The role of employment in dualistic economic development models

Traditionally employment and output have been the major two criteria for evaluating aggregate performance in a dual economic model for less developed countries. This has been mainly discussed by transferring labor from the agricultural sector to non-agricultural use. Most models are specified in such a manner that the migrated agricultural workers will be absorbed in non-agricultural jobs. In reality though, the transformation of labor to the non-agricultural sector (i. e., industrial or urban) has created an unemployment problem in that sector. The purposes of this paper are (1) to study some of the relevant common characteristics of less developed countries, (2) to review the literature related to labor employment, and (3) to demonstrate the importance of the efficiency of employment and output in dualistic development models. This paper has reviewed three classes of models. First, this study has considered the Fei-Ranis model of economic development which has drawn heavily from W. A. Lewis’s model of economic development with unlimited supplies of labor (Fei-Ranis is primarily a classical model). Second, the Jorgenson’s model of development of a dual economy has been presented. This model is based on Harrod-Domar's concept of steady growth, Third, this study looks at a more recent model, the Todaro model, which considered the practical problem of urban (industrial) unemployment. The problem of urban-unemployment is not considered by the first two models. Todaro's approach to the development of a dual economy may be the beginning of a new trend in the literature. On the basis of findings of this work it was concluded that: (1) The transformation of an agricultural surplus labor economy to an industrial surplus labor economy, can only be justified by a careful consideration of the production possibilities of each case, and the demand for the products of each sector. (2) The rate of growth of each sector will be dictated by the optimum growth path. This is the locus of optimum combination of output and employment of resources in each sector in the given time period. Because production possibilities and demand considerations operate to determine the optimum growth path, this path will rarely if ever coincide with the straight-line balanced growth path prominent in the Fei-Ranis model

A METHODOLOGY TO SUPPORT COMPANIES IN THE FIRST STEPS TOWARDS DE-MANUFACTURING

AbstractDe-manufacturing and re-manufacturing are fundamental technical solutions to efficiently recover value from post-use products. Disassembly in one of the most complex activities in de-manufacturing because i) the more manual it is the higher is its cost, ii) disassembly times are variable due to uncertainty of conditions of products reaching their EoL, and iii) because it is necessary to know which components to disassemble to balance the cost of disassembly. The paper proposes a methodology that finds ways of applications: it can be applied at the design stage to detect space for product design improvements, and it also represents a baseline from organizations approaching de-manufacturing for the first time. The methodology consists of four main steps, in which firstly targets components are identified, according to their environmental impact; secondly their disassembly sequence is qualitatively evaluated, and successively it is quantitatively determined via disassembly times, predicting also the status of the component at their End of Life. The aim of the methodology is reached at the fourth phase when alternative, eco-friendlier End of Life strategies are proposed, verified, and chosen

BenCoref: A Multi-Domain Dataset of Nominal Phrases and Pronominal Reference Annotations

Coreference Resolution is a well studied problem in NLP. While widely studied for English and other resource-rich languages, research on coreference resolution in Bengali largely remains unexplored due to the absence of relevant datasets. Bengali, being a low-resource language, exhibits greater morphological richness compared to English. In this article, we introduce a new dataset, BenCoref, comprising coreference annotations for Bengali texts gathered from four distinct domains. This relatively small dataset contains 5200 mention annotations forming 502 mention clusters within 48,569 tokens. We describe the process of creating this dataset and report performance of multiple models trained using BenCoref. We anticipate that our work sheds some light on the variations in coreference phenomena across multiple domains in Bengali and encourages the development of additional resources for Bengali. Furthermore, we found poor crosslingual performance at zero-shot setting from English, highlighting the need for more language-specific resources for this task

Microleakage of CEM cement in two different media

INTRODUCTION: Sealing ability of root-end filling materials is of great importance. It can be investigated by measuring microleakage. The purpose of this in vitro study was to evaluate microleakage of calcium enriched mixture (CEM) cement in two different media including phosphate buffer solution (PBS) and distilled water. MATERIALS AND METHODS: Twenty single-rooted human teeth were selected. All teeth were root-end filled with CEM cement. Samples were divided into two groups of 10 each and were placed in PBS or distilled water. The microleakage was measured after 12 and 24 h, 14 and 30 days with Fluid Filtration device. Data were statistically analyzed by repeated measures test. RESULTS: Sealing ability of CEM cement was significantly superior in PBS compared to distilled water (P<0.05). This study also showed that time had no significant effect on the sealing ability of CEM cement. CONCLUSION: Media can significantly affect the microleakage of CEM cement. PBS can provide more phosphorous ions for hydroxyapatite formation of CEM cement; therefore, CEM cement can seal more effectively with PBS

Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images

Diagnosing Brain Tumor with the aid of Magnetic Resonance Imaging (MRI) has gained enormous prominence over the years, primarily in the field of medical science. Detection and/or partitioning of brain tumors solely with the aid of MR imaging is achieved at the cost of immense time and effort and demands a lot of expertise from engaged personnel. This substantiates the necessity of fabricating an autonomous model brain tumor diagnosis. Our work involves implementing a deep convolutional neural network (DCNN) for diagnosing brain tumors from MR images. The dataset used in this paper consists of 253 brain MR images where 155 images are reported to have tumors. Our model can single out the MR images with tumors with an overall accuracy of 96%. The model outperformed the existing conventional methods for the diagnosis of brain tumor in the test dataset (Precision = 0.93, Sensitivity = 1.00, and F1-score = 0.97). Moreover, the proposed model's average precision-recall score is 0.93, Cohen's Kappa 0.91, and AUC 0.95. Therefore, the proposed model can help clinical experts verify whether the patient has a brain tumor and, consequently, accelerate the treatment procedure.Comment: 4th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC 2020), IEEE, 7-9 October 2020, TamilNadu, INDI

Electrocardiogram Heartbeat Classification Using Convolutional Neural Networks for the Detection of Cardiac Arrhythmia

The classification of the electrocardiogram (ECG) signal has a vital impact on identifying heart-related diseases. This can ensure the premature finding of heart disease and the proper selection of the patient's customized treatment. However, the detection of arrhythmia is a challenging task to perform manually. This justifies the necessity of a technique for automatic detection of abnormal heart signals. Therefore, our work is based on the classification of five classes of ECG arrhythmic signals from Physionet's MIT-BIH Arrhythmia Dataset. Artificial Neural Networks (ANN) have demonstrated significant success in ECG signal classification. Our proposed model is a Convolutional Neural Network (CNN) customized to categorize the ECG signals. Our result testifies that the planned CNN model can successfully categorize arrhythmia with an overall accuracy of 95.2%. The average precision and recall of the proposed model are 95.2% and 95.4%, respectively. This model can effectively be used to detect irregularities of heart rhythm at an early stage.Comment: 4th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC 2020), IEEE, 7-9 October 2020, TamilNadu, INDI

The relationship between the religious beliefs and the feeling of loneliness in elderly

The objective of this research is to study the relationship between the religious beliefs and the feeling of loneliness in elderly. In this descriptive correlation study, the statistical society included 100 individuals of the society of retired people in the Medical University of Gilan province in Iran. The sample was taken by the easy random method. The method of collecting data was the questionnaire contained 3 parts: 1) personal characteristics and social characteristics. 2) Allport's internal and external religious beliefs scale and 3) the Standard loneliness feeling of You care. Data was analyzed by means of the description and presumption statistical methods and use of the SPSS software. The findings showed that there is a meaningful correlation between the external religious beliefs and the marital status, the amount of income, socialization with family members and relatives, social activities and also between the internal religious beliefs and the attending in the religious gatherings, the emotional support of the family, friends, and the others and the general satisfaction of the mentioned supports with P<0.05 and finally with the use of the nonparametric testes, a meaningful relationship has been found between the religious beliefs and the feeling of loneliness with P<0.001. Thus this study shows that the religious believes as an important source of support in aged people, can help them to be healthier physically and psychologically and it is essential to consider it for the mental health educational plans. © Indian Society for Education and Environment (iSee)

Prediction of Temperature and Rainfall in Bangladesh using Long Short Term Memory Recurrent Neural Networks

Temperature and rainfall have a significant impact on economic growth as well as the outbreak of seasonal diseases in a region. In spite of that inadequate studies have been carried out for analyzing the weather pattern of Bangladesh implementing the artificial neural network. Therefore, in this study, we are implementing a Long Short-term Memory (LSTM) model to forecast the month-wise temperature and rainfall by analyzing 115 years (1901-2015) of weather data of Bangladesh. The LSTM model has shown a mean error of -0.38oC in case of predicting the month-wise temperature for 2 years and -17.64mm in case of predicting the rainfall. This prediction model can help to understand the weather pattern changes as well as studying seasonal diseases of Bangladesh whose outbreaks are dependent on regional temperature and/or rainfall.Comment: 4th International Symposium on Multidisciplinary Studies and Innovative Technologies, IEEE, 22-24 October, 2020, TURKE