The main purpose of this study is to present a model of warning report in the auditing profession based on organizational factors of auditing institutions in Iran. According to its nature, the present study is an applied research and in terms of method is a descriptive-survey research. The statistical population of the study includes auditors working in auditing institutions, who are the members of the Association of Certified Public Accountants. The theoretical framework and research background are based on the inductive approach and data collection. Since this research was conducted in a real environment, ie auditing institutions that are members of the Iranian Association of Certified Public Accountants and the auditing organization. It is also considered as a field research. For implementing the main phases of the research and data collection, field methods and questionnaire have been used. After collecting data using field methods and questionnaire, in the second step, for validating and analyzing the relationships between the variables of the initial model of the study, two methods of data analysis, namely descriptive and inferential statistics and appropriate statistical software were used. The results revealed, organizational factors of auditing institutions in Iran have an effect on  the warning report in the audit profession and in turn, cause timely warning reporting among auditors in accordance with legal principles, procedures and criteria as a voluntary action.

The main focus of this research is to investigate and explain the role of organizational factors and human resources characteristics in the warning report of auditors of auditing institutions in Iran. The statistical population of the research includes auditors working in audit institutions, members of the public accountants' society and audit organization. Data related to the characteristics of human resources and the variables of organizational factors have been collected using standard questionnaires. Due to the complex nature of standard questionnaires used, a multi-level structural equation model is used to analyze the collected data. The first and second levels of the model are used to confirm the validity and reliability of the research variables. The third level of the model, which explains the relationship between research variables, has been used to test the research hypotheses. The results of the research showed that the free-spirited leadership style is an important organizational factor promoting the warning report in the audit profession in Iran's official audit institutions. Also, managing age in human resource leads to the improvement of transparency of reporting and, in turn, prompts timely warning reporting among auditors in accordance with principles, procedures and legal criteria as a voluntary act.

Underreporting of adverse drug reactions (ADRs) due to time constraints, limited awareness, reluctance, and legal fears demands integrating pharmacists with physicians and implementing an extensive continuing education module on pharmacovigilance for application-based learning. Our study aimed to integrate a pharmacist with a treating physician to enhance patient safety by facilitating ADR reporting, providing a continuing education module on pharmacovigilance to senior residents and interns, and providing an ADR alert card to patients. We carried out a cross-sectional study over ten months in the general medicine department of a tertiary care hospital. We integrated Doctor of Pharmacy intern students with treating physicians to facilitate easy identification of ADR and for issuing ADR alert cards. We provided a seven-day continuing education module on pharmacovigilance concepts for senior residents and final-year medical interns. We distributed an ADR alert card to 180 patients. The largest groups of patients affected by ADRs in our study were aged between 41-50 and 51-60 years, collectively accounting for nearly 50% of the affected population. Our study reported a slight female predominance (51.1%). The gastrointestinal system (31.1%), nervous system (20%), and skin and subcutaneous tissue (13.9%) were the most frequently affected organ systems, accounting for 65% of the ADRs. Most ADRs (92.2%) were not serious. Analgesics (aspirin), HMG-CoA inhibitors (atorvastatin), and antiepileptics (phenytoin) were the top three drug classes most associated with ADRs. Out of 23 participants, 21 (91.3%) correctly completed the reporting of ADR into the ADR form from anonymous case reports, identified predisposing factors, assessed causality correctly, and suggested prevention and management strategies according to the clinical scenario. Most participants (95.7%) indicated that the module significantly enhanced their awareness and comprehension of Pharmacovigilance concepts, with only a small percentage (4.3%) expressing neutral sentiment and none disagreeing. Integrating pharmacists, continuing pharmacovigilance education, and issuing ADR alert cards significantly enhanced ADR reporting, comprehension of pharmacovigilance concepts, and patient safety measures in the healthcare setting.

Ilizarov is an accepted technique for lengthening short amputation stumps to improve prosthetic function and rehabilitation. The relation of stump lengthening and phantom limb pain (PLP) has not been reported in literature. We present here a case report of a transfemoral amputee who had a flare up of PLP following stump lengthening. He responded well to a combination of pharmacological therapy and soft tissue manipulative techniques and desired length was achieved. This report alerts the possibility of aggravation of PLP following stump lengthening and discusses its management.

