Miracle-dz/UniFAR-FTUs huggingface dataset:
Acid-base Management in Cardiopulmonary Bypass

Cardiopulmonary bypass (CPB) merupakan alat penunjang fungsi sirkulasi dan pernapasan pasien yang biasa digunakan ketika menjalani pembedahan jantung atau pembuluh darah besar. Selama prosedur CPB, kondisi hipotermia dipertahankan untuk menurunkan kebutuhan oksigen dan laju metabolisme. Kondisi hipotermia akan mempengaruhi keseimbangan asam-basa pada tubuh. Manajemen asam-basa selama prosedur CPB dicapai dengan menggunakan metode a-stat atau pH-stat. Pada metode a-stat, manajemen asam-basa dilakukan dengan menjaga pHa 7.4 dan PaCO2 40 mmHg pada suhu 37oC tanpa penambahan CO2 oksigen untuk menjaga total CO2 tetap konstan. Sedangkan, pada metode pH-stat, diberikan CO2 oksigen untuk menjaga PaCO2 40 mmHg dan pHa 7.4 secara in vivo. Masih banyak perdebatan terkait waktu penerapan masing-masing metode. Pada level mikrosirkulasi, manajemen a-stat terbukti memberikan keuntungan pada otak dan mengurangi insidensi postoperative cerebral dysfunction. Sedangkan, metode pH-stat dilaporkan meningkatkan risiko emboli otak, sehingga tidak disarankan untuk pasien yang memiliki risiko tinggi gangguan aliran darah otak. Namun, terdapat pula laporan yang menyatakan pH-stat bermanfaat pada operasi bedah jantung anak. Berdasarkan hal itu, usia pasien dapat menentukan waktu penggunaan metode a-stat dan pH-stat. Satu indikasi primer penggunaan pH-stat adalah selama proses pendinginan saat deep hypothermic circulatory arrest (DHCA), sedangkan metode a-stat lebih baik digunakan selama selective cerebral perfusion (SCP) dan rewarming.
Integrated analysis of the underlying immunomodulator and survival signature of STON1 gene in kidney renal clear cell carcinoma

Axiu Zheng Guangdong Medical University Jianrong Bai Guangdong Medical University Yanping Ha Guangdong Medical University Bingshu Wang Hainan Medical University Yuan Zou Guangdong Medical University Caixia Wu Guangdong Medical University Jingci Xing Guangdong Medical University Shaojiang Zheng Hainan Medical University Zhihua Shen Guangdong Medical University Botao Luo Guangdong Medical University Wei Jie (  wei_jie@hainmc.edu.cn ) Hainan Medical University https://orcid.org/0000-0002-1869-1458
Simple Asana: Yoga and the Elderly

Yoga is becoming very popular among young people, however it is also exceptionally beneficial for the elderly to improve mobility, strength and combat chronic ailments. It is an alternative form of medicine and can be ancillary tool for treating pain, cardiovascular issues, weight problems, depression, sleep disorders and many more. The practice goal of yoga is to challenge oneself but not be overwhelmed, develop inner awareness without judgement. In contrast to many other programs it does not involve strenuous workouts or expensive equipment and is not competitive. The simple form involves series of static positions that use isometric contraction and relaxation of different muscle groups to create specific body alignments with coordinated breathing and deep meditative relaxation. The best yoga practice for elderly or a person with limitations are that uses supporting props as blankets, pillows or chairs during asana to overcome the limitations that are coordinated with breathing exercise and meditation.
The risk for recurrent preterm birth after prior preterm birth complicated by major fetal anomaly

