INTRODUCTION: Mixed methods are used to holistically understand the "what", "how", and "why" questions within a single study by integrating quantitative and qualitative methods. Although this approach has demonstrated value in other disciplines, their use in applied clinical informatics research remains largely unexplored. This methodological review characterized the types of informatics intervention studies that used mixed methods designs and the specific methods of evaluation and reporting. MATERIALS AND METHODS: On December 13, 2024, we searched Embase, MEDLINE, PubMed, CINAHL and PsycINFO for studies that reportedly used a mixed methods design to study the development or evaluation of clinical informatics interventions. We developed themes in topics studied, designs used, and reporting issues. Quality assessment was conducted using the Mixed Methods Appraisal Tool. RESULTS: We included 54 studies. Despite being described as mixed methods, 14 studies (25.9%) lacked the integration of quantitative and qualitative data. Of the remaining studies, convergent study designs were commonly used. Mixed methods were predominantly used during pre-implementation and post-implementation evaluations. Quality issues included non-representative samples and non-reporting of qualitative approaches and paradigms. DISCUSSION AND CONCLUSIONS: The use of mixed methods to study clinical informatics development and implementation is uncommon. This review confirmed that reporting and design problems found in other disciplines extend to informatics. Calls to action include a need to disseminate mixed methods guidance within the informatics community and offer mixed methods training in graduate programs. We also synthesized an initial reporting checklist from the literature. Our findings offer baseline levels to assess educational efforts.

With the advent of sixth-generation (6 G) networks, efficient management of bandwidth and transmit power has become an increasingly important concern. With increasing network complexity and uncertainty, traditional optimization methods face multiple challenges. Large language models (LLMs) offer a flexible alternative to these methods due to their adaptability across varied conditions. In this study, we formulate a resource allocation problem involving multiple device-to-device (D2D) pairs and develop an LLM-based approach to maximize either energy efficiency (EE) or spectral efficiency (SE). We evaluate three LLM adaptation methods, namely fine-tuning, retrieval-augmented generation (RAG), and few-shot prompting, and identify fine-tuning as the most effective for the study under consideration. Finetuning achieves 94.24 % of optimal SE and 86.51 % of optimal EE, outperforming RAG and few-shot prompting in terms of performance. To further test its robustness, we examine finetuning under varied path loss distributions and increased system complexity, and observe how the LLM's predictions respond to these conditions. Additionally, fine-tuned models like Phi-3 Mini achieve inference times of less than one second (0.66s) and have a considerable time complexity advantage over exhaustive search, RAG, and few-shot prompting.

ARA is an at-scale Platform for Advanced Wireless Research (PAWR), specifically tailored to the unique community, application, and economic context of rural regions. It features the first-of-its-kind real-world implementation of long-distance, high-capacity wireless backhaul and access systems spanning over 30 km in diameter. Leveraging both software-defined radios and programmable Commercial Off-The-Shelf (COTS) systems, ARA orchestrates the wireless resources alongside the networking and compute resources for enabling end-to-end experiments involving user equipment, base stations, edge computing, and cloud infrastructure. Such an integration facilitates the coevolution of rural-focused wireless innovation and applications, while helping to advance the frontiers of advanced Next-G wireless systems such as Open RAN. As of summer 2024, ARA is publicly accessible with 7 base stations (BSes) and over 30 user equipment (UEs). In this demo, we share advanced wireless research experiments enabled by ARA, involving MU-MIMO in TV White Space (TVWS) bands, long-range mmWave and microwave backhaul communications, and open-source 5G NR protocol stacks such as srsRAN and OpenAirInterface (OAI).

Time synchronization is a critical component in network operation and management, and it is also required by Ultra-Reliable, Low-Latency Communications (URLLC) in next-generation wireless systems such as those of 5G, 6G, and Open RAN. In this context, we design and implement AraSync as an end-to-end time synchronization system in the ARA wireless living lab to enable advanced wireless experiments and applications involving stringent time constraints. We make use of Precision Time Protocol (PTP) at different levels to achieve synchronization accuracy in the order of nanoseconds. Along with fiber networks, AraSync enables time synchronization across the AraHaul wireless x-haul network consisting of long-range, high-capacity mmWave and microwave links. In this paper, we present the detailed design and implementation of AraSync, including its hardware and software components and the PTP network topology. Further, we experimentally characterize the performance of AraSync from spatial and temporal dimensions. Our measurement and analysis of the clock offset and mean path delay show the impact of the wireless channel and weather conditions on the PTP synchronization accuracy.

Software-defined networking (SDN) has introduced flexibility and ease of management in the network. SDN offers centralized and fine-grained control over the web it has become an industry trend and is adopted in various areas of communication and networking. Controllers in SDN play a pivotal role, and their performance is key to the stability and scalability of the network. This study evaluates different SDN controllers i.e., NOX, POX and RYU. The performance of the controllers is analyzed based on Quality of Service (QoS) parameters, including Flow Setup Latency, Round Trip Time (RTT), Initial Ping Delay (IPD), Jitter, Throughput, and TCP & UDP Bandwidth. Different-sized networks are simulated, by varying the number of switches in linear and tree topologies and hosts in a single topology, using Mininet SDN simulator. Our results show that the performance of RYU is very consistent and shows small variation in the performance with increasing network size and traffic loads as compared to NOX and POX.

