Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy dissipation‐based material deterioration assessment using random decrement technique and convolutional neural network: A case study of Saigon bridge in Ho Chi Minh City, Vietnam

The bridge structures must work under random and complex excitation conditions. The vibration response of these structures includes two main components as a determining component and a stochastic component. Thus, using vibration data, the structural health monitoring (SHM) process for these structures requires eliminating random parts impact. The random decrement (RD) signature, a known technique to serve this requirement, is applied to analyze the bridge's vibrations under the ambient load (random excitation) in this study. The nonlinear viscoelastic model is used to evaluate the energy dissipation of material. Then, a new damage index, called the loss factor function (LF), determined from the power spectral density (PSD) of vibration modes, is used to assess material deterioration. In fact, only some vibration modes of structures that occur with large amplitude are considered to be determined. Therefore, the article evaluates some of the first vibration modes' energy dissipation to monitor the change of material's mechanical properties. The vibration of the Saigon bridge under actual traffic loadings over 9 years is used as an illustrative example. One single beam with the same modes of the bridge span is modeled and used to extract numerical data to train convolutional neural network (CNN). CNN is widely known at the current time with outstanding capabilities in the typical image classification. The distribution of these LF values is overall evaluated by using a featured image built from their contour plot images. With the supervised learning algorithm, the proposed network is trained to assess the deterioration level of materials. The output is the label corresponding to the deterioration level, and the inputs are featured images about energy dissipation.
Finite element modeling, correlation and model updating of stiffened plate

This paper looks into modal parameter extraction of aluminium plate using finite element (FE) analysis and the correlation with experimental modal analysis. The effects of localize stiffening with ribs on modal parameters were considered. Finite element models of an aluminium plate with and without rib were developed and analyzed. Correlation and model updating of the stiffening plate were considered. Results suggest that ribs increase the local stiffness of the plate and they can be added onto a structure during modification.
Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning

The exponential growth of the internet and a multi-fold increase in social media users in the last decade have resulted in a massive growth of unstructured data. Aspect-Based Sentiment Analysis (ABSA) is challenging because it performs a fine-grain analysis; it is a text analysis technique where the opinions group is based on the aspect. The Aspect Extraction (AE) task is one of the core subtasks of ABSA; it helps to identify aspect terms in the text, comments, or reviews. The challenge of the Arabic AE task increases due to the complexity of the Arabic language. This work aims to develop the Arabic AE task by proposing transfer learning using state-of-art pre-trained contextual language models. We concatenate the Bidirectional Encoder Representation from Transformers (BERT) language model and contextualize string embeddings (Flair embedding) as a stacked embeddings layer for better word representation for Arabic language. Then, we extend it with different deep learning network architectures. For Arabic AE, the model is developed by concatenating the Arabic contextual language model, AraBERT, and Flair embedding as a contextual stacked embeddings layer with an extended layer, BiLSTM-CRF or BiGRU-CRF, for sequence labeling. Our proposed models are called BF-BiLSTM-CRF and BF-BiGRU-CRF. The proposed model is evaluated using the Arabic Hotel’s reviews dataset. For performance evaluation, we used the F1 score. The experimental results show that the proposed BF-BiLSTM-CRF configuration outperformed the baseline and other models by achieving an F1score of 79.7%.
Joint Analysis of Morphological Parameters and In Silico Haemodynamics of the Left Atrial Appendage for Thrombogenic Risk Assessment

