Structured Knowledge Research Articles

Assess the knowledge regarding thyroid disorders among patients attending medical OPD

Background: The thyroid gland is an endocrine gland consisting of two lobes connected by an isthmus, in the neck. It is found in front of the neck below the Adams apple. Thyroid gland though a small structure on the anterior aspect of neck has many important functions to perform, failing in its duty might lead to serious consequences. Methods: A non-experimental descriptive research design was conducted to assess knowledge regarding thyroid disorders among patients attending medical OPD in a selected hospital of Dehradun at Uttarakhand. A total of 100 samples were selected through purposive sampling technique. A structured knowledge questionnaire was administered through interview technique to assess the knowledge. Results: The study showed that the mean knowledge score of patients regarding thyroid disorders was 14.04±3.755. The mean percentage was 14.04. There was significant association between social habits, suffered or suffering from thyroid disorders, family history of thyroid disorders, medical history of any medicine intake and previous knowledge regarding thyroid disorders with knowledge score. Conclusions: From the findings of the study, it was conducted that Out of 100 participants 42% had good knowledge and 7% had poor knowledge, 38% had average knowledge and 13% had very good knowledge about thyroid disorders. There is a need for educational programs to create awareness and improve knowledge regarding thyroid disorders.

A descriptive study to assess the knowledge attitude and practice of employees regarding prevention of occupational health hazards in a selected sugar mill of Uttar Pradesh

Background: Approximately 30 to 50% works exposed to occupational health hazard and 120 million workers become victim in 200000 fatalities annually. Aim of the study was to assess the knowledge attitude and practice of employees regarding prevention of occupational health hazards in a selected sugar mill of Uttar Pradesh. Methods: A descriptive survey was done on co-operate sugar mill Baghat. Data has been taken from the co-operate sugar mill employees working on the machine and in field areas. 100 participants had chosen using convenient sampling technique. Structured knowledge questionnaire was developed to assess knowledge, structured checklist to measure practices and Likert scale to assess attitude of employees. Results: 60% employees worked in machinery area and 40% were in field area. 37% have not faced any hazards faced, 26% has faced eye infection, 19% got fracture,18% developed respiratory problems, 17% faced crush injuries, 16% had back problem, 13% have burn injuries and 4% had history of any kind of allergy. 6% of sugar mill employees had good knowledge, 51% sugar mill employees had average and 43% employees had poor knowledge. 15% employees had good practice; 85% employees had poor 90% employees had good attitude; 10% employees had poor attitude regarding occupational health hazard. Conclusions: Lack of knowledge regarding prevention of occupational health hazard put workers on risk of many diseases. Awareness program should be conducted regularly to enhance knowledge of workers and improve their skills.

Assess the Knowledge Regarding Antenatal and Postnatal Care among Asha Worker Working in Selected Area of Etawah

Background; ASHAs form the backbone of the NRHM and are meant to be selected by and be accountable to the village. They need to provide preventive, promotive and curative health facilities in the rural community.This study was conducted to assess the knowledge regarding antenatal and postnatal care among ASHA worker working in selected area of Etawah Kanpur U.P. The aim of the study was to assess the knowledge regarding antenatal and postnatal care among ASHA worker working in selected area of Etawah with view to develop and distribute information booklet to enhance their knowledge regarding. Objective 1. To assess the knowledge of ASHA worker regarding antenatal and postnatal care. 2.To determine the association between Demographic variable and knowledge score. Material and Method: for the present study Non experimental descriptive Cross-sectional Design was used for the study. purposive sampling was used to select 108 ASHA workers. Data were collected by structured knowledge questionnaire. Result: it showed that majority of the Participants 84 (90.72%) had Moderate knowledge, 18 (19.44) had adequate knowledge and 06 (6.48%) had Inadequate level of knowledge regarding pre and postnatal care among ASHA worker.

