Institute of Medicine Prevention Report: Recommended Prevention and Early Intervention Measures

2009 ◽  
Author(s):  
Ricardo F. Munoz
Keyword(s):  
Early Intervention ◽  
Institute Of Medicine ◽  
Intervention Measures

scholarly journals Early Intervention Measures for Refractive Error

2011 ◽  
Vol 4 (8) ◽  
pp. 1-3
Author(s):  
E. NILA ETHEL ◽  
◽  
Dr. P. NAGALAKSHMI Dr. P. NAGALAKSHMI
Keyword(s):  
Early Intervention ◽  
Refractive Error ◽  
Intervention Measures

scholarly journals Joubert syndrome related disorder (JSRD): a case report and review of literature

2017 ◽  
Vol 4 (3) ◽  
pp. 1124
Author(s):  
Seema Sharma ◽  
Ajay Sharma ◽  
Vipin Sharma ◽  
Sandesh Guleria
Keyword(s):  
Case Report ◽  
Early Intervention ◽  
Eye Movements ◽  
Joubert Syndrome ◽  
Accurate Diagnosis ◽  
Review Of Literature ◽  
Radiological Findings ◽  
Related Disorder ◽  
Molar Tooth Sign ◽  
Intervention Measures

Joubert syndrome and related disorder (JSRD) is a rare disorder of midline structure of brain having characteristic clinical and neuro-radiological findings. The hallmark of diagnosis is molar tooth sign (MTS). Early accurate diagnosis can help in planning early intervention measures to reduce the morbidity. We are hereby presenting a case of eight months old female infant with abnormal eye movements since birth along with developmental delay. Clinical and radiological evidence proved that child is having Joubert syndrome related disorder. 

scholarly journals The Concept of Recovery of Credit Institutions in the Bank Recovery and Resolution Directive

2019 ◽  
Vol 28 ◽  
pp. 103-111
Author(s):  
Märt Maarand
Keyword(s):  
Early Intervention ◽  
Direct Connection ◽  
Credit Institution ◽  
Legal Rules ◽  
Recovery Planning ◽  
Intervention Measures ◽  
Rules And Principles ◽  
Two Measures ◽  
State Liability ◽  
Credit Institutions

New regulations, obligations for credit institutions, and powers for authorities were created by the Bank Recovery and Resolution Directive (BRRD). While resolution of a credit institution is clearly defined, it is less clear whether recovery of a credit institution could and should be treated as a separate concept under the BRRD; which elements it encompasses; and how these elements enhance and are linked with the pre-existing prudential regulation, processes, and tools. The problem is that if recovery is to be deemed a differentiable concept, specific legal rules and principles could be applicable that are separate from the prudential or the resolution framework – whether existing ones or other, easily developed rules and principles. This is particularly crucial when authorities exercise powers related to recovery, because following appropriate rules and principles has a direct connection with state liability. In consideration of these issues, the article presents several important conclusions: Firstly, recovery in the sense applied in the BRRD can be distinguished from both the prudential framework and the concept of resolution, on the basis of function; the concept of recovery can be considered to consist of recovery planning, early intervention measures, and two measures of further intervention that can be employed. Some early intervention measures are recovery-specific and broaden the supervisory powers significantly, while others do not and show overlap with supervisory powers derived from the prudential framework. Recovery planning and exercising early intervention measures can take place in parallel with processes connected with the prudential framework while nonetheless maintaining recovery as a usefully separate concept.

scholarly journals Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Muhammad Adnan ◽  
Duaa H. AlSaeed ◽  
Heyam H. Al-Baity ◽  
Abdur Rehman
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Intervention ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Learning Performance ◽  
Learning Models ◽  
Intervention Measures ◽  
Artificial Neural Network Ann

