Search results for: regional knowledge networks

Authors: Lucélia Alcântara

Abstract:

Reading has been seen as a passive skill by many people for a long time. However, when one comes to study it deeply and in a such a way that the act of reading equals acquiring knowledge through living an experience that belongs to him/her, passive definitely becomes active. Material development with a focus on reading has to consider much more than reading strategies. The following questions are asked: Is the material appropriate to the students’ reality? Does it make students think and state their points of view? With that in mind a lesson has been developed to illustrate theory becoming practice. Knowledge, criticality, intercultural experience and social interaction. That is what reading is for.

Keywords: reading, culture, material development, learning

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Authors: V. K. Banga

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Robots are now playing a very promising role in industries. Robots are commonly used in applications in repeated operations or where operation by human is either risky or not feasible. In most of the industrial applications, robotic arm manipulators are widely used. Robotic arm manipulator with two link or three link structures is commonly used due to their low degrees-of-freedom (DOF) movement. As the DOF of robotic arm increased, complexity increases. Instrumentation involved with robotics plays very important role in order to interact with outer environment. In this work, optimal control for movement of various DOFs of robotic arm using various soft computing techniques has been presented. We have discussed about different robotic structures having various DOF robotics arm movement. Further stress is on kinematics of the arm structures i.e. forward kinematics and inverse kinematics. Trajectory planning of robotic arms using soft computing techniques is demonstrating the flexibility of this technique. The performance is optimized for all possible input values and results in optimized movement as resultant output. In conclusion, soft computing has been playing very important role for achieving optimized movement of robotic arm. It also requires very limited knowledge of the system to implement soft computing techniques.

Keywords: artificial intelligence, kinematics, robotic arm, neural networks, fuzzy logic

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Authors: Sidra Rizwan

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The article focuses on the training needs of secondary school teachers related to the knowledge component of instructional planning and strategies as stated in the National professional standards for teachers in Pakistan. The study aimed to determine the training needs of secondary school teachers on different aspects of knowledge & understanding component of instructional planning and strategies. The target population of the study was the secondary school teachers across Pakistan. For this purpose, a sample of 400 secondary school teachers was selected through multistage sampling from all the four provinces and Federal capital area. Survey method was adopted to assess the training needs by using a self reporting tool. The tool helped to gauge the training needs through indirect inventory questions as well as a ranking list in which the respondents themselves prioritized their training areas. The results showed variation between the direct and indirect reporting of the teachers on the basis of which it was concluded that the secondary school teachers needed awareness about the knowledge component of instructional planning and strategies in order to redefine their actual training needs. The researcher further identified the training needs of secondary school teachers within each province and Islamabad capital territory; including an analysis of variations between strata. As teachers are considered agents of change, their training according to the professional standards should provide a solid base for “rethinking education”.

Keywords: training needs, secondary school teachers, instructional planning & strategies, knowledge & understanding

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Authors: Nabil A. Albaser

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Background: There is a serious problem with adverse drug reactions (ADRs) everywhere, including Yemen. Since it helps with the detection, assessment, reporting and prevention of ADRs, pharmacovigilance (PV) is an essential part of the healthcare system. The unbiased reporting of ADRs remains the foundation of PV. Students majoring in healthcare should acquire the knowledge and skills necessary to conduct PV in a range of clinical settings. The primary objective of this study was to evaluate the understanding and attitudes of final-year Pharmacy, Nursing, and Midwifery students at Al-Razi University in Sana'a, Yemen, regarding PV and ADRs reporting. Methods: The study followed descriptive cross-sectional approach. A validated, self-administered questionnaire with three parts—demographic information, knowledge, and perceptions of Pharmacovigilance was online distributed to final-year Pharmacy, Nursing, and Midwifery students. The questionnaire was given to 175 students; 122 of them responded with a percentage (69.7%). Results: The majority of respondents were male (79.5%). More than the tow-third of the students, 68.9%, were beyond the age of 23. Although the majority of students, 80%, heard about the terms of ADRs and PV, but only 50% and 57.4% of the respondents, respectively, could define the both terms correctly. However, only 11.48 % of them, nevertheless, took a PV course. More than a half of them (56.6%) had a positive perceptions towards pharmacovigilance and ADR reporting and had a moderate degree of knowledge (68.9%). Conclusion: The study demonstrated that the participants lacked sufficient knowledge of pharmacovigilance and ADR reporting. They showed a moderate level of understanding of reporting ADRs as well as a favorable opinion of dealing with and reporting ADRs. Yemen's health care curriculum should include lessons on pharmacovigilance.

Keywords: adverse drug reaction reporting, pharmacovigilance, yemen, knowlegde

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Authors: Liudmyla Vesper

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The problems of modern education are large-scale and diverse. The aspirations of parents, teachers, and experts converge - everyone interested in growing up a generation of whole, well-educated persons. Both the family and society are expected in the future generation to be self-sufficient, desirable in the labor market, and capable of lifelong learning. Today's children have a powerful potential that is difficult to realize in the conditions of traditional school approaches. Focusing on STEM education in practice often ends with the simple use of computers and gadgets during class. "Science", "technology", "engineering" and "mathematics" are difficult to combine within school and university curricula, which have not changed much during the last 10 years. Solving the problems of modern education largely depends on teachers - innovators, teachers - practitioners who develop and implement effective educational methods and programs. Teachers who propose innovative pedagogical practices that allow students to master large-scale knowledge and apply it to the practical plane. Effective education considers the creation of stable neural structures during the learning process, which allow to preserve and increase knowledge throughout life. The author proposed a method of integrated lessons – cases based on the maths patterns for forming a holistic perception of the world. This method and program are scientifically substantiated and have more than 15 years of practical application experience in school and student classrooms. The first results of the practical application of the author's methodology and curriculum were announced at the International Conference "Teaching and Learning Strategies to Promote Elementary School Success", 2006, April 22-23, Yerevan, Armenia, IREX-administered 2004-2006 Multiple Component Education Project. This program is based on the concept of interdisciplinary connections and its implementation in the process of continuous learning. This allows students to save and increase knowledge throughout life according to a single pattern. The pattern principle stores information on different subjects according to one scheme (pattern), using long-term memory. This is how neural structures are created. The author also admits that a similar method can be successfully applied to the training of artificial intelligence neural networks. However, this assumption requires further research and verification. The educational method and program proposed by the author meet the modern requirements for education, which involves mastering various areas of knowledge, starting from an early age. This approach makes it possible to involve the child's cognitive potential as much as possible and direct it to the preservation and development of individual talents. According to the methodology, at the early stages of learning students understand the connection between school subjects (so-called "sciences" and "humanities") and in real life, apply the knowledge gained in practice. This approach allows students to realize their natural creative abilities and talents, which makes it easier to navigate professional choices and find their place in life.

Keywords: science education, maths education, AI, neuroplasticity, innovative education problem, creativity development, modern education problem

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Authors: Elżbieta Antczak

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Cities offer important opportunities for economic development and for expanding access to basic services, including health care and education, for large numbers of people. Moreover, green areas (as an integral part of sustainable urban development) present a major opportunity for improving urban environments, quality of lives and livelihoods. This paper examines, using spatial concentration and spatial taxonomic measures, regional diversification of greenery in the cities of Poland. The analysis includes location quotients, Lorenz curve, Locational Gini Index, and the synthetic index of greenery and spatial statistics tools: (1) To verify the occurrence of strong concentration or dispersion of the phenomenon in time and space depending on the variable category, and, (2) To study if the level of greenery depends on the spatial autocorrelation. The data includes the greatest Polish cities, categories of the urban greenery (parks, lawns, street greenery, and green areas on housing estates, cemeteries, and forests) and the time span 2004-2015. According to the obtained estimations, most of cites in Poland are already taking measures to become greener. However, in the country there are still many barriers to well-balanced urban greenery development (e.g. uncontrolled urban sprawl, poor management as well as lack of spatial urban planning systems).

