Background: Breast cancer (BC) is the most commonly diagnosed cancer in women. N⁶-methyladenosine (m⁶A) is the most prevalent internal modification in mammalian mRNAs and plays a crucial role in various biological processes. However, its function in Natural killer (NK) cells in BC remains unclear. NK cells are essential for cancer immunosurveillance. This study aims to assess m⁶A levels in transcripts involved in the NKG2D cytotoxicity signaling pathway in NK cells of BC patients compared to controls and find out its impact on mRNA levels. Additionally, it evaluates how deliberately altering m⁶A levels in NK cells affects mRNA and protein expression of NKG2D pathway genes and NK cell functionality. Methods: m⁶A methylation in transcripts of NKG2D-pathway-related genes in BC patients and controls was determined using methylated RNA immunoprecipitation-reverse transcription-PCR (MERIP-RT-PCR). To deliberately alter m⁶A levels in primary cultured human NK cells, the m⁶A demethylases, FTO and ALKBH5, were knocked out using the CRISPR-CAS9 system, and FTO was inhibited using Meclofenamic acid (MA). The impact of m⁶A alteration on corresponding mRNA and protein levels was assessed using RT-qPCR and Western blot analysis or flow cytometry, respectively. Additionally, NK cell functionality was evaluated through degranulation and ⁵¹Cr release cytotoxicity assays. Results: Transcripts of NKG2D, an activating receptor that detects stressed non-self tumour cells, had significantly higher m⁶A levels in the 3′ untranslated region (3’UTR) accompanied by a marked reduction in their corresponding mRNA levels in BC patients compared to controls. Conversely, transcripts of ERK2 and PRF1 exhibited significantly lower m⁶A levels escorted with higher mRNA expression in BC patients relative to controls. The mRNA levels of PI3K, PAK1 and GZMH were also significantly elevated in BC patients. Furthermore, artificially increasing transcripts’ m⁶A levels via MA in cultured primary NK cells reduced mRNA levels of NKG2D pathway genes and death receptor ligands but did not affect protein expression or NK cell functionality. Conclusion: Transcripts with higher m⁶A levels in the 3’UTR region were less abundant, and vice versa. However, changes in mRNA levels of the target genes didn’t impact their corresponding protein levels or NK cell functionality.

The development of intuitive and responsive neuroprosthetic systems remains a critical challenge in assistive technologies, particularly in decoding neural signals to enable precise and adaptive motor control. This study addresses the problem of translating EEG-based motor imagery into effective neuroprosthetic control, overcoming challenges such as limited data, overfitting in predictive models, and practical constraints in robotic actuation.A CNN-LSTM hybrid model was developed to classify motor imagery tasks using EEG signals. The application of data augmentation and regularization techniques improved the model’s performance, achieving a test accuracy of 93.5% and balanced precision and recall across motor imagery tasks. To validate its practical application, a PyBullet-based simulation demonstrated the successful control of a robotic gripper, where the model’s predictions were translated into accurate "open" and "close" actions. The gripper joints performed these actions with high precision, showcasing the system's potential for real-time neuroprosthetic applications. However, constraints such as dataset limitations and simulation-specific constraints underscore the need for further optimization.This study provides a robust proof-of-concept for integrating deep learning with brain-computer interfaces to achieve adaptive, reliable, and real-time neuroprosthetic control. By addressing key challenges, the proposed framework bridges the gap between neural signal decoding and physical actuation, offering a pathway toward advanced and responsive neuroprosthetic systems

With the vast amount of goods being shipped around the world, there is a need to track and manage various types of assets, particularly shipping containers. Logistics industries dealing with small facilities, shipments, equipment, and vehicles must be tracked. There are many applied asset tracking systems such as Radio-Frequency Identification (RFID), Bluetooth Low Energy (BLE) Beacon, and Long-Range Radio (LoRa). This paper presents these systems and focuses on locating assets using LoRaWAN technology, which has a positive impact on the responsive and sustainable cities in Egypt. IoT-based Long Range (LoRa) is a low-power, wide-area communication technology that uses radio frequencies to transmit data over long distances. The extended range, low power consumption, low maintenance, and the ability to store location data when the end node that needs to connect to the asset is out of the gateway's coverage make LoRa a good choice for developing asset-tracking applications. This paper introduces a real-time tracking experiment as a result of a project implementation, whose goals are aligned with industry, innovation, and infrastructure and Sustainable Cities and Communities, the Sustainable Development Goals (SDGs) No. 9 and 11.