With recent advancements in software-defined vehicles, over-the-air (OTA) software updates are crucial to roll out new software and patches for connected vehicles. Traditionally, outdated vehicles are recalled by the manufacturers, however, owners are notoriously difficult to reach with recall notices. Also, organizational and procedural challenges result in many outdated vehicles with insecure and unstable software. In this paper, we propose a dissemination framework for OTA updates using a federated fog environment. Pushing the update to all vehicles via the fog nodes may congest the network, leading up to a single-point failure for update dissemination, as well as, disruption to other installed services at the fog nodes. We propose a software-defined mechanism to select a pivot, which gets the update from the fog node and streams it to other vehicles. Moreover, a timer-barrier mechanism is proposed to identify any malicious vehicles that are barred from participating in pivot selection. The experimental evaluation demonstrates that the proposed scheme improves network convergence time by 40% with 95% node trustworthiness.

Due to the increasing trend in IP traffic, the placement of Data Centers (DCs) at network nodes has become a hot research topic. A proper DCs placement translates in reduced power consumption of overall network. The paper scope is to find the best placement of “k” DCs nodes out of “N” total nodes to reduce power consumption. To solve the problem, we propose two heuristics: EoDCP, based on Estimation of Distribution Algorithm (EDA), and MaxN-MinL. An exhaustive search based ESDCP algorithm is used as a lower bound to compare the performance of EoDCP and MaxN-MinL. Moreover, electronic traffic grooming technique is employed to further reduce the total network power consumption. A 20-Node Random network and a 17-Node German network are used to perform comparison of proposed heuristics. Performance of EoDCP algorithm is far better than those of MaxN-MinL, and is similar to the optimal solution obtained via ESDCP. Finally, using electronic traffic grooming improves power savings up-to 15% in the two considered topologies.

Prior provisioning of optical source technologies have techno-economic importance for the operator during the design and planning of optical network architectonics. Advancement towards the latest technology paradigm such as Elastic Optical Networks (EONs) and Software Defined Networking (SDN) open a gateway for a flexible and re-configurable optical network architecture. In order to achieve the required degree of flexibility, a flexible and dynamic behaviour is required both at the control and data plane. In this regards, SDN-enabled flexible optical transceivers are proposed to provide the required degree of flexibility. Sliceable Bandwidth Variable Transponders (SBVTs) is one of the recent type of flexible optical transceivers. Based on the type/technology of optical carrier source, the SBVTs are categorized into two types; Multi-Laser SBVT (ML-SBVT) and Multi-wavelength SBVT (MW-SBVT). Both architectures have their own pros and cons when it comes to accommodate traffic request. In this paper, we propose a selection model for the SBVTs before its actual deployment in the network. The selection model consider various design and planning phase network characteristics. In addition to this selection model, the comparison of centralized Flex-OCSM architecture is also presented with the already discussed SBVT types. The analysis in this work is performed on random network (20 nodes) and the German Network (17 nodes).

Abstract Cloud adoption has significantly increased using the infrastructure-as-a-service (IaaS) paradigm, in order to meet the growing demands of computing, storage and networking, in small as well as large enterprises. Different vendors provide their customized solutions for OpenStack deployment on bare metal or virtual infrastructure. Among these many available IaaS solutions, OpenStack stands out as being an agile and open-source platform. However, its deployment procedure is a time-taking and complex process with a learning curve. This paper addresses the lack of basic infrastructure automation in almost all of the OpenStack deployment projects. We propose a flexible framework to automate the process of infrastructure bring up for deployment of several OpenStack distributions, as well as resolving dependencies for a successful deployment. Our experimental results demonstrate the effectiveness of the proposed framework in terms of automation status and deployment time, that is, reducing the time spent in preparing a basic virtual infrastructure by four times, on average.

Flexible-grid optical networks are the most convincing candidate for the evolution of backbone optical networks thanks to their high spectral efficiency and flexibility. We propose an original approach to the logical topology design (LTD) problem in the offline planning phase. We deal with the LTD problem using heuristic algorithms incorporating a detailed transmission layer model. Several heuristic algorithms, allocating traffic demands in different orderings, are considered for lightpath provisioning. Traffic ordering schemes are mainly based on two parameters: traffic demands capacity and lightpath physical route length. Through simulative analyses, we provide a performance comparison of different heuristics, using parameters like spectral efficiency, amount of blocked traffic and total number of transceivers. We also show the importance of integrating a detailed physical layer modeling in the network design phase.

Energy-efficient design of flexible-grid networks is investigated. We focus on the design of the logical layer, usually disregarded when dealing with flexible-grid networks. More precisely, we evaluate the impact of introducing an energy-aware electronic traffic grooming in flexible-grid networks design. We propose two greedy heuristics for the network design, one exploiting traffic grooming, and we compare their energy efficiency. Results have been retrieved for several randomly generated networks of different size, with different connectivity, average physical link length and traffic scenarios. Significant energy savings can be achieved for low traffic loads and large network size when performing traffic grooming.