Keywords: greenery, urban areas, regional spatial diversification and concentration, spatial taxonomic measure

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Authors: Kamel Belammi, Houria Fatrim

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imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of imbalanced data sets. In medical diagnosis classification, we often face the imbalanced number of data samples between the classes in which there are not enough samples in rare classes. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weight and some rules of thumb to determine those weights. After the balancing phase, we applythe different classifiers (support vector machine (SVM), k- nearest neighbor (KNN) and multilayer neuronal networks (MNN)) for balanced data set. We have also compared the obtained results before and after balancing method.

Keywords: multilayer neural networks, k- nearest neighbor, support vector machine, imbalanced medical data, least mean square algorithm, diabetes

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Authors: Bhawna Verma

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Looking at the contraceptive use and unmet need specific to the different age groups would help to understand various determinants and characteristics of women from different age groups, which are often being neglected. The study explores contraceptive behavior, unmet need for family planning and its correlates among young nulliparous women aged 15-29, using data from NFHS-4 (2015-16), India. Method: The study utilized information from 26,924 currently married women, who has no child or who have had first terminated pregnancy and was aged 15-29 at the time of the survey. Chi-Square and logistic regression analysis have been used to assess the effects of socio-economic characteristics. Results: Of all the considered explanatory variables religion, caste, education, current age, age at marriage, media exposure and regional differences were found to be significantly affecting the behavior of contraceptive use. Women of the 25-29 age group are 0.6 percent less likely to have an unmet need than women of 12-19 age group. Unmet need is increasing with the increased level of education. Muslim women are 0.3 percent less likely to have an unmet need than women of Hindu category. Conclusion: Separate considerations must be given to the needs for family planning formation among nulliparous women along with the factors associated with the use and non-use of contraceptives among them. Separate considerations must be given for effective promotion of FP knowledge through print, electronic media, towards the unequal access to the contraceptives among nulliparous women. Marriages after legal minimum age and encouraging women for higher education may address existing socio-economic barriers.

Keywords: contraceptive use, unmet need, family planning, contraceptive behavior

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Authors: R. Nagarani

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An intrinsic property of software in a real-world environment is its need to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality, making software maintenance a tough problem. Refactoring is regarded as an effective way to address this problem. Many refactoring approaches at the method and class level have been proposed. But the extent of research on software refactoring at the package level is less. This work presents a novel approach to refactor the package structures of object oriented software using genetic algorithm based community detection. It uses software networks to represent classes and their dependencies. It uses a constrained community detection algorithm to obtain the optimized community structures in software networks, which also correspond to the optimized package structures. It finally provides a list of classes as refactoring candidates by comparing the optimized package structures with the real package structures.

Keywords: community detection, complex network, genetic algorithm, package, refactoring

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Authors: Devineni Vijay Bhaskar, Yendluri Raja

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We recommend a model for traffic sign detection stranded on Convolutional Neural Networks (CNN). We first renovate the unique image into the gray scale image through with support vector machines, then use convolutional neural networks with fixed and learnable layers for revealing and understanding. The permanent layer can reduction the amount of attention areas to notice and crop the limits very close to the boundaries of traffic signs. The learnable coverings can rise the accuracy of detection significantly. Besides, we use bootstrap procedures to progress the accuracy and avoid overfitting problem. In the German Traffic Sign Detection Benchmark, we obtained modest results, with an area under the precision-recall curve (AUC) of 99.49% in the group “Risk”, and an AUC of 96.62% in the group “Obligatory”.

Keywords: convolutional neural network, support vector machine, detection, traffic signs, bootstrap procedures, precision-recall curve

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Authors: Vildan Cırık, Emine Efe

