Miracle-dz/UniFAR-FTUs huggingface dataset:
Formulation and Evaluation of Cephalexin Extended Release Matrix Tablets Using 32 Factorial Design

The aim of the present investigation was to prepare extended release film coated matrix tablets of cephalexin using binary mixture of two grades of hydrophilic polymer, hydroxypropyl methyl cellulose (HPMC), by direct compression method. Results of the preliminary trials indicated that the polymers used have significant release retarding effect on the formulation. To study the effect of concentration of polymers on drug release from matrix tablets, 3 2 full factorial design was applied. The concentration of HPMC K15M and HPMC 15cps were used as independent variables, while percentage drug release was selected as dependent variable. The dissolution data were fitted into zero-order, first-order, Higuchi and Korsemeyer-Peppas models to identify the pharmacokinetics and mechanism of drug release. Comparative study of dissolution profile of final batch F3 with market preparation (Sporidex AF 375) was done by similarity factor (f 2 ) determination and it was concluded that final formulation F3 (10% HPMC K15M, 17.5% HPMC 15cps) shows good similarity with the market product. The results of the accelerated stability study of final formulation F3 for 1 month revealed that storage conditions were not found to have made any significant changes in final formulation F3. The release of cephalexin was prolonged for 6 h by using polymer combinations of HPMC and a twice daily matrix tablet was formulated.
Determination of the Impedance Parameters of Antennas and the Maximum Power Transfer Efficiency of Wireless Power Transfer

In this paper, we derive formulas that calculate the impedance parameters (Z-parameters) of multiple antennas near objects to analyze wireless power transfer. Furthermore, we propose a method to calculate the impedance parameters of antennas, the maximum power transfer efficiency, and the optimum load impedance of a wireless power transfer system using equivalent current that generate the same electromagnetic field as that generated by an antenna in transmitting mode.
Predicting High School Graduation and Dropout Using a Hierarchical Generalized Linear Model Approach

Introduction High school graduation and dropout are important outcome measures for the school systems. These outcome measures can be predicted using important variables or predictors that have day-today application in educational settings. Several researchers have predicted high school graduation and dropout using relevant predictors (see predicted high school graduation and dropout using background and demographic variables such as low grades, aggressive behavior, student poverty, and parents' education level. Similarly, Rumberger and Larson (1998) predicted high school dropout using low grades, misbehavior, and high absenteeism in the model. Initially, this study used simple correlation analysis to show the relationship between number/percent of students at each achievement level of reading/mathematics and the number/percent of graduation/dropout. In addition, a logistic regression analysis was used in order to analyze the strength and direction of prediction due to potential predictors at the student level to predict the student's status of graduation/dropout. The significant correlation between school poverty and the status of graduation/dropout further suggested using school poverty to predict the student's status of graduation/dropout employing a hierarchical generalized linear model (HGLM). Thus, this paper predicts high school graduation and dropout using important predictors, such as student achievement, discipline, absenteeism, and student/school poverty, which are of interest for a large Florida school District. The study results would be helpful for school systems to improve student chances for graduation by means of intervening potential predictors such as discipline and absenteeism associated with the students at-risk of graduation and dropout.
Depression, Stress and Anxiety among Parents of Sons with
 Disabilities

Background: The prevention of depression, stress, and anxiety is an enormous concern of the World Health Organization and it has been a topic of research over the last decades. However, studies with families with sons or daughters with disabilities are scarce, which stresses out the need to develop scientific research in this area. It is of paramount importance to understand the impact of having a child with disability, so that effective strategies of action can be delineated. Goals: This study aims to assess depression, stress and anxiety of parents with sons or daughters having intellectual deficit, multi-deficiency or autism, considering gender, age of both parents and sons/daughters, as well as education level of the parents.Methods: With a cross-sectional observational study, data were collected at schools and at special education institutions through self-administered questionnaires. Two different groups of participants were included in the sample: parents of sons/daughters having intellectual deficit, multi-deficiency or autism, and parents of sons/daughters without disability. Participants were recruited according to a random stratified (by sons/daughters’ age and gender) random sampling. The Depression Anxiety Stress Scale (DASS-21) was administered to both groups of parents, with the collaboration of special educations teachers and school psychologists. Data were analysed using SPSS version 21.0. Descriptive and parametric inferential statistic (t-tests and ANOVA) were performed. Effect sizes based on differences between means were also calculated. Results: Overall, 871 participants were enrolled in the study: 403 parents (227 mothers and 176 fathers; 21 to 81 years old, M = 45) of sons/daughters having a disability, and 468 parents (242 mothers and 226 fathers; 25 to 79 years old, M = 43)) of sons/daughters without disabilities. Most of the children had intellectual disabilities (n = 206), followed by autism (n = 143) and multi-deficiency (n = 52). Parents with children/youth with disabilities showed higher levels of anxiety, depression and stress than the other parents. Levels of anxiety, depression and stress are associated with disabled sons/daughters’ age (higher levels associated to older ages) and negatively with parent’s education level (higher levels associated to lower education). Discussion: These results highlight the need to more deeply reflect about the necessity of designing intervention programs targeting the development of coping strategies to prevent or minimize parent’s anxiety, depression and stress. This type of intervention is of key importance to capacitate families to cope with these adverse conditions, thus promoting the mental health and psychological wellbeing of parents and disabled children. Keywords: Depression, Stress, Anxiety, Children, Disability.
cDNA Cloning, Heterogeneous Expression and Biochemical Characterization of a Novel Trypsin-Like Protease from Nilaparvata lugens

