203435 research outputs found
Sort by
Efficient and Test-Time Adaptive Visual Object Tracking in the Wild
No full textTracking a single object, given the location at the first frame, has been an ongoing challenge in the vision community for decades. Most recent approaches provide reasonably good performance, especially when benchmarked on in-distribution (ID) datasets, i.e., on the testing portion of the same datasets used for training. However, they incur high computational costs and hardware constraints, making their deployment in the wild for mobile, autonomous, and IoT applications still challenging. Efficient visual trackers address the efficiency aspect of such bottlenecks; however, they tend to overfit to their training distributions and lack generalization abilities, resulting in them performing well on their respective in-distribution (ID) test sets but not as well on out-of-distribution (OOD) sequences, which again imposes limitations on their deployment in the wild under constrained resources. We introduce SiamABC, a highly efficient Siamese tracker that significantly improves tracking performance, even on OOD sequences. SiamABC takes advantage of new architectural designs in the way it bridges the dynamic variability of the target, and of new losses for training. Also, it directly addresses OOD tracking generalization by including a fast backward-free dynamic test-time adaptation method that continuously adapts the model according to the dynamic visual changes of the target. Our extensive experiments suggest that SiamABC shows remarkable performance gains in OOD sets while maintaining accurate performance on the ID benchmarks. SiamABC outperforms MixFormerV2-S by 7.6% on the OOD AVisT benchmark while being 3x faster (100 FPS) on a CPU. We also contribute by making the codebase publicly available at https://wvuvl.github.io/SiamABC/
Microbiological Examination of Yellow Mealworm (Tenebrio molitor) Larvae under Various Processing and Storage Conditions as Potential for Human Consumption
No full textYellow mealworm (Tenebrio molitor) larvae have gained attention as a sustainable and nutritionally balanced food source, yet their high natural bacterial load raises food safety concerns. This study investigates the impact of pre-processing, processing, and storage conditions on microbial load and diversity in mealworms, comparing thermal (hot) and non-thermal (cold) treatments. These treatments include freeze-drying, wet heat treatments, dry heat treatments, and the combination of wet and dry heat treatments against untreated controls. Microbial loads were quantified by plate counts after day 0, day 10, and day 28 of vacuum-sealed refrigerated (4°C) storage. Water activity (Aw), pH, and moisture content were also analyzed to assess variability of intrinsic properties. Significant log reductions occurred under thermal treatments (4.59 ± 0.35 Log CFU/g for hot treatments vs. 8.15 ± 0.43 Log CFU/g for cold treatments). While no significant microbial growth occurred by day 10 (p \u3e 0.05), a notable increase was observed by day 28 (p = 0.006), particularly in heated samples (p \u3c 0.05). Anaerobic and psychrotrophic bacteria followed similar trends, whereas yeast and mold counts remained low and consistent throughout storage. These findings highlight the effectiveness of heat processing in mitigating microbial risks, supporting safer mealworm integration into food systems. Additionally, this study examines batch-to-batch microbial variability in fresh, uncleaned and unprocessed T. molitor larvae from three seasonal batches and comparing results with literature values. Aerobic, anaerobic, psychrotrophic, and yeast/mold counts were assessed, revealing significant seasonal variations—with the September batch exhibiting the highest microbial loads (p\u3c 0.05). While aerobic and yeast/mold counts aligned with prior studies, anaerobic and psychrotrophic counts diverged, likely due to geographic and methodological differences. These variations underscore the influence of seasonal factors, rearing practices, and processing on microbial profiles, emphasizing the need for standardized monitoring in edible insect production. Together, these findings provide critical insights into microbial safety and variability in mealworms, informing strategies for their safe adoption as a sustainable food source
Peace Education: Exploring Educational Possibilities
