A Preliminary Report on a Multi-Level Learning Technique Using Production Systems.

1981 ◽  
Author(s):  
Nicholas V. Findler
Keyword(s):  
Preliminary Report ◽  
Production Systems ◽  
Learning Technique ◽  
Multi Level

A MULTI-LEVEL LEARNING TECHNIQUE USING PRODUCTION SYSTEMS

1982 ◽  
Vol 13 (1) ◽  
pp. 25-30 ◽  
Author(s):  
NICHOLAS V. FINDLER
Keyword(s):  
Production Systems ◽  
Learning Technique ◽  
Multi Level

scholarly journals Supply Chain Logistics in Multi-Level Just-in-Time Production Sequencing Problems

2016 ◽  
Vol 11 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Gyan Bahadur Thapa ◽  
Sergei Silvestrov
Keyword(s):  
Supply Chain ◽  
Production Systems ◽  
Efficient Frontier ◽  
Nonlinear Integer Programming ◽  
Just In Time ◽  
Time Production ◽  
Sequencing Problem ◽  
Single Level ◽  
Supply Chain Logistics ◽  
Multi Level

The multi-level just-in-time sequencing problem is one of the challenging research areas in supply chain management. In this paper, we present brief review and some recent research developments of just-in-time production systems together with supply chain logistics. Observing production flows and supply chain synchronization in production process, we present the mathematical models of just-in-time (JIT) sequencing problem in multi-level and single-level as nonlinear integer programming in terms of discrepancy functions under the specified constraints. Discrete apportionment approach is briefly reported as an efficient frontier for single-level.  Journal of the Institute of Engineering, 2015, 11(1): 91-100

Optimised operation of multi-level exergy analysis for complicated production systems

2013 ◽  
Vol 13 (2) ◽  
pp. 201 ◽  
Author(s):  
Yang Liu ◽  
Xinyao Xiang ◽  
Qinglin Cheng ◽  
Xuxu Wang ◽  
Tao Yu
Keyword(s):  
Exergy Analysis ◽  
Production Systems ◽  
Multi Level

scholarly journals Developing a Flipped Learning model for Teaching EAP Students Struggling with Multi-Level Challenges in a College Context

2020 ◽  
Vol 9 (6) ◽  
pp. 1
Author(s):  
Iman Alkhalidi
Keyword(s):  
Blended Learning ◽  
Classroom Practice ◽  
Learning Model ◽  
English For Academic Purposes ◽  
Learning Theories ◽  
Flipped Learning ◽  
Holistic Model ◽  
Online Engagement ◽  
Learning Technique ◽  
Multi Level

This article aims to understand students’ experiences regarding the implementation of flipped learning (FL) as a modern blended learning technique in teaching English for academic purposes (EAP) in a community college context in Toronto. Based on students’ views, blended learning theories, and several previous studies, the study also aims to develop a holistic contextualized flipped learning model that helps both students and teachers in the context of EAP to cope with the challenges of a multilevel EAP classroom. The study is guided by the epistemology and philosophy of the interpretive paradigm as an underpinning stance. Accordingly, the qualitative approach has been selected for determining the strategy and methods of sampling, and data collection and data analysis. Results revealed that students’ views are compatible with the theoretical views in validating the utilization of flipped learning as a modern technique in the context of EAP. However, results revealed that the development of a holistic model includes a further component-online engagement as an extension component to the model. The study offers a set of recommendations and implications for EAP teachers and instructors within the area of ELT for classroom practice.

Scheduling mixed-model multi-level just-in-time production systems

1989 ◽  
Vol 27 (9) ◽  
pp. 1487-1509 ◽  
Author(s):  
JOHN MILTENBURG ◽  
GORDON SINNAMON
Keyword(s):  
Mixed Model ◽  
Production Systems ◽  
Just In Time ◽  
Time Production ◽  
Multi Level

Multi-level simulation concept for multidisciplinary analysis and optimization of production systems

2019 ◽  
Vol 103 (9-12) ◽  
pp. 3993-4012 ◽  
Author(s):  
Tim Delbrügger ◽  
Matthias Meißner ◽  
Andreas Wirtz ◽  
Dirk Biermann ◽  
Johanna Myrzik ◽  
...  
Keyword(s):  
Production Systems ◽  
Multidisciplinary Analysis ◽  
Multi Level

Sustainability: Supply chains, partner linkages, and new forms of self-regulation

2006 ◽  
Vol 25 (1) ◽  
pp. 1-12
Author(s):  
Reinier de Man ◽  
Tom R. Burns
Keyword(s):  
World Trade ◽  
Government Regulation ◽  
Production Systems ◽  
Self Regulation ◽  
Business Development ◽  
Business Practices ◽  
Business Partnerships ◽  
Sustainable Business ◽  
Multi Level ◽  
Regulatory Powers

This paper outlines a multi-level approach to sustainable business development. It builds on the notion that contemporary production systems are extensive, increasingly global in their reach. National government regulation is not feasible in many cases. Attempts to nationally regulate against non-sustainable production and business practices may even result in World Trade Organization (WTO) action against improper trade barriers. And, obviously, there is no international government with proper legislative and regulatory powers. Starting with the concept of supply chain, we describe and analyze the emerging practice of forming business partnerships for sustainability. The conceptualization is illustrated with a number of current examples. Problems and potentialities are discussed briefly in a concluding discussion.

scholarly journals Multi-Level Deep Learning Model for Potato Leaf Disease Recognition

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2064
Author(s):  
Javed Rashid ◽  
Imran Khan ◽  
Ghulam Ali ◽  
Sultan H. Almotiri ◽  
Mohammed A. AlGhamdi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Disease Detection ◽  
Potato Leaf ◽  
Crop Species ◽  
Leaf Disease ◽  
Crop Diseases ◽  
Learning Technique ◽  
Multi Level ◽  
Deep Learning Model

Potato leaf disease detection in an early stage is challenging because of variations in crop species, crop diseases symptoms and environmental factors. These factors make it difficult to detect potato leaf diseases in the early stage. Various machine learning techniques have been developed to detect potato leaf diseases. However, the existing methods cannot detect crop species and crop diseases in general because these models are trained and tested on images of plant leaves of a specific region. In this research, a multi-level deep learning model for potato leaf disease recognition has developed. At the first level, it extracts the potato leaves from the potato plant image using the YOLOv5 image segmentation technique. At the second level, a novel deep learning technique has been developed using a convolutional neural network to detect the early blight and late blight potato diseases from potato leaf images. The proposed potato leaf disease detection model was trained and tested on a potato leaf disease dataset. The potato leaf disease dataset contains 4062 images collected from the Central Punjab region of Pakistan. The proposed deep learning technique achieved 99.75% accuracy on the potato leaf disease dataset. The performance of the proposed techniques was also evaluated on the PlantVillage dataset. The proposed technique is also compared with the state-of-the-art models and achieved significantly concerning the accuracy and computational cost.

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