• Journal of Microbiology and Biotechnology
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• v.31 no.2
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• pp.171-180
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• 2021

Caffeine, a methylxanthine analog of purine bases, is a compound that is largely consumed in beverages and medications for psychoactive and diuretic effects and plays many beneficial roles in neuronal stimulation and enhancement of anti-tumor immune responses by blocking adenosine receptors in higher organisms. In single-cell eukaryotes, however, caffeine somehow impairs cellular fitness by compromising cell wall integrity, inhibiting target of rapamycin (TOR) signaling and growth, and overriding cell cycle arrest caused by DNA damage. Among its multiple inhibitory targets, caffeine specifically interacts with phosphatidylinositol 3-kinase (PI3K)-related kinases causing radiosensitization and cytotoxicity via specialized intermediate molecules. Caffeine potentiates the lethality of cells in conjunction with several other stressors such as oxidants, irradiation, and various toxic compounds through largely unknown mechanisms. In this review, recent findings on caffeine effects and cellular detoxification schemes are highlighted and discussed with an emphasis on the inhibitory interactions between caffeine and its multiple targets in eukaryotic microorganisms such as budding and fission yeasts.

• Smart Structures and Systems
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• v.26 no.1
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• pp.35-47
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• 2020

This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used; one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.457-469
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• 2017

This manuscript deals with a novel approach aimed at identifying multiple damaged sites in structural components through finite frequency changes. Natural frequencies, meant as a privileged set of modal data, are adopted along with a numerical model of the system. The adoption of finite changes efficiently allows challenging characteristic problems encountered in damage detection techniques such as unexpected comparison of possible shifted modes and the significance of modal data changes very often affected by experimental/environmental noise. The new procedure extends MDLAC and exploits parallel computing on modern multicore processors. Smart filters, aimed at reducing the potential damaged sites, are implemented in order to reduce the computational effort. Several use cases are presented in order to illustrate the potentiality of the new damage detection procedure.

• Journal of Biomedical Engineering Research
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• v.27 no.6
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• pp.310-317
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• 2006

Extensive research with polarimetry and Mueller matrix has been done for chemical measurements and possible cancer detection. However, the effect of thermally denatured biological tissue on polarization changes is not well known. The purpose of this study is to characterize polarization changes in collagen due to thermal denaturation. The variations in polarized state caused by thermal damage were investigated by obtaining the Mueller matrix elements of collagen sample at multiple thermal damage levels. The changes in birefringence of denatured collagen were also investigated. This information could be used to determine the extent of thermal damage level of clinically heat treated tissues.

• Journal of Trauma and Injury
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• v.33 no.3
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• pp.181-185
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• 2020

Damage control surgery (DCS) is an abbreviated laparotomy procedure that focuses on controlling bleeding to limit the surgical insult. It has become the primary treatment modality for patients with exsanguinating truncal trauma. Herein, we present the case of a 47-year-old woman with liver, kidney, and superior mesenteric vein (SMV) injuries caused by a motor vehicle collision. The patient underwent DCS following resuscitative endovascular balloon occlusion of the aorta (REBOA). In this case report, we discuss the importance of priority setting in DCS for the treatment of multisystem damage of several abdominal organs, particularly when the patient has incurred a combination of major vascular injuries. We also discuss the implications of damage control of the SMV, perihepatic packing, and right-sided medial visceral rotation. Further understanding of DCS, along with REBOA as a novel resuscitation strategy, can facilitate the conversion of uniformly lethal abdominal injuries into rescuable injuries.