No full textPeace Education is a growing field of study that includes different approaches to understanding and promoting peace, conflict resolution, and social justice. Peace education can be a strategic area for societies that have experienced various forms of direct or indirect violence as is the case of Colombia, South America, which has undergone prolonged periods of violence and internal conflict. Considering that peace is contextual and should be defined and decided on by the people involved in the target context, this work aims to illuminate how a peace education process can be experienced by teachers and students and the forms it may take. More specifically, the purpose of this study is to explore how we, educators, can create a peace education curriculum, enact it, and live it with students in the classroom in a situated context in the Colombian Caribbean. This qualitative inquiry was based on the theoretical perspectives provided by social constructionism, feminism and Standpoint Theory, and critical peace education. I employed the methodology Participatory Action Research (PAR) and used discourse analysis as analytical lenses to engage with data. The data for this study was assembled through reflexive discussion meetings, fieldwork journals, interviews with students, student work, curricular documents, and class video-recordings. The study revealed that our histories and understandings, our metaphors, and our strong sense of partnership guided the creation of a peace education curriculum. It also suggested the suitability of PAR to facilitate peace education dynamics of solidarity and action as well as to help close the theory and practice divide in education. Findings also revealed how evolving critical peace educators experienced transformational action in the classroom based on our ongoing learning during the process of curriculum design. The ten female fifth grade participants provided us with an understanding of their perceptions of peace and violence and the agency they believed they had (or not) in exercising peace and violence. Although we experienced challenges during the implementation of the study, students demonstrated engagement with the peace education curriculum. Finally, this study suggests that we consider the potential of approaching peace and violence from situated standpoints (Standpoint Theory), since peace and violence are situated phenomena that may vary from context to context. It also pinpoints the duality of the school as an institution that may do violence or may disrupt it and highlights the value of critical reflective practice for teacher and student learning as they enact/live peace education
Physics-Informed Neural Network Based Aerodynamic Modeling Framework
No full textSignificant advances have been made in developing aerodynamic models over the years. While these advances span many modeling techniques and data collection methods, certain aerodynamic regimes still pose significant challenges. These regimes commonly occur when an aerodynamic body is under extreme flight conditions. Many of these conditions occur simultaneously in the rare and poorly understood case of a tumbling aerodynamic body. The flight of a tumbling aerodynamic body goes through the entire range of aerodynamic angles, alpha and beta, causing significant non-linearities and time-dependent effects associated with flow separation. During tumbling, the aerodynamic body simultaneously rotates about all three axes at high rotational rates, introducing additional non-linearities. This work, motivated by a need to understand the underlying dynamics of tumbling aerodynamic bodies, develops a Physics-Informed Neural Network Aerodynamic Modeling Framework (PINN-AMF) purposely developed to improve aerodynamic models in challenging regimes. PINNs are built on the concept of introducing physical knowledge into the training process of neural networks while benefiting from their universal approximation capabilities. At the center of the PINNAMF are the modifications made to the original PINN architecture to ensure the technique is suitable for aerodynamic modeling. These modifications allowed PINNs to be used on unbounded flight data for the application of aerodynamic modeling for the first time and were demonstrated on increasingly complex simulated case studies. The remaining components of the PINN-AMF consist of the data processing, flight propagation, and uncertainty quantification techniques. The primary purpose of any aerodynamic model is its use to propagate the motion of an aerodynamic body in flight. Therefore, the aerodynamic models can easily be integrated into a flight propagator to provide trajectory predictions. Small errors in the aerodynamic model can lead to significant errors in the propagated trajectories. Therefore, ensemble-based uncertainty quantification weighting schemes were developed to provide calibrated uncertainty estimates. For all techniques, accurate modeling is dependent on good data. Historically, data for aerodynamic modeling has come from multiple sources, including flight testing, wind tunnel testing, and computational fluid dynamics. While the primary data source for the PINN-AMF is assumed to be flight test data, techniques have been developed to allow multiple data sources to be used in the modeling process. This work covers the entire development of the PINN-AMF while demonstrating its applicability on both simulated case studies and real flight test data of a tumbling aerodynamic body
