bluuebunny/crossref_metadata_2025_split huggingface dataset:
Soft Sensor Digital Twin Implementation of a Pick-and-Place Operation
Authors: Schrader Brandon , Guerra-Zubiaga David , McMichael Grayson 
Published: February 2024

Abstract
This research paper proposes the use of a modular soft sensor positioned around the end effector of an industrial robotic arm to optimize human-machine interaction and safety. The smart sensor is designed to detect collisions with external objects and alert a control center through an Industrial Internet of Things (IIoT) device connected to the cloud. This innovative solution aims to improve safety in collaborative scenarios where humans work with industrial robots, reducing the risk of injuries and accidents. This research explores the integration of soft robotics, IIoT, and cloud manufacturing to obtain next-generation automation systems with digital manufacturing tools. The research is focused on developing a cost-effective and efficient solution that can be easily integrated with existing collaborative industrial robots. Simulations performed in Siemens’ Process Simulate Tecnomatix show the efficacy and usefulness of this sensor in a digital twin format. The results of this study show the possibility of using Industry 4.0 technology to improve safety conditions in factory settings and will contribute to the advancement of human-robot collaboration and the integration of IoT technology in the manufacturing industry.
A Finite Element Model for Analyzing the Shear Wave Propagation in Soft Biomaterials
Authors: Wang Jianing , Li Runze , Zhou Qifa , Gu Linxia , Dong Pengfei 
Published: February 2024

Abstract
The elastography method has been widely used to estimate the stiffness of biomaterials based on the shear wave speed. The wave propagation excited by a single indent on the surface of the biomaterials is not always an ideal shear wave. The distance from the interested region to the indent, or different algorithms for elastography may affect the calculation of stiffness. This paper aims to analyze the shear wave propagation in soft biomaterials with a finite element model. The finite element model was constructed based on the setup of our previous in-vitro experiments on the elastography of gelatin. Briefly, a shear wave propagation was induced by a single indent with a frequency of 1 kHz. Following simulation, the displacements along a path line, at three depths were extracted for analyzing the shear wave propagation. The influence of the damping behavior and three different elastography algorithms were also investigated with our data. Results have shown that the finite element simulation agreed well with the previous in-vitro experiments. The estimated stiffness increased by more than 10% as the depth increased from 1 mm to 7 mm. This increase was even larger for the material with larger damping behavior (viscoelasticity). The precise estimation was related to the distance between the interested region and the indent for the material with a larger damping behavior. The feasibility of three algorithms: wavefront slope, cross-correlation algorithm, and finite differencing method (FDM), were investigated with our model. The FDM proposed in this work can determine the shear wave speed based on local spatial and temporal data, while high-frequency data are required. The understanding from this work may provide valuable information for optimizing the performance of elastography.
Uncertainty of Thermodynamic-Entropy-Based Reliability and Remaining Useful Life Predictions Under Variable Amplitude Fatigue
Authors: Curtis Lance R. , Ayyub Bilal M. 
Published: February 2024

Abstract
Traditionally, engineers have used empirical relationships such as the modified Goodman relation and the Basquin equation to design for fatigue and Miner’s Rule to calculate reliability and to predict the remaining useful life (RUL) of structures subjected to fatigue. Research has shown the viability of using thermodynamic entropy as an alternative to traditional empirical relationships for fatigue-related design, reliability estimation, and RUL prediction. This entropy-based approach relies upon fatigue fracture entropy, a material property shown empirically to be connected with an entropy-based Miner’s Rule.
Practical application of any approach, however, demands an accounting for uncertainty; otherwise, any predictions inherently carry a higher level of risk. Entropy-based formulations observed in the literature provide no such accounting. This work provides an analytically derived formulation for uncertainty in both entropy-centered and empirically based physics-of-failure-centered (PoF-centered) approaches to estimating reliability. For both the PoF- and entropy-centered approaches, Monte Carlo simulation data of an AISI 304 stainless steel rod subjected to variable amplitude fatigue produced estimates of both cycle-based and time-based reliability using both entropy-centered and PoF-centered formulations.
This procedure includes non-parametric as well as parametric approaches. The entropy-based calculations compare well with their traditional counterparts above 80% reliability. More investigation to improve the simulation as well as comparing against empirical fatigue tests are recommended.
Utilizing Relative Frequency Shift for Defect Detection and Localization in Additively Manufactured Parts: An Analytical Methodology
Authors: Howell Quin I. , Davis Joshua I. , Perez Ennio M. , Mitchell Joseph B. , Lamon Stewart , O’Connor Dennis M. 
Published: February 2024

