Critical Policy Analysis of Resource Allocation Reviews under the Every Student Succeeds Act (ESSA) | Chapter 3| New Ideas Concerning Arts and Social Studies Vol. 5

 

Background: The distribution of funding to schools can be a complex issue, as school systems have limited financial resources with which to advance their goals. The Every Student Succeeds Act (ESSA) introduced a new provision that requires state education agencies (SEAs) to review resource allocation in districts that serve a significant number of schools identified for continuous improvement and a significant number of schools implementing targeted school improvement plans.

 

Purpose: The purpose of this study is to examine the intersection of state, local, and federal policy meant to engage school districts around allocating resources for the purpose of supporting high-need schools. This study examined the discord between policy and practice around the enactment of federal legislation centred around resource allocation through a critical policy lens aimed at assessing whether state and local guidance can promote implementation of the federal guidance on equitable and effective distribution of resources.

 

Methodology: The study reviewed publicly available data across 25 state education agencies and 10 school districts. These districts were randomly selected, but a focus was made on reviewing resources for large school districts. The study reviewed federal, state and local guidance on resource allocation pertaining to the ESSA.

 

Findings: The analysis identified examples of state and local efforts to guide the implementation of the new legislation. However, the findings also highlight the challenge that exists in implementing federal policy and the impact of those challenges across marginalised communities of poverty.

 

Conclusion: Over the past decade, numerous efforts have aimed to promote more equitable resource distribution, leading to state and local policies designed to improve allocation practices. The findings of this study may be used as a guidance tool for policy makers and educational leaders at the state and local levels as they continue to seek ways to support marginalised students. The findings have direct implications for current practitioners, parents, the community, and state/local school boards.

 

 

Author(s) Details

Carlas McCauley
Department of Education Leadership and Policy Studies, Howard University School of Education, Washington DC, USA.

 

Please see the book here :- https://doi.org/10.9734/bpi/nicass/v5/6393

Research on the Optimal Allocation of Sanya's Tourism Resources |Chapter 15 | Theoretical Key Issues and Practical Development Trends of China’s Digital Economy

This paper studies the optimal allocation of tourism resources in Sanya through an analysis of the distribution and characteristics of various tourism resources in the city. By combining the needs of tourists and market demands, the best resource allocation scheme is determined. In this scheme, a variety of tourism resources are rationally matched to form a diversified and comprehensive tourism product line. This method of resource allocation not only attracts more tourists to Sanya, promoting the development of the local tourism economy, but also maximizes the protection of the local natural environment and cultural heritage, achieving sustainable development.

 

Author (s) Details

 

Wenbo Lyu
Saxo Fintech Business School, University of Sanya, Sanya, 572000, China.

 

Qianwan Xu
Saxo Fintech Business School, University of Sanya, Sanya, 572000, China.

 

Hao Su
Saxo Fintech Business School, University of Sanya, Sanya, 572000, China.

 

Chenxi Min
Saxo Fintech Business School, University of Sanya, Sanya, 572000, China.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48388-89-6/CH15

In-Patient Bed Allocation by Using Markov Chain Model | Chapter 11 | Research Highlights in Science and Technology Vol. 6

 The use of Markov Chain for in-patient bed distribution has the potential to provide abundant benefits to hospitals, containing improved patient outcomes, shortened costs, and enhanced functional efficiency. Bed volume management is the distribution and provision of beds in hospitals, place beds in specialty wholes are considered as a limited resource.  The key determinants affecting bed allocation in clinics are related to the patient's healing condition, treatment necessities, and available resources in the way that beds, staff, and hospital ability.Hospital administrators, nurses, and doctors often manage the bed allocation process to make sure that subjects receive the proper level of consideration and assistance while they are sick. As it helps to maximize the use of possessions, reduce patient wait occasions, and guarantee that patients receive up-to-date and appropriate care, bed allocation is a critical component of hospital administration. The study found that an reformed bed allocation system take care of improve patient flow and defeat wait times, superior to better patient outcomes. Incorporating more exhaustive data, creating active and adaptable models, combining optimization algorithms, handling predictive science of logical analysis, integrating with decision support structures, and validating their productiveness in actual healthcare scenes are the key components of Markov chain models for in-patient bed distribution. The field may make excellent progress in these areas by maximizing system use, streamlining the process of allocating beds, and lifting the standard of patient care.