Background Atrial fibrillation (AF) is considered the most common human arrhythmia. In nonvalvular AF, around 99% of thrombi are formed in the left atrial appendage (LAA). Nevertheless, there is not a consensus in the community about the relevant factors to stratify the AF population according to thrombogenic risk. Objective To demonstrate the need for combining left atrial morphological and haemodynamics indices to improve the thrombogenic risk assessment in nonvalvular AF patients. Methods A cohort of 71 nonvalvular AF patients was analysed. Statistical analysis, regression models, and random forests were used to analyse the differences between morphological and haemodynamics parameters, extracted from computational simulations built on 3D rotational angiography images, between patients with and without transient ischemic attack (TIA) or cerebrovascular accident (CVA). Results The analysis showed that models composed of both morphological and haemodynamic factors were better predictors of TIA/CVA compared with models based on either morphological or haemodynamic factors separately. Maximum ostium diameter, length of the centreline, blood flow velocity within the LAA, oscillatory shear index, and time average wall shear stress parameters were found to be key risk factors for TIA/CVA prediction. In addition, TIA/CVA patients presented more flow stagnation within the LAA. Conclusion Thrombus formation in the LAA is the result of multiple factors. Analyses based only on morphological or haemodynamic parameters are not precise enough to predict such a phenomenon, as demonstrated in our results; a better patient stratification can be obtained by jointly analysing morphological and haemodynamic features.
Impact of Hydrothermal Treatments on Nutritional Value and Mineral Bioaccessibility of Brussels Sprouts (Brassica oleracea var. gemmifera)

Hydrothermal treatment of Brussels sprouts (Brassica oleracea var. gemmifera) induces both physical and chemical changes in nutrients and non-nutrients. It also affects the bioaccessibility of individual compounds. The aim of this study was to investigate the influence of hydrothermal treatment (boiling, steaming, and sous vide technique) on the concentration of the selected nutrients and non-nutrients in Brussels sprouts and in vitro bioaccessibility of the mineral components. It has been shown that, in terms of the leaching of nutrients and non-nutrients into the aqueous medium, traditional cooking in water involves the greatest percentage loss (the highest decrease in dry matter (11.8%), ash (13.3%), protein (10.4%), crude fat (43.3%), dietary fiber (9.5%), digestible carbohydrates (12.2%), and most of mineral components (7.6–39.8%)). In contrast, steam cooking and sous vide cooking of Brussels sprouts allow a higher level of preservation of the individual compounds. By using reduced process temperatures and vacuum packaging, sous vide cooking can be an alternative to traditional cooking to preserve the higher nutritional value of Brassica oleracea var. gemmifera (preservation of dry matter, ash, crude fat, and most of the mineral components at the level of the raw sample p ≤ 0.05).
Efficiency of dry bone inspection compared with two-dimensional os coxal images for age estimation in a Thai population

The auricular surface and pubic symphysis are commonly used in age estimation. This study aimed to compare the results of age estimation between dry bones and 2D images of the os coxae and to develop a tool specifically for Thai individuals. The total samples were 250 left os coxal dry bones divided into 200 samples (100 males, 100 females) for the training set and 50 samples for the test set. The age range was 26 – 94 years. We used the Suchey-Brooks method and Berg method for observing the pubic symphysis and the Buckberry-Chamberlain method for observing the auricular surface. Afterward we compared the dry bones and photo parts. Our results showed sex did not play a significant role in estimating the age-at-death. In both parts, the auricular surface yielded the highest accuracy (80 – 84%) with SEE = 13.99 – 14.24 years. The pubic symphysis showed an accuracy of 74 – 76% and SEE = 14.37 – 14.44 years. The results of the dry bone and photo parts did not differ significantly. In both dry bone and photo parts, the intra-observer agreement performed moderate to almost perfect agreement. On the other hand, the inter-observer agreement was slight to fair. In conclusion, our study can be potentially applied for distant consultation for age estimation using 2D pelvic images with a forensic anthropologist for estimating biological profiles.
Survival prediction of LGG patients based on deep learning of MRI data and genomic data integration

Low-grade gliomas (LGG) is the most common primary intracranial tumor, with high incidence rate, high recurrence rate, high mortality rate and low cure rate. Therefore, it is necessary to predict the survival of LGG patients in diagnosis. Considering the complementarity of information, the new proposed algorithm in this study is designed to integrate Magnetic Resonance Image (MRI) data and gene expression data using deep learning method to predict the Disease Specific Survival (DSS) of LGG patients. Firstly, MRI data of 44 patients is screened from TCIA database, and then the corresponding gene expression data of 44 patients is searched from TCGA database. Then, 724 image feature data extracted from MRI data are filtered and extracted by deep learning method, and DSS tags are used to train the model; deep learning method is used to extract 20530 features of gene expression data, and DSS tags are also used for training. As a contrast, the deep learning method is used to integrate the two features to train the model. Experiments are evaluated on MRI data, gene expression data and the integration data of MRI and gene expression data, respectively. The results show that by using the integration of MRI data and gene expression data performs better than using single data in terms of the time-dependent receiver operating characteristic(ROC) and the area under the curve (AUC) of the ROC curve criteria.
Anaerobic Co-Digestion of Food Waste with Livestock Manure at Ambient Temperature: A Biogas Based Circular Economy and Sustainable Development Goals

