Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural damage assessment under varying temperature conditions

Modal parameters such as natural frequencies and mode shapes are sensitive indicators of structural damage. However, they are not only sensitive to damage, but also to the environmental conditions such as, humidity, wind and most important, temperature. For civil engineering structures, modal changes produced by environmental conditions can be equivalent or greater than the ones produced by damage. This article proposes a damage detection method which is able to deal with temperature variations. The objective function correlates mode shapes and natural frequencies, and a parallel genetic algorithm handles the inverse problem. The numerical model of the structure assumes that the elasticity modulus of the materials is temperature-dependent. The algorithm updates the temperature and damage parameters together. Therefore, it is possible to distinguish between temperature effects and real damage events. Simulated data of a three-span bridge and experimental one of the I-40 Bridge validate the proposed methodology. Results show that the proposed algorithm is able to assess the experimental damage despite of temperature variations.
Modeling and Parallel Operation of Exchange-Biased Delta-E Effect Magnetometers for Sensor Arrays

Recently, Delta-E effect magnetic field sensors based on exchange-biased magnetic multilayers have shown the potential of detecting low-frequency and small-amplitude magnetic fields. Their design is compatible with microelectromechanical system technology, potentially small, and therefore, suitable for arrays with a large number N of sensor elements. In this study, we explore the prospects and limitations for improving the detection limit by averaging the output of N sensor elements operated in parallel with a single oscillator and a single amplifier to avoid additional electronics and keep the setup compact. Measurements are performed on a two-element array of exchange-biased sensor elements to validate a signal and noise model. With the model, we estimate requirements and tolerances for sensor elements using larger N. It is found that the intrinsic noise of the sensor elements can be considered uncorrelated, and the signal amplitude is improved if the resonance frequencies differ by less than approximately half the bandwidth of the resonators. Under these conditions, the averaging results in a maximum improvement in the detection limit by a factor of N. A maximum N≈200 exists, which depends on the read-out electronics and the sensor intrinsic noise. Overall, the results indicate that significant improvement in the limit of detection is possible, and a model is presented for optimizing the design of delta-E effect sensor arrays in the future.
Design rationale for computer-supported conflict mitigation

The development of large-scale engineering systems requires the collaboration of numerous specialists. Their decisions reflect their different perspectives of a project and these different perspectives typically lead to many conflicts. These conflicts, if not resolved early, create more expensive designs, delays in the design-construction process, and compromises in the final product. This paper presents research on the representation, use, and communication of design rationale for conflict mitigation in a collaborative environment. This research is based on the view that: (1) The designers’ perspectives are expressed in their design rationale; (2) a system for capturing the design rationale needs to represent and manage design intent evolution, aritfact evolution, and relationships between intents and between intent and artifact; (3) a design rationale system needs to capture its information in a nonintrusive manner by providing part of the design rationale; and (4) a system for conflict mitigation needs to provide active computer support for the negotiation between multiple participants. Based on these requirements, this paper proposes “Design Recommendation and Intent Model (DRIM) as an ontology for design rationale and SHARED-Design Recommendation and Intent Management System (SHARED-DRIMS)” as a system for conflict mitigation based on this ontology.
Deep Learning Methods for Lung Cancer Nodule Classification: A Survey

Lung cancer is one of the leading causes of cancer related deaths. It is due to the complexity of early detection of nodules. In clinical practice, radiologists find it difficult to determine whether a condition is normal or abnormal by manually analysing CT scan or X-ray images for nodule identification. Currently, various deep learning techniques have been developed to identify lung nodules as benign or malignant, but each technique has its own advantages and drawbacks. This work presents a thorough analysis based on segmentation techniques, Related features-based detection, multi-step detection, automatic detection, and deep convolutional neural network techniques. Performance comparison was conducted on a selected works based on performance measures. A potential research direction for the recognition of lung nodules is given at the end of this study.
The Fine‐Scale Structure of Long Beach, California, and Its Impact on Ground Motion Acceleration

The metropolitan Los Angeles region represents a zone of high‐seismic risk due to its proximity to several fault systems, including the San Andreas fault. Adding to this problem is the fact that Los Angeles and its surrounding cities are built on top of soft sediments that tend to trap and amplify seismic waves generated by earthquakes. In this study, we use three dense petroleum industry surveys deployed in a 16 × 16‐km area at Long Beach, California, to produce a high‐resolution model of the top kilometer of the crust and investigate the influence of its structural variations on the amplification of seismic waves. Our velocity estimates reveal substantial lateral contrasts and correlate remarkably well with the geological background of the area, illuminating features such as the Newport‐Inglewood fault, the Silverado aquifer, and the San Gabriel river. We then use computational modeling to show that the presence of these small‐scale structures have a clear impact on the intensity of the expected shaking, and can cause ground‐motion motion acceleration to change by several factors over a subkilometer horizontal scale. These results shed light onto the scale of variations that can be expected in this type of tectonic settings and highlight the importance of resolution in modern‐day seismic hazard estimates.
Swim Bladder of Farmed Totoaba macdonaldi: A Source of Value-Added Collagen