A reverse transcription-polymerase chain reaction (RT-PCR) strategy was used to clone diverse trypsin-like protease gene transcripts from midguts of the brown planthopper Nilaparvata lugens Stål (Homoptera: Delphacidae). Six individual trypsin-like protease transcripts were identifi ed. On the basis of one nucleotide sequence of the six clones, a full-length cDNA sequence (1902 bp) was obtained by rapid amplifi cation of cDNA ends (RACE). The cDNA contained an 1128-bp open reading frame encoding a putative protein of 375 amino acids with typical features of the trypsin-like protease. Heterogeneous expression of the coding sequence for the mature peptide in Escherichia coli cells showed that the expressed protease with a molecular weight of 27.0 is active, for its BApNAse activity assayed by using BApNA (N-benzoyl-D,L-arginine-p-nitroanilide) as substrate. The protease had its maximum activity at pH 8.0 and 35 °C. A much better stability was observed at pH values above 4.0 and temperatures below 40 °C. The enzyme was strongly inhibited by serine protease inhibitor. The trypsin-like protease is therefore likely one of the major digestive proteases responsible for protein hydrolysis in N. lugens gut, and multiple gene families encoding digestive proteases may help in adaptation of this sap-sucker to different rice varieties.
Analysis of clustering algorithms for spike sorting of multiunit extracellular recordings

ANALYSIS OF CLUSTERING ALGORITHMS FOR SPIKE SORTING OF MULTIUNIT EXTRACELLULAR RECORDINGS by Jayesh S Rege Various techniques have been considered in the past to identify distinct spike shapes from mulitunit extracellular recording. These techniques involve adaptive filtering techniques or template matching techniques or hierarchical clustering techniques. In this investigation, we have used Principal Component Analysis followed by various clustering techniques to identify distinct spike shapes. The amplitude filter is used to separate spikes from background neuronal activity. The correlation matrix of the spike data is used to compute principal component wave forms. Each spike is thus represented by the cOefficients of principal components. Then, We have used agglomorative hierarchical clustering algorithm to perform the initial clustering of the data set. The clustering results are then refined by the application of the Estimation Maximization Algorithm. The Bayesian Information Criteria(BIC) is used to find out best fit of the mOdel to the data set. ANALYSIS OF CLUSTERING ALGORITHMS FOR SPIKE SORTING OF MULTIUNIT EXTRACELLULAR RECORDINGS
Aircraft Vehicle Systems Modeling and Simulation under Uncertainty

In aircraft development, it is crucial to understand and evaluate behaviour, performance, safety and other aspects of subsystems before and after they are physically available for testing. Simulation models are used to gain knowledge in order to make decisions at all development stages. This paper describes the development of Saab Gripen´s vehicle systems and some methods and challenges related to uncertainties in test and model data. The ability to handle uncertain information and lack of information is the key to success in early design. The vehicle systems comprise fuel, environment control system (ECS), hydraulic, auxiliary power, escape, electrical power and landing gear system.
Reinforcement learning of walking behavior for a four-legged robot

In this paper, we investigate a reinforcement learning of walking behavior for a four-legged robot. The robot has two servo motors per leg, so this problem has eight-dimensional continuous state/action space. We present an action selection scheme for actor-critic algorithms, in which the actor selects a continuous action from its bounded action space by using the normal distribution. The experimental results show the robot successfully learns to walk in practical learning steps.
Images Restoration Using an Iterative Dynamic Programming Approach

A novel image restoration algorithm is presented in this paper. The restoration problem is formulated under the energy minimization framework and is solved using a dynamic programming-based approach. Through applying dynamic programming iteratively along both horizontal and vertical scanlines, the new algorithm can quickly converge to a near-global-optimal solution, without suffering the so-called streak artifacts. Experiments on both grayscale and color images demonstrate that the presented algorithm can effectively remove Gaussian noise and impulse noise from corrupted images, as well as to restore images with missing intensity values.
Connectivity Features for Identifying Cognitive Impairment in Presymptomatic Carotid Stenosis