Sentence Embeddings and Semantic Entity Extraction for Identification of Topics of Short Fact-Checked Claims

The objective of this research was to design a method to assign topics to claims debunked by fact-checking agencies. During the fact-checking process, access to more structured knowledge is necessary; therefore, we aim to describe topics with semantic vocabulary. Classification of topics should go beyond simple connotations like instance-class and rather reflect broader phenomena that are recognized by fact checkers. The assignment of semantic entities is also crucial for the automatic verification of facts using the underlying knowledge graphs. Our method is based on sentence embeddings, various clustering methods (HDBSCAN, UMAP, K-means), semantic entity matching, and terms importance assessment based on TF-IDF. We represent our topics in semantic space using Wikidata Q-ids, DBpedia, Wikipedia topics, YAGO, and other relevant ontologies. Such an approach based on semantic entities also supports hierarchical navigation within topics. For evaluation, we compare topic modeling results with claims already tagged by fact checkers. The work presented in this paper is useful for researchers and practitioners interested in semantic topic modeling of fake news narratives.

Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model

Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance strategies. Several AI models have been developed for the proactive maintenance of comprehensive mining face equipment, including early warnings for periodic weighting and the detection of common faults such as those in the shearer, hydraulic support, and conveyor. The platform leverages large-scale knowledge graph models and Graph Retrieval-Augmented Generation (GraphRAG) technology to build structured knowledge graphs. This facilitates intelligent Q&A capabilities and precise fault diagnosis, thereby enhancing system responsiveness and improving the accuracy of fault resolution. The practical process of implementing such a platform primarily based on open-source components is summarized in this paper.

Unsafe behavior identification on construction sites by combining computer vision and knowledge graph–based reasoning

PurposeWorkers' unsafe behavior is the main cause of construction safety accidents, thereby highlighting the critical importance of behavior-based management. To compensate for the limitations of computer vision in tackling knowledge-intensive issues, semantic-based methods have gained increasing attention in the field of construction safety management. Knowledge graph provides an efficient and visualized method for the identification of various unsafe behaviors.Design/methodology/approachThis study proposes an unsafe behavior identification framework by integrating computer vision and knowledge graph–based reasoning. An enhanced ontology model anchors our framework, with image features from YOLOv5, COCO Panoptic Segmentation and DeepSORT integrated into the graph database, culminating in a structured knowledge graph. An inference module is also developed, enabling automated the extraction of unsafe behavior knowledge through rule-based reasoning.FindingsA case application is implemented to demonstrate the feasibility and effectiveness of the proposed method. Results show that the method can identify various unsafe behaviors from images of construction sites and provide mitigation recommendations for safety managers by automated reasoning, thus supporting on-site safety management and safety education.Originality/valueExisting studies focus on spatial relationships, often neglecting the diversified spatiotemporal information in images. Besides, previous research in construction safety only partially automated knowledge graph construction and reasoning processes. In contrast, this study constructs an enhanced knowledge graph integrating static and dynamic data, coupled with an inference module for fully automated knowledge-based unsafe behavior identification. It can help managers grasp the workers’ behavior dynamics and timely implement measures to correct violations.

Knowledge Regarding Human Monkey pox (HMPX) Among Nursing Students

Monkey pox is a viral infectious disease caused by the monkey pox virus, which belongs to the same family as the virus causing smallpox. Though typically self-limiting, with symptoms lasting two to four weeks, it can be transmitted through close contact with infected individuals, animals, or contaminated materials. Materials and Methods: A pre-experimental one-group pretest post-test designed study was conducted to evaluate the effectiveness of Laptop-Assisted Teaching on knowledge regarding Human Monkey pox (HMPX) among 60 nursing students using purposive sampling technique at a selected school in Bengaluru with the Objectives, to assess the effectiveness of Laptop-Assisted Teaching in improving knowledge about Human Monkey pox (HMPX) among nursing students and to determine the association between pretest knowledge scores regarding Human monkey pox (HMPX) among Nursing students with their selected sample characteristics. Data was collected using a structured knowledge questionnaire on HMPX, validated by 10 experts. Descriptive and inferential statistics were used for analysis. Results: The study showed that Laptop-Assisted Teaching significantly improved students' knowledge of HMPX. The mean pretest score was 9.95 (SD = 4.01), while the mean post-test score was 17.84 (SD = 8.78). The computed paired ‘t’ test value of 6.36 at p>0.05 was significant. Additionally, there was a statistically significant association between pretest knowledge scores and selected sample characteristics such as age and family type. Conclusion: The study concluded that nursing students had inadequate knowledge about HMPX initially, but Laptop-Assisted Teaching was effective in enhancing their understanding.

