Miracle-dz/UniFAR-FTUs huggingface dataset:
A cross sectional survey in paediatricians pertaining to management of functional gastrointestinal disorders in infants: Indian perspectives

Background: Functional gastrointestinal disorders (FGIDs) in infants and toddlers possess extensive burden to the parents and healthcare professionals. Guidelines addressing the practices in diagnosis and management of FGIDs in infants in Indian subcontinent is unavailable. Hence this study assessed current knowledge, attitude and practice of pediatricians in diagnosis and treatment of FGIDs. Methods: A cross-sectional survey based on a structured questionnaire assessed pediatrician’s knowledge about prevalence and profile of most common GI disorders in pediatric age groups (birth to 12 months), association of FGIDs with different feeding practices, impact of FGID on quality of life, various management options and physician preferred method of treatment. Results: Colic was rated as the most common GI disorder, followed by gassiness/fussiness, regurgitation and constipation. About 59% pediatricians come across FGIDs more in formula-fed infants compared to breastfed infants (4.2%) and 93.9% pediatricians affirm that FGIDs affect quality of life. Approximately 91% pediatricians believed reassurance and education was the best management option, which was also reported as the preferred mode of management by about 89% pediatricians, whereas pharmacological therapies were the least preferred (1.6%). For breastfed infants suffering with regurgitation, colic or constipation, majority pediatricians opted for switching to 100% whey partially hydrolysed protein formula; sequentially followed by use of pre/probiotics and switch to extensively hydrolyzed protein formula. Conclusions: Parental education and reassurance offer an ideal mode of management of FGIDs. Partially hydrolysed protein formula may be considered one of the best management options irrespective of the FGID condition in infants who are breastfeed.
SJIM-V6-N2 Final Text MRV.indd

Hidradenitis suppurativa is a rare chronic / recurrent, infl ammatory and suppurative disease that aff ects apocrine gland-bearing areas of the skin, including the axillae, inframammary fold, inguinal and anogenital regions. Few reports have described the co-occurrence of Hidradenitis suppurativa and Crohn’s disease and the challenge in managing patients with both conditions. We describe two cases in which HS developed during the course of Crohn’s disease in one patient, while it preceded the onset of Crohn’s disease in the other case. Both patients showed poor response to biological therapy although one case initially responded well to induction therapy with infl iximab.
Research and application of 3D laser scanning technology in reversed modeling of hydropower station

As a high-tech, high-precision and fully automatic stereo scanning technology, the 3D laser scanning is widely applied in topographic survey, cultural relics protection and digital city construction, while the research and application of 3D laser scanning in the digital construction of hydropower industry is just starting. In this paper, the 3D laser scanning technology is used to obtain the cloud data of the switch station of Xin'anjiang hydropower station. Combined with the autonomous modeling software 3Dmax, the point cloud contour model is imported into 3Dmax to establish the 3D digital model of Xin'anjiang switch station. The 3D digital model established by this method is not only of high precision, but also of high restoration of site environment by texture mapping. Therefore, the reversed modeling method in this paper can provide reference for the 3D digital reversed modeling of similar hydropower projects.
Controlled Single-Cell Compression With a High-Throughput MEMS Actuator

