IMPACT OF NON-FINANCIAL REWARDS ON EMPLOYEES MOTIVATION IN BANKING SECTOR OF PESHAWAR, PAKISTAN

This papershows to measure the impact of non-financial rewards on the motivation of employees in different banks of Pakistan. The purpose of this paper is to know the impact of the non-financial rewards on employee’s motivation andwill classify to what extent the HR are taking care of this source of employee motivation.The paper follows an empirical study and quantitative approach. For non-financial rewards, there are fiveindependent variables taken for this study; rewards & recognition, career opportunity, promotion, participation of employees in decision-making and responsibility to identify their impact on the employee’s motivation. For effective collection of primary data from the target respondents, a Likert scaled questionnaire used, and 185 employees are taken from seven different banks in, Peshawar Pakistan. SPSS used to evaluate all of the data in order to find and measure the spearman association among the dependent and independent variables. All the data presented using tables.All non-financial reward variables found to have a significant association in the analysis. HR managers in the banking industry may find this study useful. The paper offers a basis for understanding organizational motivation problems. It is a valuable contribution to the field of HR management expertise, as it examines the factors that influence employee motivation and offers solutions to challenges that workers face at work. The paper would also force the banks managers to consider the issues of employee motivation for the sake of the company's overall success

Gender Differences in the Use of Parts of Speech in Pakistani English Newspaper Columns: A Critical Discourse Analysis

This research is a critical discourse analysis of parts of speech used by two different genders, male and female in Pakistani English newspaper columns. A corpus has been compiled from four Pakistani English newspapers i.e. The Dawn, The News, The Nation and The Express Tribune. A sample size of 240 columns (120 both by male and female writers) has been compiled in this study. This research highlights the implicit function of parts of speech in Pakistani English newspapers. These parts of speech have been limited to nouns, pronouns, verbs and adjectives. It explores how male and female writings differ in the use of parts of speech in Pakistani English newspaper columns and to uncover the manipulation through its uses. Keywords: CDA, POS, Corpus driven, English News Columns, Gender, Pakista

Genre Analysis of Conclusion Sections of Pakistani Research Articles in Natural and Social Sciences

This study has investigated macro-structures (Move Analysis) of conclusion section of Research Articles (henceforth RAs) in Social Sciences and Natural Sciences. The purpose of the study is to find out the differences in RAs conclusion sections across various disciplines in terms of moves and steps constituting each move. For this purpose, 50 RAs conclusions have been selected for analysis, 25 each from  social sciences and natural sciences. RAs have been selected randomly from different journals written by Pakistani authors. A new model has been proposed for the move analysis by following Yang & Allison’s (2003) and Bunton’s (2005) models for conclusion section. This study has revealed variations in move structure of RAs conclusions across various disciplines in Pakistani context. This study may contribute an understanding of the nature of conclusions of RAs across various disciplines. It may be helpful for both writing instructors, whose purpose is to have their students succeed in using this component of genre, and to writing students, who are willing to take part in distinct discourse communities. Key Words: Genre Analysis, Research Article Conclusion, Move Analysis, Disciplinary Variatio

Investigating Content and Language Integration in an EFL Textbook: A Corpus-Based Study

This research evaluates an English language textbook from CLIL perspective. For this purpose, an intermediate level (grade-11) English language textbook has been selected and analyzed utilizing Coyle’s conceptual framework of 4Cs (i.e. content, cognition, communication and culture).Content, communication, and culture have been explored through a checklist, whereas cognition has been explored by developing a corpus from the questions given in the exercises of the textbook and analyzing in the light of Bloom’s taxonomy. The results reveal certain breeches between CLIL features and the textbook’s contents. Layout, learning outcomes, organization of the content, subject matter, authenticity of the text, exercises, and focus on language skills does not seem to match with CLIL perspectives. Listening and speaking skills are observed to be ignored. Moreover, the exercises do not seem to foster critical thinking and interaction between students and teachers. Most of the questions are observed covering only first two levels (i.e. knowledge and comprehension) of Bloom’s taxonomy. The study concludes that CLIL principles are not integrated in the textbook. Therefore, the textbook is not suitable to an ESL/EFL setting

Deep Learning-Based Multiclass Classification of Diseases in Cucumber Fruit: Enhancing Agriculture Diagnosis