• Structural Engineering and Mechanics
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• v.60 no.4
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• pp.729-747
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• 2016

This study aims to present a novel optimization algorithm known as gravitational search algorithm (GSA) for structural damage detection. An objective function for damage detection is established based on structural vibration data in frequency domain, i.e., natural frequencies and mode shapes. The feasibility and efficiency of the GSA are testified on three different structures, i.e., a beam, a truss and a plate. Results show that the proposed strategy is efficient for determining the locations and the extents of structural damages using the first several modal data of the structure. Multiple damages cases in different types of structures are studied and good identification results can be obtained. The effect of measurement noise on the identification results is investigated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.10 s.181
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• pp.2520-2528
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• 2000

Mechanical and physical properties of composite materials make a great difference due to their cure process condition. In order to clarify the effect of cure process condition on the microscopic damage behavior and failure mechanism of Carbon/Epoxy composites, three point bend test has been performed. For this purpose, two kinds of specimens with single adhesive and multiple adhesive layers were prepared. For single adhesive layer, four different types of specimen were used, that is, non-sanding, sanding, cocured, laminated specimens. Three different types of specimen were also used for the multiple adhesive layer, non-sanding, sanding, cocured specimens. Acoustic emission technique has also been employed to monitor the damage progresses associated with each micro-failure mechanism. The characteristics of AE parameters associated with micro-failure mechanism of each specimen were discussed.

• Smart Structures and Systems
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• v.3 no.4
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• pp.455-474
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• 2007

A flexibility-based distributed computing strategy (DCS) for structural health monitoring (SHM) has recently been proposed which is suitable for implementation on a network of densely distributed smart sensors. This approach uses a hierarchical strategy in which adjacent smart sensors are grouped together to form sensor communities. A flexibility-based damage detection method is employed to evaluate the condition of the local elements within the communities by utilizing only locally measured information. The damage detection results in these communities are then communicated with the surrounding communities and sent back to a central station. Structural health monitoring can be done without relying on central data acquisition and processing. The main purpose of this paper is to experimentally verify this flexibility-based DCS approach using wired sensors; such verification is essential prior to implementation on a smart sensor platform. The damage locating vector method that forms foundation of the DCS approach is briefly reviewed, followed by an overview of the DCS approach. This flexibility-based approach is then experimentally verified employing a 5.6 m long three-dimensional truss structure. To simulate damage in the structure, the original truss members are replaced by ones with a reduced cross section. Both single and multiple damage scenarios are studied. Experimental results show that the DCS approach can successfully detect the damage at local elements using only locally measured information.

• Smart Structures and Systems
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• v.25 no.6
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• pp.733-749
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• 2020

This study presents a computer vision-based approach for representing time evolution of structural damages leveraging a database of inspection images. Spatially incoherent but temporally sorted archival images captured by robotic cameras are exploited to represent the damage evolution over a long period of time. An access to a sequence of time-stamped inspection data recording the damage growth dynamics is premised to this end. Identification of a structural defect in the most recent inspection data set triggers an exhaustive search into the images collected during the previous inspections looking for correspondences based on spatial proximity. This is followed by a view synthesis from multiple candidate images resulting in a single reconstruction for each inspection round. Cracks on concrete surface are used as a case study to demonstrate the feasibility of this approach. Once the chronology is established, the damage severity is quantified at various levels of time scale documenting its progression through time. The proposed scheme enables the prediction of damage severity at a future point in time providing a scope for preemptive measures against imminent structural failure. On the whole, it is believed that the present study will immensely benefit the structural inspectors by introducing the time dimension into the autonomous condition assessment pipeline.

• Journal of Mechanical Science and Technology
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• v.19 no.7
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• pp.1393-1404
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• 2005

Fatigue crack growth and life have been estimated based on established empirical equations. In this paper, an alternative method using artificial neural network (ANN) -based model developed to predict fatigue damages simultaneously. To learn and generalize the ANN, fatigue crack growth rate and life data were built up using in-plane bending fatigue test results. Single fracture mechanical parameter or nondestructive parameter can't predict fatigue damage accurately but multiple fracture mechanical parameters or nondestructive parameters can. Existing fatigue damage modeling used this merit but limited real-time damage monitoring. Therefore, this study shows fatigue damage model using backpropagation neural networks on the basis of X -ray half breadth ratio B / $B_o$, fractal dimension $D_f$ and fracture mechanical parameters can estimate fatigue crack growth rate da/ dN and cycle ratio N / $N_f$ at the same time within engineering limit error ($5\%$).