A shift from a linear economy to a circular economy of resource consumption is vital for diverting the value from lost resources to resource-efficient products towards developing a sustainable system. Household digesters provide one opportunity to create a biogas-based circular economy. Because household digesters are typically fed a wide and variable range of substrates, it is important to determine the ideal mixing ratios for them. In this study, an anaerobic digester startup process was analyzed and an assessment of anaerobic co-digestion of food waste with different livestock manures was carried out at ambient temperatures. Food waste (FW), cow manure (CM), poultry litter (PL) and goat manure (GM) were co-digested at mixing ratios (FW:PL:CM) of 2:1:1, 2:2:1, 1:1:2, 1:1:1 (wt/wt) and FW:PL:GM at mixing ratios of 2:1:1 and 1:1:2, at an organic loading rate of 1 g volatile solid (VS)/L/day, and 8% total solids. A maximum methane yield was obtained from co-digestion of FW:PL:GM at a mixing ratio of 2:1:1 in autumn-to-winter conditions, 21–10 °C, while the mixing ratio of FW:PL:CM at 2:2:1, showed negligible methane production under the same temperature condition. This study suggests that co-digestion of food waste and poultry litter with goat manure yields more biogas than other substrate combinations. Therefore, selecting suitable co-substrates with an optimized mixing ratio can promote several key indicators of a biogas-based circular economy towards achieving sustainable development goals 2, 3, 5, 6, 7, 9, 13 and 15.
PATH MODEL LEADING TO INTENTION TO RECOMMEND CO-WORKING SPACES TO NEW CUSTOMERS: AN EMPIRICAL EVIDENCE FROM THAILAND

According to Global Co-working Growth Study 2020 (Coworking resources, 2020), the numbers of co-working spaces are growing. However, gaining new customers still is challenging, referring to the DeskMag’s report (Kowrk Blog, 2017). From a practical standpoint, this is problematic. This research assumes that if current customers intend to recommend co-working space to new customers, the problem would become less problematic. However, there is no theoretical model that could explain the phenomenon of interest from previous researches. This is a problem from a theoretical standpoint. Therefore, a model, as such, is needed. Thus, this paper develops a conceptual model that could explain what factors drive intention to recommend. The model includes antecedents, overall satisfaction, and intention to recommend. Four statistical analyses were required to analyse the proposed model. Regression analysis was used to test all the relationships, all supported. This becomes a new model that could explain the phenomenon of interest. Then, path analysis was conducted to calculated direct, indirect, and total effects of paths leading to intention to recommend. Then, testing of the indirect effects were carried out to test the effects of antecedents on intention to recommend, all significant. Finally, the total effects were, then, ranked.
Fully Automated Thrombus Segmentation on CT Images of Patients with Acute Ischemic Stroke

Thrombus imaging characteristics are associated with treatment success and functional outcomes in stroke patients. However, assessing these characteristics based on manual annotations is labor intensive and subject to observer bias. Therefore, we aimed to create an automated pipeline for consistent and fast full thrombus segmentation. We used multi-center, multi-scanner datasets of anterior circulation stroke patients with baseline NCCT and CTA for training (n = 228) and testing (n = 100). We first found the occlusion location using StrokeViewer LVO and created a bounding box around it. Subsequently, we trained dual modality U-Net based convolutional neural networks (CNNs) to segment the thrombus inside this bounding box. We experimented with: (1) U-Net with two input channels for NCCT and CTA, and U-Nets with two encoders where (2) concatenate, (3) add, and (4) weighted-sum operators were used for feature fusion. Furthermore, we proposed a dynamic bounding box algorithm to adjust the bounding box. The dynamic bounding box algorithm reduces the missed cases but does not improve Dice. The two-encoder U-Net with a weighted-sum feature fusion shows the best performance (surface Dice 0.78, Dice 0.62, and 4% missed cases). Final segmentation results have high spatial accuracies and can therefore be used to determine thrombus characteristics and potentially benefit radiologists in clinical practice.
Mechanochemical dechlorination of petrochemical sludge through a planetary ball mill and using industrial wastes as additives

Mechanochemical treatment causes the formation of mineral Cl compounds during the degradation of polychlorinated compounds by planetary ball mill. In the present study, the three types of industrial waste comprising of sludge from petrochemical wastewater treatment plants as source of pollutants, scrap iron and petrochemical water treatment plant sludge as co‐milling reagents were used. The raw samples and the milled products were analyzed by gas chromatography–mass spectrometry, Fourier transform infrared spectroscopy, Raman Spectroscopy, and X‐ray diffraction. Further analyses such as ion chromatography and X‐ray fluorescence were performed. The analysis results confirmed that the milling process degraded persistent organic pollutants and showed that major changes occurred in the organic phases, carbon and chlorine bonds. The yield of chloride ions was investigated using Ion Chromatography. The highest percentage of de‐Cl products (95.6%) was achieved after 6 h in equal proportions of reagents. Furthermore, CaCl2 and FeCl2 were present in the final milling product.
Searching for a Longevity Food, We Bump into Hericium erinaceus Primordium Rich in Ergothioneine: The “Longevity Vitamin” Improves Locomotor Performances during Aging

Phenotypic frailty is characterized by a progressive decline in physical functioning. During ageing, morphological and functional alterations involve the brain, and chief theories involve oxidative stress, free radical accumulation, and reduced antioxidant defenses as the most implicated mechanisms. From boosting the immune system to fighting senescence, medicinal mushrooms have been found to have a number of health and longevity benefits. Among them, Hericium erinaceus (He) has been demonstrated to display a variety of physiological effects, including anti-aging properties. Thus, He represents an attractive natural source for developing novel medicines and functional foods, based on the identification of its active ingredients and metabolites. Particularly, H. erinaceus primordium (He2) extract contains a high amount of Ergothioneine (ERGO), the longevity vitamin. Herein, we revealed the preventive effect of ERGO-rich He2 extract in a preclinical model, focusing on locomotor decline during ageing monitored through spontaneous behavioral test. This effect was accompanied by a significant decrease in some oxidative stress markers (NOS2, COX2) paralleled by an increase in P53, showed in cerebellar cortex cells and fibres by immunohistochemistry. In summary, we demonstrated the neuro-protective and preventive effects of He2 extract during aging, probably due to its peculiarly high ERGO content.
Pricing Decision for a Closed-Loop Supply Chain with Technology Licensing under Collection and Remanufacturing Cost Disruptions

Closed-loop supply chain (CLSC) management faces collection and remanufacturing cost disruption challenges. This study explores a CLSC system wherein original equipment manufacturers (OEMs) license the third-party remanufacturer (TPR) to bear the remanufacturing activities and investigate pricing decisions in the CLSC, while considering collection and remanufacturing cost disruptions. To obtain the optimal pricing strategy, we develop game theory models under the disruptions of both centralized and decentralized CLSCs. Based on theoretical and numerical analyses, we obtain the following results: (1) Whether or not disruption events occur, the centralized supply chain can better encourage consumers to participate in the collection of used products than a decentralized supply chain; (2) when collection disruption in a large positive region or the remanufacturing cost disruption in a large negative region occurs, OEM and TPR profits will greatly increase, and the OEM will raise the licensing fee to extract more profit from the remanufacturing activity; (3) a certain robust region exists for the retail price and wholesale price when the supply chain faces disruption increase; (4) when the supply chain faces the disruptions, it has great influence on the OEM’s licensing fee but little on the TPR’s acquisition price. The main contributions of the study include: (1) We considered the impacts of both technology licensing and collection and remanufacturing cost disruption; (2) we developed game theory models to determine the optimal manufacturing and remanufacturing quantities, and pricing strategy under the disruptions; (3) based on theoretical and numerical analyses, we presented some interesting and important insights. The results of this paper could provide useful guidelines for supply chain members on how to effectively control costs to obtain more profit by adjusting prices and selecting a better operation mode for the closed-loop supply chain.
Engineer Morphing: Bridging the Gap Between Classroom Teaching and the Engineering Profession*

