Search Results (1,849)

This paper introduces sustainable engineering systems built using digital twin technology and circular economy principles. This research presents a framework for monitoring, modeling, and making decisions in real timusing virtual replicas of physical products, processes, and systems in product lifecycles. A digital twin was used to show that through a digital twin, waste was reduced by 27%, energy consumption was reduced by 32%, and the resource recovery rate increased to 45%. The proposed approach under the framework employs various machine learning algorithms, IoT sensor networks, and advanced data analytics to support closed-loop flows of materials. The results show how digital twins can enhance progress toward the goals the circular economy sets to identify inefficiencies, predict maintenance needs, and optimize the use of resources. This integration is a promising industry approach that will introduce more sustainable operations and maintain economic viability. Full article

The fast expansion of cloud computing has raised carbon emissions and energy usage in cloud data centers, so creative solutions for sustainable resource management are more necessary. This work presents a new algorithm—Carbon-Aware, Energy-Efficient, and SLA-Compliant Virtual Machine Placement using Deep Q-Networks (DQNs) and Agglomerative Clustering (CARBON-DQN)—that intelligibly balances environmental sustainability, service level agreement (SLA), and energy efficiency. The method combines a deep reinforcement learning model that learns optimum placement methods over time, carbon-aware data center profiling, and the hierarchical clustering of virtual machines (VMs) depending on resource constraints. Extensive simulations show that CARBON-DQN beats conventional and state-of-the-art algorithms like GRVMP, NSGA-II, RLVMP, GMPR, and MORLVMP very dramatically. Among many virtual machine configurations—including micro, small, high-CPU, and extra-large instances—it delivers the lowest carbon emissions, lowered SLA violations, and lowest energy usage. Driven by real-time input, the adaptive decision-making capacity of the algorithm allows it to dynamically react to changing data center circumstances and workloads. These findings highlight how well CARBON-DQN is a sustainable and intelligent virtual machine deployment system for cloud systems. To improve scalability, environmental effect, and practical applicability even further, future work will investigate the integration of renewable energy forecasts, dynamic pricing models, and deployment across multi-cloud and edge computing environments. Full article

Data center virtualization has grown rapidly alongside the expansion of application-based services but continues to face significant challenges, such as downtime caused by suboptimal hardware selection, load balancing, power management, incident response, and resource allocation. To address these challenges, this study proposes a combined machine learning method that uses an MDP to choose which VMs to move, the RF method to sort the VMs according to load, and NSGA-III to achieve multiple optimization objectives, such as reducing downtime, improving SLA, and increasing energy efficiency. For this model, the GWA-Bitbrains dataset was used, on which it had a classification accuracy of 98.77%, a MAPE of 7.69% in predicting migration duration, and an energy efficiency improvement of 90.80%. The results of real-world experiments show that the hybrid machine learning strategy could significantly reduce the data center workload, increase the total migration time, and decrease the downtime. The results of hybrid machine learning affirm the effectiveness of integrating the MDP, RF method, and NSGA-III for providing holistic solutions in VM placement strategies for large-scale data centers. Full article

This article describes a pilot project studying the potential benefits of using virtual reality (VR) in design reviews of cruise ship interiors. The research was conducted as part of a 2020–2022 research project targeting at sustainable shipbuilding methods. It was directly connected to an ongoing cruise ship building project, executed in cooperation with four companies constructing interiors. The goal was to use VR reviews instead of, or in addition to, constructing physical mock-up sections of the ship interiors, with expected improvements in sustainability and stakeholder communication. A number of virtual 3D models were created, imported into a virtual reality environment, and presented to customers. Experiences were collected through interviews and surveys from both the construction companies and customers. The results indicate that VR can be an efficient tool for design reviews. The designs can often be evaluated better in VR than using traditional methods. Material savings are possible by using virtual mock-ups instead of physical ones. However, it was also discovered that the visual rendering capabilities of the used software environment do not provide the realism that would be desired in some reviews. To overcome this limitation, more resources would be needed in preparing the models for VR reviews. Full article