LoRaWAN has become one of the most widely adopted LPWAN technologies, but its large-scale use has also exposed several persistent security weaknesses. Recent studies show that unprotected LoRaWAN links are still vulnerable to basic attacks such as eavesdropping (67% success rate) and replay attempts (about 43%), which highlights the need for more practical and adaptive security solutions suitable for low-power devices. In this work, we develop a security framework that combines a hybrid machine-learning model for anomaly detection with a lightweight Zero Trust authentication mechanism. The anomaly detection module merges a tuned LightGBM classifier with an autoencoder-based unsupervised detector. Across multiple attack categories, the combined model achieved an average detection accuracy of 95% and an AUC-ROC of 0.973 while keeping the memory footprint small enough for LoRa-class devices. We also design a Zero Trust authentication scheme based on an optimized Proof-of-Authority blockchain model and a low-cost mutual authentication protocol. The evaluation shows an average authentication delay of about 187 ms and system availability above 99.9%. Although the full framework introduces an energy overhead of approximately 18%, the projected device lifetime remains more than seven years, which is significantly longer than what existing blockchain-based IoT authentication systems provide.

Multi-hop Long Range Wide Area Network (LoRaWAN) deployments supporting mission-critical applications face serious limitations when static routing protocols are unable to adapt to changing network conditions or routing-layer security threats. Our analysis indicates that static routing protocols can experience up to 43% degradation in packet delivery ratio under selective forwarding attacks compared to normal operating conditions. Current approaches offer limited capability to autonomously respond to topology changes, device failures, or varying traffic loads. This paper introduces a game-theoretic adaptive routing framework combined with reinforcement learning to dynamically optimize path selection in multi-hop LoRaWAN networks. The proposed framework operates entirely on the network server, while end devices perform only lightweight state reporting, ensuring compatibility with Class A LoRaWAN devices. Trust information derived from security monitoring is incorporated to balance performance objectives with security-aware routing decisions. The approach is evaluated through large-scale simulations with network sizes ranging from 100 to 2000 nodes. The results show consistent improvements in packet delivery ratio and latency compared to static routing across the evaluated scenarios, while maintaining stable performance under attack conditions. Attack detection achieves high precision (92.0%) with a low false positive rate (0.65%), with an average detection time of 26.6±6.8 seconds. Integration overhead remains limited, resulting in minimal additional energy consumption.

This paper proposes a machine learning (ML)-driven framework for Open Radio Access Networks (ORAN) to address security, Ultra-Reliable Low-Latency Communication (URLLC), and energy efficiency challenges. By integrating Isolation Forest for security, One-Class SVM for URLLC, and Generative Adversarial Networks (GANs) for cost optimization, the framework dynamically adapts to network traffic shifts while adhering to ORAN’s open architecture principles. Evaluations on a dataset of 72,000 instances with 21 features demonstrate 92% attack detection, 15% latency reduction, and 20% energy savings, validated through rigorous metrics such as precision-recall the area under the curve (AUC) and synthetic cost modeling. The proposed solution represents a significant advancement in the field of intelligent network management, offering a comprehensive approach to the most pressing challenges in modern telecommunications infrastructure. Through extensive simulations and real-world testing, we validate that our framework outperforms existing approaches in terms of attack detection accuracy, latency optimization, and energy efficiency without compromising the essential interoperability principles of the ORAN architecture.