Miracle-dz/UniFAR-FTUs huggingface dataset:
Mantle dynamics beneath the discrete and diffuse plate boundaries of the <fc>J</fc>uan de <fc>F</fc>uca plate: Results from <fc>C</fc>ascadia <fc>I</fc>nitiative body wave tomography

We use the delay times of teleseismic S phases recorded by ocean bottom seismometers during the plate-scale Cascadia Initiative community experiment to constrain the heterogeneity of seismic velocity structure beneath young oceanic lithosphere. Our study area covers the entire Juan de Fuca (JdF) and Gorda plates, from their creation at the JdF and Gorda Ridges to their subduction beneath the North American continent, and the entire length of the Blanco transform fault. The range of the observed Vs anomalies requires variations in the melt fraction of the asthenosphere. The data require that low Vs anomalies extend to depths of at least 200 km, which is within the carbonatite melting regime. In the upper 200 km of the mantle, Vs increases rapidly to the east of the JdF Ridge, while there is no clear relationship with the age of the lithosphere in the Gorda region. The distribution of melt is asymmetric about both the JdF and Gorda Ridges. Dynamic upwelling – due to the buoyancy of the mantle – and accompanying downwelling can explain the rapid decrease in melt fraction to the east of the JdF Ridge, the asymmetry about the JdF Ridge, and the sinuous pattern of upwelling near the Blanco transform fault. Finally, mantle flow beneath the diffuse Gorda and Explorer plate boundaries is distinct from that beneath the discrete plate boundary of the JdF Ridge. In particular, shear between the Pacific and JdF plates appears to dominate mantle deformation over seafloor spreading beneath the Gorda Ridge.
A Pilot Feasibility Study on a Single-Session Stabilization Group Psychotherapy for Adults with Acute Stress Symptoms

Background: Although the field of psychology currently recommends trauma-focused cognitive behavioral therapy for early psychological intervention for acute traumatic stress, additional research is required for safe and efficient psychotherapy that can delivered to a broader population and within a brief period of time. Methods: This pilot study examined the safety and feasibility of a single-session group stabilization intervention for individuals conducted at an average of two weeks after various types of traumatic events. Further development of DSM-5 mental disorders, such as post-traumatic stress disorder (PTSD) and others, was assessed at the six-month follow-up. A total of 38 participants with acute stress symptoms participated in a single-session 90-minute group psychotherapy, which consists of psychoeducation; identification of and coping with triggers; somatosensory grounding; and containment exercise. Results: After six months, follow-up was conducted on 34 (89.5%) patients, who completed the Structured Clinical Interview for DSM-5 Mental Disorders and the PTSD Checklist-5. One (2.9%) participant met the current diagnosis of PTSD, whereas none met any other psychiatric diagnoses. A significant decrease was noted in PTSD scores between baseline and follow-up (t=7.4, df=33, p<0.001, Cohen’s d=1.27) measured using the PTSD Checklist-5. Conclusion: The finding suggests that a single stabilization session can be used in a safe and efficient manner at of the acute stage of trauma.
on an Easily Obtained Accurate

