Miracle-dz/UniFAR-FTUs huggingface dataset:
CORRELATION OF COMPUTED TOMOGRAPHY AND INTRAOPERATIVE FINDINGS WITH CLINICAL SYMPTOMS IN HEAD TRAUMA PATIENTS

Hematoma in different parts of the brain is one of the most important complications of head injury and is associated with high mortality and morbidity rate. The aim of this study was evaluation of the relationship between Computed Tomography (CT) and intraoperative findings with clinical symptoms in head trauma patients. In this study 95 patients with cerebral hemorrhage due to head trauma, referred to Taleghani Hospital in Kermanshah were studied. After an initial clinical examination, the level of consciousness determined according to the Glasgow Coma Scale (GCS) was recorded. All patients underwent brain CT scan and findings were recorded, including size and location of the hematoma. Patients in all treatment such as surgical procedures under the supervision, and the information on their status was recorded until discharge or death. It was found that most patients (38%) were between 40-20 years: 73% of patients were male, while 27% were female. The outcome of 35 patients (35.4%) were normal, 12 patients (12.3%) had moderate disability, 9 patients (9.2%) had severe disability, 11 patients (10.8%) vegetative state and 31 patients (32.3%) died. There was a significant association between location of the hematoma and hematoma in CT scan and outcome of patients with cerebral hemorrhage caused by trauma (P<0.05). We also found a significant association between size of the hematoma and midline shift in CT scan with outcome of patients with cerebral hemorrhage caused by trauma (P<0.05). The prognosis of patients with traumatic brain injury depends on location of the hematoma; volume of hematoma, midline shift in CT scan and length of trauma to surgery more than 4 hours.
SARS-CoV-2 infection results in lasting and systemic perturbations post recovery

SARS-CoV-2 has been found capable of inducing prolonged pathologies collectively referred to as Long-COVID. To better understand this biology, we compared the short- and long-term systemic responses in the golden hamster following either SARS-CoV-2 or influenza A virus (IAV) infection. While SARS-CoV-2 exceeded IAV in its capacity to cause injury to the lung and kidney, the most significant changes were observed in the olfactory bulb (OB) and olfactory epithelium (OE) where inflammation was visible beyond one month post SARS-CoV-2 infection. Despite a lack of detectable virus, OB/OE demonstrated microglial and T cell activation, proinflammatory cytokine production, and interferon responses that correlated with behavioral changes. These findings could be corroborated through sequencing of individuals who recovered from COVID-19, as sustained inflammation in OB/OE tissue remained evident months beyond disease resolution. These data highlight a molecular mechanism for persistent COVID-19 symptomology and characterize a small animal model to develop future therapeutics.
RIVER ENGINEERING ADAPTATIONS AGAINST THE GLOBAL WARMING−TOWARDS GENERALIZATION OF CLOSE-TO-NATURE RIVERS

本論文では，最初に，平成16年四国に上陸した6回の台風がもたらした水災害を事例に，河川整備とともに，維持管理の重要性を示している．次に，明治時代の自然状態の利根川とカナダの自然河川，および我が国一級河川の基準地点のデータ等を用い，次元解析から導かれた河川の無次元河幅，無次元水深と無次元河道形成流量の間に成立する力学関係式が，治水と環境の両面から望ましい川づくりと河川管理の指標となること，船底形断面形が望ましい横断面形であること，無次元河幅，無次元水深等が決まる力学関係の中で無次元掃流砂量も決まること，これらの関係式が多自然川づくりにおいて重要な役割を果たすこと，を示している．最後に，地球温暖化時代に向かい，治水，利水，河川環境を総合した多自然川づくり計画の普遍化の方向性について議論している．
A nested modelling approach to infrastructure performance characterisation

