Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of the Glomerular Filtration Rate in Living and Cadaver Donor Recipients Within 12 Months of Kidney Transplant

Background: Among patients with urologic disease, end-stage renal disease (ESRD) have the highest mortality rate. The choice of treatment for patients with ESRD is transplantation. The primary objective of this study was to assess the trend of renal function test in cadaver and living donor groups within a year after the surgery. Method: We included ESRD patients who were admitted to our hospital’s clinic from 2001 to 2014. The glomerular filtration rate (GFR) was the outcome of interest. Results: A total of 381 individuals were included in this study, of whom 246 (64.6%) were males. the mean age was 40.8 years (SD=13.2). In this study, 291 (76.4%) patients received a transplant from a live donor. In the cadaver group, the GFR decreased by 3.8 mL/min (95% confidence interval [CI], 1.2-5.0) per six months, while in the living donor group, it decreased by 1.4 mL/min (95% CI, 0.3–2.5). These trends were not significantly different. The GFR of the living donor group was 0.4 mL/min (95% CI, -1.6-2.3, p=0.72) higher than that of the cadaver donor group per six months, which was not significantly different. The rejection rate was lower in the live donor group (odds ratio=0.54 [95% CI, -0.39-0.74], p<0.001). The rejection rate decreased over the study period by 71% every six months, and this decrease did not significantly differ between the study groups. Conclusions: Although the total GFR in the live donor group was higher, given the higher GFR at the time of surgery in the live donor group, the decrease rate in GFR seemed identical in the study groups.
Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration

Large penetration of wind energy systems into electric-grids results in many power quality problems. This paper presents a classification of power quality issues, namely harmonics and short-duration voltage variation observed due to the integration of wind power. Additionally, different techniques and technologies to mitigate the effect of such issues are discussed. The paper highlights the current trends and future scopes in the improvement of the interconnection of wind energy conversion systems (WECSs) into the grid. As the voltage variation is the most severe power quality issue, case studies have been presented to investigate this problem using steady-state time-series simulations. The standard IEEE test system namely IEEE 123-node test feeder and IEEE 30-node grid are solved under different operating conditions with wind power penetration. Typical daily load profiles of a substation in Riyadh, Saudi Arabia, and an intermittent wind power generation profile are used in all case studies. Mitigation of voltage variations due to wind intermittency is achieved using reactive power compensation of the interface inverter. The results show the effectiveness of these approaches to avoid voltage variation and excessive tap setting movements of regulators and keep the voltage within the desired operating conditions.
Research on intelligent recognition of axis orbit based on Hu moment invariants and fractal box dimension

The shape feature of axis orbit is very important to judge the fault of rotor system. After the sampling and quantization of the image of axis orbit, the edge of the image will be inaccurate. Therefore, the discretization of image has a great influence on the calculation of higher order moments of the moment invariants. High order moments are mainly used to describe image details. The fractal dimension is sensitive to the details and complexity of the image. Therefore, this paper proposes a method of combining fractal dimension with moment invariants. And Canny operator is used to detect the edge of axis orbit image. The Hu invariant moments of the image and the fractal box dimension are used as the feature vectors of BP neural networks. The 40 groups of samples of the typical fault axis orbit of the experiment are trained and sampled to test. The recognition rate of the 8 groups of axis orbit is up to 100%, and the recognition effect is satisfactory. The results show that the method has high recognition speed and high recognition accuracy, and has good practical value for the intelligent fault diagnosis of the rotor system by using the axis orbit.
Peptidoglycan Hydrolases of Escherichia coli

SUMMARY The review summarizes the abundant information on the 35 identified peptidoglycan (PG) hydrolases of Escherichia coli classified into 12 distinct families, including mainly glycosidases, peptidases, and amidases. An attempt is also made to critically assess their functions in PG maturation, turnover, elongation, septation, and recycling as well as in cell autolysis. There is at least one hydrolytic activity for each bond linking PG components, and most hydrolase genes were identified. Few hydrolases appear to be individually essential. The crystal structures and reaction mechanisms of certain hydrolases having defined functions were investigated. However, our knowledge of the biochemical properties of most hydrolases still remains fragmentary, and that of their cellular functions remains elusive. Owing to redundancy, PG hydrolases far outnumber the enzymes of PG biosynthesis. The presence of the two sets of enzymes acting on the PG bonds raises the question of their functional correlations. It is difficult to understand why E. coli keeps such a large set of PG hydrolases. The subtle differences in substrate specificities between the isoenzymes of each family certainly reflect a variety of as-yet-unidentified physiological functions. Their study will be a far more difficult challenge than that of the steps of the PG biosynthesis pathway.
Detection of Surface Defects in Logs Using Point Cloud Data and Deep Learning