Abstract Major fetal anomalies (MFA) are a known risk factor for preterm birth (PTB), though the etiology of this is not well-studied, making counseling of these patients difficult. Thus, we sought to describe the rate of recurrent PTB in a second-observed pregnancy among persons with a history of PTB of an infant with a MFA in a first observed pregnancy, and to assess the difference in delivery timing between the first- and second-observed pregnancy. This was a retrospective cohort study of patients with a first-observed pregnancy complicated by MFA and a second-observed pregnancy in single healthcare system between 2013 and 2017. The primary outcome was recurrent PTB (PTB in second-observed pregnancy) and secondary outcomes were recurrent spontaneous PTB (SPTB), delivery gestational age (GA) in second-observed pregnancy, and difference in delivery GA between the first- and second-observed pregnancy. Recurrent PTB in the setting of prior PTB complicated by an MFA is common (∼1/4 patients), though median delivery timing for individuals who delivered preterm in the first-observed pregnancy was 6 weeks later in the second-observed pregnancy. These data suggest that PTB in the setting of MFA is a comparable risk factor to PTB in the absence of MFA.
Differentiating between drug-sensitive and drug-resistant tuberculosis with machine learning for clinical and radiological features.

Background Tuberculosis (TB) drug resistance is a worldwide public health problem that threatens progress made in TB care and control. Early detection of drug resistance is important for disease control, with discrimination between drug-resistant TB (DR-TB) and drug-sensitive TB (DS-TB) still being an open problem. The objective of this work is to investigate the relevance of readily available clinical data and data derived from chest X-rays (CXRs) in DR-TB prediction and to investigate the possibility of applying machine learning techniques to selected clinical and radiological features for discrimination between DR-TB and DS-TB. We hypothesize that the number of sextants affected by abnormalities such as nodule, cavity, collapse and infiltrate may serve as a radiological feature for DR-TB identification, and that both clinical and radiological features are important factors for machine classification of DR-TB and DS-TB. Methods We use data from the NIAID TB Portals program (https://tbportals.niaid.nih.gov), 1,455 DR-TB cases and 782 DS-TB cases from 11 countries. We first select three clinical features and 26 radiological features from the dataset. Then, we perform Pearson's chi-squared test to analyze the significance of the selected clinical and radiological features. Finally, we train machine classifiers based on different features and evaluate their ability to differentiate between DR-TB and DS-TB. Results Pearson's chi-squared test shows that two clinical features and 23 radiological features are statistically significant regarding DR-TB vs. DS-TB. A ten-fold cross-validation using a support vector machine shows that automatic discrimination between DR-TB and DS-TB achieves an average accuracy of 72.34% and an average AUC value of 78.42%, when combing all 25 statistically significant features. Conclusions Our study suggests that the number of affected lung sextants can be used for predicting DR-TB, and that automatic discrimination between DR-TB and DS-TB is possible, with a combination of clinical features and radiological features providing the best performance.
LoRa‐aided outdoor localization system: RSSI or TDoA?

Internet of Things (IoT) is a hot research topic, and typically involves communication between small devices in order to sense, react, and reinvent our way of living. To this end, the localization of such small devices is of great importance for most applications. Between the many applications of IoT, there are those which require low power communication between long ranges, which are entitled Low Power Wide Area (LPWA) communications. One of the main LPWA technologies is the Long Range (LoRa), which does not support native localization. In order to overcome such a problem, a possible approach is to use the Global Positioning Systems (GPS), but it can be costly when aiming for cheaper and simpler devices. This paper evaluates the accuracy of a LoRa‐aided outdoor localization system when locating GPS‐free Target Nodes (TN) through the use of Anchor Nodes (AN) with known positions. Two different indicators are used, namely the Received Signal Strength Indicator (RSSI) and the Time Difference of Arrival (TDoA). Our results indicate that, in general, TDoA presents an improved accuracy over RSSI, with errors ranging from 66 to 253 m in the considered scenario.
The Impacts of Organizational Culture on Knowledge Transfer Between Japanese Managers and Vietnamese Employees in Japanese Enterprises