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Complementary health approaches are growing in popularity worldwide and play a substantial role in health care. It is very important for paediatric nurses to have knowledge of practices affecting the medical conditions of patients and to communicate with them through integrative nursing care. The purpose of this study was to determine paediatric nurses’ knowledge and experiences of complementary health approaches (CHA) and their personal and professional attitudes to the use of complementary health approaches. This multicentre study was conducted with 1450 paediatric nurses in 18 hospitals in Turkey. Paediatric nurses included in the study were working in the following clinics: Paediatric Service, Paediatric Intensive Care, Paediatric Haematology/Oncology. Data collection focused on the paediatric nurses’ knowledge and experiences of CHA. A high proportion of our sample of paediatric nurses reported that they had used some form of CHA themselves; the most popular choices of CHA were prayer, massage, and vitamins techniques. Paediatric nurses reported positive experiences (drawing/music/art/dance therapies, prayer, herbs, thermal springs, massage, and reflexology) and negative experiences (herbs, thermal springs, prayer, and massage). This study may contribute to increased awareness of the potentially important role of paediatric nurses in the delivery of CHA. Paediatric nurses play important roles in helping patients to use complementary health approaches safely and accurately. Trainings on CHA should be organised, data collection forms including CHA should be created, and evidence-based studies should be focused towards improving the clinical practice of paediatric nurses.

Keywords: complementary health approaches, paediatric nurses, knowledge, experience, attitude, Turkey

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Authors: Pui-sze Yeung, Connie Suk-han Ho, David Wai-ock Chan, Kevin Kien-hoa Chung

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Background: Recent findings have shown that transcription skills play a unique and significant role in Chinese word reading and spelling (i.e. word dictation), and written composition development. The interrelationships among component skills of transcription, word reading, word spelling, and written composition in Chinese have rarely been examined in the literature. Is the contribution of component skills of transcription to Chinese written composition mediated by word level skills (i.e., word reading and spelling)? Methods: The participants in the study were 249 Chinese children in Grade 1, Grade 3, and Grade 5 in Hong Kong. They were administered measures of general reasoning ability, orthographic knowledge, stroke sequence knowledge, word spelling, handwriting fluency, word reading, and Chinese narrative writing. Orthographic knowledge- orthographic knowledge was assessed by a task modeled after the lexical decision subtest of the Hong Kong Test of Specific Learning Difficulties in Reading and Writing (HKT-SpLD). Stroke sequence knowledge: The participants’ performance in producing legitimate stroke sequences was measured by a stroke sequence knowledge task. Handwriting fluency- Handwriting fluency was assessed by a task modeled after the Chinese Handwriting Speed Test. Word spelling: The stimuli of the word spelling task consist of fourteen two-character Chinese words. Word reading: The stimuli of the word reading task consist of 120 two-character Chinese words. Written composition: A narrative writing task was used to assess the participants’ text writing skills. Results: Analysis of covariance results showed that there were significant between-grade differences in the performance of word reading, word spelling, handwriting fluency, and written composition. Preliminary hierarchical multiple regression analysis results showed that orthographic knowledge, word spelling, and handwriting fluency were unique predictors of Chinese written composition even after controlling for age, IQ, and word reading. The interaction effects between grade and each of these three skills (orthographic knowledge, word spelling, and handwriting fluency) were not significant. Path analysis results showed that orthographic knowledge contributed to written composition both directly and indirectly through word spelling, while handwriting fluency contributed to written composition directly and indirectly through both word reading and spelling. Stroke sequence knowledge only contributed to written composition indirectly through word spelling. Conclusions: Preliminary hierarchical regression results were consistent with previous findings about the significant role of transcription skills in Chinese word reading, spelling and written composition development. The fact that orthographic knowledge contributed both directly and indirectly to written composition through word reading and spelling may reflect the impact of the script-sound-meaning convergence of Chinese characters on the composing process. The significant contribution of word spelling and handwriting fluency to Chinese written composition across elementary grades highlighted the difficulty in attaining automaticity of transcription skills in Chinese, which limits the working memory resources available for other composing processes.

Keywords: orthographic knowledge, transcription skills, word reading, writing

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Authors: Nadia el Atlas, Mohammed el Aroussi, Mohammed Wahbi

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In this paper a novel technique of mass characterization based on robust features-fusion is presented. The proposed method consists of mainly four stages: (a) the first phase involves segmenting the masses using edge information’s. (b) The second phase is to calculate and fuse the most relevant morphological features. (c) The last phase is the classification step which allows us to classify the images into benign and malignant masses. In this step we have implemented Support Vectors Machines (SVM) and Artificial Neural Networks (ANN), which were evaluated with the following performance criteria: confusion matrix, accuracy, sensitivity, specificity, receiver operating characteristic ROC, and error histogram. The effectiveness of this new approach was evaluated by a recently developed database: INBREAST database. The fusion of the most appropriate morphological features provided very good results. The SVM gives accuracy to within 64.3%. Whereas the ANN classifier gives better results with an accuracy of 97.5%.

