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Understanding the Nexus of Industry 5.0 and Circular Economy for Enriched Lean and Environment Sustainability: Nexus of Industry 5.0 and Circular Economy
No full textIndustries that integrate Industry 5.0 technologies with Circular Economy principles can achieve enhanced resource efficiency, waste reduction, and product lifecycle optimization. This chapter presents key opportunities and challenges associated with this integration, drawing on a comprehensive review of current literature and real-world case studies. The findings underscore the potential of this convergence to deliver substantial environmental and operational benefits, while offering actionable insights for stakeholders aiming to advance environmental sustainability within industrial practices
Gradient fibro-porous materials for tailored sound absorption
Get PDFReducing the pore size of bulk sound absorbers often increases weight and introduces manufacturing challenges, limiting their practical use. To address these issues, a class of materials is introduced that uses 3D printing to seamlessly integrate fibers within porous scaffolds, allowing improved sound absorption performance without a significant weight addition. The reliance on 3D printing enables the creation of gradient fibro-porous structures with customizable acoustic properties. This study explores the effect of through-thickness gradients in the scaffold’s relative density, fiber thickness, and fiber density on the acoustical performance with the goal of identifying the optimal strategy to obtain broader and higher sound absorption performance. Experimental evaluation using a normal-incidence impedance tube setup demonstrates that gradient fibro-porous samples offer a 47% mass reduction compared to traditional porous structures while maintaining enhanced sound absorption. This research highlights gradient-engineered fibro-porous structures, manufactured using advanced 3D printing techniques, as a lightweight, efficient, and innovative solution for advanced noise control applications
Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network
No full textEarly and accurate diagnosis of pulmonary hypertension (PH), including differentiating pre-capillary from post-capillary PH, is crucial for guiding effective clinical management. This study developed and validated a deep learning-based diagnostic model to classify patients into non-PH, pre-capillary PH, or post-capillary PH categories. A retrospective dataset from 204 patients (112 pre-capillary PH, 32 post-capillary PH, and 60 non-PH controls) was collected at the First Affiliated Hospital of Nanjing Medical University, with diagnoses confirmed by right heart catheterization (RHC). Patients were randomly divided into training (186 patients, 90%) and testing sets (18 patients, 10%) stratified by diagnostic category. We trained and evaluated the model using 35 repeated random splits. The proposed deep learning model combined graph convolutional networks (GCN), convolutional neural networks (CNN), and Transformers to analyze multimodal data, including cine short-axis (SAX) sequences, four-chamber (4CH) sequences, and clinical parameters. Across test splits, the model achieved an overall area under the receiver operating characteristic curve (AUC) of 0.814 ± 0.06 and accuracy (ACC) of 0.734 ± 0.06 (mean ± SD). Class-specific AUCs were 0.745 ± 0.11 for non-PH, 0.863 ± 0.06 for pre-capillary PH, and 0.834 ± 0.10 for post-capillary PH, indicating good discriminative ability. This study demonstrated three-class PH classification using multimodal inputs. By fusing imaging and clinical data, the model may support accurate and timely clinical decision-making in PH
Top-k Document Retrieval in Compressed Space
No full textLet be a collection of D strings of total length n over an alphabet of size σ. We consider the so-called top-k document retrieval problem: given a short string P and an integer k, list the identifiers of k strings in most relevant to P, in decreasing order of relevance. Relevance may be a fixed value associated with the strings where P occurs, or the number of times P occurs in the strings. While RAM-optimal solutions using O (n log n ) bits and O (|P|/logσ n + k ) time exist, solving the problem optimally within space close to O (n log σ ) bits is open. We describe a data structure for the top-k document retrieval problem that uses O (log log n ) bits per symbol on top of any compressed suffix array (CSA) of , and supports queries in essentially optimal time, in the following sense. Given a CSA using |CSA| bits of space, that finds the suffix array range of a query string P in time tcnt, and accesses a suffix array entry in time tSA, listing any k pattern occurrences would take time O (tcnt + ktSA). Our top-k data structure uses | CSA | + O (n log log n ) bits and reports k most relevant documents that contain P in time O (tcnt + k (tSA + log log n )). On every known CSA using O (n log σ ) bits, tSA is Ω(log log n ) in virtually all cases, thus our time is O (tcnt + ktSA ) in most situations. When the query string P is sufficiently long, some CSAs reach time O (tcnt + k ) to list any k occurrences of P. Our structure achieves similar performance in this case, obtaining time O (tcnt + tsort(k, n )) on every known CSA, where tsort (k, n ) is the time to sort k integers in [1, n]. This time is already O (tcnt + k ) in the typical regimes, k = O (polylog n ) and k = Ω(nε) for any constant ε \u3e 0. If we can deliver the results in unsorted order of relevance, then the time for long patterns is always O (tcnt + k ), which is optimal with respect to the CSA, and reaches the RAM-optimal time O (|P|/logσ n + k ) on a particular CSA. No top-k solution using o (n log D ) bits of space has achieved this before
Sound absorption in uniform and layered gyroid and diamond triply periodic minimal surface porous absorbers
No full textWe investigate the acoustical properties of additively manufactured porous absorbers with gyroid and diamond triply periodic minimal surface pore geometries. Porous samples with different relative densities are fabricated using vat photopolymerization and tested using acoustic impedance and airflow resistivity measurement setups. Optical microscopy shows that the increased wall thicknesses due to polymer expansion causes the actual relative densities of the fabricated samples to exceed the intended designs. The two-microphone tests demonstrate that higher relative densities enhance sound absorption effectiveness, with the diamond geometry outperforming the gyroid at equivalent relative densities. The airflow resistivity tests indicate that the superior performance of the diamond samples stems from their increased airflow resistance, attributable to the absence of through-holes in their structure. We use the inverse characterization approach to model the absorbers using the Johnson-Champoux-Allard rigid formulation, uncovering additional variations in bulk transport properties that are linked to the differing geometries. The validated numerical models are then used to predict the sound absorption performance of sound package designs with various series and parallel relative density gradients using a transfer matrix method. Our results show that such layered configurations of additively manufactured TPMS-based absorbers can enable the design of sound packages with application-specific absorption performance
