Drug Interaction Study Of Apixaban With Cyclosporine Or Tacrolimus: Results From A Phase 1, Randomized, Open-Label, Crossover Study In Healthy Volunteers

BACKGROUND Solid organ transplant recipients commonly require anticoagulation. Apixaban (APX) is principally metabolized by CYP3A4, undergoes direct intestinal excretion, and is a substrate to P-glycoprotein (P-gp) and Breast Cancer Resistance Protein (BCRP) transporters. We examined the potential drug interaction between cyclosporine (CsA) and tacrolimus (Tac) [combined inhibitors of CYP3A4, P-gp and, BCRP] with APX.https://jdc.jefferson.edu/petposters/1005/thumbnail.jp

Tracheobronchomalacia: An Unreported Pulmonary Complication of Acute Pancreatitis

Acute Pancreatitis (AP) is a common disease with systemic complications, specifically pulmonary complications that are well-documented [1]. Here we present, to the best of our knowledge, the first reported case of tracheobronchomalacia as a respiratory complication of AP. A 54-year-old white male with multiple chronic comorbidities developed necrotizing acute pancreatitis (NAP) following a surgical procedure. Internal Medicine evaluated and managed his NAP according to protocol. Within one week of NAP onset, the patient developed rapid respiratory distress. Chest radiography and ABGs were unable to diagnose ARDS. A CT scan with IV contrast was completed to investigate a pulmonary embolus and found the tracheal diameter variations during inspiration and expiration of the respiratory cycle consistent with tracheobronchomalacia (TBM). The patient’s respiratory status continued to deteriorate requiring endotracheal intubation and mechanical ventilation with weaning trials proving to be futile. The patient eventually developed fungemia and expired after his family opted for palliative extubation. Airway collapse related to TBM is an under-recognized diagnosis which should be suspected in patients with NAP who develop acute respiratory distress in whom no specific etiology has been determined

Una observación hacia los procesos asistidos por computadora en la producción de prendas de vestir. Comparación y análisis de software CAD/CAM en Bangladesh

This research paper inquires about different attributes of Computer-aided processes in garments production. This perspective Research was done by our courageous team from 2021 to 2022. It reveals adequate information on the Garments industry's Intension and criteria for choosing CAD/CAM software. For the sake of this Research, we visited more than 600 industries to gather raw data; Every Industry tried to attend this Research from a different region of Bangladesh willingly. After collecting all raw data from the garments industry. The data of the Garments industry was coordinated by Excel program. Consequently, the data was analyzed and implemented statistically to identify the Garments Industry attribute for satisfaction with CAD/CAM software. This process also detects many challenges and defines and advises a proper solution to the problems that the Garments industry is facing in the current situation. This research paper demonstrates adequate information about the Garment's criteria and demand in purchasing garments Computer-aided software.Este trabajo de investigación indaga sobre los diferentes atributos de los procesos asistidos por computadora en la producción de prendas de vestir. Esta investigación de perspectiva fue realizada por nuestro valiente equipo de 2021 a 2022. Revela información adecuada sobre la intención de la industria de la confección y los criterios para elegir el software CAD/CAM. Por el bien de esta investigación, visitamos más de 600 industrias para recopilar datos sin procesar; Cada industria trató de asistir a esta investigación desde una región diferente de Bangladesh de buena gana. Después de recopilar todos los datos sin procesar de la industria de la confección. Los datos de la industria de Vestuario fueron coordinados por el programa Excel. En consecuencia, los datos se analizaron e implementaron estadísticamente para identificar el atributo de la industria de la confección para la satisfacción con el software CAD/CAM. Este proceso también detecta muchos desafíos y define y aconseja una solución adecuada a los problemas que enfrenta la industria de la confección en la situación actual. Este trabajo de investigación demuestra información adecuada sobre los criterios de la prenda y la demanda en la compra de prendas Software asistido por computadora

ATM Shield: Analysis of Multitier Security Issues of ATM in the Context of Bangladesh

Over the last decade, consumers have been largely dependent on and trust the Automatic Teller Machine (ATM) to conveniently meet their banking needs. However, despite the numerous advantages of ATM system, ATM fraud has recently become more widespread. In this paper, we provide an overview of the possible fraudulent activities that may be perpetrated against ATMs and investigates recommended approaches to prevent these types of frauds. In particular, we develop a prototype model for the utilization of three tier security equipped ATM to provide security solutions against must of the well-known breaches. In this research article, the tools and techniques of ATM fraud are contemplated. A secure three layer electronic transaction mechanism of ATM is developed to prevent ATM frauds. In this three layer authentication systems the users can improve ATM security against frauds and crimes

Human Behavior Analysis Using Intelligent Big Data Analytics.

