Special interest tourism is not so special after all: Big data evidence from the 2017 Great American Solar Eclipse
جهانگردی با علاقه ویژه از همه مهم تر نیست: شواهد داده های بزرگ از خورشید گرفتگی بزرگ آمریکایی 2017-2020
This study puts to empirical test a major typology in the tourism literature, mass versus special interest tourism (SIT), as the once-distinctive boundary between the two has become blurry in modern tourism scholarship. We utilize 41,747 geo-located Instagram photos pertaining to the 2017 Great American Solar Eclipse and Big Data analytics to distinguish tourists based on their choice of observational destinations and spatial movement patterns. Two types of tourists are identified: opportunists and hardcore. The motivational profile of those tourists is validated with the external data through hypothesis testing and compared with and contrasted against existing motivation-based tourist typologies. The main conclusion is that large share of tourists involved in what is traditionally understood as SIT activities exhibit behavior and profile characteristic of mass tourists seeking novelty but conscious about risks and comforts. Practical implications regarding the potential of rural and urban destinations for developing SIT tourism are also discussed.
Keywords: Big data | Instagram photos | Social media | Spatial analysis | Special interest tourism | Astro-tourism
STrategically Acquired Gradient Echo (STAGE) imaging, part III: Technical advances and clinical applications of a rapid multi-contrast multi-parametric brain imaging method
تصویربرداری گرادیان اکو (STAGE) استراتژیک ، بخش سوم: پیشرفت های فنی و برنامه های بالینی از یک روش تصویربرداری سریع مغزی چند پارامتری سریع با کنتراست-2020
One major thrust in radiology today is image standardization with a focus on rapidly acquired quantitative multi-contrast information. This is critical for multi-center trials, for the collection of big data and for the use of artificial intelligence in evaluating the data. Strategically acquired gradient echo (STAGE) imaging is one such method that can provide 8 qualitative and 7 quantitative pieces of information in 5 min or less at 3 T. STAGE provides qualitative images in the form of proton density weighted images, T1 weighted images, T2* weighted images and simulated double inversion recovery (DIR) images. STAGE also provides quantitative data in the form of proton spin density, T1, T2* and susceptibility maps as well as segmentation of white matter, gray matter and cerebrospinal fluid. STAGE uses vendors product gradient echo sequences. It can be applied from 0.35 T to 7 T across all manufacturers producing similar results in contrast and quantification of the data. In this paper, we discuss the strengths and weaknesses of STAGE, demonstrate its contrast-to-noise (CNR) behavior relative to a large clinical data set and introduce a few new image contrasts derived from STAGE, including DIR images and a new concept referred to as true susceptibility weighted imaging (tSWI) linked to fluid attenuated inversion recovery (FLAIR) or tSWI-FLAIR for the evaluation of multiple sclerosis lesions. The robustness of STAGE T1 mapping was tested using the NIST/NIH phantom, while the reproducibility was tested by scanning a given individual ten times in one session and the same subject scanned once a week over a 12-week period. Assessment of the CNR for the enhanced T1W image (T1WE) showed a significantly better contrast between gray matter and white matter than conventional T1W images in both patients with Parkinsons disease and healthy controls. We also present some clinical cases using STAGE imaging in patients with stroke, metastasis, multiple sclerosis and a fetus with ventriculomegaly. Overall, STAGE is a comprehensive protocol that provides the clinician with numerous qualitative and quantitative images.
Keywords: Quantitative magnetic resonance imaging | Susceptibility weighted imaging | T1 mapping | Quantitative susceptibility mapping | Multi-parametric magnetic resonance imaging | Strategically acquired gradient echo
Associations of hospital discharge services with potentially avoidable readmissions within 30 days among older adults after rehabilitation in acute care hospitals in Tokyo, Japan
انجمن خدمات ترخیص بیمارستان با بستری مجدد بالقوه قابل اجتناب در عرض 30 روز در میان سالمندان بعد از توانبخشی در بیمارستانهای مراقبت حاد در توکیو ، ژاپن-2020
OBJECTIVE: To examine the associations of three major hospital discharge services covered under health insurance (discharge planning, rehabilitation discharge instruction, and coordination with community care) with potentially avoidable readmissions within 30 days (30-day PAR) in older adults after rehabilitation in acute care hospitals in Tokyo, Japan.
DESIGN: Retrospective cohort study using a large-scale medical claims database of all Tokyo residents aged ≥75 years. SETTING: Acute care hospitals PARTICIPANTS: Patients who underwent rehabilitation and were discharged to home (n=31,247; mean age: 84.1 years, standard deviation: 5.7 years) between October 2013 and July 2014.
MAIN OUTCOME MEASURE: 30-day PAR.
