Bibliometric study on the literature related to dental research and education published in Journal of Pakistan Medical Association

Objective: To analyse characteristics of literature related to dental research and education published in a single medical journal, The Journal of Pakistan Medical Association.Method: The bibliometric study was conducted at the Sindh Institute of Oral Health Sciences, Jinnah Sindh Medical University from March till May 2020, and comprised electronic and hand search of scientific literature relevant to dentistry published in the Journal of Pakistan Medical Association from the first issue published in 1953 till March 2020. The selected articles were analysed for year of publication, field of study, type of article, institute and country of first author, number of authors and citation count. Keyword mapping was also carried out. Data was analysed using SPSS 19.Results: Of the 159 articles identified, 117(73.6%) were contributed from Pakistan. The most common specialties were Oral and Maxillofacial Surgery and Epidemiology with 20(12.6%) articles each, followed by Operative Dentistry and Endodontics 19(11.9%). Majority of articles were Original / Research 87(54.7%). The highest number of articles were published in 2019 26(16.35%). The Aga Khan University, contributed the most publications 30(18.9%), followed by Dow University of Health Sciences, 11(6.9%). Majority of the articles were contributed by three authors 48(38.4%). The top cited article was found to have 113 citations, followed by articles with 103 and 91 citations.Conclusions: The contribution of scientific papers related to dentistry and dental education in the Journal of Pakistan Medical Association was significant. With growth of dentistry as a discipline along with a parallel increase in the publication of dental research papers, it is imperative that a dedicated indexed journal for dental research be commenced

Deep Learning Methods for Malware and Intrusion Detection: A Systematic Literature Review

Android and Windows are the predominant operating systems used in mobile environment and personal computers and it is expected that their use will rise during the next decade. Malware is one of the main threats faced by these platforms as well as Internet of Things (IoT) environment and the web. With time, these threats are becoming more and more sophisticated and detecting them using traditional machine learning techniques is a hard task. Several research studies have shown that deep learning methods achieve better accuracy comparatively and can learn to efficiently detect and classify new malware samples. In this paper, we present a systematic literature review of the recent studies that focused on intrusion and malware detection and their classification in various environments using deep learning techniques. We searched five well-known digital libraries and collected a total of 107 papers that were published in scholarly journals or preprints. We carefully read the selected literature and critically analyze it to find out which types of threats and what platform the researchers are targeting and how accurately the deep learning-based systems can detect new security threats. This survey will have a positive impact on the learning capabilities of beginners who are interested in starting their research in the area of malware detection using deep learning methods. From the detailed critical analysis, it is identified that CNN, LSTM, DBN, and autoencoders are the most frequently used deep learning methods that have effectively been used in various application scenarios

Trust Violations and Positive Emotions: Moderating Role of Initial Trust: A Case of Kindergartens in China

This paper examines the moderating role of initial trust of parents between positive emotions of parents and apology with an internal or external attribution after a competence or integrity-based trust violation by the Kindergarten for the purpose of trust repair. We asked about 855 parents to respond to a scenario in which they read about violation of competence with apology internal or about violation of integrity with apology external. After reading a scenario based hypothetical situation they respond to a questionnaire. Each participant was presented with one of the scenarios.The results revealed a significant interaction whereby positive emotions were produced more successfully when kindergarten apologized with an internal attribution in matters of violation concerned matters of competence but apologized with an external attribution when the trust violation concerned matters of integrity. Initial trust positively moderates the relationship between apology with internal and external attributions in matters concerning competence and integrity and positive emotions. Keywords: Trust Violation, Competence, Integrity, Apology, Attribution, Kindergarten, Emotions, Initial Trust, Public Organization

Trust Repair by Public Kindergartens: Mediating Role of Positive Emotions: A case of kindergartens in Anhui Province, China

The trust of the public for public organizations is greatly damaged because of the repeated occurrence of violations of trust events, which has an outcome in the form of serious trust deficit. This paper examines the post-trust of parents in kindergarten that extends apology with an internal or external attribution after a competence or integrity-based trust violation. We asked about 855 parents to respond to a scenario in which they read about violation of competence with apology internal or about violation of integrity with apology external. After reading a scenario based hypothetical situation they respond to a questionnaire. Each participant was presented with one of the scenarios. The results revealed a significant interaction whereby parent’s leads to post trust more successfully when kindergarten apologized with an internal attribution in matters of violation concerned matters of competence but apologized with an external attribution when the trust violation concerned matters of integrity. Positive emotions mediate the relationship between apology with internal attribution in matters of competence violation or apology with external attribution in matters of integrity violation and post trust. Keywords: Trust Repair, Trust Violation, Competence, Integrity, Apology, Attribution, Kindergarten, Emotions

Fabrication and in vitro evaluation of chitosan-gelatin based aceclofenac loaded scaffold