Abstract Reliable and accurate predictions of infrastructure condition can save significant amounts of money for infrastructure management agencies through better planned maintenance and rehabilitation activities. Infrastructure deterioration is a complicated, dynamic and stochastic process affected by various factors such as design, environmental conditions, material properties, structural capacities and some unobserved variables. Previous researchers have explored different types of modelling techniques, ranging from simple deterministic models to sophisticated probabilistic models, to characterise the deterioration process of infrastructure systems; however, these models have limitations in various aspects. Traditional deterministic models are inadequate to capture the uncertainties associated with infrastructure deterioration processes. State-based probabilistic models can only predict conditions at fixed time points. Time-based probabilistic models require frequent observations that, in practice, are not easy to perform. The goal of this research is to develop a new probabilistic model that is capable of capturing the stochastic nature of infrastructure deterioration, while at the same time avoiding the limitations of previous modelling efforts. The proposed nested model is based on discrete choice model theory. It can be used to predict the probability of an infrastructure system staying at defined condition states by relating an index representing the performance of the infrastructure to a number of explanatory variables that characterise the structural adequacy, traffic loading and environmental conditions of the infrastructure. The proposed model includes different possible implementation paths (sequential versus multinomial) depending on the considered explanatory variables and the available data. In the case study, the proposed probabilistic model is implemented with pavement performance data collected in Texas, yielding promising preliminary results.
Experimental Results of Structural Health Monitoring of Wind Turbine Blades

A 9 meter TX-100 wind turbine blade, developed under a Sandia National Laboratories R&D program, was recently fatigue tested to blade failure at the National Renewable Energy Laboratories, National Wind Technology Center. The fatigue test provided an opportunity to exercise a number of structural health monitoring (SHM) techniques and nondestructive testing (NDT) systems. The SHM systems were provided by teams from NASA Kennedy Space Center, Purdue University and Virginia Tech (VT). The NASA and VT impedance-based SHM systems used separate but similar arrays of Smart Material macro-fiber composite actuators and sensors. Their actuator activation techniques were different. The Purdue SHM setup consisted of several arrays of PCB accelerometers and exercised a variety of passive and active SHM techniques, including virtual and restoring force methods. A commercial off-the-shelf Physical Acoustics Corporation acoustic emission (AE) NDT system gathered blade AE data throughout the test. At a fatigue cycle rate around 1.2 Hertz, and after more than 4,000,000 fatigue cycles, the blade was diagnostically and visibly failing at the blade spar cap termination point at 4.5 meters. For safety reasons, the test was stopped just before the blade completely failed. This paper provides an overview of the SHM and NDT system setups, and some test results.
Joint learning of morphology and syntax with cross-level contextual information flow

Abstract We propose an integrated deep learning model for morphological segmentation, morpheme tagging, part-of-speech (POS) tagging, and syntactic parsing onto dependencies, using cross-level contextual information flow for every word, from segments to dependencies, with an attention mechanism at horizontal flow. Our model extends the work of Nguyen and Verspoor ((2018). Proceedings of the CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. The Association for Computational Linguistics, pp. 81–91. ) on joint POS tagging and dependency parsing to also include morphological segmentation and morphological tagging. We report our results on several languages. Primary focus is agglutination in morphology, in particular Turkish morphology, for which we demonstrate improved performance compared to models trained for individual tasks. Being one of the earlier efforts in joint modeling of syntax and morphology along with dependencies, we discuss prospective guidelines for future comparison.
Printed

: Low cost printed electronics have gained a great deal of interest over the past 5 years because of their promise to greatly reduce the cost of many electronic applications. In particular these applications include radio frequency identification (RFID) tags, displays, printed circuit boards (PCB)/keypads, chemical sensors, etc. The talk will include:
Enhanced situation awareness for unmanned aerial vehicle operating in terminal areas with circuit flight rules