Abstract
Metal 3D printing techniques have become the primary additive manufacturing method for intricate and detailed designs, surpassing traditional techniques. These innovative methods allow for the use of novel fabrication techniques that enable greater intricacy, resolution, and detail compared to previous methods. In the aerospace industry, this printing technique is being developed to manufacture lightweight parts with complex designs. Similarly, in the biomedical engineering industry, metal 3D printing is used to manufacture implants and prosthetics that require precise designs and high-quality finishes. While research in conventional manufacturing has made advancements in the understanding of crack and defect propagation, there has been relatively less attention given to the dynamic attributes of metal 3D printed parts. If these parts are to be used in engineering applications, it is essential to have strict quality control and inspection in place to detect defects. To address this issue, this investigation proposes the use of the relative frequency shift (RFS) method, a non-destructive and efficient method for detecting and localizing internal voids in metal 3D printed parts. This study examines the vibration of an additively manufactured metal cantilever beam with a purposely placed square void, using analytical and Finite Element Methods (FEM). By considering a segmented and continuous beam, the relative frequency shifts were determined analytically. The location of voids with respect to modes and antinodes was observed to impact the magnitude of shift in each mode. To gather an array of frequency responses, void location along the beam was varied. Using an analytical transfer matrix method, a parametric map was created to display the frequency response of higher modes with respect to void location. This map is utilized to detect and locate internal voids along the length of the beam by comparing the observed shifts to those in the map and drawing inferences. This parametric map was validated using FEM. Through this investigation, it was discovered that every void location in 3D printed metal parts has a distinct dynamic “fingerprint” that can be utilized to precisely identify and localize defects. The effectiveness of the RFS method in detecting and localizing such defects is successfully demonstrated by this study. These findings have significant implications for the quality control and design optimization of metal 3D printed parts in various industries, including aerospace and biomedical engineering. By providing a non-destructive and efficient method for detecting defects, the RFS method can help to ensure the safety and reliability of 3D printed metal parts used in critical applications.
Study on Molecular Dynamics Simulation With Boiling Water and Copper for Different Nanostructured Surfaces
Authors: Afreen Tanjee , Gebre Fisseha L. , Xu JiaJun 
Published: February 2024

Abstract
This study utilized molecular dynamics simulation to investigate how boiling a liquid water film on a hot copper plate generates distinctive features of nanostructured surfaces. This study aims to explore the impact of nanostructured surfaces on boiling water and observe heat flow for microelectronic systems. The molecular system comprises a solid copper wall, liquid water, and water vapor. LAMMPS (open-source large-scale atomic/molecular massively parallel simulator) was used to conduct the simulations and OVITO was used to visualize the results.
This study compared the results for hot plain surfaces with different concave surfaces. The purpose is to observe how the nanostructured surface reacts to bubble formation in contrast to the smooth surfaces. The result shows that boiling initiates faster on the largest concave nanostructured surface than on the plain surface. The effect of nanostructured surface on the boiling liquid film has extensive applications in Engineering and nanotechnology. Furthermore, different studies show that at a macroscopic level, smooth surfaces can have nanoscale and microscale structures. Therefore, understanding the boiling process on different surfaces is essential to gain insights into these physical phenomena.
Real-World Deep Reinforcement Learning for Position Tracking of a Pendulum Driven by a Series Elastic Actuator
Authors: Sambhus Ruturaj , Gokce Aydin , Welch Stephen , Leonessa Alexander 
Published: February 2024

Abstract
In this study, deep reinforcement learning (DRL) is employed to train a policy for position trajectory tracking of a pendulum driven by a series elastic actuator (SEA) on hardware. SEAs are commonly used in various robotics applications, but often exhibit non-linearities, motivating a model-free control approach. Real-time learning of DRL policies is conducted using the Proximal Policy Optimization (PPO) algorithm for tracking position trajectories within a 0.05–0.5 Hz frequency interval and 0.12 rad amplitude. The impact of an additional velocity-tracking reward term on position-tracking performance is also explored. The SEA in this work is controlled by a low-level controller, with learning performed on a high-level PC. The environment trains on target sinusoidal trajectories sampled randomly from 0.05 to 0.5 Hz with safety measures, including a physical emergency stop, low-level position safety bounds, and input current limits. The modified reward function investigates the effect of including a term proportional to the negative square of velocity error for smoother tracking. The performance is characterized by metrics of episode mean square position error for tracking accuracy and episode mean absolute acceleration for smoothness. This research demonstrates real-time learning of DRL policy for position tracking of a pendulum driven by a SEA with non-linearities using freely available software tools.
Air-Aluminum Foam Applications for Cooling Systems With Heat Generation
Authors: Carbajal Gerardo 
Published: February 2024

Abstract
The present numerical study provides valuable information about air-aluminum foams for cooling systems with heat generation. Different aluminum foam porosities, permeabilities, and air mass flow rates were conducted. This study focuses on increasing the effective thermal conductivity by using air-aluminum foam. The numerical simulations include cylindrical and rectangular samples distributed in one array consisting of two rows and 10 columns. The pressure and the speed of the fluid along the center line of the cylindrical samples experienced a harmonic pressure and speed response. In the case of the rectangular samples, the air-aluminum foam was tested on three air passage configurations: I, Z, and U. The numerical results showed that the hot spot temperature for the heat source is evenly spread in the system. The results show that the air-aluminum foam significantly reduces the hot spot temperature of the modules. For the same heat generation, the cylindrical modules performed better than the rectangular modules due to better flow interaction between the fluid and the solid surface of the cylindrical samples compared with the sharp corners of the rectangular modules. Like the air-cooling system, the I-combination resulted in less pressure drop than the U and Z configurations. The pressure drops for air-aluminum foam increased was inversely proportional to the porosity.
Predicting Pressure Gradient in Aortic Coarctation Based on Geometrical Features Using Design of Experiments and Machine Learning Models
Authors: Asadbeygi Alireza , Abazari Mohammad Amin , Alimohammadi Mona 
Published: February 2024

Abstract
Aortic coarctation is the term used for narrowing of the aorta that can result in a range of cardiovascular complications, such as hypertension and heart failure, making it a condition that warrants timely diagnosis and management. Although surgical and catheter-based interventions have progressed, the treatment of aortic coarctation remains a challenge, especially in patients with complex anatomy or comorbidities. The current risk assessment routines for aortic coarctation are based on pressure gradient and narrowing diameter measurements. In This study, predictive models of pressure gradient in aortic coarctation are developed based on geometrical parameters using machine learning algorithms. Parametric idealized geometry of aortic coarctation is developed, and multiple coarctation cases were generated based on central composite design of experiment (CCD). Transient computational fluid dynamics (CFD) simulations were performed on the generated geometries. Three different pressure gradient indices were defined based on the cardiac time point and extracted from the CFD results. These indices were then used as the training dataset. Finally, linear quadratic, SVM, RIDGE and MARS regression models were developed for pressure gradient indices and the performance of the models were determined. Based on the obtained results, the RIDGE and MARS models outperformed the others to predict the pressure gradient indices with RSME values of 1.26 Pa and 1.94 Pa, respectively.
Pathogenic
Rickettsia
spp. as emerging models for bacterial biology
Authors: Sit Brandon , Lamason Rebecca L. 
Published: February 2024

ABSTRACT

Our understanding of free-living bacterial models like
Escherichia coli
far outpaces that of obligate intracellular bacteria, which cannot be cultured axenically. All obligate intracellular bacteria are host-associated, and many cause serious human diseases. Their constant exposure to the distinct biochemical niche of the host has driven the evolution of numerous specialized bacteriological and genetic adaptations, as well as innovative molecular mechanisms of infection. Here, we review the history and use of pathogenic
Rickettsia
species, which cause an array of vector-borne vascular illnesses, as model systems to probe microbial biology. Although many challenges remain in our studies of these organisms, the rich pathogenic and biological diversity of
Rickettsia
spp. constitutes a unique backdrop to investigate how microbes survive and thrive in host and vector cells. We take a bacterial-focused perspective and highlight emerging insights that relate to new host–pathogen interactions, bacterial physiology, and evolution. The transformation of
Rickettsia
spp. from pathogens to models demonstrates how recalcitrant microbes may be leveraged in the lab to tap unmined bacterial diversity for new discoveries.
Rickettsia
spp. hold great promise as model systems not only to understand other obligate intracellular pathogens but also to discover new biology across and beyond bacteria.
The end of the reign of a “master regulator’’? A defect in function of the LasR quorum sensing regulator is a common feature of
Pseudomonas aeruginosa
isolates
Authors: Trottier Mylène C. , de Oliveira Pereira Thays , Groleau Marie-Christine , Hoffman Lucas R. , Dandekar Ajai A. , Déziel Eric 
Published: February 2024

ABSTRACT

Pseudomonas aeruginosa
, a bacterium causing infections in immunocompromised individuals, regulates several of its virulence functions using three interlinked quorum sensing (QS) systems (
las
,
rhl
, and
pqs
). Despite its presumed importance in regulating virulence, dysfunction of the
las
system regulator LasR occurs frequently in strains isolated from various environments, including clinical infections. This newfound abundance of LasR-defective strains calls into question existing hypotheses regarding their selection. Indeed, current assumptions concerning factors driving the emergence of LasR-deficient isolates and the role of LasR in the QS hierarchy must be reconsidered. Here, we propose that LasR is not the primary master regulator of QS in all
P. aeruginosa
genetic backgrounds, even though it remains ecologically significant. We also revisit and complement current knowledge on the ecology of LasR-dependent QS in
P. aeruginosa
, discuss the hypotheses explaining the putative adaptive benefits of selecting against LasR function, and consider the implications of this renewed understanding.
mSphere of Influence: Compartmentalized cAMP signals in American trypanosomes
Authors: Lander Noelia 
Published: February 2024

ABSTRACT

Noelia Lander works on cell signaling in American trypanosomes and studies the role of cyclic adenosine monophosphate (cAMP) microdomains in environmental sensing and differentiation. In this mSphere of Influence, Dr. Lander reflects on three research articles in different eukaryotic models that had impacted on the way she thinks about the regulation of cAMP signals in
Trypanosoma cruzi
, the etiologic agent of Chagas disease. The articles “FRET biosensor uncovers cAMP nano-domains at β-adrenergic targets that dictate precise tuning of cardiac contractility” (N. C. Surdo, M. Berrera, A. Koschinski, M. Brescia, et al., Nat Commun 8:15031, 2017,
https://doi.org/10.1038/ncomms15031
), “Cyclic AMP signaling and glucose metabolism mediate pH taxis by African trypanosomes” (S. Shaw, S. Knüsel, D. Abbühl, A. Naguleswaran, et al., Nat Commun 13:603, 2022,
https://doi.org/10.1038/s41467-022-28293-w
), and “Encystation stimuli sensing is mediated by adenylate cyclase AC2-dependent cAMP signaling in
Giardia
” (H. W. Shih, G. C. M. Alas, and A. R. Paredez, Nat Commun 14:7245, 2023,
https://doi.org/10.1038/s41467-023-43028-1
) influenced her current hypothesis that cAMP signals are generated in response to environmental cues leading to changes in membrane fluidity at the flagellar tip and the contractile vacuole complex of
T. cruzi
, structures where cAMP mediates key cellular processes for developmental progression.
Identification of a microbial sub-community from the feral chicken gut that reduces
Salmonella
colonization and improves gut health in a gnotobiotic chicken model
Authors: Wongkuna Supapit , Ambat Achuthan , Ghimire Sudeep , Mattiello Samara Paula , Maji Abhijit , Kumar Roshan , Antony Linto , Chankhamhaengdecha Surang , Janvilisri Tavan , Nelson Eric , Doerner Kinchel C. , More Sunil , Behr Melissa , Scaria Joy 
Published: February 2024

ABSTRACT

A complex microbial community in the gut may prevent the colonization of enteric pathogens such as
Salmonella
. Some individual or a combination of species in the gut may confer colonization resistance against
Salmonella
. To gain a better understanding of the colonization resistance against
Salmonella enterica
, we isolated a library of 1,300 bacterial strains from feral chicken gut microbiota which represented a total of 51 species. Using a co-culture assay, we screened the representative species from this library and identified 30 species that inhibited
Salmonella enterica
subspecies enterica serovar Typhimurium
in vitro
. To improve the
Salmonella
inhibition capacity, from a pool of fast-growing species, we formulated 66 bacterial blends, each of which composed of 10 species. Bacterial blends were more efficient in inhibiting
Salmonella
as compared to individual species. The blend that showed maximum inhibition (Mix10) also inhibited other serotypes of
Salmonella
frequently found in poultry. The
in vivo
effect of Mix10 was examined in a gnotobiotic and conventional chicken model. The Mix10 consortium significantly reduced
Salmonella
load at day 2 post-infection in gnotobiotic chicken model and decreased intestinal tissue damage and inflammation in both models. Cell-free supernatant of Mix10 did not show
Salmonella
inhibition, indicating that Mix10 inhibits
Salmonella
through either nutritional competition, competitive exclusion, or through reinforcement of host immunity. Out of 10 species, 3 species in Mix10 did not colonize, while 3 species constituted more than 70% of the community. Two of these species were previously uncultured bacteria. Our approach could be used as a high-throughput screening system to identify additional bacterial sub-communities that confer colonization resistance against enteric pathogens and its effect on the host.

IMPORTANCE

Salmonella
colonization in chicken and human infections originating from
Salmonella-
contaminated poultry is a significant problem. Poultry has been identified as the most common food linked to enteric pathogen outbreaks in the United States. Since multi-drug-resistant
Salmonella
often colonize chicken and cause human infections, methods to control
Salmonella
colonization in poultry are needed. The method we describe here could form the basis of developing gut microbiota-derived bacterial blends as a microbial ecosystem therapeutic against
Salmonella
.