Deep learning classification based on 3D point clouds has gained considerable research interest in recent years.The classification and quantitative analysis of wood defects are of great significance to the wood processing industry. In order to solve the problems of slow processing and low robustness of 3D data. This paper proposes an improvement based on littlepoint CNN lightweight deep learning network, adding BN layer. And based on the data set made by ourselves, the test is carried out. The new network bnlittlepoint CNN has been improved in speed and recognition rate. The correct rate of recognition for non defect log, non defect log and defect log as well as defect knot and dead knot can reach 95.6%. Finally, the "dead knot" and "loose knot" are quantitatively analyzed based on the "integral" idea, and the volume and surface area of the defect are obtained to a certain extent,the error is not more than 1.5% and the defect surface reconstruction is completed based on the triangulation idea.
Theoretical study of anharmonic force field and spectroscopic constants for 1-chlorophosphaethene, CH2PCl and CD2PCl

Abstract The anharmonic force field and spectroscopy constants for CH2PCl are determined using CCSD(T), VPT2, and density functional theory employing cc-pVQZ basis sets. The molecule structure, rotational spectroscopic constants, and vibrational wave numbers are compared with the available experimental data. Anharmonicity constants, vibration-rotation interaction constants and cubic force constants are predicted. Vibrational wave numbers and rotational constants for CD2PCl are also determined using the same levels. The isotopic shifts of vibrational wave numbers are remarkable by D atom substitution for 1-chlorophosphaethene. GRAPHICAL ABSTRACT
HOW ACCURATE DO YOU WANT IT? DEFINING MINIMUM REQUIRED ACCURACY FOR MEDICAL ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) is becoming a more and more common component of biomedical engineering solutions, and these latter systems are getting promising results in terms of diagnostic and prognostic accuracy. Medical AI (MAI) is then reaching the maturity level for its appropriate use in clinical practice, but to this end, its efficacy needs to be demonstrated first. Currently, this efficacy is proven in terms of the reported accuracy of the algorithm, especially for diagnostic tasks. But also in this case, how much accurate is “enough” accurate? To address this question means to define the minimum required accuracy for a system to be valid, that is fit to its intended use. To this aim, we propose a risk-based approach to the definition of adequate accuracy, in accordance with a risk-based regulatory classification. We investigated whether the current state of the art is already compliant with this standard-based approach, by performing a literature review in four application domains, one for each of the four risk classes we identified: the diagnosis of psoriasis, of knee osteoarthritis, the screening of breast cancer screening, and the detection of influenza outbreaks. The evaluation of the literature review highlighted that this approach is still not widely adopted, but that there is a partial presence of an implicit, conventional scheme that is similar to our proposal, especially in the high-impact literature. We also provide some guideline to assess the minimum required accuracy but also sheds light on the need for further official guidelines that ensure the wider application of the regulatory risk-based approach by the scholarly community of MAI.
Taylor-Couette flow with mixed convection heat transfer and variable properties in a horizontal annular pipe

Taylor-Couette flows in a horizontal annular gap between finite coaxial cylinders in rotor-stator configuration are numerically investigated. The inner cylinder (rotor) rotates at a constant angular velocity while the outer cylinder (stator) is at rest. They are limited at their extremities by two fixed walls that prevent axial fluid flow. In addition, a heat transfer is generated by an imposed temperature difference, with the rotor hotter than the stator while the end-walls are adiabatic. The fluid physical properties are temperature dependent. This non-linear physics problem, with a strong coupling of the conservation equations and boundary conditions, is solved by a finite volume method with numerical schemes of second order space and time accuracies. The radius and aspect ratios and the Taylor, Grashof and Prandt numbers are the control parameters. The developed numerical code has been tested for different meshes and perfectly validated. Extensive calculations have been made in large ranges of the Taylor and Grashof numbers to analyze the Taylor-Couette flow in convection modes. The results highlight the dynamic and thermal instabilities generated in the Taylor Couette flow from the appearance of Ekman cells to the Taylor vortex propagation in the entire annulus. The combined effect of these vortices with the secondary flow improves the heat transfer. Furthermore, the influence of the physical properties in the radial direction is more marked in the vicinity of the walls. Finally, we propose an empirical correlation of the Nusselt number in the studied parameter ranges.
Applying Systems Engineering framework for architecting a Smart Parking System within a Smart City

A Smart Parking System is an important feature in any Smart City, as it hopes to address a persistent challenge in urban mobility. Various technology‐based solutions have been developed. However, in such complex societal systems, it is important to consider various stakeholders and interfaces with other systems to choose an architecture that works efficiently for the given context. In this paper, we present the application of the Systems Engineering framework to develop a Smart Parking System architecture to meet the needs of a hypothetical Smart City. The framework shows the application of methods to analyse the context, prioritise needs, define and formally model alternate architectures, choose between the alternatives, and the use of a simulation model to validate requirements derived for the chosen architecture. Such systematic application of Systems Engineering methods can help planners and designers to negotiate complexities and avoid getting locked into sub‐optimal technological solutions.
Staphylococcus aureus costochondritis and chest wall abscess in a COVID-19 patient treated with tocilizumab