Knowledge transfer effectiveness is considered one of the most important factors for ensuring the success of any enterprise, especially for multinational enterprises. However, in the case of Japan, the effectiveness of knowledge transfer between Japanese managers and foreign employees is not high. This limited effectiveness is understood as linked to the cultural distance between Japanese managers and foreign employees. The main goal of this study is to explore the impact of organizational culture on knowledge transfer in Japanese enterprises. Quantitative survey research was conducted with 365 respondents, who are Vietnamese labourers working in Japan. Analysis showed that two factors had a positive impact on the effectiveness of knowledge transfer: cultural openness and managers' communication ability. The study draws on these results to make recommend improvements in the knowledge transfer process between Japanese managers and Vietnamese employees.
Cancer mortality in Italian populations: differences between Italy and the USA

Background The comparison of cancer mortality rates and risk factors among foreign-born populations in a host country with those in the country of origin provides insights into differences in access to care, timely diagnosis, and disease management between the two countries. Methods Using 2008–2018 cancer mortality data for the Italian population and for Italy-born Americans, we calculated age-standardized mortality rates (ASMRs) and standardized mortality ratios (SMRs). Results ASMRs were lower in Italy-born Americans (201.5 per 100 000) compared to Italians (255.1 per 100 000). For all neoplasms combined, SMRs for Italy-born American men and women were 0.75 [95% confidence interval (CI), 0.73–0.77] and 0.78 (95% CI, 0.76–0.80), respectively. Among men, the SMRs were significantly below 1 for oral cavity, stomach, colorectal, liver, lung, prostate, bladder and kidney cancer. Among women, the SMRs were 0.69 for oral, 0.40 for stomach, 0.61 for colorectal, 0.72 for liver, 0.73 for breast and 0.53 for kidney cancers. Mortality was not reduced for lung (1.02, 95% CI, 0.94–1.10) cancer in women. Conclusion Generational differences in smoking prevalence patterns between the US and Italy may explain the advantages for Italy-born Americans for lung and other tobacco-related cancers compared to their Italian men counterparts. Lower prevalence of Helicobacter pylori, alcohol consumption, hepatitis B and C virus in the USA may justify the lower mortality for stomach and liver cancer, among Italy-born Americans. Earlier and more widespread adoption of cancer screening and effective treatments in the USA is likely to be influential in breast, colorectal and prostate cancer mortality.
Suitability of Graph Representation for BGP Anomaly Detection

The Border Gateway Protocol (BGP) is in charge of the route exchange at the Internet scale. Anomalies in BGP can have several causes (mis-configuration, outage and attacks). These anomalies are classified into large or small scale anomalies. Machine learning models are used to analyze and detect anomalies from the complex data extracted from BGP behavior. Two types of data representation can be used inside the machine learning models: a graph representation of the network (graph features) or a statistical computation on the data (statistical features). In this paper, we evaluate and compare the accuracy of machine learning models using graph features and statistical features on both large and small scale BGP anomalies. We show that statistical features have better accuracy for large scale anomalies, and graph features increase the detection accuracy by 15% for small scale anomalies and are well suited for BGP small scale anomaly detection.
RISE-Based Composite Adaptive Control of Electro-Hydrostatic Actuator with Asymptotic Stability

The electro-hydrostatic actuator (EHA), the actuator of electric drive and hydraulic transmission, is competitive since it is small in size, light in weight and high in power density. However, the existence of the velocity loop error of servo motors, unmodeled dynamics and highly nonlinear uncertainties restrict the improvement of the tracking accuracy of the EHA system. In order to achieve high-precision motion control of EHAs, a RISE-based composite adaptive control scheme is proposed in this paper. In the proposed composite adaptive control design, a novel parameter adaptive law is synthesized to compensate for the parametric uncertainties and a robust integral of the sign of error (RISE) feedback is utilized to suppress the adverse effects caused by the lumped disturbances, including the velocity loop error of a servo motor and other unmodeled dynamics. The synthesized parameter adaptive law possesses the advantage of fast convergence, which is beneficial to achieve transient tracking performance improvement. In addition, the proposed controller is more suitable for practical applications since it is chattering free. The closed-loop system stability analysis shows that the proposed control scheme guarantees an excellent asymptotic tracking performance. Finally, comparative simulations are conducted to verify the high-performance nature of the proposed controller.
What Happens to Wood after a Tree Is Attacked by a Bark Beetle?

