EGFR-mutant non–small cell lung cancer (NSCLC) initially responds well to EGFR tyrosine kinase inhibitors (EGFR-TKIs), yet resistance is nearly inevitable and responses to immune checkpoint inhibitors (ICIs) remain limited. In a retrospective cohort of 57 patients with advanced EGFR-mutant NSCLC treated with nivolumab or pembrolizumab, the objective response rate was 12.3%, although responses were concentrated in a small subgroup with concurrent high PD-L1 expression and dense intratumoral CD8⁺ immune-cell infiltration. Emerging evidence links these outcomes to chemokine-network dysregulation that alters immune trafficking and spatial organization. Oncogenic EGFR signaling shifts chemokine networks away from effector T-cell recruitment

This paper proposed a new method for network self-fault management (NSFM) based on two technologies: intelligent agent to automate fault management tasks, and Windows Management Instrumentations (WMI) to identify the fault faster when resources are independent (different type of devices). The proposed network self-fault management reduced the load of network traffic by reducing the request and response between the server and client, which achieves less downtime for each node in state of fault occurring in the client. The performance of the proposed system is measured by three measures: efficiency, availability, and reliability. A high efficiency average is obtained depending on the faults occurred in the system which reaches to

It is believed that Organizations around the world should be prepared for the transition to IPv6 and make sure they have the " know how" to be able to succeed in choosing the right migration to start time. This paper focuses on the transition to IPv6 mechanisms. Also, this paper proposes and tests a deployment of IPv6 prototype within the intranet of the University of Baghdad (BUniv) using virtualization software. Also, it deals with security issues, improvements and extensions of IPv6 network using firewalls, Virtual Private Network ( VPN), Access list ( ACLs). Finally, the performance of the obtainable intrusion detection model is assessed and compared with three approaches.

Abstract

Zigbee is considered to be one of the wireless sensor networks (WSNs) designed for short-range communications applications. It follows IEEE 802.15.4 specifications that aim to design networks with lowest cost and power consuming in addition to the minimum possible data rate. In this paper, a transmitter Zigbee system is designed based on PHY layer specifications of this standard. The modulation technique applied in this design is the offset quadrature phase shift keying (OQPSK) with half sine pulse-shaping for achieving a minimum possible amount of phase transitions. In addition, the applied spreading technique is direct sequence spread spectrum (DSSS) technique, which has

Physicians are likely to expend significant labor and time while manually calculating blood smears. Automatic computer-based methods for classifying acute lymphoblastic leukemia have trouble correctly lighting stained white blood cell microscopy images and accurately separating cells that touch or overlap. Additionally, incorporating machine learning techniques into medical services is very hard because doctors can deal with rough guesses as long as the results aren't too bad, but they can't use these calculations for actual medical care. Enabling a A deep network having knowledge of the accuracy of its own predictions is a fascinating and crucial issue. Most instances segmentation frameworks weigh the mask quality during the instance

In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha