Communication Capabilities Research Articles

Team Tracker: A Project Management Tool

This paper introduces "Team Tracker," a project management tool developed using Asana and Next.js. The tool integrates key functionalities such as audio and video calling, real-time task tracking, and an embedded browser, creating an all-in-one solution for remote team collaboration. The objective of Team Tracker is to streamline workflow by eliminating the need for multiple platforms, thus improving team efficiency and communication. A comparative analysis with existing tools like Jira and Slack demonstrates how Team Tracker addresses the gaps in current project management solutions by offering enhanced real-time communication and task management capabilities. The system's architecture, challenges encountered during development, expected outcomes, and potential future enhancements are also discussed. Team Tracker is poised to provide a comprehensive, scalable solution for modern project management needs. Keywords: project management, Asana, Next.js, real-time communication, task tracking, Team Tracker.

Research on Chinese Universities' Communication Work for Global Southern Z-Generation Youth under the Theory of "The Bias of Communication" —A Case Study Based on Z University's Communication with Africa

At present, the significant role of strengthening international communication efforts towards the Global South is increasingly highlighted, with communication work targeting Z-Generation youth being a top priority in promoting the construction of international communication capabilities. The rise and development of the Theory of The Bias of Communication holds certain contemporary value. In the new era, China's communication work towards the Global Southern Z-Generation youth should apply this theory critically, which is specifically manifested in facing China's modernization development strategy and the concept of a community with a shared future, emphasizing the status of communication media and the renewal of new media, and promoting the shift from media dominance to content as king. Faced with challenges such as China's international communication capacity does not match the level of its comprehensive national strength and the disadvantages of “time-biased” and “space-biased” media in a late-developing context, based on the practical experience of Z University's communication work with African Z-Generation youth, Chinese universities' international communication work for Global Southern Z-Generation youth should create a fluid channel for the transformation of time and space on the basis of balancing time and space, thereby actively responding to national strategic needs. It should undertake the mission of the times to tell Chinese stories and spread Chinese voices, and build an international communication discourse system with Chinese characteristics, becoming an important position for comprehensively improving international communication efficiency.

Assessing the accuracy and reproducibility of ChatGPT for responding to patient inquiries about otosclerosis.

Patients increasingly use chatbots powered by artificial intelligence to seek information. However, there is a lack of reliable studies on the accuracy and reproducibility of the information provided by these models. Therefore, we conducted a study investigating the ChatGPT's responses to questions about otosclerosis. 96 otosclerosis-related questions were collected from internet searches and websites of professional institutions and societies. Questions are divided into four sub-categories. These questions were directed at the latest version of ChatGPT Plus, and these responses were assessed by two otorhinolaryngology surgeons. Accuracy was graded as correct, incomplete, mixed, and irrelevant. Reproducibility was evaluated by comparing the consistency of the two answers to each specific question. The overall accuracy and reproducibility rates of GPT-4o for correct answers were found to be 64.60% and 89.60%, respectively. The findings showed correct answers for accuracy and reproducibility for basic knowledge were 64.70% and 91.20%; for diagnosis & management, 64.0% and 92.0%; for medical & surgical treatment, 52.95% and 82.35%; and for operative risks & postoperative period, 75.0% and 90.0%, respectively. There were no significant differences found between the answers and groups in terms of accuracy and reproducibility (p = 0.073 and p = 0.752, respectively). GPT-4o achieved satisfactory accuracy results, except in the diagnosis & management and medical & surgical treatment categories. Reproducibility was generally high across all categories. With the audio and visual communication capabilities of GPT-4o, under the supervision of a medical professional, this model can be utilized to provide medical information and support for otosclerosis patients.

Perceived Health Risks Associated with Smartphone Use Among Health College Students at Qassim University in Saudi Arabia

Background Smartphones have evolved into a fusion of traditional personal digital assistants and cellular phones, integrating mobile communication capabilities with features parallel to those of a handheld computer. Objective This study aimed to assess the health risks associated with smartphone use and the prevalence and nature of observed health issues among health college students at Qassim University in Saudi Arabia. Methods Employing a descriptive cross-sectional research design, this study surveyed a convenience sample of 511 health college students at Qassim University. The data collection instrument comprised two sections: Section 1 assessed sociodemographic, academic, and health-related information, while Section 2 evaluated the health risks as perceived by students. Results The majority of the participants were women (71%) and were studying nursing (26%). A significant proportion (39.9%) reported blurred vision, while 38.6% indicated a lack of adequate sleep. Notably, no significant association was observed between the health complaints and sociodemographic information. The analysis also revealed no significant relationship between health complaints and smartphone usage patterns. Conclusion The findings highlight a critical need for further research to investigate the factors contributing to the effect of smartphone use on the physiological well-being of university students. Such research is essential for developing targeted interventions to mitigate the associated health risks.

Cluster consensus of finite-field networks with stochastic communication topology

Most of the multi-agent systems (MASs) in real life are with limited storage and communication capabilities; studies on such systems, including consensus problem, however have been mainly based on real number field models. In this article, the cluster consensus problem of MASs with stochastic communication topology over finite-field known as the cluster consensus of finite-field networks (FFNs), is considered. Using the semi-tensor product of matrices, the dynamics of FFNs with stochastic communication topology are equivalently converted into a logical algebraic form, which facilitates further studies. Then a permutation system is introduced. The relationship between cluster consensus of FFNs with stochastic communication topology and the set stability with probability one (SSPO) of the permutation system is revealed. In such an approach, some verifiable criteria can be derived for the cluster consensus of FFNs with stochastic communication topology, without requesting the connectivity of the whole network. Finally, an example is presented to validate the main results.

Exosome Cargo in Neurodegenerative Diseases: Leveraging Their Intercellular Communication Capabilities for Biomarker Discovery and Therapeutic Delivery

The inexorable progression of neurodegenerative diseases (NDs), including Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, and multiple sclerosis, is closely related to irreversible brain decline. Accurately characterizing pathophysiological features and identifying reliable biomarkers for early diagnosis and optimized treatment are critical. Hindered by the blood–brain barrier (BBB), obtaining sensitive monitoring indicators for disease progression and achieving efficient drug delivery remain significant challenges. Exosomes, endogenous nanoscale vesicles that carry key bioactive substances, reflect the intracellular environment and play an important role in cell signaling. They have shown promise in traversing the BBB, serving dual roles as potential biomarkers for NDs and vehicles for targeted drug delivery. However, the specific mechanisms by which exosome influence NDs are not fully understood, necessitating further investigation into their attributes and functionalities in the context of NDs. This review explores how exosomes mediate multifaceted interactions, particularly in exacerbating pathogenic processes such as oxidative stress, neuronal dysfunction, and apoptosis integral to NDs. It provides a comprehensive analysis of the profound impact of exosomes under stress and disease states, assessing their prospective utility as biomarkers and drug delivery vectors, offering new perspectives for tackling these challenging diseases.

행동기준 평정척도(BARS)를 적용한 대학생 핵심역량 측정도구 개발 및 타당화 연구: D대학의 의사소통역량을 중심으로

Objectives This study attempted to develop the core competency’s measurement tool and secure its validity by focusing on the D University’s communication capabilities and applying the behaviorally anchored rating scale based on experiences or behaviors closely related to students’ university education and life. Methods This study applied appropriate research methods according to a six-step’s systematic research procedure, including core competency analysis and competency modeling, analysis of behavioral characteristics of excellent students through FGI for professors and BEI for students, development of core competency’s measurement tool through expert council, validation through expert delphi survey, pilot test and analysis, and finalizations stage of system, definition, and measurement tool. Results The research results are as follows. First, the core competency’s system and definition with a high consistent were derived through a systematic competency modeling process. Second, the behavioral characteristics of hyperformers(high-performing students) were analyzed through interview with professors and students and reflected in the behaviorally anchored rating scale. Third, the core competency’s measurement tool(draft) with a high consistent were confirmed. Fourth, a core competency measurement tool(revised version) was tentatively derived by verifying its validity through two expert delphi surveys. Fifth, a pilot test was conducted to improve effectiveness of the core competency’s measurement tool, and reliability and validity were secured. Sixth, the finalizations stage was completed, and the system, definition, and measurement tool of core competencies were confirmed. Conclusions By developing a core competency’s measurement tool based on the behaviorally anchored rating scale focusing on D University’s communication capabilities and then revealing the process of securing reliability and validity, It could play a significant role in suggesting alternative methods to objectively measure the core competencies of each university.