Severe asymptomatic stenosis of the internal carotid artery (ICA) leads to increased incidence of mild cognitive impairment (MCI) likely through silent embolic infarcts and/or chronic hypoperfusion, but the brain dysfunction is poorly understood and difficult to diagnose. Thirty cognitively intact subjects with asymptomatic, severe (≧70%), unilateral stenosis of the ICA were compared with 30 healthy controls, matched for age, sex, cardiovascular risk factors and education level, on a battery of neuropsychiatric tests, voxel-based morphometry of magnetic resonance imaging (MRI), diffusion tensor imaging and brain-wise, seed-based analysis of resting-state functional MRI. Multivariate regression models and multivariate pattern classification (support vector machines) were computed to assess the relationship between connectivity measures and neurocognitive performance. The patients had worse dizziness scores and poorer verbal memory, executive function and complex visuo-spatial performance than controls. Twelve out of the 30 patients (40%) were considered to have MCI. Nonetheless, the leukoaraiosis Sheltens scores, hippocampal and brain volumes were not different between groups. Their whole-brain mean fractional anisotropy (FA) was significantly reduced and regional functional connectivity (Fc) was significantly impaired in the dorsal attention network (DAN), frontoparietal network, sensorimotor network and default mode network. In particular, the Fc strength at the insula of the DAN and the mean FA were linearly related with attention performance and dizziness severity, respectively. The multivariate pattern classification gave over 90% predictive accuracy of individuals with MCI or severe dizziness. Cognitive decline in stroke-free individuals with severe carotid stenosis may arise from nonselective widespread disconnections of long-range, predominantly interhemispheric non-hippocampal pathways. Connectivity measures may serve as both predictors for cases at risk and therapeutic targets for mitigating vascular cognitive impairment.
Aortic Wave Dynamics and Its Influence on Left Ventricular Workload

The pumping mechanism of the heart is pulsatile, so the heart generates pulsatile flow that enters into the compliant aorta in the form of pressure and flow waves. We hypothesized that there exists a specific heart rate at which the external left ventricular (LV) power is minimized. To test this hypothesis, we used a computational model to explore the effects of heart rate (HR) and aortic rigidity on left ventricular (LV) power requirement. While both mean and pulsatile parts of the pressure play an important role in LV power requirement elevation, at higher rigidities the effect of pulsatility becomes more dominant. For any given aortic rigidity, there exists an optimum HR that minimizes the LV power requirement at a given cardiac output. The optimum HR shifts to higher values as the aorta becomes more rigid. To conclude, there is an optimum condition for aortic waves that minimizes the LV pulsatile load and consequently the total LV workload.
Civil Engineering Application for Virtual Collaborative Environment

Abstract The motivation for this project was to find a way to give engineers, and the community affected by Civil Engineering work, information that is often difficult to visualise via computer screens, 2D plans or even scale models. We developed a Digital Scale Model using the existing expertise at the University of Nottingham on Space Geodesy and Mixed Reality. The user works in a collaborative Virtual Environment (VE) where it is possible to share with other users, new ideas, points of view and solutions that appear in the timeline of a construction, pursuing reduction of costs and improving agility. This paper is an introduction of the project developed at the University of Nottingham and describes the concept of Digital Scale Models. Hardware, software and results will be presented and discussed . Key words: Augmented Reality, Civil Engineering, Collaborative Environment 1. Introduction Civil Engineering scenarios have been the subject of study of Augmented Reality researchers for a long time. Piekarski[8] and Feiner[3] are just a few examples of what has been developed involving the visualisation of VE in outdoor areas using AR. But due to the low-cost technology and easy access to mobile computers, most engineers are still using the scale models or 2D layouts to demonstrate the modifications that will be implemented in the area of a new construction. The investors, builders and the public affected have to use a lot of imagination to realise how the environment will look in the future. The problem with this representation is that it does not give us the sensation of being side by side with the changes. In our approach, the engineers will not only have the facility to make decisions on, for instance, the best position for the Civil Engineering project, but they will also be able to observe, analyse, share information and discuss with their team the best way to carry out the work. The users of our system are divided into two main groups: field and office users. The first refers to the person that will wear the developed system in the target area, where the project is to be (or is being) carried out. The field user will see, with the help of a see-through Head Mounted Display (HMD), the digital and real environments seamlessly. The outdoor user shares the VE with the office users thanks to a wireless network. The latter, with the help of a computer screen and a mouse, can operate the VE from their desks. The public affected by the construction is a sub-set of the field user. With only part of the equipment used by the field users, they will be able to liaise with the engineers, solving possible doubts about what will be modified and giving suggestions about the new investment.
Glass Transition Phenomenon for Conjugated Polymers