ThermalTrack Dataset- Training Images- Fused RGB LWIR- sequence 11
Get PDFWe present a wheel track detection system that leverages RGB- Thermal (RGB-T) imaging, where thermal channels reveal critical temperature differentials between compacted tracks and loose snow- tracks exhibit higher thermal inertia and lower reflectivity, emitting stronger radiation signatures even in visually homogeneous conditions. By fusing these distinctive thermal patterns with RGB spatial information, our method reliably identifies navigable tracks, enabling robust path-following in complete white-out conditions where snow textures and terrain features become indistinguishable
ENERGY TRANSITIONS IN UNDERSERVED COMMUNITIES: MEANINGFUL ENGAGEMENT TO ADDRESS ENERGY BURDENS AND AIR POLLUTION
No full textThis report presents two connected studies focused on environmental justice and community engagement in energy transitions. The first study examines engagement practices in EPA-funded projects using interviews and public abstracts. Applying the EngageINFEWS framework, it analyses stakeholder dynamics and offers recommendations for working with underserved communities. Key insights include addressing power imbalances, securing funding, building capacity, and fostering communication. The second study centres on the REJuST project, which supports energy justice in a rural northern community impacted by public health concerns linked to a bioenergy facility burning wood, paper, plastics, and tire-derived fuels. I use survey data to explore resident perceptions on air pollution, energy burdens, and high energy service needs. Qualitative and quantitative methods used in this report aim to support just energy transitions and build transferable skills for climate and sustainability work
LIFE CYCLE ASSESSMENT OF RENEWABLE HYDROCARBON FUELS PRODUCED BY THE NEW COOL GAS TO LIQUID (COOL GTL) PROCESS
Get PDFThe Cool Gas-to-Liquid (Cool GTL) process converts biogas and captured CO2 into renewable hydrocarbon fuels, contributing to GHG emissions reduction in transportation. This report explains a Life Cycle Assessment (LCA) study that was completed to evaluate the environmental impact of different feedstocks, hydrogen sources, and electricity inputs. The analysis considers biogas from food waste, manure, and landfill sources, as well as biogenic, fossil, and direct air capture (DAC) CO2 feedstocks. Results show that biogas pathways provide the greatest GHG reductions, with the best case (food and manure biogas + solar hydrogen + solar electricity) achieving a net-negative GWP of -60.2 g CO2eq/MJ fuel, representing a 171% reduction compared to conventional fossil jet fuel emissions. Biogenic CO2 feedstocks also lead to a 69% reduction in emissions (26.1 g CO2eq/MJ fuel) but require renewable inputs. Fossil CO2 and DAC pathways often yield higher emissions, with the worst DAC scenario reaching 205.8 g CO2eq/MJ fuel, surpassing fossil jet fuel emissions. Findings emphasize the need to prioritize biogas, transition to green hydrogen, and use renewable electricity for sustainability
GENERALIZING MEDICAL IMAGE SEGMENTATION TASK WITH EFFICIENT DEEP LEARNING MODELS
No full textMedical Image Segmentation is a critical task in the field of medical imaging, playing a crucial role in diagnostics, treatment planning, and disease monitoring. The emergence of Deep Learning (DL) has ushered in a new era in Artificial Intelligence (AI), propelling remarkable advancements in key domains like language translation, object recognition, and recommendation systems. This evolution has been accompanied by continuous enhancements in computational efficiency and improvements in predictive accuracy. The introduction of sophisticated algorithms, such as convolutional neural networks (CNNs) and transformers, exemplifies these advancements. DL algorithms have demonstrated exceptional efficacy in medical image segmentation tasks, showcasing the potential for AI-driven early diagnostics. However, the deployment of AI systems in clinical environments is often hindered by the substantial computational demands and complexity of cutting-edge DL models. In this research proposal, we explore various methodologies to enrich the visual feature representation for medical images. We focus on integrating global context-oriented techniques, such as attention mechanisms, into the development of parameter-efficient deep learning models. Our goal is to create a generalized, end-to-end medical image segmentation framework that can accurately and efficiently segment medical images across different modalities and conditions. By leveraging advanced deep learning techniques and optimizing model architectures, we aim to enhance the performance and generalization capabilities of medical image segmentation models, ultimately contributing to improved clinical outcome
An Ancient Vanished Race: Industrial Mythistory and the Alternative Archaeology of Lake Superior Copper
Get PDFThis thesis explores how the perspectives, biases, and values of Euro-Americans regarding the copper mining industry have shaped a mythology which centers Lake Superior copper in an imagined pre-Columbian global trade network. Notably, many iterations of this mythology are rooted in a rejection of Indigenous agency in the creation of these sites, ascribing them instead to a ‘vanished race’ which had surely once occupied the region. The core of this thesis is formed around a set of semi-structured interviews with representatives of regional heritage institutions and avocationalists which were conducted with the goal of identifying how and why these stories still resonate with people today. Supporting these interviews with historical research and field observations, this study demonstrates that the myth of the ‘vanished race’ exists as a means by which proponents redefine their own Euro-American, Judeo-Christian, and (in rare cases) Indigenous identities, as well as the identities of post-industrial communities in the Lake Superior region