REFINING THE GOALS OF PUBLIC EDUCATION IN THE UNITED STATES: AN EXPLORATORY SINGLE-CASE EMBEDDED STUDY OF A STUDENT-CENTERED PATH-GOALS SETTING

The public higher education system in the United States has inherited a multitude of aims and missions in order to fulfill its social and educational objectives. As a result, many higher education institutions suffer from unclear goals. Nevertheless, the researcher identified the student\u27s goal-oriented process as the fundamental aspect of this educational system from its inception until the present. The exploratory single-case embedded study conducted at a regional comprehensive institution highlighted the differences, difficulties, and issues faced by students, administrators, and the institution itself in its pursuit of specific goals in higher education. Indepth interviews were employed to investigate the data obtained from cohorts of undergraduate students (n = 6) and administrators (n = 10) at the chosen university. By combining an adjustment model of Path-Goal leadership theory with a psychological Goal-Setting theory, the study proposes a clear and data-driven approach to reorganizing student support programs and initiatives within the constraints of limited institutional resources and ambiguous policies. This study aims to advance research on educational leadership within complex systems. The study examines various strategies and programs employed by Institution X using the Student-Goal Setting approach to align these goals with the institution\u27s leadership processes. The results emphasize the significance of transparent communication, encouraging inclusive initiatives, and efficient goal-setting methods in improving the development of students\u27 path goals. Obstacles institutions face include a lack of coordination, imprecise strategic planning, and deficiencies in program implementation and communication strategies. Recommendations include implementing transparent, data-driven strategies that effectively balance student needs and institutional objectives, as well as a possible application of artificial intelligence in higher education system

The time to shut down

At each time, a firm facing uncertainty over future market conditions have to make a decision whether they should continue to produce or stop the process? As the traditional principle, the firm will go out of production when the price of the typical unit does not cover the average variable cost that it must incur to produce the typical unit. In reality the firm can suffer losses today however it can get more gains tomorrow that is enough to make up the losses. It means that this rule seems not be suitable absolutely in an uncertainty environment. And it leads to a rule that the firm only stop producing if average variable costs of unit exceed the price of unit by a positive amount. This paper expects to find this exceeding amount and when a firm will stop producing. Under uncertainty, the price of unit and the average variables cost are assumed to follow a continuous time stochastic process. We wish to apply the optimal stopping time approach in order to solve it.

Influences of heating temperatures on physical properties, spray characteristics of bio-oils and fuel supply system of a conventional diesel engine

Alternative fuels need to satisfy the strict requirements of the use for diesel engines aiming at enhancing the performance and reducing pollutant emissions. The use of straight bio-oils for diesel engines entails improving their disadvantages such as high density, high surface tension and kinematic viscosity (tri-physical parameters). There have been some as-used methods for reduction of the above-mentioned negative effects related to straight bio-oil disadvantage, however, the adequately-heating method may be considered as a simple one helping the physical parameters of straight bio-oils to reach stable and highly-confident values which are close to those of traditional diesel fuel. As a consequence, the spray and atomization, combustion, performance, and emissions of diesel engines fueled with preheated bio-oils are improved. In this work, a study of the dependence of the density, surface tension and kinematic viscosity of coconut oil (a type of bio-oils) on temperatures (from 40-110oC) within a wide variety are conducted. In the first stage, the influence study of temperature on tri-physical parameters is carried out on the basis of experimental correlation and as-described mathematical equation. In the second stage, the influence study of tri-physical parameters on spray and atomization parameters including penetration length (Lb) and Sauter mean diameter (SMD), and the influence of tri-physical parameters on fuel supply system are investigated. The optimal range of temperature for the as-used bio-oils is found after analyzing and evaluating the obtained results regarding the physical properties and spray characteristics, as well as compared with those of diesel fuel. The confident level over 95% from the regression correlation equation between the above-mentioned tri-physical parameters and temperature is presented. Additionally, the measured spray parameters, the calculated values of frictional head loss and fuel flow rate are thoroughly reported. 

What Good are Models for Neuroscience? An Example from Modelling the Thalamic Reticular Nucleus

Generally, modelling in the sciences bring out many benefits. It is taking the known relationships about different physical parameters in order to develop new hypothesis due to the ability to scan through different possibilities systematically. Since my field is in neuroscience, I will focus my efforts on explaining to you how modelling can be useful to understanding the brain in a theoretical manner, as well as assisting experimentalists develop new experiments (Fig.1A), via two specific examples of modelling the network involving the thalamic reticular nucleus in Dr. Julie Haas\u27 lab. Modelling can never achieve realistic results that experiments can, simply because we do not know of all of the variables that come into play. But it can provide us with insights due to the fact that modelling can scan through multitudes of possibilities that would take too long and a whole lot of resources to achieve in experiments. These insights can then guide experimentalists in designing experiments to discover what the brain might be using to achieve such amazing computational capabilities