This systematic review examines state-of-the-art artificial intelligence applications in energy systems, assessing their performance, real-world deployments and transformative potential. Guided by PRISMA 2020, we searched Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language studies published between January 2015 and January 2025 that reported novel AI uses in energy, empirical results, or significant theoretical advances and passed peer review. After title–abstract screening and full-text assessment, it was determined that 129 of 3000 records met the inclusion criteria. The methodological quality, reproducibility and real-world validation were appraised, and the findings were synthesised narratively around four critical themes: reinforcement learning (35 studies), multi-agent systems (28), planning under uncertainty (25), and AI for resilience (22), with a further 19 studies covering other areas. Notable outcomes include DeepMind-based reinforcement learning cutting data centre cooling energy by 40%, multi-agent control boosting virtual power plant revenue by 28%, AI-enhanced planning slashing the computation time by 87% without sacrificing solution quality, battery management AI raising efficiency by 30%, and machine learning accelerating hydrogen catalyst discovery 200,000-fold. Across domains, AI consistently outperformed traditional techniques. The review is limited by its English-only scope, potential under-representation of proprietary industrial work, and the inevitable lag between rapid AI advances and peer-reviewed publication. Overall, the evidence positions AI as a pivotal enabler of cleaner, more reliable, and efficient energy systems, though progress will depend on data quality, computational resources, legacy system integration, equity considerations, and interdisciplinary collaboration. No formal review protocol was registered because this study is a comprehensive state-of-the-art assessment rather than a clinical intervention analysis. Full article

Virtual power plants achieve the flexible scheduling and management of power systems by integrating distributed energy resources such as renewable energy sources, energy storage systems, and controllable loads. However, due to the instability of renewable energy generation, load demand fluctuations, and market price uncertainty, virtual power plants face a gigantic challenge operating and participating in electricity markets. First, this paper outlines the functions and uncertainties of virtual power plants; then, it describes the uncertainties of virtual power plants in terms of aggregation, participation in market bidding, and optimal dispatch; finally, it summarizes the review. Full article

This research aims to understand the evolving role of academic libraries, focusing on student perceptions of current services and their vision for the future. Data was collected using a survey at a midwestern research university in the United States. The survey contained both quantitative and qualitative questions. The objective of the survey was to understand the current utilization of library services and students’ future visions for academic libraries. Qualitative and quantitative analysis techniques were utilized as part of the study. Thematic analysis was employed as part of the qualitative analysis, while descriptive and inferential analysis techniques were utilized in the quantitative analysis. The findings reveal that many students use libraries for traditional functions such as studying and accessing resources. There is also an inclination toward digitalization due to convenience, accessibility, and environmental sustainability; however, print materials remain relevant as well. Another finding was a lack of awareness among some students regarding available library services, indicating a need for better marketing and communication strategies. Students envision future libraries as technology-driven spaces integrating artificial intelligence (AI), augmented reality (AR), virtual reality (VR), and innovative collaborative environments. Ethical considerations surrounding AI, including privacy, bias, and transparency, are crucial factors that must be addressed. Some of the actionable recommendations include integrating ethical AI, implementing digital literacy initiatives, conducting ongoing usability and user experience (UX) research within the library, and fostering cross-functional collaboration to enhance library services and student learning. Full article

Testbeds are widely used in experiential learning, providing practical assessments and bridging classroom material with real-world applications. However, manually managing and provisioning student lab environments consumes significant preparation time for instructors. The growing demand for advanced technical skills, such as network administration and cybersecurity, is leading to larger class sizes. This stresses testbed resources and necessitates continuous design updates. To address these challenges, we designed an efficient Environment Management Platform (EMP). The EMP is composed of a set of 4 Command Line Interface scripts and a Web Interface for secure administration and bulk user operations. Based on our testing, the EMP significantly reduces setup time for student virtualized lab environments. Through a cybersecurity learning environment case study, we found that setup is completed in 15 s for each student, a 12.8-fold reduction compared to manual provisioning. When considering a class of 20 students, the EMP realizes a substantial saving of 62 min in system configuration time. Additionally, the software-based management and provisioning process ensures the accurate realization of lab environments, eliminating the errors commonly associated with manual configuration. This platform is applicable to many educational domains that rely on virtual machines for experiential learning. Full article