Adaptive Sensing for Internet of Robotic Things Platforms With Integrated Sensing, Computing, and Communication Capabilities

Adaptive Sensing for Internet of Robotic Things Platforms With Integrated Sensing, Computing, and Communication Capabilities

Project Monitoring Based on View of All Project Partners At Digital Project Management Platforms

In the dynamic landscape of digital project management, effective project monitoring is critical for successful project outcomes. The emergence of digital project management platforms has revolutionized the way projects are monitored, enabling real-time collaboration and data-driven decision-making. Periodic site visits and gathering input from all project stakeholders are crucial for ensuring clear understanding of project progress by states institutional. With digital project management systems, on-site progress should be monitored in real-time and inclusive of all project stakeholders. The research investigates how collaborative monitoring fosters project progress of transparency and active engagement among project partners. By providing real-time visibility into project progress, tasks, and milestones, these platforms empower stakeholders with up-to-date information, enabling agile decision-making and prompt risk mitigation. This study explores how digital project management platforms integrate site monitoring evaluations and reporting tools, providing valuable data-driven insights into project performance. By considering periodic analytics, project partners can improve resource allocation, decision making process against possible bottlenecks at construction stages and boost productivity and inform states institutional to monitor project progress closely. Furthermore, the research examines the influence of these platforms on communication dynamics, promoting communication and efficient information exchange among project partners. Such enhanced communication capabilities result in improved team cohesion and collaboration, driving the project forward with synchronized efforts. In this study, the focus lies on tracking the progress of contractual agreements through digital platforms, enabling all stakeholders to monitor advancements. Specifically, the investigation centers on how the designated contractor responsible for project execution and the consulting firm appointed by the project owner can effectively track project progress on a periodic basis using digital platforms. In the context of transportation projects in Turkey, a digital project tracking system has been developed for the Ministry of Transport and Infrastructure, through which data obtained from on-site visits and consultant opinions regarding project advancements are shared with the Ministry. Subsequently, the responsible contracting authority, guided by the consultant's feedback, can respond and evaluate progress accordingly. This approach ensures that project advancements are accurately monitored by all stakeholders of the project. To do so, this study investigates the utilization of the prepared digital platform to facilitate rapid and precise contract monitoring by all project stakeholders, encompassing the opening information and assessment evaluations based on stakeholders' perspectives.

Identifying Tampered Radio-Frequency Transmissions in LoRa Networks Using Machine Learning.

Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference and signal manipulation, which can undermine both data integrity and security. This paper presents an innovative method for identifying tampered radio frequency transmissions by employing five sophisticated anomaly detection algorithms-Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure. The novelty of this work lies in applying image-based tampered frequency techniques with these algorithms, offering a new perspective on securing LoRa transmissions. We generated a dataset of over 26,000 images derived from real-world experiments with both normal and manipulated frequency signals by splitting video recordings of LoRa transmissions into frames to thoroughly assess the performance of each algorithm. Our results demonstrate that Local Outlier Factor achieved the highest accuracy of 97.78%, followed by Variational Autoencoder, traditional Autoencoder and Principal Component Analysis at 97.27%, and Isolation Forest at 84.49%. These findings highlight the effectiveness of these methods in detecting tampered frequencies, underscoring their potential for enhancing the reliability and security of LoRa networks.