This paper considers the situation awareness function associated with an unmanned aerial vehicle arriving at an uncontrolled airfield. Given no air traffic control service available within such a terminal area, the unmanned aerial vehicle needs to establish a good level of situation awareness by using its onboard sensors to detect and track other traffic aircraft. Comparing to the existing works which mainly use sensor observations in the filtering process, this paper exploits the circuit flight rules to provide extra knowledge about the target behaviour. This is achieved by using multiple models to describe the target motions in different flight phases and characterising the phase transition in a stochastic manner. Consequently, an interacting multiple model particle filter with state-dependent transition probabilities is developed to provide the required situation awareness function.
Research on the Relationship between Learning Engagement and Learning Completion of Online Learning Students

With the deep integration of the Internet and education, new educational models, such as micro classrooms, large-scale open online courses, and flipped classrooms have gradually appeared. In the information age, online learning has become popular in most countries all over the world, especially in the face of public crises such as the COVID-19 pandemic. Online learning can effectively connect teachers and students through the Internet. However, the low participation and low cognitive level of e-learning also affect the quality of e-learning, which depends largely on the learning investment of students. Based on self-efficacy theory and exploratory community theory, this paper puts forward the hypothesis that students’ e-learning investment affects learning completion and tests the mediating effect of incorporating self-efficacy into students’ e-learning investment on learning completion. The results show that the Cronbach’s α coefficient is 0.968, which has high reliability. KMO value is 0.880, indicating that data information can be extracted effectively. The three aspects of students’ e-learning input (behavioral investment, cognitive input, and emotional input) constructed in this paper can significantly improve the learning completion level of students. The results of this study have positive value for understanding the advantages and disadvantages of e-learning, promoting the reform of e-learning methods in universities, and implementing e-learning strategies for students familiar with different e-learning years.
Acoustic Evidence for the Development of Aspiration Feature in Putonghua Stops

This study was investigated developmental temporal features in Putonghua-speaking children. The total of 212children between the ages 2;6 and 6;5 participated in Shanghai. Speech materials were constructed according to aspiration feature in stop sounds of Putonghua. Six words were selected in this study. A voice onset time was measured. Non-parametric procedures were employed for all the analyses. The VOT value across bilabial, alveolar, and velar stops was significantly differed between aspirated and unaspirated stops for each age group. Effect of age is significant for unaspirated stops. It is clear that each of Putonghua stops showed decreasing mean and standard deviation. The overshoot phenomenon of VOT was apparent from the age of 2;6-2;11 to 4;6-4;11. There was high variability in the production of lag time for aspirated stops.
Enhancing start performance in the sport of skeleton

A fast start is considered to be crucial in skeleton with marginal gains in start performance perceived to make meaningful improvements to overall chances of success. Currently, knowledge surrounding the underlying determinants of start performance is sparse and training practices are based on limited scientific evidence. A series of investigations were conducted to advance this understanding. Initial observations revealed similarities between dry-land push-starts and those on ice tracks. However, the number of steps taken before loading was adjusted to seemingly accommodate unique track profiles and appeared to be influenced by physical capacity. Consequently, skeleton athletes completed multiple two day testing sessions (four physical tests and biochemical analyses) across two seasons, alongside dry land push track tests. Additionally, body composition was assessed either side of selected training and competition blocks. Three independent physical factors (countermovement jump, sprint and force-power characteristics) were identified as fundamental to a fast push start and a regression equation comprising these variables provided an accurate prediction of start ability (R^2 = 0.86). Testosterone appeared to influence push track performance and lean mass accrual, however, retrospective biochemical analyses were deemed to have limited utility in applied practice. Conversely, the importance of monitoring body composition, particularly across competition seasons, was apparent and dual energy X ray absorptiometry is an appropriate tool to detect meaningful changes. A continuous sled velocity measure confirmed the contribution of physical capabilities to both the distance and velocity attained before loading. Importantly, loading phase success appeared independent of physical ability, perhaps warranting specific loading technique training. Finally, a trade off between pre load velocity and load effectiveness was evident, and experimentally modifying loading distance provided a promising approach to improve performance in developing athletes. This thesis has informed skeleton training by identifying factors which contribute to performance, alongside approaches to thoroughly evaluate athlete progression and has introduced processes through which start performance can be enhanced.
Variable pay and job performance of shop-floor workers in lean production

