School improvement - innovative practices and experiences

This paper reflects field experiences in setting up and running the Academic Cell and the Model Teachers Resource Centre. The paper is based on work done in April 2001 up to August 2003. As a Professional Development Teacher (PDT), school improvement has been the core objective for me. This paper points out to the contributions made for the purpose of school improvement and curriculum enrichment through material development, , which focuses on developing two main areas: firstly, a Guide for Low Cost Teaching Aids and secondly, textbooks and corresponding Teacher’s Guides. Also, it discusses the procedures that were adopted in setting up a model Teacher’s Resource Centre. The fact that this centre functioned as backup for the Academic Cell’s activities and also as a source of facilitation for district based Teacher’s Resource Centres has also been elaborated on. It was found that material developing is a lengthy and complex process and requires detailed knowledge of a particular subject and the ability to clearly and effectively communicate with an audience. There are certain criteria that need to be considered in developing textbooks such as: 1) Textbooks must address the objectives of the National Curriculum. 2) The content and language should be appropriate enough in making a smooth transition from the previous level to the one that is aimed at. 3) The content must provide to students with room for creativity. 4) The presentation has to be engaging and interesting. For this purpose, an appropriate ratio of text and illustration needs to be considered. Gender balance and cultural appropriateness must also be addressed. As part of my responsibilities I was also involved with the development of Indicators for Monitoring and Supporting Schools. It is hoped that this document will play a significant role in making judgments based on evidence whether schools are progressing or not. I also worked towards setting standards for quality education in colleges through teaching, arranging workshops, making lists of teaching and learning material plus library books all of which play a vital role in achieving that purpose. I learnt that within their scope of work, Professional Development Teachers can make a big difference by providing opportunities for school improvement. In doing this however the system needed to be thoroughly understood and a personal commitment also counted

3-Methyl-1H-pyrrolo[2,1-c][1,4]oxazin-1-one

In the title mol­ecule, C8H7NO2, all the non-H atoms lie essentially in the same plane (r.m.s. deviation = 0.019 Å) In the crystal structure, weak inter­molecular C—H⋯O inter­actions link mol­ecules into chains along [100]. In addition, there are π–π stacking inter­actions between mol­ecules related by the c-glide plane, with alternating centroid–centroid distances of 3.434 (2) and 3.639 (2) Å

A Study to Assess the Impact of Self-instructional Module on Knowledge among Adolescent Students regarding First Aid Measures for Selected Emergencies in a Selected Higher Secondary School, Devsar, Kulgam

Introduction: First aid is an important life-saving skill that everybody should know. As young people move towards independence and take on responsibilities in their own lives, they should know how to help others, whether it is a family member, friend or fellow citizen. While there are moves underway to introduce first aid training into the secondary school curriculum, there are other ways to give our kids and ourselves at least an elementary knowledge of the first aid basics. This study intended to assess the impact of a self-instructional module on knowledge among adolescent students regarding first aid measures for selected emergencies in a selected higher secondary school of Devsar, Kulgam. Method: A quantitative methodology was used with a pre-experimental one group pre-test and post-test design on a convenient sample of 50 adolescent students. A self-structured questionnaire was used to collect the data. Result: Data were analysed using descriptive and inferential statistics. Data analysis through SPSS-16 version by using t-test exposed significant difference (21.18) and p &lt; 0.001 amongst pre-test and post-test knowledge scores of respondents. Conclusion: Thus the study revealed a substantial increase in knowledge scores amongst adolescent students regarding first aid measures for selected emergencies after administration of the self-instructional module.</jats:p

Effect of Partial Shading on a PV Array and Its Maximum Power Point Tracking Using Particle Swarm Optimization