Agriculture plays a key role in the economies of many developing nations. cucumber is cultivated vegetable that are grown in large quantities, but the production is regularly affected by diseases, with its yield loss impacted by diseases which include Belly Rot and Pythium Fruit Rot. Early and accurate disease diagnosis is critical for minimizing economic losses and improving crop quality. Traditional method techniques are based on visual identification and time-consuming and often inaccurate, especially for the early stages of the disease. In this work, we aim to tackle these problems and present an automatic cucumber disease classification system by transfer learning. Three convolutional neural network models (pre-trained VGG16, MobileNetV2 and ResNet-50) were retrained on a set of 2400 images containing two disease classes and one normal class. The images were preprocessed with the Contrast Limited Adaptive Histogram Equalization (CLAHE) and background removal by deep learning segmentation to eliminate the background noise and focus only on the informative feature of the image. The models were trained and tested by using training, validation, and test sets with the respective accuracies of 95.28%, 98.06%, and 57.5%. MobileNetV2 showed superior performance to all other models including the highest precision, recall, and F1 score of 0.98, confirming that it was robust and appropriate for real-time disease classification. The results demonstrate that the transfer learning method is conducive to improving the issues of lack of labeled samples and variations in image acquisition and strength, thus providing a reliable model for early disease detection in cucumbers. The system we propose can support farmers and agronomists in early disease management decisions and reduce chemical usage. In the future, we will increase the data set with more disease classes, and develop a mobile APP for field level disease detection

Deep Learning-Based Multiclass Classification of Diseases in Cucumber Fruit: Enhancing Agriculture Diagnosis

Agriculture plays a key role in the economies of many developing nations. cucumber is cultivated vegetable that are grown in large quantities, but the production is regularly affected by diseases, with its yield loss impacted by diseases which include Belly Rot and Pythium Fruit Rot. Early and accurate disease diagnosis is critical for minimizing economic losses and improving crop quality. Traditional method techniques are based on visual identification and time-consuming and often inaccurate, especially for the early stages of the disease. In this work, we aim to tackle these problems and present an automatic cucumber disease classification system by transfer learning. Three convolutional neural network models (pre-trained VGG16, MobileNetV2 and ResNet-50) were retrained on a set of 2400 images containing two disease classes and one normal class. The images were preprocessed with the Contrast Limited Adaptive Histogram Equalization (CLAHE) and background removal by deep learning segmentation to eliminate the background noise and focus only on the informative feature of the image. The models were trained and tested by using training, validation, and test sets with the respective accuracies of 95.28%, 98.06%, and 57.5%. MobileNetV2 showed superior performance to all other models including the highest precision, recall, and F1 score of 0.98, confirming that it was robust and appropriate for real-time disease classification. The results demonstrate that the transfer learning method is conducive to improving the issues of lack of labeled samples and variations in image acquisition and strength, thus providing a reliable model for early disease detection in cucumbers. The system we propose can support farmers and agronomists in early disease management decisions and reduce chemical usage. In the future, we will increase the data set with more disease classes, and develop a mobile APP for field level disease detection

Efficacy and safety of quinine loading dose in patients with severe falciparum malaria at a tertiary care hospital in Pakistan

OBJECTIVE: To compare the clinical outcomes of a loading dose regimen of quinine with a uniform dose regimen in patients with severe falciparum malaria. METHODS: A retrospective chart review of 315 patients admitted with severe falciparum malaria and treated with quinine at a tertiary care teaching hospital of Karachi, Pakistan during 1999-2006 was conducted. Group A with 103 patients (32.7%) was given an initial loading dose of quinine while group B with 212 patients (67.3%) did not receive the loading dose. The two groups were compared in terms of reduction of parasite load, resolution of fever, recovery of consciousness and incidence of adverse effects. Outcome parameters were measured on the third day of therapy. RESULTS: More individuals in group A (62.1%) were afebrile as compared to group B (54.7%) at day 3 of therapy. Patients in group B showed greater reduction in parasitaemia (47.2% at baseline to 4.7% on day 3) as compared to group A (56.3 % at baseline to 9.7% on day 3). Following therapy, fewer patients in group B had altered consciousness (7.1% at baseline to 4.7% on day 3) as compared to patients in group A (7.8% at baseline to 5.8% on day 3). However, these associations were not statistically significant. The incidence of thrombocytopenia was higher in Group A (5.8%) as compared to Group B (0.9%). CONCLUSION: Although quinine loading dose may be more effective than uniform dose in rapid fever clearance; it also appears to be associated with higher toxicity. Uniform dose of quinine may be prescribed in severe falciparum malaria in view of its better safety profile