The electromagnetically actuated MEMS <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Hammer was used to evaluate the effects of mechanical impact on the membrane permeability, apoptotic induction, and proliferation of human neural progenitor cells. The <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Hammer enabled application of two strain magnitudes (<inline-formula> <tex-math notation="LaTeX">$\varepsilon = 42{\%}$ </tex-math></inline-formula> and 69%) for two strain durations (<inline-formula> <tex-math notation="LaTeX">$10~\mu \text{s}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$100~\mu \text{s}$ </tex-math></inline-formula>) to individual cells at unprecedented high strain rates (<inline-formula> <tex-math notation="LaTeX">$\dot {\varepsilon }\mathrm { } \boldsymbol {\sim } {200\times }10^{3}\,\,\text{s}^{-1}$ </tex-math></inline-formula>) and high throughput (up to 36,000 cells/min). This enables large numbers of cells to be analyzed and the effects of strain magnitude and duration to be decoupled. The magnitude of applied strain significantly affected cell membrane permeability shortly after compression, whereas the duration of strain significantly increased early apoptosis in cells 24 hours after compression. Strain magnitude also significantly affected cell quantity over a 70-hour period, despite no significant difference in the cell doubling times. Understanding the relationship between mechanical strain and cellular response will ultimately lead to improved diagnostics and treatment of high strain rate mechanical injury conditions such as Traumatic Brain Injury. [2020-0171]
Effect of Inhibitor Concentration and Immersion Time on the Corrosion Rate and Inhibition Efficiency of AISI 1019 Steel in Inhibited Seawater Environment

The effect of inhibitor concentration and immersion time on the corrosion rate and inhibition efficiency of AISI 1019 steel in seawater environment were investigated by means of weight loss measurement (WLM) at an interval of 6days for a period of 60 days. AISI 1019 steel were immersed in seawater solution in the absence and presence of varying inhibitor concentrations of potassium chromate (PC), Sodium Nitrite (SN), Methyl Orange (MO), Methyl Red (MR), Terminalia Catappa Leaves (TCL) Extract, Carica papaya Leaves (CPL) Extract. The results showed that there is an increase in inhibition efficiency with increase in inhibitor concentration and decrease in inhibition efficiency with increase in immersion time. The inorganic inhibitor (potassium chromate), inorganic inhibitor (Sodium Nitrite), the organic Inhibitor (Methyl Orange), the organic inhibitor (Methyl Red), the green inhibitor (Terminalia Catappa Leaves) and green inhibitor (Carica Papaya Leaves) produced their best inhibition efficiency of 71.94%, 634%, 68.94%, 68.32%, 62.7%, 59.79% respectively at a concentration of 10g/L. From the result obtained, the potassium chromate inhibitor has better inhibitory property than other inhibitors, which revealed that it is best suited for inhibition of corrosion of mild steel in seawater environment.
SENN: Stock Ensemble-based Neural Network for Stock Market Prediction using Historical Stock Data and Sentiment Analysis

Stock market prediction is one of the most appealing and challenging problems in the realm of data science. In this paper, authors investigate the potential of exploiting sentiment score extracted from microblog text data along with historical stock data to improve the stock market prediction performance. The sentiment score is extracted by using an ensemble-based model which utilize the power of Long Short- Term Memory (LSTM) and Multi-Layer Perceptron (MLP) along with Convolutional Neural Network (CNN). We propose Stock Ensemble-based Neural Network (SENN) model which is trained on the Boeing historical stock data and sentiment score extracted from StockTwits microblog text data in 2019. Furthermore, we propose a novel way to measure the stock market prediction model performance which is the modification of the classic Mean Absolute Percentage Error (MAPE) metric, namely Adjusted MAPE (AMAPE). It has been observed from the experiments that utilizing SENN to integrate sentiment score as additional features could improve the stock market prediction performance up to 25% and also decreasing the margin of error up to 48%. With the training data limitation, the proposed model achieves a superior performance of 0.89% AMAPE. Our codes are available at https://www.github. com/louisowen6/SENN.
Measurement of Dynamic On-State Resistance of High-Voltage GaN-HEMTs under Real Application Conditions

GaN-HEMTs gain a lot of attention to power electronics engineers. However, they could exhibit increased on-state resistance due to charge trapping. This paper focusses on measuring this effect for high voltage devices. Measurements reveal an unexpected blocking voltage dependency of on-state resistance. Therefore, a second measurement setup serves as verification.
The Effectiveness of Compassion Focused Therapy on Depression and Rumination after romantic Relationship Breakup: A single case study