With the increase in attacks, the different mechanisms in different layers of defense are applied in order to detect and prevent attacks. In this case we are encountered with massive amounts of alerts with low level and scattered information. Alert correlation is one of the solutions that are used to combine alerts and create a high level view of under controlled network security situations, and a lot of researches have been done in this field. In this paper, we describe the OSSIM and introduce its features. In addition with a combined approach to the alert correlation problem, a new categorization is done on the scientific researches and with respect to these researches, we described the process of alert correlation in the OSSIM and established a correspondence between components of the OSSIM and one of the researches. In most researches the focus is on the alerts correlation of intrusion detection systems, we have shown in this paper that the other resources are effective in multi-stage attacks correlation.

To report a patient with Urrets-Zavalia (U-Z) syndrome after penetrating keratoplasty (PKP) in both eyes Case report: A 22-year-old woman with keratoconus associated with a central full thickness scar in both eyes underwent PKP in her right eye that was postoperatively associated with a fixed dilated pupil and severe fibrin reaction, posterior synechiae and mild anterior sub capsular cataract. However he developed fixed dilated pupil leading to posterior synechiae after PKP in the second eye. This case shows an intrinsic susceptibility for U-Z syndrome which alerts surgeons in the case of second eye surgery.

Alert correlation systems attempt to discover the relations among alerts produced by one or more intrusion detection systems to determine the attack scenarios and their main motivations. In this paper a new IDS alert correlation method is proposed that can be used to detect attack scenarios in real-time. The proposed method is based on a causal approach due to the strength of causal methods in practice. To provide a picture of the current intrusive activity on the network, we need a real-time alert correlation. Most causal methods can be deployed offline but not in real-time due to time and memory limitations. In the proposed method, the knowledge base of the attack patterns is represented in a graph model called the Causal Relations Graph. In the offline mode, we construct Queue trees related to alerts'' probable correlations. In the real-time mode, for each received alert, we can find its correlations with previously received alerts by performing a search only in the corresponding tree. Therefore, the processing time of each alert decreases significantly. In addition, the proposed method is immune to deliberately slowed attacks. To verify the proposed method, it was implemented and tested using DARPA2000 dataset. Experimental results show the correctness of the proposed alert correlation and its efficiency with respect to the running time.

Today, intrusion detection systems are extremely important in securing computers and computer networks. Correlated systems are next to intrusion detection systems by analyzing and combining the alarms received from them, appropriate reports for review and producing security measures. One of the problems face by intrusion detection systems is generating a large volume of false alarms, so one of the most important issues in correlated systems is to check the alerts received by the intrusion detection system to distinguish true-positive alarms from false-positive alarms. The main focus of this research is on the applied optimization of classification methods to reduce the cost of organizations and security expert time in alert checking. The proposed intrusion detection model using correlation(IIDMC) is tested on a valid test dataset and the results show the efficiency of the proposed model and consequently its high accuracy.

In recent decades air pollution and its associated health risks are in growing numbers. Detecting ‎air pollution in the environment and alarming the people may accomplish various advantages ‎among health monitoring, telemedicine, and industrial sectors. A novel method of detecting air ‎pollution using supervised learning models and an alert system using IoT is proposed. The main ‎aim of the research is manifold: a) Air pollution data is preprocessed using the feature scaling ‎method, b) The feature selection and feature extraction process done followed by performing a ‎Recurrent Neural Network and c) The predicted data is stored in the cloud server, and it provides ‎the end-users with an alert when the threshold pollution index exceeds. The proposed RNN ‎reports enhanced performance when tested against traditional machine learning models such as ‎Convolutional Neural Networks (CNN), Deep Neural Networks(DNN), and Artificial Neural ‎Networks(ANN) for parameters such as accuracy, specificity, and sensitivity.‎

Today, intrusion detection systems are extremely important in securing computers and computer networks. Correlated systems are next to intrusion detection systems by analyzing and combining the alarms received from them, appropriate reports for review and producing security measures. One of the problems face intrusion detection systems is generating a large volume of false alarms, so one of the most important issues in correlated systems is to check the alerts received by the intrusion detection system to distinguish true-positive alarms from false-positive alarms. The main focus of this research is on the applied optimization of classification methods to reduce the cost of organizations and security expert time in alert checking. The proposed Incrimental Intrusion Detetection Model using Correlator (IIDMC) is tested on a valid test dataset and the results show the efficiency of the proposed model and consequently its high accuracy.