooner they prefer to switch to secondary recovery, and the longer they would run the secondary recovery. The value of the information increases as the decision-maker is more risk-seeking. The differences in the production lifetime decisions with the consideration of future information versus the decisions ignoring future information also increase as the decision-maker is more risk-seeking. A change in the problem setting to a more marginal and uncertain case shows that risk-averse decision-makers would not run the project. Risk-neutral decision-makers would only run the project if future information were incorporated. This reinforces the importance of sequential decision-making, where value is created from information. Risk-seeking decision-makers would run the project with or without information. The novelties and contributions from the present work include: • Modelling, demonstration, and discussion of the impact of different risk attitudes on decisions. • Selection and application of the best machine learning method for non-linear regression in the LSM approach. • Demonstration of the value of considering future information in solving sequential decision-making problems

Effects of COVID-19 on Mental Health Workers\u27 Job Satisfaction, Employee Burnout, and Intent to Leave

The COVID-19 disease emerged in December 2019 and created a worldwide pandemic. As the COVID-19 virus spread, healthcare workers faced increased workloads and burnout due to increased stress. With a current abundance of research to better understand how the pandemic affected healthcare workers, minimal research has been conducted to investigate the effects on mental health workers. It is imperative to better understand how the consequences of the pandemic affected mental health workers due to their importance in supporting the mental well-being of our communities. This study focused on how the COVID-19 pandemic influenced job satisfaction, burnout syndrome, and intent to leave in mental health workers before and after the first 3 years of the COVID-19 pandemic. Using an online survey format on JotForm, 103 mental health professionals completed an online survey to measure job satisfaction, burnout syndrome, and intent to leave before and after the first 3 years of the COVID-19 pandemic. The results of the one-way repeated measures MANOVA showed a statistically significant difference in levels of job satisfaction, burnout syndrome, and intent to leave before and after the first 3 years of the COVID-19 pandemic. Results of the multiple linear regression indicated the COVID-19 pandemic did not act as a significant moderator for the relationship between job satisfaction and intent to leave, but did for the relationship between burnout and intent to leave. Implications encourage increased support for mental health workers because of the pandemic. Recommendations for future research are continued efforts in studying how the COVID-19 pandemic affects mental health employees, as well as other professions

Rendimiento de la madera rolliza de Pinus radiata D.Don, en la manufactura de parihuela para la agroexportación.

Universidad Nacional Agraria La Molina. Facultad de Ciencias Forestales. Departamento Académico de Industrias ForestalesEl presente estudio se realizó en la empresa North Pallet S.A., ubicado en la ciudad de Trujillo, La Libertad; con el objetivo de estimar el rendimiento en la producción de Parihuelas a partir de trozas de la especie de Pino radiata, proveniente de plantaciones de la ciudad de Cajamarca.Para el análisis del rendimiento se determinó una muestra de 61 trozas, evaluándose la calidad de cada troza, clasificándola como calidad A (Buena) y calidad B (regular a mala) y clase diamétrica grande (G), mediana (M) y pequeña (P), las cuales se sometieron al proceso de transformación para la obtención de tablas y tacos para la elaboración de parihuelas en el modelo convencional de 1.00 m x 1.20 m x 0.15 m. Como resultado del estudio, se determinó el rendimiento promedio por troza de 46.26% de madera útil y 0.05 m3 promedio de residuos obtenidos en la producción de madera rolliza a parihuela; así mismo, se comprobó la influencia positiva de los factores cualitativos como la calidad de la troza, obteniendo un rendimiento de 48.04% (calidad A) y 42.6% (calidad B) y la influencia positiva en el aumento de la clase diamétrica, obteniendo rendimientos según la clase diamétrica de 50.43% G (grande), 45.3% M(mediana) y 42.00% P(pequeña). Para la determinación del rendimiento por etapa de producción se obtuvo como resultado: 86.3% (encuadrado), 76.9% (formación de tablón) y 86.2% (formación de cubos), mostrando que existe una mayor pérdida de madera en la etapa de la formación de tablón. Así mismo, mediante la ecuación de regresión lineal simple se elaboró una tabla de rendimiento en base al diámetro promedio de troza, la cual permitirá la estimación del volumen aserrado en pies tablares para la producción de parihuelaThe present study was carried out in the company North Pallet S.A., located in the city of Trujillo, La Libertad; with the objective of estimating the yield in the production of Parihuelas from logs of the species of Pine radiata, coming from plantations of the city of Cajamarca. For performance analysis, a sample of 61 logs was determined, evaluating the quality of each log, classifying it as quality A (Good) and quality B (regular to poor) and large (G), medium (M) and small (P), which were submitted to the transformation process to obtain boards and blocks for the elaboration of pallets in the conventional model of 1.00 m x 1.20 m x 0.15 m. As a result of the study, the average yield per log of 46.26% of useful wood and 0.05 m3 average of residues obtained in the production of round wood on stretcher was determined; Likewise, the positive influence of qualitative factors such as the quality of the log was verified, obtaining a yield of 48.04% (quality A) and 42.6% (quality B) and the positive influence on the increase in the diameter class, obtaining yields according to the diameter class of 50.43% G (large), 45.3% M (medium) and 42.00% P (small). To determine the yield by production stage, the following results were obtained: 86.3% (framed), 76.9% (plank formation) and 86.2% (cube formation), showing that there is a greater loss of wood in the formation stage. of plank. Likewise, through the simple linear regression equation, a yield table was prepared based on the average log diameter, which will allow the estimation of the sawn volume in board feet for stretcher production

The Relationship between Nonprofit Organizations and Cloud Adoption Concerns

Many leaders of nonprofit organizations (NPOs) in the United States do not have plans to adopt cloud computing. However, the factors accounting for their decisions is not known. This correlational study used the extended unified theory of acceptance and use of technology (UTAUT2) to examine whether performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit can predict behavioral intention (BI) and use behavior (UB) of NPO information technology (IT) managers towards adopting cloud computing within the Phoenix metropolitan area of Arizona of the U.S. An existing UTAUT2 survey instrument was used with a sample of IT managers (N = 106) from NPOs. A multiple regression analysis confirmed a positive statistically significant relationship between predictors and the dependent variables of BI and UB. The first model significantly predicted BI, F (7,99) =54.239, p -?¤ .001, R^2=.795. Performance expectancy (β = .295, p = .004), social influence (β = .148, p = .033), facilitating conditions (β = .246, p = .007), and habit (β = .245, p = .002) were statistically significant predictors of BI at the .05 level. The second model significantly predicted UB, F (3,103) = 37.845, p -?¤ .001, R^2 = .527. Habit (β = .430, p = .001) was a statistically significant predictor for UB at a .05 level. Using the study results, NPO IT managers may be able to develop strategies to improve the adoption of cloud computing within their organization. The implication for positive social change is that, by using the study results, NPO leaders may be able to improve their IT infrastructure and services for those in need, while also reducing their organization\u27s carbon footprint through use of shared data centers for processing