Keywords: breast cancer, mammography, CAD system, features, fusion

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Authors: Zainab Idrees Ahmad, Mahjabeen Qureshi, Zainab Hussain

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Standard precautions are a set of infection control practices used to prevent the transmission of diseases that can be acquired by contact with body fluids, non-intact skin, and mucous membranes. Lack of practice of SPs can result in a considerable increase in morbidity and mortality rates. Medical students (the future physicians) should have the highest knowledge of standard precautions to prevent the spread of nosocomial infections and ensure their safety as well. This study was designed. To assess the knowledge of medical students regarding standard precautions. And explore the attitude of medical students of MBBS in the third, fourth and final year towards standard precautions.: A descriptive cross-sectional study was conducted in the setting of Rawalpindi Medical University, Pakistan including the students of MBBS in their 3rd, 4th and final years. The study duration was from October 2022 to February 2023. The sample size calculated was 282 with a confidence interval of 95%. A questionnaire was structured utilizing the WHO guidelines on SPs assessing knowledge and attitude regarding hand hygiene, needle stick injury, use of gloves and mask, and sharp disposal. A total of 300 responses were received utilizing the technique of non-random convenience sampling. Data was analyzed using the latest version of SPSS.:Knowledge score regarding components of SPs, hand hygiene, and moments of hand hygiene was satisfactory. However, score regarding the use of PPE, needle stick injury, and sharp disposal was low. Almost all the students were compliant with the proper washing of hands but the observation of recommended time length was lacking. Compliance with the use of correct PPE and informing the supervisor upon getting a needle stick injury was low. This study signifies that medical students lack knowledge regarding standard precautions. This is alarming as this can be the vehicle for the spread of nosocomial infections. Proper training should be given to medical students to prevent the spread of hospital-acquired infections.

Keywords: attitude, knowledge, medical students, standard precautions

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Authors: Shahnaz Piroozfar, Seyed Arash Halajzadeh, Abouzar Ilkhani

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Nowadays, in unpredictable business environments, enterprises face a great challenge to meet customer’s requirements. The performance of an enterprise has a significant impact on its activities and has to be evaluated continuously. One of the most important indicators to evaluate performance is named ‘innovation’. There are various indicators of innovation in the product/service sectors. These cause different innovation types to emerge, in product sectors. Additionally there are basic enablers to these innovation types, including an innovative culture, a customer oriented organizational culture, etc. Also one of these enablers is called enterprise system, which includes: SCM, CRM, ERP, KM etc. Knowledge management as a solution is a necessity in a competitive world. Implementation solutions such as KM are expensive, so enterprises need to answer how KM systems affect different performance indicators like innovation. This paper aims to develop a model to evaluate the impact of KM on innovation in SMEs.

Keywords: innovation, knowledge management, SMEs, enterprise

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Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

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Authors: Ghazala Kausar

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This study investigates Pakistani first year university students’ perception of the role of pragmatics in their general approach to learning English. The research is triggered by National Curriculum’s initiative to provide holistic opportunities to the students for language development and to equip them with competencies to use English language in academic and social contexts (New English National Curriculum for I-XII). The traditional grammar translation and examination oriented method is believed to reduce learners to silent listener (Zhang, 2008: Zhao 2009). This lead to the inability of the students to interpret discourse by relating utterances to their meaning, understanding the intentions of the users and how language is used in specific setting (Bachman & Palmer, 1996, 2010). Pragmatic competence is a neglected area as far as teaching and learning English in Pakistan is concerned. This study focuses on the different types of pragmatic knowledge, learners perception of such knowledge and learning strategies employed by different learners to process the learning in general and pragmatic in particular. This study employed three data collecting tools; a questionnaire, discourse completion task and interviews to elicit data from first year university students regarding their perception of pragmatic competence. Results showed that Pakistani first year university learners have limited pragmatic knowledge. Although they acknowledged the importance of linguistic knowledge for linguistic competence in the students but argued that insufficient English proficiency, limited knowledge of pragmatics, insufficient language material and tasks were major reasons of pragmatic failure.

Keywords: pragmatic competence, Pakistani college learners, linguistic competence

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Authors: Bangwon Seo

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In this paper, we consider multicast device-to-device (D2D) direct communication systems in cellular networks. In multicast D2D communications, nearby mobile devices exchanges, their data directly without going through a base station and a D2D transmitter send its data to multiple D2D receivers that compose of D2D multicast group. We consider wiretap channel where there is an eavesdropper that attempts to overhear the transmitted data of the D2D transmitter. In this paper, we propose a secure transmission scheme in D2D multicast communications in cellular networks. In order to prevent the eavesdropper from overhearing the transmitted data of the D2D transmitter, a precoding vector is employed at the D2D transmitter in the proposed scheme. We perform computer simulations to evaluate the performance of the proposed scheme. Through the simulation, we show that the secrecy rate performance can be improved by selecting an appropriate precoding vector.

Keywords: device-to-device communications, wiretap channel, secure transmission, precoding

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Authors: Avatharam Ganivada, Krishna Shah

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The Mask R-CNN is recently introduced by the team of Facebook AI Research (FAIR), which is mainly concerned with instance segmentation in images. Here, the Mask R-CNN is based on ResNet and feature pyramid network (FPN), where a single dropout method is employed. This paper provides a modified Mask R-CNN by adding multiple dropout methods into the Mask R-CNN. The proposed model has also utilized the concepts of Resnet and FPN to extract stage-wise network feature maps, wherein a top-down network path having lateral connections is used to obtain semantically strong features. The proposed model produces three outputs for each object in the image: class label, bounding box coordinates, and object mask. The performance of the proposed network is evaluated in the segmentation of every instance in images using COCO and cityscape datasets. The proposed model achieves better performance than the state-of-the-networks for the datasets.

Keywords: instance segmentation, object detection, convolutional neural networks, deep learning, computer vision

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Authors: Romisaa Ali

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The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

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Authors: Montaceur Zaghdoud, Mohamed Moussaoui, Jalel Akaichi

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Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks.

Keywords: approximate graph matching, hybrid frequent subgraph mining, graph mining, possibility theory

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Authors: Enrique Claver Cortés, Bartolomé Marco Lajara, Eduardo Sánchez García, Pedro Seva Larrosa, Encarnación Manresa Marhuenda, Lorena Ruiz Fernández, Esther Poveda Pareja

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In recent decades, the analysis of the effects of clustering as an essential factor for the development of innovations and the competitiveness of enterprises has raised great interest in different areas. Nowadays, companies have access to almost all tangible and intangible resources located and/or developed in any country in the world. However, despite the obvious advantages that this situation entails for companies, their geographical location has shown itself, increasingly clearly, to be a fundamental factor that positively influences their innovative performance and competitiveness. Industrial clusters could represent a unique level of analysis, positioned between the individual company and the industry, which makes them an ideal unit of analysis to determine the effects derived from company membership of a cluster. Also, the absorptive capacity (hereinafter 'AC') can mediate the process of innovation development by companies located in a cluster. The transformation and exploitation of knowledge could have a mediating effect between knowledge acquisition and innovative performance. The main objective of this work is to determine the key factors that affect the degree of generation and use of knowledge from the environment by companies and, consequently, their innovative performance and competitiveness. The elements analyzed are the companies' membership of a cluster and the AC. To this end, 30 most relevant papers published on this subject in the "Web of Science" database have been reviewed. Our findings show that, within a cluster, the knowledge coming from the companies' environment can significantly influence their innovative performance and competitiveness, although in this relationship, the degree of access and exploitation of the companies to this knowledge plays a fundamental role, which depends on a series of elements both internal and external to the company.