Advancing climate change is affecting the health and vitality of forests in many parts of the world. Europe is currently facing spruce bark beetle outbreaks, which are most often caused by wind disturbances, hot summers, or lack of rainfall and are having a massive economic impact on the forestry sector. The aim of this research article was to summarize current scientific knowledge about the structure and physical and mechanical properties of wood from bark beetle-attacked trees. Spruce stands are attacked by a number of beetles, of which Ips typographus is the most common and widespread in Central Europe. When attacking a tree, bark beetles introduce ophiostomatoid fungi into the tree, which then have a greater effect on the properties of the wood than the beetles themselves. Fungal hyphae grow through the lumina of wood cells and spread between individual cells through pits. Both white rot and brown rot fungi are associated with enzymatic degradation of lignin or holocellulose, which is subsequently reflected in the change of the physical and mechanical properties of wood. Wood-decay fungi that colonize wood after infestation of a tree with bark beetles can cause significant changes in the structure and properties of the wood, and these changes are predominantly negative, in the form of reducing modulus of rupture, modulus of elasticity, discolouration, or, over time, weight loss. In certain specific examples, a reduction in energy consumption for the production of wood particles from beetle-attacked trees, or an increase in surface free energy due to wood infestation by staining fungi in order to achieve better adhesion of paints or glues, can be evaluated positively.
Automated detection and screening of depression using continuous wavelet transform with electroencephalogram signals

Nearly 264 million people around the globe currently suffer from clinical depression, according to the World Health Organization. Although there are diagnostic techniques and treatments presently used by professionals, they are not always helpful. Herein, we suggest the use of advanced technological methods to diagnose depressed patients correctly. A machine learning approach is presented, which uses the electroencephalogram for diagnostics. The model extracts multiple features by applying a continuous wavelet transform (CWT) for each recording. These recordings are employed to train and test the model, with data gathered from 15 depressed and 15 normal patients. After the features are extracted from these recordings, it is organized into matrix form. The features are dimensionally reduced using kernel‐principal component analysis and principal component analysis techniques, ranked using Student's t‐test, and then labelled as normal or depressed with various classifiers. Accuracies of 99.33% and 99.13% were achieved for the right and left hemispheres of the brain, respectively, and 99.26% for the combined hemispheres of the brain. As compared to the discrete and empirical wavelet transform feature extraction methods, the CWT attained the best results. A depression severity index was also developed, using two features for discriminating the classes: normal versus depressed.
Critical Factors Affecting Consumer Buying Behaviour of Organic Vegetables in Vietnam

In Vietnam, the desire to consume organic vegetables is increasing as people become increasingly concerned about foods that contain several chemicals and toxins that are hazardous to their health. However, numerous subjective and objective factors continue to influence customers’ purchase intention and behavior, indicating that this market has not yet reached its full potential. The study analyzes the impacts of trust, price, and convenience on consumers’ consumption behavior and frequency of using organic vegetables in Northern Vietnam. We surveyed 312 consumers in the region. After classifying the data, 178 participants have used or regularly used organic vegetables selected for further study. Next, the author applied SmartPLS software with version 3.3 to test the hypotheses and analyze the effects of the observations. The result shows that three main factors affect customers’ organic vegetable consumption behavior, especially perceived convenience. This study contributes to the development of comprehensive policy mechanisms and regulations on product traceability. Furthermore, market management authorities must have a strategy in place to inspect the product quality of organic vegetable store chains regularly to build customer trust and support the growth of organic vegetable production and supply chains in Vietnam’s northern region.
Comparison Between Steepest Descent Method and Conjugate Gradient Method by Using Matlab