Abstract The maximum power point (MPP) of a Solar Photovoltaic (SPV) array varies with temperature and irradiation. Shadow of various objects falling on a certain portion of the SPV array causes partial shading condition (PSC) which results in the formation of hot spot. Thus, reducing the power (output) by 33% on a single cell in addition to the occurrence of various peaks on a P-V curve. To detect global maxima among the multiple peaks is a challenge for researchers. Hence, different Maximum Power Point Tracking (MPPT) techniques are used to overcome this challenge. In this paper, the impact of partial shading on SPV array has been analysed and the Particle Swarm Optimization (PSO) based MPPT technique is used to obtain global maxima under partial shading conditions. The MPPT controller is incorporated with a converter (boost) to vary the input voltage as per the duty cycle of the switch generated by PSO algorithm-based controller.</jats:p

Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images

Liver disease is one of the most prominent causes of the increase in the death rate worldwide. These death rates can be reduced by early liver diagnosis. Computed tomography (CT) is a method for the analysis of liver images in clinical practice. To analyze a large number of liver images, radiologists face problems that sometimes lead to the wrong classifications of liver diseases, eventually resulting in severe conditions, such as liver cancer. Thus, a machine-learning-based method is needed to classify such problems based on their texture features. This paper suggests two different kinds of algorithms to address this challenging task of liver disease classification. Our first method, which is based on conventional machine learning, uses texture features for classification. This method uses conventional machine learning through automated texture analysis and supervised machine learning methods. For this purpose, 3000 clinically verified CT image samples were obtained from 71 patients. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues by using supervised learning methods. Our proposed method correctly quantified asymmetric patterns in CT images using machine learning. We evaluated the effectiveness of the feature vector with the K Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers. The second algorithm proposes a semantic segmentation model for liver disease identification. Our model is based on semantic image segmentation (SIS) using a convolutional neural network (CNN). The model encodes high-density maps through a specific guided attention method. The trained model classifies CT images into five different categories of various diseases. The compelling results obtained confirm the effectiveness of the proposed model. The study concludes that abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques, which may also assist radiologists and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases.</jats:p

An empirical investigation of socio-economic impacts of agglomeration economies in major cities of Punjab, Pakistan

Agglomeration economies are the external benefits earned from clustering of industries and people in cities. The study assumes unbridled clustering of population in emerging urban agglomerations turning economies into diseconomies. This study empirically investigates the heterogeneous socioeconomic impacts of agglomeration economies in selected cities of Punjab, Pakistan, from 1998 to 2018, using the Pooled Mean Group and the Mean Group techniques of Panel ARDL. Agglomeration economies are determined by population density, number of registered factories, employment size, and housing, in the cities of Punjab. The study designed four indices for socioeconomic conditions using principal component analysis. These include: education-index, healthcare-index, water & sanitation-index, and economic conditions-index. Research findings reveal pressures of high population density, unemployment, and costly housing on educational & healthcare facilities, poor sanitation & waste management, in cities of Punjab, Pakistan. The study suggests that policy makers and urban planners to develop short term and long term policies and development plans for villages and secondary cities to uplift wellbeing of the local population. Nonetheless, cities need to decentralize for sustainable development and management

Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images

Liver disease is one of the most prominent causes of the increase in the death rate worldwide. These death rates can be reduced by early liver diagnosis. Computed tomography (CT) is a method for the analysis of liver images in clinical practice. To analyze a large number of liver images, radiologists face problems that sometimes lead to the wrong classifications of liver diseases, eventually resulting in severe conditions, such as liver cancer. Thus, a machine-learning-based method is needed to classify such problems based on their texture features. This paper suggests two different kinds of algorithms to address this challenging task of liver disease classification. Our first method, which is based on conventional machine learning, uses texture features for classification. This method uses conventional machine learning through automated texture analysis and supervised machine learning methods. For this purpose, 3000 clinically verified CT image samples were obtained from 71 patients. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues by using supervised learning methods. Our proposed method correctly quantified asymmetric patterns in CT images using machine learning. We evaluated the effectiveness of the feature vector with the K Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers. The second algorithm proposes a semantic segmentation model for liver disease identification. Our model is based on semantic image segmentation (SIS) using a convolutional neural network (CNN). The model encodes high-density maps through a specific guided attention method. The trained model classifies CT images into five different categories of various diseases. The compelling results obtained confirm the effectiveness of the proposed model. The study concludes that abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques, which may also assist radiologists and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases