Experimental verification of a self-consistent theory of the first-, second-, and third-order (non)linear optical response

We show that a combination of linear absorption spectroscopy, hyper-Rayleigh scattering, and a theoretical analysis using sum rules to reduce the size of the parameter space leads to a prediction of the two-photon absorption cross-section of the dye AF455 that agrees with two-photon absorption spectroscopy. Our procedure, which demands self-consistency between several measurement techniques and does not use adjustable parameters, provides a means for determining transition moments between the dominant excited states based strictly on experimental characterization. This is made possible by our new approach that uses sum rules and molecular symmetry to rigorously reduce the number of required physical quantities.Comment: 10 pages, 9 figure

Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data

Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, and hip joint angles, torques, and powers, as well as ground reaction forces (GRF). ML was able to classify those with and without PAD using Neural Networks or Random Forest algorithms with 89% accuracy (0.64 Matthew’s Correlation Coefficient) using all laboratory-based gait variables. Moreover, models using only GRF variables provided up to 87% accuracy (0.64 Matthew’s Correlation Coefficient). These results indicate that ML models can classify those with and without PAD using gait signatures with acceptable performance. Results also show that an ML gait signature model that uses GRF features delivers the most informative data for PAD classification

Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data

Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, and hip joint angles, torques, and powers, as well as ground reaction forces (GRF). ML was able to classify those with and without PAD using Neural Networks or Random Forest algorithms with 89% accuracy (0.64 Matthew’s Correlation Coefficient) using all laboratory-based gait variables. Moreover, models using only GRF variables provided up to 87% accuracy (0.64 Matthew’s Correlation Coefficient). These results indicate that ML models can classify those with and without PAD using gait signatures with acceptable performance. Results also show that an ML gait signature model that uses GRF features delivers the most informative data for PAD classification

Design, analysis, and feedback control of a nonlinear micro-piezoelectric–electrostatic energy harvester

A nonlinear micro-piezoelectric–electrostatic energy harvester is designed and studied using mathematical and computational methods. The system consists of a cantilever beam substrate, a bimorph piezoelectric transducer, a pair of tuning parallel-plate capacitors, and a tip–mass. The governing nonlinear mathematical model of the electro-mechanical system including nonlinear material and quadratic air-damping is derived for the series connection of the piezoelectric layers. The static and modal frequency curves are computed to optimize the operating point, and a parametric study is performed using numerical methods. A bias DC voltage is used to adapt the system to resonate with respect to the frequency of external vibration. Furthermore, to improve the bandwidth and performance of the harvester (and achieve a high level of harvested power without sacrificing the bandwidth), a nonlinear feedback loop is integrated into the design

Effect of production process and high-pressure processing on viability of Listeria innocua in traditional Italian dry-cured coppa

In this study the effect of the application of High Pressure Treatment (HPP) combined with four different manufacturing processes on the inactivation of Listeria innocua, used as a surrogate for L. monocytogenes, in artificially contaminated coppa samples was evaluated in order to verify the most suitable strategy to meet the Listeria inactivation requirements needed for the exportation of dry-cured meat in the U.S. Fresh anatomical cuts intended for coppa production were supplied by four different delicatessen factories located in Northern Italy. Raw meat underwent experimental contamination with Listeria innocua using a mixture of 5 strains. Surface contamination of the fresh anatomical cuts was carried out by immersion into inoculum containing Listeria spp. The conditions of the HPP treatment were: pressure 593 MPa, time 290 seconds, water treatment temperature 14\ub0C. Listeria innocua was enumerated on surface and deep samples post contamination, resting, ripening and HPP treatment. The results of this study show how the reduction of the microbial load on coppa during the production process did not vary among three companies (P>0.05) ranging from 3.73 to 4.30 log CFU/g, while it was significantly different (P<0.01) for the fourth company (0.92 log CFU/g). HPP treatment resulted in a significant (P<0.01) deep decrease of L. innocua count with values ranging between 1.63-3.54 log CFU/g with no significant differences between companies. Regarding superficial contamination, HPP treatment resulted significant (P<0.01) only in Coppa produced by two companies. The results highlight that there were processes less effective to inhibit the pathogen; in particular for company D an increase of L. innocua count was shown during processing and HPP alone cannot be able to in reaching the Listeria inactivation requirements needed for exportation of dry-cured meat in the U.S. According to the data reported in this paper, HPP treatment increases the ability of the manufacturing process of coppa in reducing Listeria count with the objective of a lethality treatment

Effect of production process and high-pressure processing on viability of Salmonella spp. in traditional Italian dry-cured coppa

The aim of the study was to investigate the combined effect of the manufacturing process followed by HPP treatment on the inactivation of Salmonella spp. in artificially contaminated coppa samples, in order to verify the ability of the combined processes to achieve the objective of a 5-log reduction of Salmonella spp. needed for exportation to the U.S. Fresh anatomical cuts intended for coppa production were supplied by four different delicatessen factories located in Northern Italy. Raw meat underwent experimental contamination with Salmonella spp. using a mixture of 3 strains. Surface contamination of the fresh anatomical cuts was carried out by immersion into inoculum containing Salmonella spp. The conditions of the HPP treatment were: pressure 593 MPa, time 290 seconds, water treatment temperature 14\ub0C. Surface and deep samples were performed post contamination (T0), end of the cold phase (T1), end of process (Tend), and after HPP treatment (postHPP) and Salmonella spp. Enumerated. The results of this study show a significant reduction of Salmonella spp. all through the production process (P<0.01) for all companies, followed by an additional reduction of bacterial counts due to HPP treatment (P<0.01), both in superficial and deep contaminations (P<0.01). The superficial overall reduction resulted of 1.58 to 5.04 log CFU/g during the production process. HPP treatment resulted in a significant (P<0.01) superficial and deep decrease in Salmonella spp. enumeration varying from 0.61 to 4.01 log and from 1.49 to 4.13 log. According to the data presented in this study, only the combined approach of coppa manufacturing process followed by HPP treatment always led to a 5-log reduction of Salmonella spp. required by USDA/FSIS guidelines

A thermosensitive electromechanical model for detecting biological particles

Miniature electromechanical systems form a class of bioMEMS that can provide appropriate sensitivity. In this research, a thermo-electro-mechanical model is presented to detect biological particles in the microscale. Identification in the model is based on analyzing pull-in instability parameters and frequency shifts. Here, governing equations are derived via the extended Hamilton’s principle. The coupled effects of system parameters such as surface layer energy, electric field correction, and material properties are incorporated in this thermosensitive model. Afterward, the accuracy of the present model and obtained results are validated with experimental, analytical, and numerical data for several cases. Performing a parametric study reveals that mechanical properties of biosensors can significantly affect the detection sensitivity of actuated ultra-small detectors and should be taken into account. Furthermore, it is shown that the number or dimension of deposited particles on the sensing zone can be estimated by investigating the changes in the threshold voltage, electrode deflection, and frequency shifts. The present analysis is likely to provide pertinent guidelines to design thermal switches and miniature detectors with the desired performance. The developed biosensor is more appropriate to detect and characterize viruses in samples with different temperatures

Efficient primary and parametric resonance excitation of bistable resonators

We experimentally demonstrate an efficient approach to excite primary and parametric (up to the 4th) resonance of Microelectromechanical system MEMS arch resonators with large vibrational amplitudes. A single crystal silicon in-plane arch microbeam is fabricated such that it can be excited axially from one of its ends by a parallel-plate electrode. Its micro/nano scale vibrations are transduced using a high speed camera. Through the parallel-plate electrode, a time varying electrostatic force is applied, which is converted into a time varying axial force that modulates dynamically the stiffness of the arch resonator. Due to the initial curvature of the structure, not only parametric excitation is induced, but also primary resonance. Experimental investigation is conducted comparing the response of the arch near primary resonance using the axial excitation to that of a classical parallel-plate actuation where the arch itself forms an electrode. The results show that the axial excitation can be more efficient and requires less power for primary resonance excitation. Moreover, unlike the classical method where the structure is vulnerable to the dynamic pull-in instability, the axial excitation technique can provide large amplitude motion while protecting the structure from pull-in. In addition to primary resonance, parametrical resonances are demonstrated at twice, one-half, and two-thirds the primary resonance frequency. The ability to actuate primary and/or parametric resonances can serve various applications, such as for resonator based logic and memory devices