Machine learning (ML) and deep learning (DL) algorithms work well where future estimations and predictions are required. Particularly, in educational institutions, ML and DL algorithms can help instructors in predicting the learning performance of learners. Furthermore, the prediction of the learning performance of learners can assist instructors and intelligent learning systems (ILSs) in taking preemptive measures (i.e., early engagement or early intervention measures) so that the learning performance of weak learners could be increased thus reducing learners’ failures and dropout rates. In this study, we propose an intelligent learning system (ILS) powered by the mobile learning (M-learning) model that predicts learners’ performance and classify them into various performance groups. Subsequently, adaptive feedback and support are provided to those learners who struggle in their studies. Four M-learning models were created for different learners considering their learning features (study behavior) and their weight values. The M-learning model was based on the artificial neural network (ANN) algorithm with the aim to predict learners’ performance and classify them into five performance groups, whereas the random forest (RF) algorithm was used to determine each feature’s importance in the creation of the M-learning model. In the last stage of this study, we performed an early intervention/engagement experiment on those learners who showed weak performance in their study. End-user computing satisfaction (EUCS) model questionnaire was adopted to measure the attitude of learners towards using an ILS. As compared to traditional machine learning algorithms, ANN achieved the highest prediction accuracy for all four learning models, i.e., model 1 = 90.77%, model 2 = 87.69%, model 3 = 83.85%, and model 4 = 80.00%. Moreover, the five most important features that significantly affect the students’ final performance were MP3 = 0.34, MP1 = 0.26, MP2 = 0.24, NTAQ = 0.05, and AST = 0.018.

scholarly journals Modelling the Impacts of Early Intervention on Desert Locust Outbreak Prevention

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Dejen Ketema Mamo ◽  
Dejene Shewakena Bedane
Keyword(s):  
Early Intervention ◽  
Crop Production ◽  
Significant Feature ◽  
Early Interventions ◽  
Desert Locust ◽  
Asymptotically Stable ◽  
Interior Equilibrium ◽  
Intervention Measures ◽  
Theoretical Results ◽  
Production Losses

To preserve crop production losses, monitoring of desert locust attacks is a significant feature of agriculture. In this paper, a mathematical model was formulated and analyzed to protect crops against desert locust attack via early intervention tactics. We consider a triple intervention approach, namely, proaction, reaction, and outbreak prevention. The model integrates a stage-structured locust population, logistics-based crop biomass, and blended early intervention via pesticide spray. We assume that the amount of pesticide spray is proportional to the density of the locust population in the infested area. Conventional short residual pesticides within ultralow volume formulation and equipment control operations are considered. The trivial and locust-free equilibrium of the model is unstable, whereas the interior equilibrium is asymptotically stable. Numerical simulations validate the theoretical results of the model. In the absence of intervention measures, desert locust losses are approximately 71% of expected crop production. The model projection shows that effective proactive early intervention on hopper stage locust contained locust infestation and subdued public health and environmental hazards. Relevant and up-to-date combined early interventions control desert locust aggression and crop production losses.

Developing an Early Intervention Preservice Specialization in Speech-Language Pathology

1995 ◽  
Vol 4 (2) ◽  
pp. 31-36 ◽  
Author(s):  
Joanne E. Roberts ◽  
Elizabeth Crais ◽  
Thomas Layton ◽  
Linda Watson ◽  
Debbie Reinhartsen
Keyword(s):  
Early Childhood ◽  
Early Intervention ◽  
Intervention Program ◽  
Graduate Program ◽  
Master's Level ◽  
Clinical Experiences ◽  
Speech Language Pathology ◽  
Early Intervention Program ◽  
Language Pathology ◽  
Family Centered

This article describes an early intervention program designed for speech-language pathologists enrolled in a master's-level program. The program provided students with courses and clinical experiences that prepared them to work with birth to 5-year-old children and their families in a family-centered, interdisciplinary, and ecologically valid manner. The effectiveness of the program was documented by pre- and post-training measures and supported the feasibility of instituting an early childhood specialization within a traditional graduate program in speech-language pathology.

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