Finding strategies to use the swim bladder of farmed totoaba (Totoaba macdonaldi) is of the utmost need to reduce waste. Fish swim bladders are rich in collagen; hence, extracting collagen is a promising alternative with benefits for aquaculture of totoaba and the environment. The elemental biochemical composition of totoaba swim bladders, including their proximate and amino acid compositions, was determined. Pepsin-soluble collagen (PSC) was used to extract collagen from swim bladders, and its characteristics were analyzed. Alcalase and papain were used for the preparation of collagen hydrolysates. Swim bladders contained 95% protein, 2.4% fat, and 0.8% ash (on a dry basis). The essential amino acid content was low, but the functional amino acid content was high. The PSC yield was high, at 68% (dry weight). The amino acid composition profile, electrophoretic pattern, and structural integrity analyses of the isolated collagen suggested it is a typical type-I collagen with high purity. The denaturalization temperature was 32.5 °C, probably attributable to the imino acid content (205 residues/1000 residues). Papain-hydrolysates (≤3 kDa) of this collagen exhibited higher radical scavenging activity than Alcalase-hydrolysates. The swim bladder from the farmed totoaba could be an ideal source to produce high-quality type I collagen and may be considered an alternative to conventional collagen sources or bioactive peptides.
Corporate social responsibility, continuous process improvement orientation, organizational commitment and turnover intentions

Purpose – The purpose of this paper is to examine how employee's perceptions of corporate social responsibility (CSR) relate to their perceptions of an organization's orientation toward continuous process improvement orientation (CPIO). In addition, this research considers how employee perceptions of CSR and CPIO relate to work attitudes and behavioral intentions. Design/methodology/approach – Using structural equations analysis, the paper examines survey data obtained from employees of a USA-based financial services firm to test hypotheses regarding employee's perceptions of corporate engagement in CSR and continuous process improvement, as well as their work attitudes. Findings – Employee perceptions of CSR and of CPIO are strongly related. Both CSR and CPIO have similar, but still unique effects upon organizational commitment (OC). CSR and CPIO do not directly impact employee's intentions to leave, but they do have an indirect effect on intentions to leave, which operates through OC. Research limitatio.
Modular multi-dimensional tool for emergency evacuation including location-based social network data

ABSTRACT This paper presents the concept of a modular multi-dimensional tool (MMDT) for evacuation planning models. The goal of MMDT is to propose alternative route and destination locations that can be evaluated and compared to one another. The proposed tool can represent a very large number of scenarios and its strength is in its modularity and efficiency. The MMDT can be applied using both conventional evacuation models and decentralised personalised evacuation models based on Location-Based Social Networks (LBSN) to reduce overall evacuation times. Large-scale test cases using anonymous LBSN data illustrate the MMDT on several scenarios. Results indicate a significant reduction in evacuation times when using decentralised personal evacuation.
ROMA robots for inspection of steel based infrastructures

The robotics laboratory of the Universidad Carlos III of Madrid has developed the ROMA climbing robots, which are able to travel along 3D complex environments to carry out inspection tasks. The ROMA robots family is able to self‐support its locomotion system and moves in a similar way to a caterpillar. During this motion the robots generate in real‐time an optimal path and grasp planning in order to ensure a stable self‐support and avoid obstacles in the environment. In order to move the robots in a teleoperated or automatic way a man‐machine interface has been developed. The robots were developed primarily for use during the inspection of complex metallic structures. There are a large number of operations to be performed on metallic structures such as those encountered in bridges, oil rigs and building skeletons which represent a danger for human operators. Nevertheless, they are not limited to these structures due to fact that they are equipped with the necessary tools for new environments like concrete.
Power losses analysis in the windings of electromagnetic gear

Presents 3D method for the computation of the winding current distribution and power losses of the electromagnetic gear. For a prescribed current obtained from measurement, the transient eddy current field is defined in terms of a magnetic vector potential and an electric scalar potential. From numerically obtained potentials the power losses are determined. The winding power losses calculation of an electromagnetic gear shows that a given course of the current generates skin effect and significantly changes the windings resistances. Also presents the designing method for reducing power losses.
Retrieval of Chlorophyll Content from Leaf Reflectance Spectra Using Support Vector Machine