RESULTS: Among the patients, 883 (2.9%) experienced 30-day PAR. A multivariable logistic generalized estimating equation model (with a logit link function and binominal sampling distribution) that adjusted for patient characteristics and clustering within hospitals showed that the discharge services were not significantly associated with 30-day PAR. The odds ratios were 0.962 (95% confidence interval [CI]: 0.805-1.151) for discharge planning, 1.060 (95% CI: 0.916-1.227) for rehabilitation discharge instruction, and 1.118 (95% CI: 0.817-1.529) for coordination with community care. In contrast, the odds of 30-day PAR among patients with home medical care services were 1.431 times higher than those of patients without these services (P<0.001), and the odds of 30-day PAR among patients with a higher number (median or higher) of rehabilitation units were 2.031 times higher than those of patients with a lower number (below median) (P<0.001). Also, the odds of 30-day PAR among patients with a higher hospital frailty risk score (median or higher) were 1.252 times higher than those of patients with a lower score (below median) (P=0.001).
CONCLUSIONS: The insurance-covered discharge services were not associated with 30-day PAR, and the development of comprehensive transitional care programs through the integration of existing discharge services may help to reduce such readmissions.
Copyright © 2020. Published by Elsevier Inc.
KEYWORDS: Big data; health services for the aged; patient readmission; rehabilitation; transitional care
Imaging of microdefects in ZnGeP2 single crystals by X-ray topography
تصویربرداری از ریزگردها در بلورهای تک ZnGeP2 توسط توپوگرافی با اشعه X-2020
The contrast from microdefects in ZnGeP2 crystals is studied. Simulation of images in X-ray topography based on the Borrmann effect is carried out for a model of a coherent inclusion of spherical form in an infinite isotropic matrix. For this simulation, a semiphenomenological theory of contrast from defects with a slowly changing deformation field is applied. It is shown that the contrast from the inclusion is a complex function, depending on the nature of defect (sign of the deformation of the matrix), the magnitude of the deformation caused by the defect, its depth in the crystal, the modulus of the diffraction vector g and the topography used (reflection or transmission). The most common images are intensity rosettes of double or triple contrast, whose lobes are elongated along the diffraction vector. These are created by inclusions, located near the X-ray exit surface of the sample. Analysis of experimental data shows that the majority of microdefects in ZnGeP2 revealed by Borrmann method (~96%) show good agreement with proposed model. All the features of the experimental images are explained by the theory. Additionally, the contrast from dislocation loops and from groups of big inclusions which have non-Coulombic deformation fields is observed
Keywords: B2. Nonlinear optic materials | A2. Bridgman technique | A2. Seed crystals | A1. Xray topography | A1. Defects| A1. Computer simulation
TUORIS: A middleware for visualizing dynamic graphics in scalable resolution display environments
TUORIS: واسط برای تجسم گرافیک پویا در محیطهای با وضوح مقیاس پذیر-2020
In the era of big data, large-scale information visualization has become an important challenge. Scalable resolution display environments (SRDEs) have emerged as a technological solution for building high-resolution display systems by tiling lower resolution screens. These systems bring serious advantages, including lower construction cost and better maintainability compared to other alternatives. However, they require specialized software but also purpose-built content to suit the inherently complex underlying systems. This creates several challenges when designing visualizations for big data, such that can be reused across several SRDEs of varying dimensions. This is not yet a common practice but is becoming increasingly popular among those who engage in collaborative visual analytics in data observatories. In this paper, we define three key requirements for systems suitable for such environments, point out limitations of existing frameworks, and introduce Tuoris, a novel open-source middleware for visualizing dynamic graphics in SRDEs. Tuoris manages the complexity of distributing and synchronizing the information among different components of the system, eliminating the need for purpose-built content. This makes it possible for users to seamlessly port existing graphical content developed using standard web technologies, and simplifies the process of developing advanced, dynamic and interactive web applications for large-scale information visualization. Tuoris is designed to work with Scalable Vector Graphics (SVG), reducing bandwidth consumption and achieving high frame rates in visualizations with dynamic animations. It scales independent of the display wall resolution and contrasts with other frameworks that transmit visual information as blocks of images
Keywords: distributed visualization | large-scale visualization | SVG
Personality, cardiovascular, and cortisol reactions to acute psychological stress in the Midlife in the United States (MIDUS) study
واکنش های شخصیت، قلب و عروق و کورتیزول حاد استرس روانی در میانسالی در (MIDUS) مطالعه ایالات متحده-2020
Recent research has suggested that diminished, as well as elevated reactivity to acute psychological stress is maladaptive. These differences in stress reactions have been hypothesized to relate to the Big Five personality traits, which are said to be biologically-based and stable across adulthood; however, findings have been inconclusive. This study sought to replicate the findings of the largest study conducted to date (Bibbey et al., 2013), with a sample of participants from the Midlife in the United States Study (MIDUS), aged between 35 and 84 years (M = 56.33, SD = 10.87). Participants (N = 817) undertook a standardized, laboratory-based procedure during which their cardiovascular and neuroendocrine reactivity to acute stress was measured. In contrast to Bibbey et al. (2013), associations between neuroticism and blunted reactivity did not withstand adjustment for confounding variables. Further, following adjustment for multiple tests, no significant positive association between agreeableness and HR reactivity was observed. Methodological differences between the studies, which may account in part for the contrasting findings, are discussed. Further conceptual replication research is needed to clarify associations between the Big Five personality traits and stress reactivity, across the lifespan.