The purpose of the present study was to investigate the efficacy of compassion-focused therapy on Depression and Rumination after a romantic breakup. The present study was carried out using the single-case quasi-experimental method and a simple baseline method. For this purpose, three female participants were selected through targeted sampling and they were treated through an individual Compassion-focused Therapy (CFT) during eight 90-min sessions. Participants completed the Beck Depression Inventory (BDI-II) and Rumination (RRS) questionnaires in the baseline stage (pre-treatment) during the second, fourth, sixth and eighth sessions, and in the one-month follow-up stage. Moreover, in the pre-treatment stage, the participants completed the Love Trauma Inventory (LTI) and the MMPI-2RF questionnaires to identify the severity of disturbance after breakup and to detect the serious disorder in axis, respectively. Then they were interviewed in order to identify their personality disorder. For data analysis, clinical significance method and the recovery percentage index were used. The results showed that compassion-focused therapy has a significant effect on the reduction of Depression and mental Rumination. The percentage of non-overlap data (PND) on the Depression Scale was 100 for the first and second participants and 75 for the third participant; it was 100 on the rumination scale for all three participants. The rate of recovery of depression in the first, second, third participant was 65%, 72%, 25%, the rumination rate in the first, second, third participant was 47%, 42%, 33%, respectively. Therefore, it can be concluded that people who experienced a breakup after a compassion-focused therapy look at themselves with a new and compassionate look at their own.
Charge storage observation in corona-charged oxy-hydroxyapatite ceramics

Oxy-hydroxyapatite (OHA), which is a OH-deficient hydroxyapatite (Ca<inf>10</inf>(PO<inf>4</inf>)<inf>6</inf>(OH)<inf>2</inf>O<inf>x/2</inf>) showing ionic (H<sup>+</sup>, O<sup>2−</sup>, and OH<sup>−</sup>) conductivity, can be converted to an OHA electret with a stable surface potential of more than several kilovolts by applying a DC electric field under heating (thermal electret method). This report presents a potential corona irradiation method for producing OHA electrets. The OHA ceramic was heated to a predetermined temperature, and corona irradiation was performed in a state in which carrier diffusion was activated inside the OHA ceramic. The state of the stored charges in OHA ceramics was verified by thermally stimulated depolarization current analysis.
A Semi-Supervised Autoencoder With an Auxiliary Task (SAAT) for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis

This paper proposes a semi-supervised autoencoder with an auxiliary task (SAAT) to extract a health feature space for power transformer fault diagnosis using dissolved gas analysis (DGA). The health feature space generated by a semi-supervised autoencoder (SSAE) not only identifies normal and thermal/electrical fault types, but also presents the underlying characteristics of DGA. In the proposed approach, by adding an auxiliary task that detects normal and fault states in the loss function of SSAE, the health feature space additionally enables visualization of health degradation properties. The overall procedure of the new approach includes three key steps: 1) preprocessing DGA data, 2) extracting two health features via SAAT, and 3) visualizing the two health features in two-dimensional space. In this paper, we test the proposed approach using massive unlabeled/labeled Korea Electric Power Corporation (KEPCO) databases and IEC TC 10 databases. To demonstrate the effectiveness of the proposed approach, four comparative studies are conducted with these datasets; the studies examined: 1) the effectiveness of an auxiliary detection task, 2) the effectiveness of the visualization method, 3) conventional fault diagnosis methods, and 4) the state-of-the-art, semi-supervised deep learning algorithms. By examining several evaluation metrics, these comparative studies confirm that the proposed approach outperforms SSAE without the auxiliary task, existing methods, and state-of-the-art deep learning algorithms, in terms of defining health degradation performance. We expect that the proposed SAAT-based health feature space approach will be widely applicable to intuitively monitor the health state of power transformers in the real world.
Wireless Longitudinal Motion Controller Design for Quadrotors*