Teaching engineering is a highly significant task. Engineering is not a self-taught discipline; it requires supervision, guidance, and instruction. This paper discusses some of the main features of current engineering education in many institutions and its challenges. A number of suggestions are provided to enhance the engineering education process. These include the importance of teaching communication skills, broadening the students’ perspective with the ethical issues of engineering, and implementing the inductive instruction technique as an effective approach for teaching engineering. Suggestions are also given regarding assessment of the performance of engineering students.
Sequence Capture of Mitochondrial Genome with PCR-Generated Baits Provides New Insights into the Biogeography of the Genus Abies Mill.

Mitochondrial DNA (mtDNA), being maternally inherited in plants of the family Pinaceae, is an important source of phylogeographic information. However, its use is hindered by a low mutation rate and frequent structure rearrangements. In the present study, we tested the method of genomic libraries enrichment with mtDNA via the sequence capture method yielding mtDNA data which were further used to reconstruct the phylogenetic tree of the genus Abies. The baits for hybrid capture were obtained by long-range PCR using primers designed on the basis of the assembly of Abies sibirica Ledeb. mitochondrial genome. Mitochondrial genomes of Picea sitchensis (Bong.) Carr. , Larix sibirica Ledeb. , and Keteleeria davidiana (Bertrand) Beissn. were used as an outgroup. The resulting phylogenetic tree consists of two sister branches, including the Eurasian and American species, respectively, with some exceptions. The subclade of A. sachalinensis (F. Schmidt) Mast. and A. veitchii Lindl. (Japan and Sakhalin islands) occupies a basal position in the branch of American firs, probably due to the complex history of fir migrations from North America to Eurasia. The tree has high support for majority of clades. For species represented by more than one sample an intraspecific variability was found which is suitable to design mtDNA markers for phylogeographic and population studies.
Effect of Temporal Sampling Rate on Estimates of the Perfusion Parameters for Patients with Moyamoya Disease Assessed with Simultaneous Multislice Dynamic Susceptibility Contrast-enhanced MR Imaging

Purpose The effect of temporal sampling rate (TSR) on perfusion parameters has not been fully investigated in Moyamoya disease (MMD); therefore, this study evaluated the influence of different TSRs on perfusion parameters quantitatively and qualitatively by applying simultaneous multi-slice (SMS) dynamic susceptibility contrast-enhanced MR imaging (DSC-MRI). Methods DSC-MRI datasets were acquired from 28 patients with MMD with a TSR of 0.5 s. Cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time to peak (TTP), and time to maximum tissue residue function (Tmax) were calculated for eight TSRs ranging from 0.5 to 4.0 s in 0.5-s increments that were subsampled from a TSR of 0.5 s datasets. Perfusion measurements and volume for chronic ischemic (Tmax ≥ 2 s) and non-ischemic (Tmax < 2 s) areas for each TSR were compared to measurements with a TSR of 0.5 s, as was visual perfusion map analysis. Results CBF, CBV, and Tmax values tended to be underestimated, whereas MTT and TTP values were less influenced, with a longer TSR. Although Tmax values were overestimated in the TSR of 1.0 s in non-ischemic areas, differences in perfusion measurements between the TSRs of 0.5 and 1.0 s were generally minimal. The volumes of the chronic ischemic areas with a TSR ≥ 3.0 s were significantly underestimated. In CBF and CBV maps, no significant deterioration was noted in image quality up to 3.0 and 2.5 s, respectively. The image quality of MTT, TTP, and Tmax maps for the TSR of 1.0 s was similar to that for the TSR of 0.5 s but was significantly deteriorated for the TSRs of ≥ 1.5 s. Conclusion In the assessment of MMD by SMS DSC-MRI, application of TSRs of ≥ 1.5 s may lead to deterioration of the perfusion measurements; however, that was less influenced in TSRs of ≤ 1.0 s.