The future of AI clinicians: assessing the modern standard of chatbots and their approach to diagnostic uncertainty

BackgroundArtificial intelligence (AI) chatbots have demonstrated proficiency in structured knowledge assessments; however, there is limited research on their performance in scenarios involving diagnostic uncertainty, which requires careful interpretation and complex decision-making. This study aims to evaluate the efficacy of AI chatbots, GPT-4o and Claude-3, in addressing medical scenarios characterized by diagnostic uncertainty relative to Family Medicine residents.MethodsQuestions with diagnostic uncertainty were extracted from the Progress Tests administered by the Department of Family and Community Medicine at the University of Toronto between 2022 and 2023. Diagnostic uncertainty questions were defined as those presenting clinical scenarios where symptoms, clinical findings, and patient histories do not converge on a definitive diagnosis, necessitating nuanced diagnostic reasoning and differential diagnosis. These questions were administered to a cohort of 320 Family Medicine residents in their first (PGY-1) and second (PGY-2) postgraduate years and inputted into GPT-4o and Claude-3. Errors were categorized into statistical, information, and logical errors. Statistical analyses were conducted using a binomial generalized estimating equation model, paired t-tests, and chi-squared tests.ResultsCompared to the residents, both chatbots scored lower on diagnostic uncertainty questions (p < 0.01). PGY-1 residents achieved a correctness rate of 61.1% (95% CI: 58.4–63.7), and PGY-2 residents achieved 63.3% (95% CI: 60.7–66.1). In contrast, Claude-3 correctly answered 57.7% (n = 52/90) of questions, and GPT-4o correctly answered 53.3% (n = 48/90). Claude-3 had a longer mean response time (24.0 s, 95% CI: 21.0-32.5 vs. 12.4 s, 95% CI: 9.3–15.3; p < 0.01) and produced longer answers (2001 characters, 95% CI: 1845–2212 vs. 1596 characters, 95% CI: 1395–1705; p < 0.01) compared to GPT-4o. Most errors by GPT-4o were logical errors (62.5%).ConclusionsWhile AI chatbots like GPT-4o and Claude-3 demonstrate potential in handling structured medical knowledge, their performance in scenarios involving diagnostic uncertainty remains suboptimal compared to human residents.

Information Extraction of Aviation Accident Causation Knowledge Graph: An LLM-Based Approach

Summarizing the causation of aviation accidents is conducive to enhancing aviation safety. The knowledge graph of aviation accident causation, constructed based on aviation accident reports, can assist in analyzing the causes of aviation accidents. With the continuous development of artificial intelligence technology, leveraging large language models for information extraction and knowledge graph construction has demonstrated significant advantages. This paper proposes an information extraction method for aviation accident causation based on Claude-prompt, which relies on the large-scale pre-trained language model Claude 3.5. Through prompt engineering, combined with a few-shot learning strategy and a self-judgment mechanism, this method achieves automatic extraction of accident-cause entities and their relationships. Experimental results indicate that this approach effectively improves the accuracy of information extraction, overcoming the limitations of traditional methods in terms of accuracy and efficiency in processing complex texts. It provides strong support for subsequently constructing a structured knowledge graph of aviation accident causation and conducting causation analysis of aviation accidents.

Integrating AI with medical industry chain data: enhancing clinical nutrition research through semantic knowledge graphs.