The chlorophyll content in crop leaf is an indicator of health situation and the crop yield. Hence, it is very important to retrieval of accurate chlorophyll content in paddy rice. This research selected Zhengyi town of Suzhou city as the study area, measurements were acquired during the summer of 2009, in a field campaign in which for 288 rice leaf samples, rice hyperspectral data was measured by ASD FieldSpec3 spectrometer, chlorophyll content was measured by using a SPAD-502 chlorophyll meter. And the parameters of support vector machine were optimized by genetic algorithm, then support vector machine and PROSPECT radiative transfer model were adopted to build estimation model, which used to retrieve the chlorophyll content of rice. The results indicate that: the coefficient of determination for the rice chlorophyll estimation model is 0.8825, and RMSE is 8.7491. Research of this paper provides some reference for quickly and accurately estimating the chlorophyll content in rice.
The Electronic Eye: The Rise of Surveillance Society

Preface and Acknowledgements. Part I: Situating Surveillance:. 1. Introduction: Body, Soul and Credit Card. 2. Surveillance in Modern Society. 3. New Surveillance Technologies. 4. From Big Brother to the Electronic Panopticon. Part II: Surveillance Trends:. 5. The Surveillance State: Keeping Tabs on You. 6. The Surveillance State: From Tabs to Tags. 7. The Transparent Worker. 8. The Targetted Consumer. Part III: Counter--Surveillance:. 9. Challenging Surveillance. 10. Privacy, Power, Persons. 11. Against Dystopia, Distance, Division. 12. Beyond Postmodern Paranoia. Bibliography. Index.
The German vocational education and training system: Its institutional configuration, strengths, and challenges

Germany is widely known for its high-quality vocational education and training (VET) system. The two key features of that system are (a) firm-based training programs accompanied by a schoolbased component (of one to two days per week), in which apprentices acquire upper secondary general education in core subjects (like math and German) and theoretical knowledge in their training occupation. This duality of practical and theoretical knowledge acquired at the workplace and at vocational schools is (b) accompanied by the private-public duality in the governance structure (i.e., public governance of the vocational schools, provide governance of the firm-based training). In the recent recession, this so-called dual system has received much international attention, for instance in the US, UK, or Spain. Whereas youth unemployment has increased enormously in the last years in many (European) countries, this has not been the case in Germany. From the outside, therefore, it may look as if Germany's low youth unemployment rate is to be credited to the dual system. That observation, however, is only partly correct, as we will discuss in this paper. The aim of this paper is to provide information on the German VET system, enabling international readers to better understand its institutional setting, its strengths and appeal, but also its challenges and weaknesses. Therefore, the paper is structured as follows. We will start with the institutional configuration of the German VET system (Section 2), describing its institutional prerequisites and its different sectors. As we will see, there is more to Germany's VET system than the well-known dual system. We will proceed by presenting some historical developments, necessary to understand the longevity of Germany's VET system and the ways in which it has dealt with the challenges of transitioning towards a "knowledge-based" society (Section 3). In Section 4, we will discuss both the potential and the problems of the apprenticeship system with regard to including low-achieving or disabled youth. Afterwards, we will briefly compare Germany to the dual systems of Austria, Denmark and Switzerland (Section 5). This comparison will reveal that the framework of a dual system allows for a variety of configurations - an information that might be of special interest to international readers who want to better understand Germany's "exceptionalism" regarding the divide between vocational and general higher education. We will conclude the paper with some findings regarding the importance of the German VET system in terms of labor market entry (Section 6) and some lessons that can be learnt from the insights presented in the paper.
Identification of anthraquinone dye colourants in red fibres from an Ohio Hopewell mound site by direct analysis in real time mass spectrometry

AbstractAmong prehistoric native groups of North America, the Ohio Hopewell was a particularly notable cultural manifestation that prevailed during the Middle Woodland Period (ca. 2100–1500 BP). The group is defined by the construction of elaborate burial mounds, evidence of ritual burning and cremation ceremonies, and a multitude of intricately decorated objects formed from materials obtained through extensive trade networks. While relatively few textile artefacts survive, those that have been preserved reflect a comprehensive knowledge of plant and animal fibre, dye, and pigment sources, and manipulation of those materials to create complex decorated fabric structures. The research reported herein is part of a programme to understand the materials used and the processes of colouration employed prehistorically. To investigate the sources of dye colourants, we applied direct analysis in real time – time-of-flight mass spectrometry (DART-MS) to both comparative materials and archaeological fibres from thre.
Behavior modeling through CHAOS for simulation of dismounted soldier operations