Scaffold development is a nascent field in drug development. The scaffolds mimic the innate microenvironment of the body. The goal of this study was to formulate a biocompatible and biodegradable scaffold, loaded with an analgesic drug, aceclofenac (Ace). The bioscaffold is aimed to have optimum mechanical strength and rheology, with drug released in a sustained manner. It was prepared via chemical cross-linking method: a chitosan (CS) solution was prepared and loaded with Ace; gelatin (GEL) was added and the mixture was cross-linked to get a hydrogel. 20 formulations were prepared to optimize different parameters including the stirring speed, drug injection rate and crosslinker volume. The optimal formulation was selected based on the viscosity, drug solubility, homogeneity, porosity and swelling index. A very high porosity and swelling index were attained. In vitro release data showed sustained drug delivery, with effective release at physiological and slightly acidic pH. SEM analysis revealed a homogeneous microstructure with highly interconnected pores within an extended polymer matrix. FT-IR spectra confirmed the absence of polymer-drug interactions, XRD provided evidences for efficient drug entrapment within the scaffold. Rheological analysis corroborated the scaffold injectability. Mathematical models were applied to in-vitro data, and the best fit was attained with Korsmeyer-Peppas

Empirical Evidence of Co-Movement between the Canadian CDS, Stock Market And TSX 60 Volatility Index: A Wavelet Approach

Purpose- The prime objective of this study was to find the co-movement between the Canadian credit default swaps market, the Stock market and volatility index (TSX 60 Index) Design/ Methodology- To achieve this purpose, daily data containing 2870 observations starting from the 1st of January 2009 to the 30th of December 2019 were analyzed. This study employed the wavelet approach to present results in short-term, medium-term, long-term, and very long time. Findings- The findings of this study showed a negative correlation between the CDS market, stock market, and the TSX 60 index in the short-term as well as in the long-term term, while in medium-term and very long-term period correlation is strongly positive. The wavelet co-movement results in the short-term and long-term were negative, while this relationship in the medium-term and very long-term period was strongly positive. Practical Implications- This research provides simultaneous valuable information for investment decisions in the short, medium, and long term time horizons, as well as for the policymakers in the Canadian credit default swaps market, stock market, and the volatility index (TSX 60 Index)

Explainable Malware Detection System Using Transformers-Based Transfer Learning and Multi-Model Visual Representation

Android has become the leading mobile ecosystem because of its accessibility and adaptability. It has also become the primary target of widespread malicious apps. This situation needs the immediate implementation of an effective malware detection system. In this study, an explainable malware detection system was proposed using transfer learning and malware visual features. For effective malware detection, our technique leverages both textual and visual features. First, a pre-trained model called the Bidirectional Encoder Representations from Transformers (BERT) model was designed to extract the trained textual features. Second, the malware-to-image conversion algorithm was proposed to transform the network byte streams into a visual representation. In addition, the FAST (Features from Accelerated Segment Test) extractor and BRIEF (Binary Robust Independent Elementary Features) descriptor were used to efficiently extract and mark important features. Third, the trained and texture features were combined and balanced using the Synthetic Minority Over-Sampling (SMOTE) method; then, the CNN network was used to mine the deep features. The balanced features were then input into the ensemble model for efficient malware classification and detection. The proposed method was analyzed extensively using two public datasets, CICMalDroid 2020 and CIC-InvesAndMal2019. To explain and validate the proposed methodology, an interpretable artificial intelligence (AI) experiment was conducted

Computational modeling of animal behavior in T-mazes: Insights from machine learning

This study investigates the intricacies of animal decision-making in T-maze environments through a synergistic approach combining computational modeling and machine learning techniques. Focusing on the binary decision-making process in T-mazes, we examine how animals navigate choices between two paths. Our research employs a mathematical model tailored to the decision-making behavior of fish, offering analytical insights into their complex behavioral patterns. To complement this, we apply advanced machine learning algorithms, specifically Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and a hybrid approach involving Principal Component Analysis (PCA) for dimensionality reduction followed by SVM for classification to analyze behavioral data from zebrafish and rats. The above techniques result in high predictive accuracies, approximately 98.07% for zebrafish and 98.15% for rats, underscoring the efficacy of computational methods in decoding animal behavior in controlled experiments. This study not only deepens our understanding of animal cognitive processes but also showcases the pivotal role of computational modeling and machine learning in elucidating the dynamics of behavioral science

Role of Absorptive Capacity Dominant Logic on the Performance of the Public Organization

In this study the performance of the public organizations is focused. The study is based on the intangible resources those have been used in the private sector organizations such as absorptive capacity and dominant logic. The performance of the organization is based on the information, knowledge, and prior experience. Through absorptive capacity and dominant logic, the organizations are able to enhance their performance. The study is conducted in the Hefei, Anhui, China which is one of the fastest growing city in the China. In the data collection process, the target group was the managerial level individual of the organizations. The data was collected through structured questionnaire adapted from the past literature. The questionnaire was translated into the Chinese language with the help of Chinese native and then translated into the English language to check the actual meaning of the questionnaire. Furthermore, to perform the data analysis process SPSS has been used. From the results its shows that absorptive capacity and dominant logic has significance impact on the organizations in the public sector and facilitates the organizations to get the superior performance. Moreover, the results also show that public organizations should adapt the techniques of the private organizations in order to improve their efficiency. Keywords: Absorptive capacity, dominant logic, performance, public organization