A 58-year-old Caucasian man was admitted for evaluation of a nephrotic syndrome. He had a past history of mild hypertension which had been treated with a combination of hydrochlorothiazide and amiloride. Blood pressure was 130/70 mmHg; there was no oedema. Serum electrolytes were normal, creatinine 112umol/l, albumin 29 g/l, gammaglobulin 5.3 g/1, total cholesterol 10.2 mmol/1; 24-h urinary protein ranged from 3 to 5 g; urinary sediment was normal, and urine culture sterile. GFR was 86 ml/min per 1.73 m, as assesssed by I Iothalamate clearance. Light microscopy and immunofluorescence studies of the specimens obtained by percutaneous renal biopsy revealed a typical pattern of stage 2 membranous nephropathy. Further evaluation showed normal blood cell count, serum complement, alphafetoprotein, and carcinoembryonic antigen; there was absence of anti-DNA antibodies, hepatitis B virus markers, antithyroglobulin and antimicrosomal antibodies, cryoglobulinaemia, and mercury in urine. VDRL and FTA tests were negative. There was no evidence of neoplasia, sarcoidosis, or multisystemic or infectious diseases. Drug inquiry revealed that the patient had been taking fenoprofen 600-1500 mg daily for the preceding 12 months, because of loin pain following a vertebral trauma. Fenoprofen was withdrawn while antihypcrtensive treatment was maintained, and complete remission of proteinuria occurred within 6 months, without steroid or immunosuppressive therapy, and was persisting 2 years later. Nephrotic syndrome induced by NSAID has been related to minimal-change glomerulopathy and has been most often associated with acute renal failure and severe intersitial nephritis [1]. NSAID-associated membranous glomerulonephritis is extremely unusual. We are aware of only four reported cases, three with diclofenac [2-4] and one with ketoprofen, a propionic-acid derivative like fenoprofen [5]. All patients presented with the nephrotic syndrome, and complete remission occurred in all within 12 months of stopping the presumed causative drug. Thus, a search for a prior NSAID treatment should be included in the drug inquiry conducted in every patient with membranous glomerulonephritis.
PLS-SEM Model of Leadership Characteristics facing Challenges in Malaysia Construction Industry

Ensuring successful completion of construction projects within the stipulated time, cost and quality; there are many challenges need to be addressed by construction leadership’s team. In addressing these challenges, the team members should possess relevant leadership characteristics. Hence, this paper presents a development of PLS-SEM model on the relationship between the required leadership characteristics with the related construction’s challenges. This model was generated using Smart PLS-SEM software based on questionnaire survey data from 50 valid responses. The data consisted of 43 leadership characteristics which are clustered in 5 groups and 9 construction’s challenges clustered in 1 group. The developed model indicated that Constructive Relationship (CR) group is the most influential group with strength of impact path (β) value of 0.451 in dealing the construction challenges with Genuine with subordinates as the most influential leadership characteristic. While, Quality is the most challenges item in construction challenges group. The model is beneficial to the industry community in facing dynamism of the construction challenges ahead.
ROLE OF SOCIOECONOMIC STATUS AND LITERACY LEVELS TOWARDS USAGE OF TOBACCO PRODUCTS AND ITS DEPENDENCY- A RETROSPECTIVE ANALYSIS

: Socioeconomic status (SES) and literacy level is a major determinant of tobacco use but little is known whether SES affects nicotine dependency or not. Socioeconomic status (SES) is strongly related to smoking behaviour. The association is so strong that smoking is regarded as a marker for deprivation and one can identify disadvantaged groups by simply observing their smoking prevalence. The main objective is to examine the effect of socioeconomic status (SES) on nicotine dependence. A retrospective study was conducted using case records of patients attending private dental college from July 2019- March 2020 . A total of 201 case sheets of patients who had the habit of tobacco use with recorded nicotine dependence score and their income and educational status were retrieved. The study included two measures of SES education and income using the Modified Kuppuswamy scale(revised version february 2019). Nicotine dependencies are assessed using Fagerstrom nicotine dependence scale. Descriptive statistics, chi-square test was used to analyze the data. More prevalence of literate participants(78.6%) and middle income smokers(<25000) (43.78%) are more prevalent among the study population . No statistically significant association was found between education and nicotine dependence scale(p = 0.750); income and nicotine dependence ( p=0.432) . Chi-square tests were used for association and descriptive statistics were used. Nicotine dependency shows insignificant variations across different indicators of SES- income and education among patients attending private dental college in Chennai.
Ef � cient Resource Allocation in Cooperative Co-evolution for Large-scale Global Optimization