This research addresses the optimization and deployment of convolutional neural networks for eyeglasses detection on low-power edge devices. Multiple convolutional neural network architectures were trained and evaluated using the FFHQ dataset, which contains annotated eyeglasses in the context of faces with diverse facial features and eyewear styles. Several post-training quantization techniques, including Float16, dynamic range, and full integer quantization, were applied to reduce model size and computational demand while preserving detection accuracy. The impact of model architecture and quantization methods on detection accuracy and inference latency was systematically evaluated. The optimized models were deployed and benchmarked on Raspberry Pi 5 and NVIDIA Jetson Orin Nano platforms. Experimental results show that full integer quantization reduces model size by up to 75% while maintaining competitive detection accuracy. Among the evaluated models, MobileNet architectures achieved the most favorable balance between inference speed and accuracy, demonstrating their suitability for real-time eyeglasses detection in resource-constrained environments. These findings enable efficient on-device eyeglasses detection, supporting applications such as virtual try-ons and IoT-based facial analysis systems. Full article

Background: Chronic pain affects one in five youth globally and is frequently accompanied by mental health challenges that extend into adulthood. Caregivers play a vital role in supporting youth with chronic pain, yet their own mental and physical health needs are often overlooked. While caregiver well-being is linked to child outcomes, few interventions directly address caregivers’ health, especially among those facing systemic barriers. This study explored the lived experiences of caregivers to better understand their unmet needs and inform the co-design of a supportive digital health solution. Methods: We conducted a qualitative exploratory study involving 32 caregivers of youth with chronic pain across Canada and Australia. Semi-structured interviews were co-facilitated by caregiver partners. Thematic analysis was applied to interview data. Results: Two overarching themes were identified: (1) bearing the weight and sacrifice of caregiving and (2) deep interrelatedness and blurred boundaries. Caregivers reported profound emotional, physical, and financial burdens; strained relationships; and social isolation. Many struggled with self-neglect, prioritizing their child’s needs over their own. Fathers’ evolving caregiving roles challenged traditional gender norms, though mothers continued to bear a disproportionate load. Despite challenges, caregivers demonstrated resilience and recognized their well-being as interconnected with their child’s health. Conclusions: Findings underscore the need for systemic investment in caregiver well-being. Digital health solutions, including virtual peer networks, mental health resources, and tailored education, offer scalable, accessible pathways for support. These insights will inform the development of Power over Pain for Primary Caregivers, a digital solution and knowledge hub aimed at improving caregiver well-being and family outcomes, aligning with global efforts to enhance family-centred pediatric pain care. Full article

The establishment of conservation areas is considered one of the most effective approaches to address biodiversity loss with limited resources. Identifying hotspots of plant diversity and conservation gaps has played a crucial role in optimizing conservation areas. Utilizing diverse types of research data can effectively enhance the recognition of hotspots and conservation gaps. Phenotypic trait diversity is a functional biogeography that analyzes the geographic distribution patterns, formation, and reasons for the development of specific or multiple phenotypic traits of organisms. Flower color and fruit color phenotypic traits are primary characteristics through which plants interact with other organisms, affecting their own survival and reproduction, and that of their offspring. This study utilized data from 1923 Phenotypic Trait Diversity Species (PTDS) with flower and fruit color characteristics to optimize conservation areas in the Shaanxi Qinling Mountains. Additionally, data from 1838 endemic species (ES), 190 threatened species (TS), and 119 protected species (PS) were used for validation. The data were primarily sourced from the Catalogue of Vascular Plants in Shaanxi, supplemented by the Chinese Virtual Herbarium and the Shaanxi Digital Herbarium. The results reveal that by comparing the existing conservation area boundaries with those determined by four types of data, conservation gaps are found in 14 counties in the Qinling Mountains of Shaanxi. The existing conservation area only accounts for 13.3% of the area determined by the four types of data. There are gaps in biodiversity conservation in the Qinling Mountains of Shaanxi, and the macroscopic use of plant phenotypic trait data contributes to optimizing these conservation gaps. Full article