In clinical nutrition research, the medical industry chain generates a wealth of multidimensional spatial data across various formats, including text, images, and semi-structured tables. This data's inherent heterogeneity and diversity present significant challenges for processing and mining, which are further compounded by the data's diverse features, which are difficult to extract. To address these challenges, we propose an innovative integration of artificial intelligence (AI) with the medical industry chain data, focusing on constructing semantic knowledge graphs and extracting core features. These knowledge graphs are pivotal for efficiently acquiring insights from the vast and granular big data within the medical industry chain. Our study introduces the Clinical Feature Extraction Knowledge Mapping ( ) model, designed to augment the attributes of medical industry chain knowledge graphs through an entity extraction method grounded in syntactic dependency rules. The model is applied to real and large-scale datasets within the medical industry chain, demonstrating robust performance in relation extraction, data complementation, and feature extraction. It achieves superior results to several competitive baseline methods, highlighting its effectiveness in handling medical industry chain data complexities. By representing compact semantic knowledge in a structured knowledge graph, our model identifies knowledge gaps and enhances the decision-making process in clinical nutrition research.

Preface for LLMs4OL 2024: The 1st Large Language Models for Ontology Learning Challenge at the 23rd ISWC

We are pleased to present the proceedings of the “1st Large Language Models for Ontology Learning Challenge (LLMs4OL 2024)”, held at the 23rd International Semantic Web Conference (ISWC). This challenge marks a significant a dvancement in utilizing Large Language Models (LLMs) for Ontology Learning (OL)—a key Semantic Web component that facilitates the automatic extraction of structured knowledge from unstructured data. The challenge features three main tasks: Term Typing (identifying categories for terms), Taxonomy Discovery (uncovering hierarchical relationships), and Non-Taxonomic Relation Extraction (identifying other meaningful relationships between terms). Each task is designed to test different facets of ontology construction and to encourage the exploration of innovative techniques. This challenge seeks to foster collaboration, inspire innovation, and expand the capabilities of LLMs in OL. The proceedings include a collection of innovative solutions and insights from global participants, highlighting the crucial role of LLMs in enhancing the web with structured knowledge. We believe the outcomes of this challenge will propel further advancements in OL and its applications on the semantic web. We would like to extend our gratitude to all the participants for their invaluable contributions, which their solutions and dedication have greatly enriched this challenge. Our sincere thanks also go to the conference organizers and committee for their efforts in hosting this event. We are deeply appreciative of the reviewers for their evaluations and feedback. Their reviews have been instrumental in enhancing the quality of the submissions. We would like to specifically acknowledge: Dr. Amin Keshavarzi (Postdoctoral Researcher, L3S &amp; TIB, Germany) Mostafa Rahgouy (Lead Project Coordinator, Auburn University, USA) Sahar Tahmasebi (Doctoral Researcher, TIB, Germany) Milad Molazadeh Oskuee (Lead NLP Researcher, Iran) Aida Usmanova (Doctoral Researcher, Leuphana Universit ¨ at L¨uneburg, Germany) Emetis Niazmand (Doctoral Researcher, TIB, Germany) Lastly, we would like to express our gratitude to ”Xenia Felice van Edig” and ”Karolina Linerová” from the TIB Open Publishing, for their support in bringing these proceedings online. We also would like to acknowledge that the 1st LLMs4OL Challenge @ ISWC 2024 jointly supported by the NFDI4DataScience initiative (DFG, German Research Foundation, Grant ID: 460234259) and the SCINEXT project (BMBF, German Federal Ministry of Education and Research, Grant ID: 01lS22070).

LLMs4OL 2024 Overview: The 1st Large Language Models for Ontology Learning Challenge

This paper outlines the LLMs4OL 2024, the first edition of the Large Language Models for Ontology Learning Challenge. LLMs4OL is a community development initiative collocated with the 23rd International Semantic Web Conference (ISWC) to explore the potential of Large Language Models (LLMs) in Ontology Learning (OL), a vital process for enhancing the web with structured knowledge to improve interoperability. By leveraging LLMs, the challenge aims to advance understanding and innovation in OL, aligning with the goals of the Semantic Web to create a more intelligent and user-friendly web. In this paper, we give an overview of the 2024 edition of the LLMs4OL challenge and summarize the contributions.