Keywords: absorptive capacity, clusters, innovation, knowledge

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Authors: Natalia Strobel, Jan Kratzer

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In this paper, we test whether different types of research and development (R&D) alliances positively impact the radical innovation performance of firms. We differentiate between the R&D alliances without extern R&D orders and embeddedness in knowledge source network. We test the differences between the domestically diversified R&D alliances and R&D alliances diversified abroad. Moreover, we test how non-price competition influences the impact of domestically diversified R&D alliances, and R&D alliance diversified abroad on radical innovation performance. Our empirical analysis is based on the comprehensive Swiss innovation panel, which allowed us to study 3520 firms between the years between 1996 and 2011 in 3 years intervals. We analyzed the data with a linear estimation with Swamy-Aurora transformation using plm package in R software. Our results show as hypothesized a positive impact of R&D alliances diversity abroad as well as domestically on radical innovation performance. The effect of non-price interaction is in contrast to our hypothesis, not significant. This suggests that diversity of R&D alliances is highly advantageous independent of non-price competition.

Keywords: R&D alliances, partner diversity, knowledge source diversity, non-price competition, absorptive capacity

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Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

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We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

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Authors: Tanu Aneja, Harsha Malaviya

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Capturing pose information in cricket shots poses several challenges, such as low-resolution videos, noisy data, and joint occlusions caused by the nature of the shots. In response to these challenges, we propose a CondDGConv-based framework specifically for cricket shot prediction. By analyzing the spatial-temporal relationships in batsman shot sequences from an annotated 2D cricket dataset, our model achieves a 97% accuracy in predicting shot types. This performance is made possible by conditioning the graph network on batsman 2D poses, allowing for precise prediction of shot outcomes based on pose dynamics. Our approach highlights the potential for enhancing shot prediction in cricket analytics, offering a robust solution for overcoming pose-related challenges in sports analysis.

Keywords: action recognition, cricket. sports video analytics, computer vision, graph convolutional networks

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Authors: Salehah Tahkin, Norlaila Mustafa, Dayang Anita Abdul Aziz

Abstract:

Knowledge of medical ethics is important to all practitioners so the best care can be delivered to all patients through safe practice. Surgeons are not exceptions to this. Knowledge, attitude, and practice (KAP) of medical ethics among paediatric surgeons and trainees in Malaysia has not been evaluated before. This study aims to determine the level of KAP regarding medical ethics among these groups. This was a cross-sectional study involving three groups of samples, i.e., paediatric surgeons (PS), paediatric surgical trainees (PST), and medical officers with a special interest in paediatric surgery (MO). A validated KAP questionnaire was used. Standard formulas were used to calculate objective indexes for measuring KAP, which were then compared for statistical significance across different sample groups; p less than 0.05 is taken as significant. The index is rated into 5 classes using a score of 0 to 10, i.e., poor (1-2.99), fair (3-4.99), good (5-6.99), very good (7-8.99), and excellent (9-10). There were 117 samples, i.e., PS n=45 (38.5%), PST n=25 (21.3%), and MO n=47 (40.2%). For knowledge, all three groups display a good index score (mean score of 5.44). For attitude, PS and MO also display an index score of good (mean score of 5.81), while the PST index score was fair (4.82). For practice, our study shows a highest score of 7.14 (very good) among PST. However, these differences were not statistically significant (p> 0.05). Conclusion: Training in paediatric surgery must continue to emphasize professionalism and medical ethics education to deliver the best health care services.