 Cooperative Co-evolution (CC) is an explicit means of problem decomposition in multi-population evolutionary algorithms for solving large-scale optimization problems. For CC, subpopulations representing subcomponents of a large-scale optimization problem co-evolve, and are likely to have different contributions to the improvement of the overall objective value of the original problem. Hence it makes sense that more computational resources should be allocated to the subpopulations with greater contributions. In this paper, we study how to allocate computational resources in this context and subsequently propose a new CC framework named CCFR to ef � ciently allocate computational resources among the subpopulations according to their contributions to the improvement of the best objective value. Our experimental results and analysis suggest that CCFR can make ef � cient use of computational resources and is a highly competitive CC framework for solving large-scale optimization problems.
Building a Multilingual Taxonomy of Olfactory Terms with Timestamps

Olfactory references play a crucial role in our memory and, more generally, in our experiences, since researchers have shown that smell is the sense that is most directly connected with emotions. Nevertheless, only few works in NLP have tried to capture this sensory dimension from a computational perspective. One of the main challenges is the lack of a systematic and consistent taxonomy of olfactory information, where concepts are organised also in a multi-lingual perspective. WordNet represents a valuable starting point in this direction, which can be semi-automatically extended taking advantage of Google n-grams and of existing language models. In this work we describe the process that has led to the semi-automatic development of a taxonomy for olfactory information in four languages (English, French, German and Italian), detailing the different steps and the intermediate evaluations. Along with being multi-lingual, the taxonomy also encloses temporal marks for olfactory terms thus making it a valuable resource for historical content analysis. The resource has been released and is freely available.
Proficient Product Quantization for Large-Scale High Dimensional Data Using Approximate Nearest Neighbor Search

: K-nearest neighbor's classification and regression is broadly utilized as a part of data mining because of its easiness and precision. At the point when a prediction is required for an inconspicuous data case, the KNN algorithm will search through the preparation dataset for the k most comparable occasions. Finding the esteem k is application subordinate, thus a nearby esteem is set which expands the exactness of the issue. Grouping the question the lion's share class of its k neighbors is called K-nearest neighbors classification. In this paper the occurrence or question be grouped is known as the issue protest or venture in short. Worldwide KNN approach utilizes the entire data for searching the k-nearest neighbors of the venture. For data KNN approach is utilized where test objects are arbitrarily chosen from the preparation data space. Keeping in mind the end goal to enhance the exactness of finding the correct k-neighbors of nearby KNN, among various ANN approaches proposed in the current years, the ones in light of vector quantization emerge, accomplishing best in class comes about. Product quantization (PQ) decays vectors into subspaces for independent handling, taking into account quick query based separation estimations. This postulation work intends to lessen the intricacy of AQ by changing a solitary most costly stride in the process – that of vector encoding. Both the remarkable search execution and high expenses of AQ originated from its all-inclusive statement, along these lines by forcing some novel outside imperatives it is conceivable to accomplish a superior trade off: lessen many-sided quality while holding the precision advantage over other ANN strategies.
Syapse at the NTCIR-16 RealMed-NLP task

In this paper, we present our approach to subtasks 1-RR-EN, 2-RR-EN, and 3 ADE and CI of the NTCIR-16 RealMed-NLP challenge. For these challenges, the English language corpora (CR-EN and RR-EN) were used. In subtasks 1 and 2, the goal was to create an NLP system which could add tags to case reports (CR) or radiology reports (RR). In subtask 3, two applications of this system were tested: the ability to determine which RRs from a group referred to the same sample, and the ability to determine the probability that a medication caused side effects in a report. Our approach leveraged keyword extraction through a medical metathesaurus (MetaMap), sentence structuring using a SciSpacy model, and word embeddings using a trained BERT model. Using this approach, we were able to complete these three subtasks with high levels of accuracy.
University of Birmingham Presenteeism, stress resilience, and physical activity in older manual workers:

This study used a person-centred approach to explore typologies of older manual workers based on presenteeism, stress resilience, and physical activity. Older manual workers ( n =217; 69.1% male; age range: 50 – 77; M age = 57.11 years; SD = 5.62) from a range of UK-based organisations, representing different manual job roles, took part in the study. A cross-sectional survey design was used. Based on the three input variables: presenteeism, stress resilience and physical activity, four distinct profiles were identified on using Latent Profile Analysis. One group (‘High sport/exercise and well-functioning’; 5.50%) engaged in high levels of sport/exercise and exhibited low levels of stress resilience and all types of presenteeism. Another profile (‘Physically burdened’; 9.70%) reported high levels of work and leisure-time physical activity, low stress resilience, as well as high levels of presenteeism due to physical and time demands. A ‘Moderately active and functioning’ group (46.50%) exhibited moderate levels on all variables. Finally, the fourth profile (‘Moderately active with high presenteeism’; 38.20%) reported engaging in moderate levels of physical activity and had relatively high levels of stress resilience, yet also high levels of presenteeism. The profiles differed on work affect and health perceptions largely in the expected directions. There were no differences between the profiles in socio-demographics. These results highlight complex within-person interactions between presenteeism, stress resilience, and physical activity in older manual workers. The identification of profiles of older manual workers who are at risk of poor health and functioning may inform targeted interventions to help retain them in the workforce for longer.
D EVELOPMENT OF A Q UALITY O UTCOME M ATRIX FOR M ALAYSIAN

The objective of this research was to develop a quality outcome matrix by exploring a systematic pattern for selecting quality tools and techniques in the Industrial Revolution 4.0, particularly in the smart manufacturing context. By conducting this analysis, the researchers found homogeneous patterns of sufficient quality case studies that can provide a basis for selecting appropriate groups of quality tools and techniques in different scenarios. Multiple case studies and an in-depth literature review were used as the research design approach. Two qualitative data collection methods were used: (1) primary data from face-to-face interviews with employees at Toyo Memory Technology and Intel Malaysia, and (2) secondary data from a previous study. Our study aimed to answer the following research question: “What are the appropriate tools and techniques concerning circumstances of quality dimensions and smart manufacturing? ” Thus, our research developed a diagnostic matrix by developing an outcome matrix for selecting appropriate quality tools and techniques. The scientific novelty of this work is that this is a real-world case study describing the authors’ theoretical and practical prerequisites that can help other researchers and industrial practitioners or manufacturers in selecting the appropriate sets of quality tools and techniques for solving quality problems.
Advanced statistical analysis for high-resolution space-time phenomena

Summary. Many spatiotemporal statistical applications exhibit complex patterns at various scales from local to global. One such example is nonstationarity, which can appear, for example, in: (i) the diurnal ﬂuctuations of solar irradiance as a driver for short term (hourly) weather processes such as temperature and wind speed [2], (ii) mesoscale atmospheric phenomena emerging from ﬂows past islands (hours, min-utes) or other geographical features, [1], (iii) switching mechanisms due to control actions in power systems (minutes, seconds), [7] (iv) geophysical and seismic ground motions (ms) due to the ringing after earth-quakes and the slow motion of tectonic plates in the Earth’s mantle, [6] (v) decadal processes such as global mean temperature and its relation to atmospheric CO 2 [10]. Temporal nonstationarity may be characterized by nonlinear mechanisms, switching regimes, cyclic correlation structure, or evolutionary changes in the distribution of the process. Conventional models fall short of proper modeling and prediction of truly nonstationary series except on short time horizons over which the process can be reasonably approximated with a stationary model. Proper treatment of these systems requires careful experimental design, exploratory methods which capture changes in distribution, and multivariate models that adequately contain coevolu-tionary mechanisms. important induction settling
Realistic and Practical Modeling of Tritium Deposition to Bare Soil