“A Study To Develop And Evaluate The Effectiveness Of Module On Cognitive Behavior Therapy In Terms Of Knowledge And Attitude Among Students Of Selected College Of Nursing, New Delhi”

Emotional experiences are ubiquitous in nature and important and perhaps even critical in academic settings, as emotion modulates virtually every aspect of cognition. Tests, examinations, homework, and deadlines are associated with different emotional states that encompass frustration, anxiety, and boredom. This is followed by the emotional-cognitive interactions in the various brain regions that are intimately involved in emotion and memory systems and influence behavior. A pre-experimental study was conducted with an aim to develop and evaluate the effectiveness of Module on cognitive behavior therapy in terms of knowledge and attitude among students of selected College of Nursing, New Delhi. Quantitative approach with one group pre-test post-test was adopted for the study. A module was developed and administered with certain activities and assignment to practice for the students. The module was based on the INC guidelines given for Cognitive Behavior Therapy included concepts, CBT model, development and tracking of automatic thoughts, biofeedback, Socratic questioning, behavior experiments, thought records and action plan components. The data was collected from 52 B.Sc. (Hons.) Nursing students of Holy Family College of Nursing using Structured knowledge Questionnaire and structured attitude scale. The data was tabulated and analyzed using descriptive and inferential statistics. The findings shows that mean post-test knowledge score and attitude score 21.2127 and 38.7021 was higher than mean pre-test knowledge score and attitude score 13.8723 and 35.4680. The result also revealed that module on cognitive behavior therapy is effective in enhancing the level of knowledge and attitude among nursing students as the calculated t-value is greater than table value at 2.012 at p&lt;0.05 level of significance

Providing Education on Basic Life Support to Increase Student Knowledge

Background: Basic Life Support (BLS) is an essential first aid action performed on individuals experiencing cardiac or respiratory arrest. The primary goal of BLS is to restore and maintain vital organ function through interventions like chest compressions and respiratory support. Knowledge, which arises from experience and observation, is enhanced through education. This process aims to improve understanding and, consequently, decision-making abilities. Purpose: The aim of this Community Service (PkM) project is to increase the knowledge of students at MAN 1 Banyumas regarding Basic Life Support. Method: The methods used in this PkM included lectures and discussions. The students' knowledge was assessed before and after the educational intervention using a structured knowledge questionnaire. Findings: The participants of this PkM were predominantly middle adolescents aged 15-17 years, with 29 participants (96.7%) falling within this age range. The majority were female, comprising 26 participants (86.7%). Most of the participants were in grade 11, accounting for 22 participants (73.3%). Before the educational intervention, the knowledge levels were as follows: Good: 2 participants (6.7%), Sufficient: 12 participants (40.0%), Poor: 16 participants (53.3%). After the BLS education, there was a marked improvement in knowledge, with all 30 participants (100%) achieving a "Good" level of knowledge. Conclusions: Education on basic life support at MAN 1 Banyumas significantly improved students' knowledge, with a higher percentage achieving a good understanding after the training.

Knowledge graph of agricultural engineering technology based on large language model

Agriculture is an industry that has evolved alongside human evolution and has faithfully fulfilled its core mission of food supply. With the reduction of rural labor, the progress of artificial intelligence and the development of Internet of Things technology, it is hoped that the efficiency and productivity of the agricultural industry can be improved. Recently, with the development of information and intelligent technology, agricultural production and management have been significantly enhanced. However, there is still a considerable challenge in effectively integrating the vast amount of fragmented information for downstream applications. An agricultural knowledge graph (AGKG) will serve as the foundation for achieving these goals. Knowledge graphs can be general or domain-specific, and are the basis for many applications, such as search engines, online question-and-answer services, and knowledge inference. Therefore, there are many knowledge graphs, including Wikidata and DBpedia, for accessing structured knowledge. Although some general knowledge graphs contain some entities and relationships related to agriculture, there are no domain-specific knowledge graphs specifically for agricultural applications. Therefore, this paper proposes an agricultural knowledge graph (AGKG) for automatically integrating large amounts of agricultural data from the Internet. By applying natural language processing and deep learning technologies, AGKG can automatically identify agricultural entities from unstructured text and connect them to form a knowledge graph. In addition, we have described the typical scenarios of our AGKG and validated it through real-world applications such as agricultural entity retrieval and agricultural question-answering.