Keywords: Replication | Acute stress | Personality | Cortisol | Cardiovascular reactivity
Temporal and spatial deep learning network for infrared thermal defect detection
شبکه یادگیری عمیق زمانی و مکانی برای تشخیص نقص حرارتی مادون قرمز-2019
Most common types of defects for composite are debond and delamination. It is difficult to detect the inner defects on a complex shaped specimen by using conventional optical thermography nondestructive testing (NDT) methods. In this paper, a hybrid of spatial and temporal deep learning architecture for automatic thermography defects detection is proposed. The integration of cross network learning strategy has the capability to significantly minimize the uneven illumination and enhance the detection rate. The probability of detection (POD) has been derived to measure the detection results and this is coupled with comparison studies to verify the efficacy of the proposed method. The results show that visual geometry group-Unet (VGG-Unet) cross learning structure can significantly improve the contrast between the defective and non-defective regions. In addition, investigation of different feature extraction methods in which embedded in deep learning is conducted to optimize the learning structure. To investigate the efficacy and robustness of the proposed method, experimental studies have been carried out for inner debond defects on both regular and irregular shaped carbon fiber reinforced polymer (CFRP) specimens.
Keywords: Deep learning | Segmentation | Thermography defect detection | Nondestructive testing
Image quality recognition technology based on deep learning
فن آوری تشخیص کیفیت تصویر مبتنی بر یادگیری عمیق-2019
Image plays an important role in today’s society and is an important information carrier. However, due to the problems in shooting or processing, image quality is often difficult to be guaranteed, and low-quality images are often difficult to identify, which results in the waste of information. How to effectively identify low-quality images has become a hot research topic in today’s society. Deep learning has a good application in image recognition. In this paper, it is applied to low-quality image recognition. An image quality recognition technology based on deep learning is studied to effectively realize low-quality image recognition. Firstly, in the stage of image preprocessing, a low-quality image enhancement method is proposed, which uses non-linear transformation to enhance image contrast image, restore image details and enhance image quality. Secondly, the convolutional neural network is used to extract image features, and the L2 regularization method is introduced to optimize the over-fitting problem. Finally, SVM is used to recognize the output of convolutional neural network to realize low quality image recognition. Through simulation analysis, it is found that the image enhancement method proposed in the preprocessing stage can effectively enhance the image quality, and deep learning can effectively realize the recognition of the enhanced image and improve the recognition accuracy.
Keywords: Low quality image | Deep learning | Image recognition | Support vector machines(SVM)
Practical card-based implementations of Yao’s millionaire protocol
پیاده سازی های عملی مبتنی بر کارت پروتکل میلیونر یائو-2019
Yao’s millionaire protocol enables Alice and Bob to know whether or not Bob is richer than Alice by using a public-key cryptosystem without revealing the actual amounts of their properties. In this paper, we present simple and practical implementations of Yao’s millionaire protocol using a physical deck of playing cards; we straightforwardly implement the idea behind Yao’s millionaire protocol so that even non-experts can easily understand their correctness and secrecy. Our implementations are based partially on the previous card-based scheme proposed by Nakai, Tokushige, Misawa, Iwamoto, and Ohta; their scheme admits players’ private actions on a sequence of cards called Private Permutation (PP), implying that a malicious player could make an active attack (for example, he/she could exchange some of the cards stealthily when doing such a private action). By contrast, our implementations rely on a familiar shuffling operation called a random cut, and hence, they can be conducted completely publicly so as to avoid any active attack. More specifically, we present two card-based implementations of Yao’s millionaire protocol; one uses a two-colored deck of cards (which consists of black and red cards), and the other uses a standard deck of playing cards. Furthermore, we also provide card-based protocols that rely on a logical circuit representing the comparison.
Keywords: Card-based protocols | Real-life hands-on cryptography | Secure multi-party computations | Yao’s millionaire protocol | Deck of cards
Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches
ایجاد پیوندهای محلی سازی ساختار و خاصیت برای تغییر شکل الاستیک کامپوزیت های کنتراست بالا سه بعدی با استفاده از روشهای یادگیری عمیق-2019
Data-driven methods are attracting growing attention in the field of materials science. In particular, it is now becoming clear that machine learning approaches offer a unique avenue for successfully mining practically useful process-structure-property (PSP) linkages from a variety of materials data. Most previous efforts in this direction have relied on feature design (i.e., the identification of the salient features of the material microstructure to be included in the PSP linkages). However due to the rich complexity of features in most heterogeneous materials systems, it has been difficult to identify a set of consistent features that are transferable from one material system to another. With flexible architecture and remarkable learning capability, the emergent deep learning approaches offer a new path forward that circumvents the feature design step. In this work, we demonstrate the implementation of a deep learning feature-engineering-free approach to the prediction of the microscale elastic strain field in a given threedimensional voxel-based microstructure of a high-contrast two-phase composite. The results show that deep learning approaches can implicitly learn salient information about local neighborhood details, and significantly outperform state-of-the-art methods.
Keywords: Materials informatics | Convolutional neural networks | Deep learning | Localization | Structure-property linkages