In November 2024, the fourth annual Symposium focusing on early-age onset cancer (EAOC) was hosted by the Colorectal Cancer Resource & Action Network (CCRAN), assembling clinicians, researchers, and patients virtually to discuss challenges in early detection and diagnosis of individuals afflicted with EAOC across tumour types. The meeting addressed the rising rates of EAOC and identified strategies to overcome barriers to timely detection and diagnosis by closing gaps in public and healthcare provider knowledge on symptoms of cancer in younger adults and reducing inequities in standard screening for younger age groups. Discussions also encompassed the various factors that serve as impediments to accessing diagnostic testing and obtaining results, as well as the critical need for access to diagnostics such as comprehensive genomic profiling (CGP), the results of which could be imperative in helping to guide clinical decisions regarding effective and well-tolerated targeted therapies. The Symposium generated key calls to action regarding increasing EAOC education and awareness among primary care providers and the public, re-evaluation of cancer screening programs’ eligibility criteria to include younger populations, and mechanisms to reduce waiting times for diagnostic testing by addressing technologist shortages and improving access to CGP through national collaborative strategies and increased funding. Full article

Sexual dysfunction is a prevalent and often under-addressed concern among prostate cancer survivors, significantly affecting quality of life for patients and their partners. The True North Sexual Health and Rehabilitation eClinic (SHAReClinic) is a virtual, biopsychosocial intervention developed to improve access to sexual health support for prostate cancer survivors and their partners. This study used a qualitative descriptive design to examine barriers and facilitators influencing the integration of SHAReClinic into oncology care across nine Canadian health care centres. Semi-structured interviews were conducted with 17 knowledge users, including health care providers and institutional leaders. Data were analyzed using a hybrid deductive–inductive thematic approach guided by the Consolidated Framework for Implementation Research (CFIR) 2.0. Participants described SHAReClinic as a much-needed resource, particularly in the absence of standardized sexual health pathways in oncology care. The virtual format was seen as accessible and well suited to addressing sensitive topics. However, limited funding, lack of institutional support, and workflow integration challenges emerged as primary barriers to implementation. Findings offer practical, theory-informed guidance for integrating SHAReClinic into oncology care and highlight key considerations for developing sustainable and scalable survivorship care models. Full article

With the increasing level of inverter-based resources (IBRs) in modern power systems, this paper presents a small-signal stability analysis for power systems comprising synchronous generators (SGs) and IBRs. Four types of inverter controls are considered: two grid-following (GFL) controls, with or without grid support functions; droop-based grid-forming (GFM) controls; and virtual oscillator control-based GFM. We also analyze the impact of STATCOM and synchronous condensers on system stability to assess their role in the energy mix transition. With the small-signal dynamic behavior of the major technologies modeled, this paper provides stringent stability assessments using the IEEE 39-bus benchmark system modified to simulate future power systems. The exhaustive test cases allow for (a) assessing the impacts of different types and controls of generation and supplementary grid assets, as well as system inertia and line impedance on grid stability, and (b) elucidating pathways for the stabilization of IBR-dominated power systems. The analysis also indicates that future power systems can be stabilized with only a fraction of the total generation as voltage sources without SGs or significant system inertia if they are well distributed. This study provides insights into future power system operations with a high level of IBRs that can also be used for planning and operation studies. Full article

The rapid increase in the deployment of Internet of Things (IoT) sensor networks has led to an exponential growth in data generation and an unprecedented demand for efficient resource management infrastructure. Ensuring end-to-end communication across multiple heterogeneous network domains is crucial to maintaining Quality of Service (QoS) requirements, such as low latency and high computational capacity, for IoT applications. However, limited computing resources at multi-access edge computing (MEC), coupled with increasing IoT network requests during task offloading, often lead to network congestion, service latency, and inefficient resource utilization, degrading overall system performance. This paper proposes an intelligent task offloading and resource orchestration framework to address these challenges, thereby optimizing energy consumption, computational cost, network congestion, and service latency in dynamic IoT-MEC environments. The framework introduces task offloading and a dynamic resource orchestration strategy, where task offloading to the MEC server ensures an efficient distribution of computation workloads. The dynamic resource orchestration process, Service Function Chaining (SFC) for Virtual Network Functions (VNFs) placement, and routing path determination optimize service execution across the network. To achieve adaptive and intelligent decision-making, the proposed approach leverages Deep Reinforcement Learning (DRL) to dynamically allocate resources and offload task execution, thereby improving overall system efficiency and addressing the optimal policy in edge computing. Deep Q-network (DQN), which is leveraged to learn an optimal network resource adjustment policy and task offloading, ensures flexible adaptation in SFC deployment evaluations. The simulation result demonstrates that the DRL-based scheme significantly outperforms the reference scheme in terms of cumulative reward, reduced service latency, lowered energy consumption, and improved delivery and throughput. Full article