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Background: 5G technology will revolutionize wireless communications, emphasizing spectrum efficiency. Pervasive device connectivity requires unprecedented spectrum resources to allow flawless Internet of Things (IoT) communication. Thus, dynamic spectrum sharing (DSS) is essential for 5G and IoT cohabitation without spectral congestion. Objective: This study examines how DSS improves 5G network speed and efficiency, especially in an IoT-dominated context. The main goals are strategies and methods to maximize spectrum sharing among 5G and IoT devices for resilient, scalable, and uninterrupted connectivity. Methods: Using simulation and testing, this study assesses 5G DSS algorithms and models for latency, throughput, and reliability. The current study compares conventional spectrum allocation and dynamic sharing strategies in various IoT applications. Results: 5G networks with DSS have improved spectrum usage and performance. Optimized algorithms reduce latency and increase throughput even in IoT-heavy situations. Conclusion: DSS is vital to strengthening 5G networks for IoT applications. DSS supports and expands IoT ecosystems by reducing spectrum scarcity and improving network performance, laying the groundwork for the 5G era's rush of connected devices and applications. DSS algorithms need further research to meet changing technology and application needs.

In the fourth industrial revolution robots, voice control interfaces and 5G networks play significant role towards advanced real-time autonomous control systems in an industrial domain as well as a social domain. With Arduino-based robots controlled by voice commands for more natural human-machine communications and 5G enabling low latency, high-reliability connectivity as degradation due to typical interference does not matter. This article explores the efficiency and accuracy of voice-controlled interfaces to function robotic attributes via ultra-low latency associated with 5G network links intended for Arduino robots. Methods: An Arduino robot equipped with a voice-controlled interface that utilizes speech recognition and Natural Language Processing (NLP) to understand and carry out commands. This setup was trialed on a 5G network to guarantee connectivity and operational efficiency. Various scenarios and loads were examined in controlled experiments to evaluate response times, precision, and reliability. The 5G has lower latency response time, which led to better command execution. Accurate interpretation of voice commands and real-time adaption to robotic operations for continuous effective behavior across operating conditions and environmental scenarios are demonstrated to highlight the robustness of voice control interface. The combination of voice-activated Arduino robots and 5G networks has paved the way for industrial automation and smart applications, showcasing an innovative approach to human-robot interaction and collaborative robotics. Nevertheless, it is important to analyze the scalability, security, and ethical considerations of these technologies on a wider scale in different domains and applications to guarantee their safe, effective, and equitable implementation.

The growing frequency of natural and manufactured catastrophes necessitates urgently developing efficient response systems to lessen their effects and save lives. This article provides a complete disaster response architecture based on the Internet of Things (IoT) and 5G technologies. The suggested paradigm tackles fundamental flaws in emergency response systems, such as delay, data overload, and fragmented communication routes. The framework attempts to develop a more coordinated, real-time response mechanism by exploiting the high-speed, low-latency capabilities of 5G networks and IoT devices' sensing and data-gathering capabilities. The framework's key components are as follows: advanced sensing capabilities for immediate and accurate data collection from affected areas, centralized data analytics to assess the severity and prioritize actions, secure and real-time communication channels for all stakeholders, and dynamic resource allocation based on evolving scenarios. Theseingredients work together to not only give instant comfort but also to initiate long-term healing processes quickly. The article examines the framework's technical design, data security problems, and prospective applications, including proof-of-concept demos and case studies. The integrated approach optimizes decision-making and increases the overall efficacy of disaster management techniques by making the best use of available resources. This study adds to continuing efforts to harness new technology for societal well-being and lays the groundwork for future disaster response system advancements.

Major depressive disorder (MDD) is the most common non-motor manifestation of Parkinson’s disease (PD) affecting 50% of patients. However, little is known about the cognitive correlates of MDD in PD. Using a computer-based cognitive task that dissociates learning from positive and negative feedback, we tested four groups of subjects: (1) patients with PD with comorbid MDD, (2) patients with PD without comorbid MDD, (3) matched patients with MDD alone (without PD), and (4) matched healthy control subjects. Furthermore, we used a mathematical model of decision-making to fit both choice and response time data, allowing us to detect and characterize differences between the groups that are not revealed by cognitive results. The groups did not differ in learning accuracy from negative feedback, but the MDD groups (PD patients with MDD and patients with MDD alone) exhibited a selective impairment in learning accuracy from positive feedback when compared to the non-MDD groups (PD patients without MDD and healthy subjects). However, response time in positive feedback trials in the PD groups (both with and without MDD) was significantly slower than the non-PD groups (MDD and healthy groups). While faster response time usually correlates with poor learning accuracy, it was paradoxical in PD groups, with PD patients with MDD having impaired learning accuracy and PD patients without MDD having intact learning accuracy. Mathematical modeling showed that both MDD groups (PD with MDD and MDD alone) were significantly slower than non-MDD groups in the rate of accumulation of information for stimuli trained by positive feedback, which can lead to lower response accuracy. Conversely, modeling revealed that both PD groups (PD with MDD and PD alone) required more evidence than other groups to make responses, thus leading to slower response times. These results suggest that PD patients with MDD exhibit cognitive profiles with mixed traits characteristic of both MDD and PD, furthering our understanding of both PD and MDD and their often-complex comorbidity. To the best of our knowledge, this is the first study to examine feedback-based learning in PD with MDD while controlling for the effects of PD and MDD.