Holacracy: redefining organizational ontology and epistemology

Purpose This paper aims to examine Holacracy as a self-management system and its implications for organizational theory. By exploring its distinctive ontology and epistemology, the paper explains how Holacracy redefines organizational structures and contributes to organizational change. Design/methodology/approach Adopting a conceptual approach, this essay draws upon existing literature to explore Holacracy’s departure from conventional organizational theories. Findings The paper discusses how Holacracy challenges traditional organizational concepts, defining its holarchical approach, and emphasizing dynamic hierarchies and role-based organization. It also highlights Holacracy’s focus on emergent intelligence, continuous feedback loops and structured knowledge acquisition through roles and organization spaces. By examining its unique ontology and epistemology, the paper offers insights into how Holacracy aligns with an emergent paradigm known as integral, metamodern or teal. Research limitations/implications Further empirical research is needed to assess its practical implementation and long-term effects on organizational performance and employee well-being. Future studies could also explore challenges and limitations associated with adopting Holacracy in different organizational contexts. Practical implications Organizations considering alternative ways of working should examine Holacracy. Implementing Holacracy requires understanding its principles and may involve significant changes, with potential benefits such as increased agility and employee engagement. Social implications Holacracy’s departure from traditional structures and approach to power can have broader social implications and contribute to more democratic and participatory cultures. Originality/value This paper is a pioneering contribution to the under-researched domain of alternative organizational models. It sheds light on the originality and distinctiveness of Holacracy, highlighting its unique approach to hierarchy, role-based organization and consciousness within organizations.

ORPP—An Ontology for Skill-Based Robotic Process Planning in Agile Manufacturing

Ontology plays a significant role in AI (Artificial Intelligence) and robotics by providing structured data, reasoning, action understanding, context awareness, knowledge transfer, and semantic learning. The structured framework created by the ontology for knowledge representation is crucial for enabling intelligent behavior in robots. This paper provides a state-of-the-art analysis on the existing ontology approaches and at the same time consolidates the terms in the robotic task planning domain. The major gap identified in the literature is the need to bridge higher-level robotic process management and lower-level robotic control. This gap makes it difficult for operators/non-robotic experts to integrate robots into their production processes as well as evaluate key performance indicators (KPI) of the processes. To fill the gap, the authors propose an ontology for skill-based robotics process planning (ORPP). ORPP not only provides a standardization in the robotic process planning in the agile manufacturing domain but also enables non-robotic experts to design and plan their production processes using an intuitive Process-Task-Skill-Primitive structure to control low-level robotic actions. On the performance level, this structure provides traceability of the KPIs down to the robot control level.

A Study to Assess Knowledge Regarding Fire Safety and Their Practices among Third Year B.Sc. Nursing Students with a view to Develop on Hand Training Module