Electro-Mechanical Behavior of Core-Shell Carbon Grease-Silicone Fibers Fabricated via Coaxial Direct Ink Writing
No full textHighly stretchable strain sensors are essential components in soft electronics, enabling integration of sensing and signal transmission into deformable systems. This thesis explores the fabrication and characterization of coaxial core–shell fibers composed of a conductive carbon grease core and an elastomeric silicone shell. These fibers were produced using coaxial direct ink writing (DIW), a versatile additive manufacturing technique that allows continuous deposition of multi-material filaments. The goal was to achieve mechanically compliant yet electrically stable fibers capable of withstanding large deformations, suitable for applications in wearable electronics and soft robotics. The printed fibers were systematically examined through mechanical tensile testing, cyclic loading, and electromechanical measurements to understand their deformation behavior and conductivity response under strain. Cross-sectional imaging was used to evaluate the structural integrity of the core–shell geometry, and potential failure mechanisms were investigated. Emphasis was placed on analyzing the interplay between material properties, printing architecture, and interface dynamics in determining overall performance. To enable coaxial printing, custom coaxial nozzles were designed and fabricated, allowing for stable co-flow of the inks and consistent formation of the core-shell structure. This work demonstrates that coaxial DIW is a viable method for producing stretchable, fiber shaped strain sensors with tailored mechanical and electrical properties. The findings contribute to a growing body of research on soft, deformable materials and offer practical insights for engineering robust and reliable components for future soft electronic and sensing systems
Selective Recovery of Critical Minerals (Ni, Zn, Co, and Mn) From Acid Mine Drainage Utilizing Ion Exchange and Selective Precipitation Processes
No full textCritical minerals (CMs) are essential for U.S. national security and technological development. The U.S. heavily depends on imports of these minerals from other countries, making supply chain disruptions a likely scenario. In consequence, finding alternative sources to conventional mining for these elements has become a topic of interest in recent years. Acid Mine Drainage (AMD), once solely viewed as an environmental problem, has emerged as a potential alternative source for CMs. AMD is a naturally occurring process where surface and underground water interact with sulfur-bearing minerals in the presence of oxygen and microorganisms. This interaction causes the water to become acidic due to the generation of sulfuric acid, triggering a chain reaction in which the acidic water leaches minerals from the rock, thereby increasing both the acidity and the amount of dissolved elements. It has been identified that mining processes can accelerate the generation of AMD since more sulfur-bearing minerals are exposed during these activities. The remediation of AMD has been extensively studied, and various active and passive treatment methods have been proposed. Furthermore, research into the potential for recovering valuable minerals through remediation has been ongoing. Several processes have been suggested to treat AMD; the most effective techniques typically involve treating the AMD sludge resulting from active treatment methods and separating the elements of interest through selective leaching, selective precipitation, and solvent extraction. However, the high operational costs associated with reagent consumption have made it difficult for these processes to be implemented on a large scale. This research investigates the development of a process that combines ion exchange, a more cost-efficient method, with selective precipitation for recovering critical metals (Ni, Co, Zn, and Mn) from AMD. The proposed methodology begins with the characterization and selection of the AMD source. Once the feedstock AMD is selected, most impurities, such as iron and aluminum, are removed using caustic precipitation, which removes 66% of iron and 96% of aluminum at a pH value of 5.00. After the impurity removal stage, a two-stage ion exchange process was developed using Ambersep M4195 to selectively separate and enrich nickel in the first stage and zinc and cobalt in the second stage. These ion exchange processes were optimized through systematic single-variable testing, where the optimized variables included AMD pH, AMD contact time, sulfuric acid concentration, and volume during resin rinsing, as well as sulfuric acid concentration and volume during resin elution. The optimal AMD pH during the nickel enrichment stage was 1.3, and the achieved nickel enrichment factor was 122.9. During the separation