Adaptive collective responses to local stimuli in anonymous dynamic networks

We develop a framework for self-induced phase changes in programmable matter in which a collection of agents with limited computational and communication capabilities can collectively perform appropriate global tasks in response to local stimuli that dynamically appear and disappear. Agents are represented by vertices in a dynamic graph G whose edge set changes over time, and stimuli are placed adversarially on the vertices of G where each agent is only capable of recognizing a co-located stimulus. Agents communicate via token passing along edges to alert other agents to transition to an Aware state when stimuli are present and an Unaware state when the stimuli disappear. We present an Adaptive Stimuli Algorithm that can handle arbitrary adversarial stimulus dynamics, while an adversary (or the agents themselves) reconfigures the connections (edges) of G over time in a controlled way. This algorithm can be used to solve the foraging problem on reconfigurable graphs where, in addition to food sources (stimuli) being discovered, removed, or shifted arbitrarily, we would like the agents to consistently self-organize, using only local interactions, such that if the food remains in a position long enough, the agents transition to a gather phase in which many collectively form a single large component with small perimeter around the food. Alternatively, if no food source has existed recently, the agents should undergo a self-induced collective phase change and switch to a search phase in which they distribute themselves randomly throughout the graph to search for food. Unlike previous approaches to foraging, this process is indefinitely repeatable, withstanding competing broadcast waves of state transition that may interfere with each other. Like a physical phase change, such as the ferromagnetic models underlying the gather and search algorithms used for foraging, microscopic changes in the environment trigger these macroscopic, system-wide transitions as agents share information and respond locally to get the desired collective response.

Multi-Layered Unsupervised Learning Driven by Signal-to-Noise Ratio-Based Relaying for Vehicular Ad Hoc Network-Supported Intelligent Transport System in eHealth Monitoring.

Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations' 2030 target. Additionally, this framework's robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper's contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring.

Lung cancer cell-derived exosomes: progress on pivotal role and its application in diagnostic and therapeutic potential.

Lung cancer is frequently detected in an advanced stage and has an unfavourable prognosis. Conventional therapies are ineffective for the treatment of metastatic lung cancer. While certain molecular targets have been identified as having a positive response, the absence of appropriate drug carriers prevents their effective utilization. Lung cancer cell-derived exosomes (LCCDEs) have gained attention for their involvement in the development of cancer, as well as their potential for use in diagnosing, treating, and predicting the outcome of lung cancer. This is due to their biological roles and their inherent ability to transport biomolecules from the donor cells. Lung cancer-associated cell-derived extracellular vesicles (LCCDEVs) have the ability to enhance cell proliferation and metastasis, influence angiogenesis, regulate immune responses against tumours during the development of lung cancer, control drug resistance in lung cancer treatment, and are increasingly recognised as a crucial element in liquid biopsy evaluations for the detection of lung cancer. Therapeutic exosomes, which possess inherent intercellular communication capabilities, are increasingly recognised as effective vehicles for targeted drug delivery in precision medicine for tumours. This is due to their exceptional biocompatibility, minimal immunogenicity, low toxicity, prolonged circulation in the bloodstream, biodegradability, and ability to traverse different biological barriers. Currently, multiple studies are being conducted to create new means of diagnosing and predicting outcomes using LCCDEs, as well as to develop techniques for utilizing exosomes as effective carriers for medication delivery. This paper provides an overview of the current state of lung cancer and the wide range of applications of LCCDEs. The encouraging findings and technologies suggest that the utilization of LCCDEs holds promise for the clinical treatment of lung cancer patients.

A Framework for the Communication Capabilities of scientists

ABSTRACT The ability to communicate science effectively is a key attribute for science graduates, and considerable work has been done to establish the techniques and skills associated with effective communication. What is less clear however, is the learning progression students traverse as they develop proficiency in science communication. In this study we evaluated aspects of university-level student science communication capabilities pre- and post- explicit teaching of core science communication capabilities. We used this data to identify the characteristics of early science communication learners and synthesise a robust learning progression Framework for Communication Capabilities (FCC). The FCC was created to support educators in identifying the characteristics of their learners to facilitate the constructive alignment of learning objectives, curriculum, and assessment rubrics for learners of science communication through early phases of their development.