Keywords: KAP, medical ethics, paediatric, surgeons, trainees

Procedia PDF Downloads 80

Authors: Mojtaba Mahmoodian, Joshua Phelan, Mehdi Shahparvari

Abstract:

With a large percentage of countries’ total infrastructure expenditure attributed to water network maintenance, it is essential to optimise maintenance strategies to rehabilitate or replace underground pipes before failure occurs. The aim of this paper is to provide water utility managers with a maintenance management approach for underground water pipes, subject to external loading and material corrosion, to give the lowest life cycle cost over a predetermined time period. This reliability-based maintenance management methodology details the optimal years for intervention, the ideal number of maintenance activities to perform before replacement and specifies feasible renewal options and intervention prioritisation to minimise the life cycle cost. The study was then extended to include feasible renewal methods by determining the structural condition index and potential for soil loss, then obtaining the failure impact rating to assist in prioritising pipe replacement. A case study on optimisation of maintenance plans for the Melbourne water pipe network is considered in this paper to evaluate the practicality of the proposed methodology. The results confirm that the suggested methodology can provide water utility managers with a reliable systematic approach to determining optimum maintenance plans for pipe networks.

Keywords: water pipe networks, maintenance management, reliability analysis, optimum maintenance plan

Procedia PDF Downloads 156

Authors: Bohli Afef

Abstract:

The fifth-generation (5G) wireless systems is featured by extreme densities of cell stations to overcome the higher future demand. Hence, interference management is a crucial challenge in 5G ultra-dense cellular networks. In contrast to the classical inter-cell interference coordination approach, which is no longer fit for the high density of cell-tiers, this paper proposes a novel virtual coordination based on the dynamic common cognitive monitor channel protocol to deal with the inter-cell interference issue. A tractable and flexible model for the coverage probability of a typical user is developed through the use of the stochastic geometry model. The analyses of the performance of the suggested protocol are illustrated both analytically and numerically in terms of coverage probability.

Keywords: ultra dense heterogeneous networks, dynamic common channel protocol, cognitive radio, stochastic geometry, coverage probability

Procedia PDF Downloads 326

Authors: Zahid Ullah, Atlas Khan

Abstract:

The rapid growth of data in various domains has created a pressing need for effective methods to transform this data into meaningful knowledge. In this era of big data, mathematical and statistical innovations play a crucial role in unlocking insights and facilitating informed decision-making in data analytics. This abstract aims to explore the transformative potential of these innovations and their impact on converting raw data into actionable knowledge. Drawing upon a comprehensive review of existing literature, this research investigates the cutting-edge mathematical and statistical techniques that enable the conversion of data into knowledge. By evaluating their underlying principles, strengths, and limitations, we aim to identify the most promising innovations in data analytics. To demonstrate the practical applications of these innovations, real-world datasets will be utilized through case studies or simulations. This empirical approach will showcase how mathematical and statistical innovations can extract patterns, trends, and insights from complex data, enabling evidence-based decision-making across diverse domains. Furthermore, a comparative analysis will be conducted to assess the performance, scalability, interpretability, and adaptability of different innovations. By benchmarking against established techniques, we aim to validate the effectiveness and superiority of the proposed mathematical and statistical innovations in data analytics. Ethical considerations surrounding data analytics, such as privacy, security, bias, and fairness, will be addressed throughout the research. Guidelines and best practices will be developed to ensure the responsible and ethical use of mathematical and statistical innovations in data analytics. The expected contributions of this research include advancements in mathematical and statistical sciences, improved data analysis techniques, enhanced decision-making processes, and practical implications for industries and policymakers. The outcomes will guide the adoption and implementation of mathematical and statistical innovations, empowering stakeholders to transform data into actionable knowledge and drive meaningful outcomes.

Keywords: data analytics, mathematical innovations, knowledge extraction, decision-making

Procedia PDF Downloads 75

Authors: Anu Malhotra

Abstract:

An Ad-hoc network is one that is an autonomous, self configuring network made up of mobile nodes connected via wireless links. Ad-hoc networks often consist of nodes, mobile hosts (MH) or mobile stations (MS, also serving as routers) connected by wireless links. Different routing protocols are used for data transmission in between the nodes in an adhoc network. In this paper two protocols (OLSR and AODV) are analyzed on the basis of two parameters i.e. time delay and throughput with different data rates. On the basis of these analysis, we observed that with same data rate, AODV protocol is having more time delay than the OLSR protocol whereas throughput for the OLSR protocol is less compared to the AODV protocol.

Keywords: routing adhoc, mobile hosts, mobile stations, OLSR protocol, AODV protocol

Procedia PDF Downloads 508