In the present paper, the problem of remotely controlling a quadrotor in longitudinal motion and under atmospheric disturbances is studied. The disturbances are considered not to be measurable. The controller - communication system is enriched with a synchronization / signal reconstruction algorithm imposing constant delays. The design goal is to independently control the horizontal and vertical velocity of the quadrotor while simultaneously attenuating the influence of the disturbances to these two velocities. To this end, a linear dynamic time delay controller will be designed. The satisfactory performance of the proposed control scheme is illustrated through computational experiments for the case of a climbing manoeuvre.
System Identification of High-Dimensional Linear Dynamical Systems With Serially Correlated Output Noise Components

We consider identification of linear dynamical systems comprising of high-dimensional signals, where the output noise components exhibit strong serial, and cross-sectional correlations. Although such settings occur in many modern applications, such dependency structure has not been fully incorporated in existing approaches in the literature. In this paper, we explicitly incorporate the dependency structure present in the output noise through lagged values of the observed multivariate signals. We formulate a constrained optimization problem to identify the space spanned by the latent states, and the transition matrices of the lagged values simultaneously, wherein the constraints reflect the low rank nature of the state information, and the sparsity of the transition matrices. We establish theoretical properties of the estimators, and introduce an easy-to-implement computational procedure for empirical applications. The performance of the proposed approach, and the implementation procedure is evaluated on synthetic data, and compared with competing approaches, and further illustrated on a data set involving weekly stock returns of 75 US large financial institutions for the 2001–2017 period.
Object Classification on a High-Resolution Tactile Floor for Human-Robot Collaboration

The growing trend in the manufacturing industry towards human-robot collaboration as a new paradigm has the potential to increase the efficiency of robot-enabled production lines. This development requires safety-oriented sensor systems, frameworks and reliable algorithms in order to avoid hazardous situations. These systems should be able to distinguish between different objects, mobile vehicles and human workers to allow adaptive control strategies of the robots. In this paper, we introduce a tactile flooring system with high spatial resolution to determine the humans in a workplace and to continue the robot’s task when mobile units are entering the workspace and no life-threatening situation is expected. The pressure data of the tactile floor is transformed to image data which is used to determine the objects presented. There are several categories to distinguish between specific vehicles, humans and users resulting in a multi-label classification problem with up to three categories presented in one pattern. Our CNN classifies five different objects with an exact match of 75%.
Nitrofurantoin and Fosfomycin for the CRE and MDR uropathogens: are the choices still same?

Antibiotic resistance among bacteria causing common infections is increasing in all regions of the world. It is interesting that pattern of resistance observed varies from hospital to community, large hospital to small hospital, state to state, and even vary from country to country. Emergence of resistance to antibiotics illustrates importance of using evidence-based strategies for treatment. In urinary tract infection (UTI) cases, antibiotic treatment is often started empirically before the results of urine culture and susceptibility testing are available. Hence, it becomes important to regularly monitor the resistance or susceptibility patterns of uropathogens, so that the guidelines for empirical therapy can be improved to include antibiotics with low resistance, aiding clinicians in proper management of UTIs with minimal therapeutic failures. ABSTRACT
Rapid Prediction of Brain Injury Pattern in mTBI by Combining FE Analysis With a Machine-Learning Based Approach

Mild traumatic brain injury (mTBI) is a significant issue worldwide. Public awareness of the dangers of mTBI has increased sharply in recent years, yet there is no easy-to-use tool available for early detection and post injury management. Computational models of the head impact, usually in the form of finite element analysis, are a method of choice for characterizing how mechanical impacts lead to brain damage by causing high strains in certain regions of the brain. However, those models require a prohibitively large amount of computational power as well as pre and post processing expertise, making them unrealistic to be used in clinical settings. In this study, we propose a framework that combines finite element analysis with a machine learning based approach where a large number of pre-computed FE results are used to train a statistical model. We analyzed a number of different head impact scenarios in which a football player would sustain a minor brain injury and computed brain internal strain patterns. These pre-computed strain patterns were then used to train a partial least squares regression model to be able to predict the general strain pattern and the location and magnitude of peak strains. Our models were able to predict the overall distribution pattern, including the location of the peak strain, with an average error of 3%. The peak strain magnitudes were also predicted accurately with the average error of 9% at almost real time speed (less than 10 seconds). This model may play an important role in developing a diagnostic tool for mTBI that can predict the severity of head impacts.
Design and synthesis of reversible data selectors for low power application