Exploring the protective capabilities developed by hospital nurses to deflect workplace violence: a qualitative study in Taiwan

BackgroundWorkplace violence is a serious safety hazard in the healthcare sector and has attracted much attention worldwide, especially for nursing staff. Equipping nursing personnel with protective capabilities for workplace violence can reduce the risk they face in work settings. This study explored scenarios of common violent patient–nurse conflicts in the workplace of hospital nursing staff and their capabilities for de-escalating such conflicts.MethodsWe used a qualitative content analysis for this study. Qualitative interviews were conducted with 21 nurses in two teaching hospitals in New Taipei City, Taiwan, until data saturation was reached. The data were transcribed, encoded, and analyzed and similar concepts were grouped under the same category.ResultsFour categories of workplace violence scenarios common to hospital nursing staff were identified: unreasonable requests, caring for high-risk patients, long waiting times for medical consultation, and close contact when caring for patients. Two protective capabilities were recognized: communication and interpersonal capabilities and problem-solving skills. Each scenario may require a different combination of abilities.ConclusionOur findings suggest that a violence-prevention training program could be designed for various workplace violence scenarios to enhance nurses’ abilities to de-escalate workplace violence in hospitals.

Sensor-Aware Data Imputation for Time-Series Machine Learning on Low-Power Wearable Devices

Wearable devices that have low-power sensors, processors, and communication capabilities are gaining wide adoption in several health applications. The machine learning algorithms on these devices assume that data from all sensors are available during runtime. However, data from one or more sensors may be unavailable due to energy or communication challenges. This loss of sensor data can result in accuracy degradation of the application. Prior approaches to handle missing data, such as generative models or training multiple classifiers for each combination of missing sensors are not suitable for low-energy wearable devices due to their high overhead at runtime. In contrast to prior approaches, we present an energy-efficient approach, referred to as Sensor-Aware iMputation (SAM), to accurately impute missing data at runtime and recover application accuracy. SAM first uses unsupervised clustering to obtain clusters of similar sensor data patterns. Next, it learns inter-relationship between clusters to obtain imputation patterns for each combination of clusters using a principled sensor-aware search algorithm. Using sensor data for clustering before choosing imputation patterns ensures that the imputation is aware of sensor data observations. Experiments on seven diverse wearable sensor based time-series datasets demonstrate that SAM is able to maintain accuracy within 5% of the baseline with no missing data when one sensor is missing. We also compare SAM against generative adversarial imputation networks (GAIN), transformers, and k-nearest neighbor methods. Results show that SAM outperforms all three approaches on average by more than 25% when two sensors are missing with negligible overhead compared to the baseline.

A systematic literature review on the role of UAV-enabled communications in advancing the UN’s sustainable development goals

Unmanned Aerial Vehicles (UAVs), commonly known as drones, show great potential in advancing the United Nations (UN) Sustainable Development Goals (SDGs), a collection of 17 worldwide targets outlined by the United Nations (UN) to tackle diverse social, economic, and environmental issues and foster sustainable development by 2030. As per the Federal Aviation Administration (FAA), UAVs can significantly improve areas of sustainability like wildlife and wildfire monitoring, precision agriculture, as well as healthcare. Furthermore, drones with communication capabilities are seen as key elements of non-terrestrial networks, and as enabling technologies for future 6G communication networks. This systematic literature review focuses on the role of UAV-enabled communications to achieve the SDGs. Our study is based on a thorough selection and analysis of scientific articles. Initially, we performed a broad search across three major databases: IEEE Xplore, Web of Science, and Scopus, yielding a set of 10,499 papers. Following a strict selection methodology (Preferred Reporting Items for Systematic Review and Meta-Analysis - PRISMA), we narrowed this set down to 28 key journal articles. Our analysis highlights six global targets that greatly benefit from UAV-enabled aerial communication. In order of impact, these are: “Sustainable Cities and Communities,” where UAVs improve security and disaster management; “Life on Land,” where drones help with wildlife monitoring and reforestation; “Industry, Innovation, and Infrastructure,” where UAVs (Unmanned Aerial Vehicles) aid in infrastructure inspection; “No Hunger,” where drones improve agricultural practices; “‘Good Health and Wellbeing,” where UAVs assist with disease prediction and monitoring; and “Clean water and sanitation,”where drones play a crucial role in efficiently monitoring water quality and distribution. In summary, this paper offers a detailed look at the applications of UAVs in achieving the SDGs through aerial communications. By identifying areas of major impact and spotting gaps and weaknesses in current research, this review article aims to guide future research and policy-making in sustainable drone applications.