One of the major challenges in human behavior modeling for military applications is dealing with all factors that can influence behavior and performance. In a military context, behavior and performance are influenced by the task at hand, the internal (cognitive and physiological) and external (climate, terrain, threat, equipment, etc.) state. Modeling the behavioral effects of all these factors in a centralized manner would lead to a complex rule-base that is difficult to maintain or expand. To better cope with this complexity we have developed the Capability-based Human-performance Architecture for Operational Simulation (CHAOS). CHAOS is a multi-agent system for human behavior modeling that is based on pandemonium theory. Every agent in CHAOS represents a specific part of behavior, such as 'reaction to threat' or 'performing a patrol task'. These agents are competing over a limited set of resources that represent human capabilities. By combining the element of competition with multiple limited resources, CHAOS allows us to model stress, strain and multi-tasking in an intuitive manner. The CHAOS architecture is currently used in firefighter and dismounted soldier simulations and has shown itself to be suitable for human behavior and performance modeling.
Modelling road accidents: An approach using structural time series

In this paper, the trend of road accidents in Malaysia for the years 2001 until 2012 was modelled using a structural time series approach. The structural time series model was identified using a stepwise method, and the residuals for each model were tested. The best-fitted model was chosen based on the smallest Akaike Information Criterion (AIC) and prediction error variance. In order to check the quality of the model, a data validation procedure was performed by predicting the monthly number of road accidents for the year 2012. Results indicate that the best specification of the structural time series model to represent road accidents is the local level with a seasonal model.
Differences in Multi-Faceted Lifestyles in Response to the COVID-19 Pandemic and Their Association with Depression and Quality of Life of Older Adults in South Korea: A Cross-Sectional Study

Background: The World Health Organization declared COVID-19 a global pandemic on 11 March 2020, due to the number of newly reported confirmed cases and the rapid increase in deaths. Therefore, countries around the world limited their population to policies such as “social distancing” or “staying at home” to prevent the spread of the virus. The purpose of this study was to evaluate differences in lifestyle pre and post the outbreak COVID-19 among older adults in South Korea and to identify the impact of lifestyle differences on depression and quality of life. Methods: An online single questionnaire covering sociodemographic data, lifestyle details, depression status, and quality of life level was distributed using mailing lists and social media. To assess lifestyles differences in older people pre and post the outbreak of COVID-19 pandemic, the online single questionnaire was used post COVID-19 pandemic. Based on the participants’ memories, they responded lifestyles at two time points (pre and post COVID-19 pandemic). Results: The results showed that there was a significant decrease in physical activity and activity participation during the pandemic. In terms of nutrition, there was no statistically significant change pre and post the outbreak COVID-19, except for the intake of protein, fat, and vitamins. Additionally, the results showed that the resulting lifestyle differences seem to have had a negative impact on depression and quality of life among older adults in South Korea. Conclusion: There was a significant difference the lifestyle patterns among the participants in South Korea between the current period and pre COVID-19 pandemic. Additionally, it is observed that these differenced lifestyles were associated with depression and quality of life among the participants. Our findings may help to develop public health programs that support healthy lifestyles in pandemic conditions.
A deep learning method for eliminating head motion artifacts in computed tomography.

PURPOSE Involuntary patient movement results in data discontinuities during computed tomography (CT) scans which lead to a serious degradation in the image quality. In this paper, we specifically address artifacts induced by patient motion during a head scan. METHOD Instead of trying to solve an inverse problem, we developed a motion simulation algorithm to synthesize images with motion induced artifacts. The artifacts induced by rotation, translation, oscillation and any possible combination are considered. Taking advantage of the powerful learning ability of neural network, we designed a novel 3D network structure with both a large reception field and a high image resolution to map the artifact free images from artifact contaminated images. Quantitative results of the proposed method were evaluated against the results of U-Net and proposed networks without dilation structure. 30 sets of motion contaminated images from two hospitals were selected to do clinical evaluation. RESULT Facilitating the training dataset with artifacts induced by variable motion patterns and the neural network, the artifact can be removed with good performance. Validation dataset with simulated random motion pattern showed outperformed image correction, and quantitative results showed the proposed network had the lowest normalized root mean-square-error, highest peak signal-to-noise ratio and structure similarity, indicating our network gave the best approximation of gold standard. Clinical image processing results further confirmed the effectiveness of our method. CONCLUSION We proposed a novel deep learning based algorithm to eliminate motion artifacts. The CNNs trained with synthesized image pairs achieved promising result in artifacts reduction. The corrected images increased the diagnostic confidence compared with artifacts contaminated images. We believe that the correction method can restore the ability to successfully diagnose and avoid repeated CT scans in certain clinical circumstances. This article is protected by copyright. All rights reserved.