Abstract This paper develops a theory of tritium deposition, which is then combined with the experimental results, depicting the temperature and moisture dependence of the HT oxidation rate. The model describes the atmosphere-to-soil HT deposition process in terms of the atmospheric transport in the surface boundary layer, the transport in soil and the oxidation process by microbial activity. This model is favorably validated with the field data from the Canadian tritium release experiment. Based on this fundamental but sophisticated model, a practical model of HT deposition velocity is developed as a simple function of the soil temperature and the soil moisture. This model drastically reduces the uncertainty in the deposition velocity from a few orders of magnitude to a factor of 2.
Bayesian Model Selection for Constrained Multivariate Normal Linear Models

The expectations that researchers have about the structure in the data can often be formulated in terms of equality constraints and/or inequality constraints on the parameters in the model that is used. In a (M)AN(C)OVA model, researchers have expectations about the differences between the (adjusted) group means; in a repeated measures model, expectations can be stated between the measuremenet means over time; and in a (multivariate) regression model, expectations can be stated between the (standardized) regression coefficients. Based on different theories, different expectations can be formulated into a set of competing equality and inequality constrained models. The researcher is then interested which model receives most support from the data. This dissertation explores how the Bayes factor, a Bayesian model selection, can be used for this purpose.
C ONTINUOUS I NNOVATIONS AND S ERVICE E NGINEERING IN T OTAL S ERVICE S OLUTIONS : A C ASE S TUDY E XPLORING THE C OMBINATION

This paper presents a case study containing five mini-cases within a service provider of complex hardware with long operation times, and the innovations needed to succeed in a transition process towards total service solutions. The study describes how a generic engineering approach towards total service solutions for business customers can be supported by continuous innovations to enable customization. An important finding is that a journey towards being a substantial provider of total service solutions successfully can start with a radical innovation connected to the business model and continue with recombinative and formalization innovations to further build up the capabilities. The case indicates that a close cooperation with the customer is crucial and seems to increase the innovative performance. Some innovative solutions for structuring the offerings and the way of working have been identified in the case. Further, the case indicates the importance for a total service solutions provider to add more businesses and customers and continuously search for synergies between the different businesses.
Double Attention Convolutional Neural Network for Sequential Recommendation

The explosive growth of e-commerce and online service has led to the development of recommender system. Aiming to provide a list of items to meet a user’s personalized need by analyzing his/her interaction1 history, recommender system has been widely studied in academic and industrial communities. Different from conventional recommender systems, sequential recommender systems attempt to capture the pattern of users’ sequential behaviors and the evolution of users’ preferences. Most of the existing sequential recommendation models only focus on user interaction sequence, but neglect item interaction sequence. An item interaction sequence also contains rich contextual information for capturing the item’s dynamic characteristic, since an item’s dynamic characteristic can be reflected by the users who interact with it in a period. Furthermore, existing dual sequential models use the same method to handle the user interaction sequence and item interaction sequence, and do not consider their different characteristics. Hence, we propose a novel Double Attention Convolution Neural Network (DACNN), which incorporates user interaction sequence and item interaction sequence into an integrated neural network framework. DACNN leverages the strength of attention mechanism to capture the temporary suitability and adopts CNN to extract local sequential features. Experimental evaluations on the real datasets show that DACNN outperforms the baseline approaches.
Sublingual Microcirculation Imaging with Novel Optically-parallel Probe and Accurate Microcirculation Vessel Segmentation

Assessment of the perfusion state for capillaries provides important guidances in diagnosis and treatment of critically ill patients. A high-quality capture of video and image, convenient and effective analysis of vessels are the foundation of microcirculation assessment in clinical application. Here, we experimentally develop a sublingual microcirculation monitoring and imaging system that uses a front-end probe parallel to the bottom of tongue to control the force on sublingual tissues, and propose a U-Net based segmentation approach for extracting the vascular structures. Human sublingual microcirculation videos are acquired from both sidestream dark field and self-built imaging system to train and test the algorithm. The model locates most of blood vessels accurately and the area under receiver operating characteristic curve measure of the model achieves 0.95, which demonstrates an excellent performance and robustness of our algorithm.