and enrichment of zinc and cobalt, the optimal pH value for AMD was 3.5, resulting in enrichment factors of 83.2 for zinc and 80.4 for cobalt. After the two stages of ion exchange, three enriched streams are obtained: a nickel-enriched sulfuric acid solution, a zinc and cobalt-enriched sulfuric acid solution, and the discharge AMD enriched with manganese and other impurities. Selective precipitation processes were specifically developed to partially separate zinc from cobalt and manganese from impurities. Sodium sulfide (Na2S) was selected as a selective reagent to precipitate zinc sulfide (ZnS). The optimal parameters for this precipitation were determined to be a maximum precipitation pH of 8.2 and an S2-/Zn2+ ratio of 2.0:1.0. Precipitation of 96% for zinc and 26% of cobalt co-precipitation was achieved, with the purity of the zinc solids calculated to be 38% (w/w). Sodium hydroxide (NaOH) was utilized to precipitate nickel and cobalt products at a pH of 11.00 with a precipitation percentage of 99% for both elements, yielding solids with a nickel purity of 41% (w/w) and a cobalt purity of 30% (w/w). Oxidative precipitation was chosen for separating manganese from impurities using ammonium persulfate (APS), (NH4)2S2O8. To optimize the recovery and selectivity of this process, the precipitation time, initial pH, temperature, APS/Mn2+ ratio, and APS concentration were studied using systematic single-variable testing. The optimal conditions identified were a temperature of 80°C, a precipitation time of 2 to 3 hours, an initial pH of 3.5, an APS/Mn2+ ratio of 1.6:1.0, and an APS concentration of 0.6 M. The manganese precipitation percentage was nearly 100%, and the purity of the solids was calculated to be 34% (w/w) manganese. Following the removal of the critical minerals of interest, the remediated AMD has a pH value between 7.0 and 7.5 and a concentration of less than 1 ppm for all heavy elements initially present
Beyond My Control: The Role of Sense of Control on Cognitive Functioning and Depressive Symptoms in Older Adults
No full textDepression and depressive symptoms commonly coincide with mild cognitive impairment (MCI) and dementia in older adults (Ismail et al., 2017; Panza et al., 2010), with its prevalence varying based on dementia severity, diagnostic criteria, and dementia subtype (Chi et al., 2015; Ismail et al., 2017; Leung et al., 2021; Lopez et al., 2003). This relationship is hypothesized to exist for a variety of reasons, including that depressive symptoms occur as an emotional response to cognitive functioning (Bennett & Thomas, 2014; Richard et al., 2013). Emotional responses may be explained by a decline in sense of control, or the belief in one’s ability to influence their environment to achieve desired outcomes, which declines with age as cognitive and physical abilities change and limit independence (Heckhausen et al., 2010; Lachman & Weaver, 1998; Mirowsky, 1995; Skinner, 1996). Sense of control has also been found to mediate the relation between subjective cognitive decline, or perceived cognitive dysfunction, and depressive symptoms (Su et al., 2022). Using data taken from years 2006 to 2018 of the Health and Retirement Study (HRS), we found that, across 7 data collection points, cognitive functioning on the Modified Telephone Interview for Cognitive Status (TICS-27) significantly predicted depressive symptoms on the Center for Epidemiologic Studies Depression (CES-D-8; Lachman & Weaver, 1997; Turvey et al., 2009; Welsh et al., 1993). Further, a mediated regression using data from years 2016 and 2018 indicated that ‘sense of control,’ measured using the subscale of the Midlife Development Inventory, mediated the relation between cognitive functioning and depressive symptoms. The findings of this study illustrate the importance that cognitive functioning has on sense of control and helps to better explain how depressive symptoms occur as an emotional reaction to cognitive dysfunction. These findings have critical implications for treatment of depressive symptoms in older adults, particularly those with signs of cognitive impairment
Five Prairie Reflections on Reviving Rural America
Get PDFThis Essay offers five reflections on Ann E. Eisenberg’s book Reviving Rural America: Toward Policies for Resilience. Each reflection approaches the book from the positionality of writing from rural America, specifically the prairies of South Dakota. Collectively, the reflections cover the politics of voting in a red rural state, policies of local municipal government, the central economic principles in the book, the presence of energy production in rural America, and environmentalism’s relevancy and impact
Artificial Intelligence in Education
Get PDFThis section explores the intersection of AI and education, including an overview of WVU\u27s AI statement and submissions from WVU faculty, classes, students, and staff on AI related projects