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface (API) of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted

Exploring Challenges of Deploying BERT-based NLP Models in Resource-Constrained Embedded Devices

BERT-based neural architectures have established themselves as popular state-of-the-art baselines for many downstream NLP tasks. However, these architectures are data-hungry and consume a lot of memory and energy, often hindering their deployment in many real-time, resource-constrained applications. Existing lighter versions of BERT (eg. DistilBERT and TinyBERT) often cannot perform well on complex NLP tasks. More importantly, from a designer's perspective, it is unclear what is the "right" BERT-based architecture to use for a given NLP task that can strike the optimal trade-off between the resources available and the minimum accuracy desired by the end user. System engineers have to spend a lot of time conducting trial-and-error experiments to find a suitable answer to this question. This paper presents an exploratory study of BERT-based models under different resource constraints and accuracy budgets to derive empirical observations about this resource/accuracy trade-offs. Our findings can help designers to make informed choices among alternative BERT-based architectures for embedded systems, thus saving significant development time and effort

Smart detection and prevention procedure for DoS attack in MANET

A self-organized wireless communication short-lived network containing collection of mobile nodes is mobile ad hoc network (MANET). The mobile nodes communicate with each other by wireless radio links without the use of any pre-established fixed communication network infrastructure or centralized administration, such as base stations or access points, and with no human intervention. In addition, this network has potential applications in conference, disaster relief, and battlefield scenario, and have received important attention in current years. There is some security concern that increases fear of attacks on the mobile ad-hoc network. The mobility of the NODE in a MANET poses many security problems and vulnerable to different types of security attacks than conventional wired and wireless networks. The causes of these issues are due to their open medium, dynamic network topology, absence of central administration, distributed cooperation, constrained capability, and lack of clear line of defense. Without proper security, mobile hosts are easily captured, compromised, and attacked by malicious nodes. Malicious nodes behavior may deliberately disrupt the network so that the whole network will be suffering from packet losses. One of the major concerns in mobile ad-hoc networks is a traffic DoS attack in which the traffic is choked by the malicious node which denied network services for the user. Mobile ad-hoc networks must have a safe path for transmission and correspondence which is a serious testing and indispensable issue. So as to provide secure communication and transmission, the scientist worked explicitly on the security issues in versatile impromptu organizations and many secure directing conventions and security measures within the networks were proposed. The goal of the work is to study DoS attacks and how it can be detected in the network. Existing methodologies for finding a malicious node that causes traffic jamming is based on node’s retains value. The proposed approach finds a malicious node using reliability value determined by the broadcast reliability packet (RL Packet). In this approach at the initial level, every node has zero reliability value, specific time slice, and transmission starts with a packet termed as reliability packet, node who responded properly in specific time, increases its reliability value and those nodes who do not respond in a specific time decreases their reliability value and if it goes to less than zero then announced that it’s a malicious node. Reliability approach makes service availability and retransmission time

Privacy-Aware Data Forensics of VRUs Using Machine Learning and Big Data Analytics

The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results

Security requirement management for cloud-assisted and internet of things⇔enabled smart city

The world is rapidly changing with the advance of information technology. The expansion of the Internet of Things (IoT) is a huge step in the development of the smart city. The IoT consists of connected devices that transfer information. The IoT architecture permits on-demand services to a public pool of resources. Cloud computing plays a vital role in developing IoT-enabled smart applications. The integration of cloud computing enhances the offering of distributed resources in the smart city. Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability, security, performance, confidentiality, and privacy. The key reason for cloud- and IoT-enabled smart city application failure is improper security practices at the early stages of development. This article proposes a framework to collect security requirements during the initial development phase of cloud-assisted IoT-enabled smart city applications. Its three-layered architecture includes privacy preserved stakeholder analysis (PPSA), security requirement modeling and validation (SRMV), and secure cloud-assistance (SCA). A case study highlights the applicability and effectiveness of the proposed framework. A hybrid survey enables the identification and evaluation of significant challenges

Prevalence of gaming addiction and its impact on sleep quality: a cross-sectional study from Pakistan

Background: Gaming addiction has become a topic of increasing research interest worldwide but little research has been carried out in Pakistan. Aims: The present study assessed the prevalence of gaming addiction among a Pakistani sample of adults in the general population. It also explored the effects of online gaming addiction upon sleep quality. Method: A cross-sectional survey was carried out during a national lockdown due to the COVID-19 pandemic in Pakistan. Using a convenience sampling technique, an online survey comprising demographic information, the Game Addiction Scale (GAS), and the Pittsburgh Sleep Quality Index (PSQI) was completed by 618 participants (67.5% male) aged 18–56 years (M = 24.53 years, SD = ±5.016). Results: Out of 618 participants, 57.0% (n = 352) did play online games. Among gamers, 12.5% (n = 44) were classed as addicted to the gaming based on GAS scores. Compared to those not addicted to gaming, participants with gaming addiction had significantly poorer subjective sleep quality, higher sleep disturbance, lesser sleep duration, and higher daytime dysfunction. Gaming addiction was also more prevalent among males compared to females. Conclusion: Gaming addiction among the Pakistani general population is significantly associated with poor sleep quality. This problem needs to be addressed at the individual and societal levels to avoid adverse long-term health impacts