Purpose – The purpose of this paper is to investigate design features of variable pay plans adopted for shop-floor workers engaged in manufacturing firms that had implemented lean production systems, and the effects of design features on their job performance. Design/methodology/approach – In total, 892 shop-floor workers attached to lean implemented manufacturing firms in Sri Lanka responded for the study. Structural equation modelling was used for the data analysis. Findings – It was found that the performance evaluation-base for variable payments, variable pay calculation-base and goal setting for variable pay significantly predict job performance of the shop-floor workers. Originality/value – It could be expected that the academics and practitioners alike are motivated by their desire to clearly apprehend the contribution of variable pay plans on job performance of the shop-floor workers engaged in lean production systems. This demands more investigations to better understand the design features of va.
Artificial Intelligent Models for Improved Prediction of Residential Space Heating

AbstractUsing artificial intelligence (AI) models, cost effective electricity meter, and easily accessible weather data, this paper discusses a methodology for improved prediction of hourly residential space heating electricity use. Four AI models [back propagation neural network (BPNN), radial basis function neural network (RBFNN), general regression neural network (GRNN), and support vector regression (SVR)] were used for predicting hourly residential heating electricity use. For this study, a typical single-family house was used to obtain the data used for AI prediction models. Results showed SVR’s ability to predict hourly residential heating electricity use was better when compared with other AI models. Furthermore, through comparison of prediction performance in different time periods, additional investigation was conducted to evaluate the effect of dynamic human behaviors on the prediction accuracy of the AI models. Results revealed that dynamic human behaviors have a negative effect on the predict.
The Multimodal Dimension of Claims in Food Packaging

Abstract Despite the proliferation of regulations governing health and content claims on food packages, customers continue to be confused and sometimes deceived. One reason for this is that most regulations adopt a rather restricted, language-centric view of what a claim is, a view that focuses on how claims represent reality as opposed to what claims “do”, the kinds of actions and human relationships they make possible. This paper introduces a perspective on package claims that takes account of their performative and multimodal dimensions. The pragmatic force of a food package, it argues, always depends on multiple conditions such as the identities of manufacturers, retailers and customers, the conditions under which packages are encountered, and the way multiple semiotic modes such as language, graphics, colors, fonts, the shape of packages, and even the shape and texture of the food itself interact. The multimodal dimension of claims is not limited to the intratextual links among different modes and different materialities on the package, but also the intertextual links among the elements on the package and other modes and materialities in the environments in which customers make purchasing decisions. A multimodal view of package claims attempts to understand how claims are situated within specific material contexts, specific social actions, and specific social practices, and how they function to index social identities for manufacturers, retailers, customers, and even government officials who make and enforce regulations governing their use.
Evaluating diffusion dispersion across an extended range of b‐values and frequencies: Exploiting gap‐filled OGSE shapes, strong gradients, and spiral readouts

To address the long echo times and relatively weak diffusion sensitization that typically limit oscillating gradient spin‐echo (OGSE) experiments, an OGSE implementation combining spiral readouts, gap‐filled oscillating gradient shapes providing stronger diffusion encoding, and a high‐performance gradient system is developed here and utilized to investigate the tradeoff between b‐value and maximum OGSE frequency in measurements of diffusion dispersion (i.e., the frequency dependence of diffusivity) in the in vivo human brain. In addition, to assess the effects of the marginal flow sensitivity introduced by these OGSE waveforms, flow‐compensated variants are devised for experimental comparison.
Comparison of Different Dosages of Alteplase in Atrial Fibrillation–Related Acute Ischemic Stroke After Intravenous Thrombolysis: A Nationwide, Multicenter, Prospective Cohort Study in Taiwan