Coronavirus disease 2019 (COVID-19) is a worldwide pandemic, causing a global health threat. Up to 15% of the confirmed cases develop severe disease, requiring hospitalization or intensive care unit (ICU) admission. Tocilizumab, an IL-6 receptor antagonist, is a promising treatment of severe pneumonia with acute respiratory distress syndrome (ARDS) or cytokine release syndrome (CRS) in the course of COVID-19. We report a suppurative costochondritis and chest wall abscess in a severe COVID-19 patient treated with tocilizumab.
Optimization of Infectious Disease Prevention and Control Policies Using Artificial Life

The spread of an infectious disease such as COVID-19 is governed by complex social interactions that are challenging to model. Policy makers must take measures to control the spread of infection despite the unknowns that accompany a novel epidemic. The principles of artificial life govern the intricacies of social interaction through which diseases can spread. Agent-based models can capture these complexities for a subset of the population by defining the behavior of individual agents. While they can be computationally expensive for large populations, their outcomes are stochastic. Therefore, they can be used to test disease prevention policies, that can be difficult to simulate using deterministic approaches. We developed an agent-based model that is inspired by several interactive simulations on the internet for describing the COVID-19 pandemic. We define metrics to estimate the socio-economic cost of disease prevention policies on the population. We present a policy-making tool based on blackbox optimization and evolutionary computation that provides well-rounded intervention measures in terms of socio-economic cost and disease control. Several intervention measures are suggested by the algorithms with varying degrees of disease control and socio-economic cost. Policy makers can choose an intervention measure based on their preference. This research recommends combining computational intelligence principles and the use of mathematical algorithms for identifying the critical amount of intervention necessary to control infectious diseases and formulate intervention policies that minimize socio-economic cost.
High-Speed Adder Design Space Exploration via Graph Neural Processes

Adders are the primary components in the data-path logic of a microprocessor, and thus, adder design has been always a critical issue in the very large-scale integration (VLSI) industry. However, it is infeasible for designers to obtain optimal adder architecture by exhaustively running EDA flow due to the extremely large design space. Previous arts have proposed the machine learning-based framework to explore the design space. Nevertheless, they fall into suboptimality due to a two-stage flow of the learning process and less efficient nor effective feature representations of prefix adder structures. In this article, we first integrate a variational graph autoencoder and a neural process (NP) into an end-to-end, multibranch framework, which is termed the graph neural process. The former performs automatic feature learning of prefix adder structures, whilst the latter one is designed as an alternative to the Gaussian process. Then, we propose a sequential optimization framework with the graph NP as the surrogate model to explore the Pareto-optimal prefix adder structures with tradeoff among Quality-of-Result (QoR) metrics, such as power, area, and delay. The experimental results show that compared with state-of-the-art methodologies, our framework can achieve a much better Pareto frontier in multiple QoR metric spaces with fewer design-flow evaluations.
Investigating the Potential of Vietnamese Tea Seed Oil (Camellia sinensis O.Kuntze) for the Enhancement of Oxidative Stability in Vegetable Oils

This study examined the effectiveness of different antioxidative compounds, namely 0.2% BHA (Butylated hydroxyanisole) + BHT (butylated hydroxytoluene), 0.03% α – tocopherol, and 3% and 6% tea seed oil (TSO) on the oxidative stability of vegetable oils. Four commonly used oils, viz. rapeseed oil (RSO), peanut oil (PNO), sunflower oil (SFO), and soybean oil (SBO), were assessed by the Schall Oven test method and monitored during the 12-day preservation period under 60°C. The total oxidation values (TOTOX) of the samples treated with 6% TSO were lower than those treated with 0.2% BHA+BHT. The results indicated the potential of TSO as a novel natural antioxidant for dietary vegetable oils. Our study also suggested that TSO could serve as an effective substitution for currently used synthetic antioxidants such as BHA and BHT.
Abusive Language and Hate Speech Detection for Javanese and Sundanese Languages in Tweets: Dataset and Preliminary Study

Indonesia’s demography as an archipelago with lots of tribes and local languages added variances in their communication style. Every region in Indonesia has its own distinct culture, accents, and languages. The demographical condition can influence the characteristic of the language used in social media, such as Twitter. It can be found that Indonesian uses their own local language for communicating and expressing their mind in tweets. Nowadays, research about identifying hate speech and abusive language has become an attractive and developing topic. Moreover, the research related to Indonesian local languages still rarely encountered. This paper analyzes the use of machine learning approaches such as Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest Decision Tree (RFDT) in detecting hate speech and abusive language in Sundanese and Javanese as Indonesian local languages. The classifiers were used with the several term weightings features, such as word n-grams and char n-grams. The experiments are evaluated using the F-measure. It achieves over 60 % for both local languages.
Machine Learning as a Tool for Wildlife Management and Research: The Case of Wild Pig-Related Content on Twitter