Reversible logic design has paved way for a new area of research and is gaining a lot of research attention as it is very useful in various areas such as low power VLSI design, quantum computing, and nanotechnology. Combinational logic dissipates the significant amount of energy due to bit loss. So, if circuits are designed using reversible logic, where information bits are not destroyed and reduced the significant amount of power dissipation. Reversible circuits are designed using parity preserving gates, which has the property of detecting not more than a single error in the circuit's primary output. If a circuit is both reversible and fault tolerant, it can recover bit-loss at the input as well as bit-error at output. This paper presents the design and FPGA synthesis of optimized data selectors. This paper also highlights the low switching power consumption characteristics of data selectors, which were typically around 1 to 3µW that finds its use in low power applications that are targeting ultra-speed.
Birds in Pine and Native Forests

The Monterey Pine Pinus radiata has been planted in Australia for about ninety years. In recent years, large amounts of native forest have been removed and planted with pines, which has greatly affected the original fauna. Almost any bird can be expected to forage in a pine plantation of mixed age-classes. This paper reports the birds seen and breeding in sample plots of 8.09 hectares in pine forest, dry sclerophyll and wet sclerophyll. Many more individuals were observed in the wet sclerophyll than in the pines or dry sclerophyll and thediversity of species was also much greater. Dry schlerophyll was slightly more diverse than the pines. The breeding diversity was two or three times greater in the native forest than in the pines. Of Australian landbirds 19.7 per cent usually or always nest in holes or on stumps. In native forest 35-40 per cent of the birds may use holes but only I I per cent do so in the pines. Most trees in the native forest did not have suitable holes for breeding birds until they were 500 millimetres in diameter or at least fifty years old.
PARTITIONING OF SPACE IN COMMUNITIES OF ANTS

SUMMARY (1) The dispersion patterns of nests, size of foraging areas and overlap in foraging areas were measured for all species of ants at ten study sites in the Mojave and Great Basin Deserts of western North America. (2) Overdispersed patterns of nest distribution were common amongst the colonies of conspecifics. Such patterns are believed to result directly from defence of foraging areas and indirectly from intraspecific competition for food. Little evidence of non-random dispersion patterns was found when the colony positions of two species were considered together. (3) The dispersion pattern of all the colonies of all species in a community is uniformly spaced at lower elevations, random at intermediate elevations and aggregated at higher elevations. The trend towards increased aggregations is believed to be due to the increase in vegetation density with elevation. This would tend to limit the areas where ant colonies can be successfully established to unshaded areas between perennial plants. (4) Size of foraging area was found to decrease with increases in both elevation and latitude. It is postulated that size of the foraging area for these ants is negatively correlated with density of food. Overlap in foraging areas decreases with increases in species diversity and is not related to either elevation or latitude.
PWM-Based Adaptive Trajectory Tracking of Switched Buck Power Converters

Based on pulse width modulation (PWM) technique, adaptive trajectory tracking of switched Buck power converters with parameter uncertainty is investigated in this article. By the aid of the backstepping technique, an equivalent continuous adaptive tracking controller is designed to guarantee the asymptotic stability of the closed-loop error continuous system. For the switched Buck converter with unknown parameters, PWM-based adaptive tracking with the equivalent adaptive continuous control input is proposed such that the closed-loop error digital system is practically asymptotically stable, and the tracking error converges to an arbitrarily small neighborhood of the origin. Simulation results are shown to illustrate the effectiveness of the proposed new control schemes.