Out-of-Pocket Expenditure on Delivery Care in Public and Private Health Sectors – A Study in a Rural District of Pakistan

Pakistan witnessed a significant improvement in maternal health outcomes during the past two decades. However, persistent urban-rural and socio-economic inequalities exist in access to maternal healthcare services across the country. The objective of this study was to estimate out-of-pocket expenditure (OOPE) on delivery care by women in the public and private health sectors in RajanPur district. This was a cross-sectional study conducted, among 368 randomly selected mothers who had childbirths from 1st October to 31st December 2020. The study applied multi-stage random sampling technique to select the study participants. The results showed that about two-thirds of mothers preferred public hospitals for most recent delivery. The percentage of cesarean deliveries conducted in private hospitals (43.8%) was 4.7 times higher than in public hospitals (9.3%). About 99% of mothers incurred OOPE during delivery care, and the mean OOPE incurred during delivery care was PKR 2840 (US$17.75) in public hospitals and PKR 25596 (US$159.9) in private hospitals. OOPE on cesarean delivery in private hospitals (PKR 39654.7, US$247.8) was 2.5 times higher than the public hospitals (PKR16111.9, US$100.69), whereas OOPE incurred on normal delivery care in private hospitals (PKR14339, US$89.62) was 9.5 times higher than OOPE in public hospitals(PKR 1501.4, US$9.38).To conclude, the findings and recommendations drawn from the research would provide some insights to health policymakers and planners in developing an integrated and viable maternal healthcare program in Pakistan

A Comparative Corpus-based Analysis of Collocational Patterns in Self and Other-translators

With the dawn of post-colonialism and a surge in migrations, several bilingual authors started translating their original texts into the target language. As a result, translation studies started distinguishing it from other-translations owing to its special status based on various extra-linguistic features. Consequently, now it goes by the term self-translation studies (Anselmi, 2012) -a field of its own. However, none of the studies have distinguished self and other-translations at the basic linguistic level. This study aims to trace and compare the patterns of collocations in other-translations and self-translations with reference to non-translated texts. For this purpose, a corpus-based on a monolingual comparable model (Baker, 1993) and consisting of three further sub-corpora i.e. other-translators, self-translators, and Pakistani writers is used. The lexical collocations model proposed by Benson et al. (1997) provides a theoretical framework for this study. The sub-corpora are tagged by TagAnt 1.2.0 and treated further using AntConc 3.5.8. The findings of the study reveal that self-translators employ more collocate types and they are more homogeneously distributed around a single node in comparison to the other-translators. The results are significant for the theoretical understanding of self-translations and invite more investigations at the linguistic level to set apart the features of the two categories

A Comparative Corpus-based Analysis of Collocational Patterns in Self and Other-translators

With the dawn of post-colonialism and a surge in migrations, several bilingual authors started translating their original texts into the target language. As a result, translation studies started distinguishing it from other-translations owing to its special status based on various extra-linguistic features. Consequently, now it goes by the term self-translation studies (Anselmi, 2012) -a field of its own. However, none of the studies have distinguished self and other-translations at the basic linguistic level. This study aims to trace and compare the patterns of collocations in other-translations and self-translations with reference to non-translated texts. For this purpose, a corpus-based on a monolingual comparable model (Baker, 1993) and consisting of three further sub-corpora i.e. other-translators, self-translators,and Pakistani writersis used. The lexical collocations model proposed by Benson et al. (1997) provides a theoretical framework for this study. The sub-corpora are tagged by TagAnt 1.2.0 and treated further using AntConc 3.5.8. The findings of the study reveal that self-translators employ more collocate types and they are more homogeneously distributed around a single node in comparison to the other-translators. The results are significant for the theoretical understanding of self-translations and invite more investigations at the linguistic level to set apart the features of the two categories