Background: Fire safety is the set of practices intended to prevent the destruction caused by Fire. Fire safety is the set of practices intended to reduce destruction caused by fire. Fire safety measures include those that are intended to prevent the ignition of an uncontrolled fire and those that are used to limit the spread and impact of a fire.[4] Fire related accidents have, on average, killed 35 people every day over the five years between 2016 and 2020, according to a report by the National Crime Records Bureau (NCRB). Aims and Objectives: To Assess the knowledge regarding Fire Safety and their practices among the students of 3rd year B.Sc. Nursing, Ambaji Nursing College, Ganeshpura, and to find out Association between selected demographic variable and to develop on hand training module. Materials and Method: The study was conducted among 60 students of Ambaji Nursing College, Ganeshpura and using simple random sampling technique. Data were obtained using structured knowledge questionnaire to assess the knowledge regarding Fire safety and their practices. Descriptive and Inferential statistics were use to analyze the Demographic data and used structured questionnaire. Karl person formula is used to determine Correlation whereas the Association is determined by using Chi -Square formula. Results and Conclusion: The study revealed that majority of students level of knowledge are 36.70% poor knowledge, 61.70% students are average knowledge and 1.70% students are good knowledge regarding fire safety and their practices. There exist positive moderate correlation (r = 0.75) between knowledge and their practices. Study also revealed there is no significant Association between the knowledge and their demographic variables such as Age, Gender, Annual household income, Religion and attending any training of fire safety practices at p ≤ 0.05 level.

Effectiveness of Power Point Presentation on Knowledge regarding impact of social networking on lifestyle among Adolescence of selected college

Introduction: The present study was aimed to assess the “Effectiveness of PowerPoint presentation on knowledge regarding impact of social networking on lifestyle among adolescence of selected college”. Objectives: In view of the nature of the problem under study and to accomplish the objectives of the study, A quasi experimental one group pre-test post-test research design is used. The main objective of the study was evaluating the effectiveness of power point presentation on impact of social networking on lifestyle among adolescence. Material and Methods: Total 100 adolescence were selected as sample for study by using simple random sampling technique. The data was collected by using structured knowledge questionnaires. The Power point presentation was also administered on the same day following the pre-test. Findings: The comparisons of the pretest and post-test means of the knowledge regarding impact of social networking on lifestyle among adolescence of selected college in selected area were done by the paired t-‘test. The test was conducted at 5% level of significance. The pretest average score was 10.64 with standard deviation of 3.37 The post-test average score was 17.14 with standard deviation of 2.98. The chi square test was used to see the association between pre-test and post-test knowledge score of adolescence regarding impact of social networking on lifestyle at selected college. Conclusion: The study concludes that the Power point presentation was effective in imparting knowledge of Impact of Social Networking on Lifestyle. And as seen adolescence was not fully aware about impact social networking so it is important to guide them by providing knowledge on social networking.

Effectiveness of Planned Teaching Programme on Knowledge regarding Blood Donation among Degree students

A study to evaluate the effectiveness of planned teaching programme on knowledge regarding blood donation among students studying in Selected Degree Colleges, Aurangabad. OBJECTIVES OF THE STUDY: To assess the knowledge regarding blood donation among students before planned teaching programme. To prepare and administer planned teaching programme regarding blood donation among students. To assess the knowledge regarding blood donation among students after planned teaching programme. To evaluate the effectiveness of planned teaching programme regarding blood donation among students. To find out the association between post test knowledge scores with selected demographic variables. MATERIAL AND METHODS: This pre experimental one group pre test post test study was conducted to evaluate the effectiveness of planned teaching programme on knowledge regarding blood donation among the students studying in selected degree colleges. Non – probability convenient sampling technique was used for the sample collection of 50 students studying in Maharashtra Institute of Technology college, Aurangabad. Structured knowledge questionnaire was used to evaluate effectiveness of planned teaching programme. The present study was evaluative in nature. The conceptual framework used for the study was based on ‘MODIFIED VON BERTTLANFFY’S’ theory. RESULT: In the comparison between pre test and post test the knowledge score of students was more in post test than pre test. In pre test 3[6%] subjects had good knowledge regarding blood donation, 5[10%] subjects had poor knowledge and 42[84%] subjects had average knowledge regarding blood donation. Whereas in post test, 49[98%] subject had good knowledge and only 01[2%] subject had average knowledge regarding blood donation. The mean difference of pre test and post test was 10.22 and paired t value was 55.72. CONCLUSION: This study revealed that planned teaching program was helpful to increase knowledge regarding blood donation among degree students.