Author(s) Details:

Balagopal Ramdurai,
IEEE, Researcher & Product Innovator, Chennai, India.

Please see the link here: https://stm.bookpi.org/RHST-V6/article/view/11346    

Study on Social-sine Cosine Algorithm-based Cross Layer Resource Allocation in Wireless Network | Chapter 6 | Recent Advances in Mathematical Research and Computer Science Vol. 1

Cross-layer resource allocation in wireless networks has historically been addressed using communication networks or information theory. A major difficulty in networking is the distribution of limited resources across network users. The resource is allotted at the Medium Access Control (MAC) level in a typical multilayer network, and the network layers use bit pipes to deliver data at a set pace with some random errors. As a result, this research demonstrates how to use the recommended Social-Sine cosine algorithm to allocate cross-layer resources in a wireless network (SSCA). The fundamental purpose of this study topic is to allocate resources between layers using the Social Sine Cosine Algorithm (SSCA). For Cross layer optimization, the MAC and physical layers provide Queue State Information (QSI) and Channel State Interference (CSI), respectively. The cross-layer optimization entity makes the resource allocation choice in order to maximise the network's sum rate. By changing the channel conditions, the Cross layer entity for optimization adapts the judgement based on new input data.

The recommended SSCA is built by combining the Social Ski Driver (SSD) and the Sine Cosine Algorithm (SSA). In addition, the suggested SSCA analyses max-min, hard-fairness, proportional fairness, mixed-bias, and maximum throughput fitness based on energy and fairness for further improving the resource allocation approach. To minimise the network's sum rate, the cross-layer optimization entity decides on resource distribution based on energy and fairness. Energy, throughput, and fairness are used to evaluate the proposed model's resource allocation performance. The created model achieves a maximum energy of 258213, a maximum throughput of 3.703, and a maximum fairness of 0.868.

 

Author (S) Details

T. Praveena
Department of Computer Science and Engineering, RV College of Engineering, Bengaluru, Karnataka, India.

G. S. Nagaraja
Department of Computer Science and Engineering, RV College of Engineering, Bengaluru, Karnataka, India.

View Book:- https://stm.bookpi.org/RAMRCS-V1/article/view/4336

A Detailed Conceptual Approach to Resources Allocation Scheduling | Chapter 8 | Theory and Practices of Mathematics and Computer Science Vol. 6

In everyday human activities, the issue of planning and allocating resources, both space and time, has created a lot of problems and has become of great concern. Several seminars and conferences were held to address the distribution of resources in various fields to select the best or suitable computational algorithms to solve this problem. The resource scheduling question deals with different parameters that are involved in the allocation of resources and related differences in the allocation of resources. The scheduling of resource distribution involves careful resource and time management for different users to prevent discrepancies in the timing of events. This paper provides a conceptual structure for solving problems with resource allocation scheduling using timetabling as a representative scheduling problem that involves careful resource and time management for different users to prevent conflicts in event timing. The work identifies the shortcomings of algorithms used to solve many timetabling problems such as the airplane roster, lecture schedules, etc. The paper aims to identify a good structure for the implementation of fuzzy algorithms that will be of great use to those who solve issues related to timetabling. Implementation of these system principles for the grouping of resources requires the requisite future works where there are no venue capacities to deal with such resources. A web implementation of the system is highly desirable and it could also integrate into it the development of other constraint-based scheduling activities such as review timetable.

Aurthor(s) Details:

A. A. Eludire
Department of Computer Science, Joseph Ayo Babalola University, Ikeji Arakeji, Nigeria.

C. O. Akanbi
Department of Information Communication Technology, Osun State University, Osogbo, Nigeria.

View Book :- https://stm.bookpi.org/TPMCS-V6/issue/view/6