Background Insufficient evidence is available for patients with acute ischemic stroke with atrial fibrillation (AF) to determine the efficacy and safety of different dosages of intravenous thrombolysis treatment. This study examined clinical outcomes in Chinese patients with stroke with and without AF after intravenous thrombolysis treatment with different intravenous thrombolysis doses. Methods and Results This multicenter, prospective cohort study recruited 2351 patients with acute ischemic stroke (1371 with AF and 980 without AF) treated with intravenous thrombolysis using alteplase. The Totaled Health Risks in Vascular Events score is a validated risk‐scoring tool used for assessing patients with acute ischemic stroke with and without AF. We evaluated favorable functional outcome at day 90 and symptomatic intracranial hemorrhage within 24 to 36 hours and outcomes of the patients receiving different doses of alteplase. Compared with the non‐AF group, the AF group exhibited a 2‐ to 3‐fold increased risk of symptomatic intracranial hemorrhage according to the National Institute of Neurological Disorders and Stroke standard (relative risk [RR], 2.10 [95% CI, 1.35–3.26]). Favorable functional outcome at 90 days and symptomatic intracranial hemorrhage rates according to the European Cooperative Acute Stroke Study II and the Safe Implementation of Thrombolysis in Stroke‐Monitoring Study standards did not significantly differ between the AF and non‐AF groups. In addition, the low‐dose alteplase subgroup exhibited an increased risk of symptomatic intracranial hemorrhage according to the National Institute of Neurological Disorders and Stroke standard (RR, 2.84 [95% CI, 1.63–4.96]). A validation study confirmed these findings after adjustment for scores determined using different stroke risk‐scoring tools. Conclusions Different alteplase dosages did not affect functional status at 90 days in the AF and non‐AF groups. Thus, the adoption of low‐dose alteplase simply because of AF is not recommended.
Implementing and Evaluating the Integration of Critical Thinking into Problem Based Learning in Environmental Building

Abstract The theorising of critical thinking has long been attempted in education. Problem Based Learning (PBL) has also been reviewed and utilised in higher education building and construction related subjects. However, how certain ‘key skills’, such as critical thinking, can be integrated into PBL in order to promote deeper learning in these complex multidisciplinary environments, warrants further exploration. This paper intends to address this knowledge gap by evaluating the integration of critical thinking into PBL, drawing on a recent Higher Education Academy funded research project. The research employed a multi-methodological research design, and was carried out on a large and innovative building ‘design and build’, multi-disciplinary group project, undertaken by 67 second-year undergraduate students in the environmental building discipline at a UK university. A process model developed in the previous research was adopted for integrating critical thinking into PBL and its integration and use indicated a number of impacts on student learning. The content and timing of the integration of critical thinking theory and practice requires modification to suit student learning more effectively. Greater emphasis on critical thinking in the project, particularly linked with assessment, is also needed. The holistic approach adopted provides a useful mechanism to maximise the benefits of integrating critical thinking into PBL, which supports students in achieving optimised design solutions and enhanced employability. The findings provide evidence of capitalising on the marriage of critical thinking with PBL in environmental building education, and should contribute to future debate on critical thinking and PBL in the wider knowledge community.
Finite Element Analysis of Total Knee Arthroplasty

Total knee arthroplasty (TKA) has become one of the most common operations in all of medicine, with an average of 966,000 performed annu - ally in the United States. Since its introduction in 1968, TKA surgical techniques and implant designs have improved to increase prosthesis longevity. However, aseptic loosening of TKA components continues to be the most common reason for revision surgery. Ideal implant characteristics, which minimize stress shielding have yet to be determined. The purpose of this study was to investigate how implant design parameters including fixation technique, tibial stem geometry, cement stiffness, and implant-bone frictional interface affect stress shielding within the tibia, and to determine the optimal combination that minimizes this effect. A CT scan of a tibia was used to simulate multiple tibial stem configurations for finite element analysis in ABAQUS. Stress shielding was calculated by using the average minimum principal stress at interval cuts along the length of the tibia. We found that short, fully-cemented stems, high-stiffness hybrid cemented stems, low-stiffness for fully-cemented stems, and a sliding friction interface to be ideal.