Tennessee Technological University

Abstract The demand for efficient and sustainable Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. One of the key reasons for this growth in UAVs is their adaptability and agility across diverse conditions. Flight modes are implemented using control surfaces. Conventional fixed-wing UAVs rely on discrete control surfaces, such as flaps and ailerons, for maneuvering. Although these control surfaces are widely used, they introduce aerodynamic discontinuities, flow separation, and increased drag. The concept of morphing wings is more substantial, but conformal shape change is necessary to avoid these limitations while maintaining flight control authority. One of the most challenging aspects in morphing is its implementation and application to actual aircraft to validate its effectiveness. This study explores the feasibility of using a 3D-printed conformal camber-morphing mechanism in the design and construction of small UAVs. Two different conformal camber morphing mechanisms have been designed, developed, and tested in flight and in the wind tunnel to assess their performance relative to conventional flap-based wings. CFD analysis and wind-tunnel results indicate up to 51.4% and 49.5% improvements in the Coefficient of Lift (C L ), respectively, at the same Drag Coefficient (C D ) of flap-based and morphing wings, highlighting the superior aerodynamic efficiency of the morphing design. The results show that 3D-printed modular morphing can be a practical and effective alternative to conventional flap-based wings, thereby improving UAV aerodynamic efficiency. This research establishes a foundational design flow for integrating morphing technologies into lightweight, low-cost UAV applications, validated through experimental testing and flight tests. The major contribution of this paper is to demonstrate the effectiveness of a camber-morphing wing aircraft through implementation and a rigorous design process, culminating in wind-tunnel and flight testing on a small UAV in its final stage of development.

Abstract In this Review article, a brief description of the stochastic mean-field (SMF) theory for describing reaction dynamics in low-energy heavy-ion collisions at bombarding energies in the vicinity of the Coulomb barrier is presented. In these collisions, as a result of strong Pauli blocking, binary nucleon collisions do not have a significant effect on the dissipation and fluctuations. At low energies, the mean-field fluctuations, due to initial correlations, have a dominant effect on fluctuations of macroscopic variables. The SMF theory proposes the determination of an ensemble of single-particle density matrices by specifying random initial fluctuations according to a distribution law. Employing an ensemble of single-particle density matrices, not only the mean values but also the distribution functions of the one-body observables can be determined. If the di-nuclear structure is maintained in heavy-ion collisions, such as deep inelastic collisions and fast quasi-fission reactions, a much simpler description of the reaction mechanism can be derived in terms of several macroscopic variables such as mass and charge asymmetry, and relative linear and relative angular momentum. In this case, by geometric projection of the SMF equations, it is possible to derive the quantal Langevin equations for macroscopic variables. As an application of quantal transport description, an analysis of multinucleon transfers and kinetic energy dissipation and fluctuations is presented for selected quasi-fission reactions.

Endosomal escape is a central barrier to efficient nucleic acid delivery by lipid nanoparticles (LNPs) and remains challenging to quantify in vivo. We report a library of branched ionizable phospholipids that markedly enhance messenger RNA delivery to the liver. The lead candidate BiP-20 outperformed the clinical benchmark LP01 by eightfold for CRISPR-Cas9 editing of the TTR gene at low dose with rapid pharmacokinetics. To quantify the endosomal escape kinetics of BiP-20, we used LysoTag mice, which allow immunoisolation of liver lysosomes, and our Lysosomal Barcoding method, finding that ~8% of BiP-20 LNPs reach the cytosol within 30 min of administration. Lysosomal proteomics revealed mechanistic regulators of escape and BiP-20-induced alterations in endosomal maturation and recycling pathways. Loss of Rab7, a mediator of late endosomal maturation, increased LNP escape. These findings provide a potent class of ionizable lipids for RNA delivery, a method to quantify endosomal escape in vivo, and mechanistic insight into the endolysosomal determinants of LNP trafficking.

The oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are two processes that occur during the operation of the cathodic electrode in Zn–Air batteries, which enable the integration of alternative energy sources into electrical energy distribution systems. Transition metal oxides, such as perovskites based on LaNiO3, are promising electrocatalysts for the ORR and OER in alkaline medium due to their versatile structure, allowing for the substitution of certain atoms with dopants, which enhances the catalytic activity for both reactions. This work reports an electrochemical study of the catalytic activity toward ORR and OER of perovskite catalysts (LaNiO3 doped with transition metals (Fe, Mn, or Pd)) in the presence of carbon-based materials as supports (multiwalled carbon nanotubes (MWCNT), graphene oxide nanosheets (GO), and graphitic carbon (C)). The results revealed interesting catalytic properties in both reactions, particularly La(Ni0.9Pd0.1)O3/MWCNT, which showed an ORR activation potential of 0.87 V vs. RHE, comparable to that of the commercial Pt/C catalyst (0.99 V vs. RHE), while the overpotential for OER was lower than that of the Pt/C catalyst (1.68 V vs. RHE for La(Ni0.9Pd0.1)O3/MWCNT and 1.79 V vs. RHE for the commercial Pt/C).

The proliferation of malicious programs for the Windows operating system has highlighted the need for detection frameworks that protect these vital systems. Traditional malware detection approaches struggle to generalize across the diverse and evolving behavioral patterns that are demonstrated by modern malware. In order to gain an upper hand against malware developers, researchers have experimented with varying representations of programs that can be used in conjunction with machine learning models to automatically detect and mitigate malware threats.This work presents Graphene, a graph-based malware detection framework that leverages a RoBERTa-based classification model for malware detection. Graphene encodes program behavior through graph representations that are subsequently linearized into function-call sequences, enabling the use of advanced language-model architectures for malware detection. We evaluate Graphene on a dataset of approximately 150,000 Windows Portable Execubable (PE) files and benchmark its performance against two conventional machine learning models: a deep neural network (DNN) and a recurrent neural network (RNN). Experimental results demonstrate that Graphene significantly outperforms these baselines, achieving 94% accuracy with a false positive rate below 4%. These findings show the effectiveness of transformer-based architectures, when informed by graph representations, in advancing the state of the art automated malware detection.

To examine how the individual experiences of sexual assault nurse examiners (SANEs) changed across a 6-month period. SANEs from a prior study participated in a 6-month follow-up survey regarding secondary trauma, burnout, support, work-related challenges and training needs. Some also completed the Secondary Traumatic Stress Scale and the Professional Quality of Life Scale. There were a few changes in the feelings of secondary trauma and burnout, with most SANEs reporting little to none. However, there was a decrease in feelings of support. Further, many challenges and training needs remained stable across time. While secondary trauma and burnout were both low and stable, it is concerning that SANEs felt less supported over time. In addition, the lack of change in challenges and training needs may suggest that the needs of SANEs are not being addressed.

ABSTRACT Previous NMR 1 J (C13,H) –s correlations exhibited only marginal degrees of success. Presented here is a new such correlation where it is proposed NMR coupling data is passed by H–C–H and H–H pathways as suggested by the theoretical results of Provasi et al. Used where the current best values of the 1 J (C13,H)'s and molecular structure data for the hydrocarbons methane ( 1 ), ethane ( 2 ), ethene ( 3 ), benzene ( 4 ), propadiene ( 5 ), and ethyne ( 6 ). Simple equations for calculation of s character and bond angles are presented. The correlation has been extended to the case of a fluorine substituent. How the s character frameworks of molecules can be mapped out is demonstrated and compared to natural bond orbital (NBO) analysis estimations from density functional theory calculations. The definitions and calculation of the parameters in the master equation, 1 J (C13,H) = A s + n B + m C + o D + E + F , are explained in detail.

The growing availability of AI writing tools presents both opportunities and challenges for how secondary students revise their work. While tools such as Grammarly and ChatGPT offer immediate feedback that can scaffold surface-level accuracy, concerns remain about their potential to discourage deeper, conceptual revision. Guided by socio-cognitive writing theory, this mixed-methods empirical study examines the impact of AI writing tools on revision practices in secondary English Language Arts (ELA) classrooms. Participants revised argumentative essays with or without access to AI tools, using data from writing samples, surveys, and think-aloud protocols. Quantitative analyses indicate that AI-assisted students produced significantly more revisions overall, particularly at the surface level, while qualitative findings reveal that engagement with global revision was strongly mediated by teacher guidance and students’ digital literacy. The results suggest that AI writing tools can function as effective cognitive scaffolds when embedded within intentional pedagogical frameworks. Implications are discussed for classroom instruction, teacher professional development, and the responsible integration of AI in secondary education.

Modern GPUs play a crucial role in accelerating a wide range of computational workloads. It is important to understand the performance limitations of these workloads on specific hardware to optimize them or to choose more appropriate configurations or algorithms. The world’s fastest supercomputer, El Capitan, consists of 11,520 AMD MI300A Accelerated Processing Units (APUs). The MI300A APU supports three logical partitioning modes—SPX, TPX, and CPX, for single, triple, and core-partitioned, respectively—to optimize compute resource allocation, offering new opportunities for performance tuning. In this work, we evaluate the performance of the MI300A APUs in different GPU partitioning modes and demonstrate the performance impact across a wide range of computational kernels represented in the RAJA Performance Suite. We use hardware counter analysis to explain the up to 30% difference in execution time in different modes for stencil kernels. We also explore the dynamic power sharing among the accelerator compute units of the APU. Power sharing enables the APU to dynamically allocate power resources between the CPU and GPU based on the workload execution behaviors. We observed throttling of the power resulted in up to 20% slowdown particularly for FLOP-bound kernels.