Conjugated polymers are emerging as promising building blocks for a broad range of modern applications including skin-like electronics, wearable optoelectronics and sensory technologies. In the past three decades, the optical and electronic properties of conjugated polymers have been extensively studied, while their mechanical properties, especially the glass transition phenomenon which fundamentally represents the polymer chain dynamics, has received much less attention. Currently, there is a lack of design rules that underpin the glass transition temperature of these semi-rigid conjugated polymers, putting a constraint on the rational polymer design for flexible stretchable devices and stable polymer glass that is needed for the devices’ long-term morphology stability. In this review article, the glass transition phenomenon for polymers, glass transition theories and characterization techniques are first discussed. Then previous studies on the glass transition phenomenon of conjugated polymers are reviewed and a
Sub-event detection during natural hazards using features of social media data

Social networking sites such as Flickr, YouTube, Facebook, etc. contain a huge amount of user-contributed data for a variety of real-world events. These events can be some natural calamities such as earthquakes, floods, forest fires, etc. or some man-made hazards like riots. This work focuses on getting better knowledge about a natural hazard event using the data available from social networking sites. Rescue and relief activities in emergency situations can be enhanced by identifying sub-events of a particular event. Traditional topic discovery techniques used for event identification in news data cannot be used for social media data because social network data may be unstructured. To address this problem the features or metadata associated with social media data can be exploited. These features can be user-provided annotations (e.g., title, description) and automatically generated information (e.g., content creation time). Considerable improvement in performance is observed by using multiple features of social media data for sub-event detection rather than using individual feature. Proposed here is a two-step process. In the first step, clusters are formed from social network data using relevant features individually. Based on the significance of features weights are assigned to them. And in the second step all the clustering solutions formed in the first step are combined in a principal weighted manner to give the final clustering solution. Each cluster represents a sub-event for a particular natural hazard.
House Prices, Home Equity-Based Borrowing, and the U.S. Household Leverage Crisis

Using individual-level data on homeowner debt and defaults from 1997 to 2008, we show that borrowing against the increase in home equity by existing homeowners is responsible for a significant fraction of both the sharp rise in U.S. household leverage from 2002 to 2006 and the increase in defaults from 2006 to 2008. Employing land topology-based housing supply elasticity as an instrument for house price growth, we estimate that the average homeowner extracts 25 to 30 cents for every dollar increase in home equity. Money extracted from increased home equity is not used to purchase new real estate or pay down high credit card balances, which suggests that borrowed funds may be used for real outlays (i.e., consumption or home improvement). Home equity-based borrowing is stronger for younger households, households with low credit scores, and households with high initial credit card utilization rates. Homeowners in high house price appreciation areas experience a relative decline in default rates from 2002 to 2006 as they borrow heavily against their home equity, but experience very high default rates from 2006 to 2008. Our estimates suggest that home equity-based borrowing is equal to 2.8% of GDP every year from 2002 to 2006, and accounts for at least 34% of new defaults from 2006 to 2008.
Spatial Data Mining of Colocation Patterns for Decision Support in Agriculture

Computer technologies have been introduced into the area of agriculture recently. Precision agriculture, as an example, is a popular concept of using GIS, GPS and other new technologies in helping farmers optimize agricultural production. Colocation pattern mining is a technique for discovering relationships between different thematic features in a spatial domain. For example, an observation that large cities are often close to riversides is obtained with a reliable statistic. Such desired capability is of importance in agricultural applications, like insect pest management. In this paper, a two-phase hierarchical clustering method is proposed to assist people in making decisions based on spatial colocation patterns implicitly existing inside the geographical data sets. It is designed to be a generic system for any data sets in point format. In the first phase, the point features being close together are grouped into a number of clusters. An LC matrix is generated to describe the relationship between the clusters and the layers of feature points. The LC matrix is then analyzed by the second hierarchical clustering to generate a dendrogram. The support and confidence of each single cluster in the dendrogram are calculated to show the concurrent occurrence of features, regardless of their geographical locations.
Mirror therapy with an exoskeleton upper-limb robot based on IMU measurement system

This paper develops a novel system which includes an exoskeleton upper-limb robot and an inertia motion unit (IMU) measurement system based on mirror therapy concepts with assistive robotics. By integrating the IMU measurement system and the exoskeleton robot, we can execute measurement and control over larger range of motion (ROM) and also improve the safety and user-friendliness of the system. In this paper, two methods using IMU to measure various orientations of parts of the master-side's upper-limb have been proposed, and then the posture trajectory of the disabled part of the slave's side will be automatically generated. Conceivably, these two methods can create more complex trajectories than conventional approaches which design specific type of trajectories. Besides that, the accuracy and safety of orientation measurement are very important issues when robot-assisted exercises are conducted. To achieve this objective, we particularly develop a point-to-point control system consisting of two different types of position measurement components along with a PID controller, and both monitor each other to make the system more robust and to prevent the single point of failure problem while the system is working.