Strategic redesign of business processes in the digital age: A framework

Organizations constantly seek ways to improve their business processes by using digital technologies as enablers. However, simply substituting an existing technology with a new one has limited value compared to using the capabilities of digital technologies to redesign business processes. Therefore, process analysts try to understand how the capabilities of digital technologies can enable the redesign of business processes. In this paper, we conduct a systematic literature review and examine 40 case studies where digital technologies were used to redesign business processes. We identified that, within the context of business process improvement, capabilities of digitalization, communication, analytics, digital representation, and connectivity can enable business process redesign. Furthermore, we note that these capabilities enable applying nine redesign heuristics. Based on our review, we map how each capability can facilitate the implementation of specific redesign heuristics. Finally, we illustrate how such a capability-driven approach can be applied to Metaverse as an example of a digital technology. Our mapping and classification framework can aid analysts in identifying candidate redesigns that capitalize on the capabilities of digital technologies.

ECBoA-OFS: An Ensemble Classification Model for Botnet Attacks based on Optimal Feature Selection using CPR in IoT

The rapid growth of the Internet of Things (IoT) has indeed introduced new security challenges, and the proliferation of compromised IoT devices has become a significant concern. Botnet attacks, where multiple corrupted devices are managed by a particular object, have become a widespread threat in IoT environments. These are used for a variety of malicious activities, including distributed DDoS attacks, data breaches, and malware distribution. However, detecting IoT botnets poses several challenges due to the resource constraints inherent in many IoT devices. The limitations in computation, storage, and communication capabilities make it challenging to deploy complex ML and deep learning models directly on these devices. This paper proposes an ensemble classification model ECBoA-OFS (Ensemble Classification for Botnet Attack Prediction using Optimal Feature Selection). It focuses on enhancing the accuracy of botnet attack prediction through the integration of ensemble methods and optimal feature selection. It describes a method for optimal feature selection in the context of analyzing the behavior of BoA and malicious traffic flow features in a network using Central Pivot Ranges (CPR). Feature selection is an important step in machine learning and data analysis because it supports to identification of the most important features for a given problem, thereby improving model performance and interpretation. The extracted features are used for model training and ensemble classification for prediction. To evaluate ECBoA-OFS, the N-BaIoT-2021 dataset consisting of regular IoT network traffic and BoA traffic records of corrupted IoT devices is utilized, considering detection precision, sensitivity, specificity, accuracy, and F1-score. Although all ensemble classifier models achieved better detection accuracy through optimal feature selection, the proposed ECBA-OFS shows better results compared to other ensemble classifier results.

Standardization Trends and Military Communication Applications of Core Technologies of 5G NR

5G New Radio(NR) technology, with its focus on high speed, low latency, and connectivity, introduces crucial advancements like MIMO for enhanced reliability and transmission rates, edge computing for reduced latency, and IAB for extended coverage. However, military communication networks face challenges such as limited capacity, unpredictable mobility and communication dead zones. This paper delves into the trends of the above 5G NR technologies outlined by the 3GPP standard, exploring their potential applications within military infrastructure networks. Our aim is to underscore the benefits of harnessing these technologies in military settings. Additionally, through simulation, we forecast the advantages associated with integrating these core 5G NR technologies, thereby paving the way for enhanced military communication capabilities.