: Wild pigs ( Sus scrofa ) are a non-native, invasive species that cause considerable damage and transmit a variety of diseases to livestock, people, and wildlife. We explored Twitter, the most popular social media micro-blogging platform, to demonstrate how social media data can be leveraged to investigate social identity and sentiment toward wild pigs. In doing so, we employed a sophisticated machine learning approach to investigate: (1) the overall sentiment associated with the dataset, (2) online identities via user profile descriptions, and (3) the extent to which sentiment varied by online identity. Results indicated that the largest groups of online identity represented in our dataset were females and people whose occupation was in journalism and media communication. While the majority of our data indicated a negative sentiment toward wild pigs and other related search terms, users who identified with agriculture-related occupations had more favorable sentiment. Overall, this article is an important starting point for further investigation of the use of social media data and social identity in the context of wild pigs and other invasive species.
Relationship Between Child-Mother Secure Attachment Style and Violence in Adolescent Romantic Relationship

This study aimed to assess the correlation between the secure attachment style of child-mother and violence in adolescents’ romantic relationships. A quantitative approach with a correlational method is used in this research. The subject in this study were 408 adolescents aged 18-22 years who were in romantic relationships and lives in the Special Region of Yogyakarta. The data collection method used in this study is the questionnaire method with the instrument used are the IPVAS-R scale and IPPA-M scale. Both scales used convergent validity with correlations of 0,18-0,78. Then the two scales were re-validated by expert judgement. Reliability for the IPVAS-R was 0,76, and the IPPA-M was 0,87. The data analysis used a non-parametric correlation test. The research results showed a negative correlation between child-mother secure attachment style and dating violence with r=-0,221 and α= 0,000 (p<0,01). The higher the secure attachment style between child and mother, the lower the violence in romantic relationships.
Lower airway clinical outcome measures for use in primary ciliary dyskinesia research: a scoping review

Objectives Disease-specific, well-defined and validated clinical outcome measures are essential in designing research studies. Poorly defined outcome measures hamper pooling of data and comparisons between studies. We aimed to identify and describe pulmonary outcome measures that could be used for follow-up of patients with primary ciliary dyskinesia (PCD). Methods We conducted a scoping review by systematically searching MEDLINE, Embase and the Cochrane Database of Systematic Reviews online databases for studies published from 1996 to 2020 that included ≥10 PCD adult and/or paediatric patients. Results We included 102 studies (7289 patients). 83 studies reported on spirometry, 11 on body plethysmography, 15 on multiple-breath washout, 36 on high-resolution computed tomography (HRCT), 57 on microbiology and 17 on health-related quality of life. Measurement and reporting of outcomes varied considerably between studies (e.g. different scoring systems for chest HRCT scans). Additionally, definitions of outcome measures varied (e.g. definition of chronic colonisation by respiratory pathogen), impeding direct comparisons of results. Conclusions This review highlights the need for standardisation of measurements and reporting of outcome measures to enable comparisons between studies. Defining a core set of clinical outcome measures is necessary to ensure reproducibility of results and for use in future trials and prospective cohorts. Measurement and reporting of lower airway outcome measures in primary ciliary dyskinesia research are not standardised. Validated, disease-specific clinical outcomes are needed to monitor disease progression in future trials and prospective cohorts. https://bit.ly/3yHERQm
Simultaneous detection of hydrogen and methane in breath for the diagnosis of small intestinal bacterial overgrowth by fast gas chromatography

The hydrogen and methane breath test has been widely used in the diagnosis of SIBO (small intestinal bacterial overgrowth), which is noninvasive and convenient with good accuracy. However, most studies use commercial GC-MS to analyze breath components which requires complex operation and has a high cost, while the most common simplified devices only have a single function with some drawbacks. This study proposes a convenient, small-sized and multi-functional fast gas chromatograph (Fast GC) with a low detection limit and sufficient resolution to simultaneously determine hydrogen and methane concentrations, corrected using the carbon dioxide concentration, in breath. The system can eliminate the interference caused by dilution with outside gases, the breathing mode of the patient and other factors of alveolar gas. The detection range of the Fast GC system was 2–200 ppm, the resolution was 1 ppm, the precision was less than 10%, and the preheating time was significantly shortened to 2 hours, as well as the size being reduced to 40 cm × 30 cm × 15 cm. Two SIBO diagnostic models were established and samples from forty-seven healthy people and patients were collected and analyzed by our system. This method has the advantages of high specificity, and non-invasive and simple operation, so that it can be used for the diagnosis of increasing volumes of clinical and domestic patients.