The Steepest descent method and the Conjugate gradient method to minimize nonlinear functions have been studied in this work. Algorithms are presented and implemented in Matlab software for both methods. However, a comparison has been made between the Steepest descent method and the Conjugate gradient method. The obtained results in Matlab software has time and efficiency aspects. It is shown that the Conjugate gradient method needs fewer iterations and has more efficiency than the Steepest descent method. On the other hand, the Steepest descent method converges a function in less time than the Conjugate gradient method.
Respon Investor atas Tanggung Jawab Sosial Tata Kelola Perusahaan di Indonesia: Perspektif Resource Based View

This study aims to examine the effect of corporate social responsibility disclosure and corporate governance disclosure on firm value in Indonesia. This study uses a Resource Based View perspective which is still rarely used in testing firm value. This study uses data and information from manufacturing companies listed on the IDX from 2016 to 2019. After purposive sampling, the final sample that can be used is 260 observations. Testing the data using multiple linear analysis with panel data. The test results show that corporate social responsibility and corporate governance have no effect on firm value. The test results in this study indicate that the Financial Services Authority as the supervisor of issuers in the Indonesian capital market, needs to improve corporate governance and corporate social responsibility arrangements. Keywords: Governance; Sustainability; Disclosure; Firm Value.
An Efficient Obstacle-Avoiding Rectilinear Steiner Tree Construction Method Using PB-SAT

ABSTRACT Rectilinear Steiner tree (RST) construction is an important part of recent VLSI physical design. This article presents an efficient satisfiability (SAT) based approach to construct an obstacle-avoiding Rectilinear Steiner tree for a given set of pins in the presence of a given set of rectilinear obstacles. Initially, a spanning tree is generated without considering the obstacles and the net is decomposed into 2-pin sub-nets. Then, the RST is constructed for each subnet considering obstacles. RST is generated by applying a Pseudo-Boolean (PB) SAT-based technique. The proposed technique has been verified on several benchmark circuits. The results indicate that the proposed technique is able to produce a shorter wire length than other state-of-the-art RST tools in many cases.
Stochastic Process Regression for Cross-Cultural Speech Emotion Recognition

In this work, we pose continuous apparent emotion recognition from speech as a problem of learning distributions of functions, and do so using Stochastic Processes Regression. We presume that the relation between speech signals and their corresponding emotion labels is governed by some underlying stochastic process, in contrast to existing speech emotion recognition meth-ods that are mostly based on deterministic regression models (static or recurrent). We treat each training sequence as an instance of the underlying stochastic process which we aim to discover using a neural latent variable model, which approximates the distribution of functions with a stochastic latent variable using an encoder-decoder composition: the encoder infers the distribution over the latent variable, which the decoder uses to predict the distribution of output emotion labels. To this end, we build on the previously proposed Neural Processes theory by using (a). noisy label predictions of a backbone instead of ground truth labels for latent variable inference and (b). recurrent encoder-decoder models to alleviate the effect of commonly encountered temporal misalignment between audio features and emotion labels due to annotator reaction lag. We validated our method on AVEC’19 cross-cultural emotion recognition dataset, achieving state-of-the-art results.
The effect of carbon black on the curing and mechanical properties of natural rubber/ acrylonitrile- butadiene rubber composites

The present work investigates the effect of the amount of carbon black on curing and mechanical properties such as tensile strength, elongation at break, hardness and abrasion resistance of the natural rubber (NR)/ acrylonitrile-butadiene rubber (NBR) blend. For that purpose, a blend composed of 65% NR and 35% NBR filled with different content of carbon black N330 was used. The curing results indicate that the viscosity and the crosslink density of rubber composites increase and the scorch and curing times decrease with increasing the filler content. Mechanical properties such as tensile modulus and hardness of the CB filled NR/NBR blend were remarkably improved, indicating the inherent reinforcing potential of CB. Regarding tensile strength and abrasion resistance, they increase with the addition of carbon black, up to 50 phr. After that, these properties decrease slightly with filleraddition.
The Good Place in Your Classroom: Recommendations for Teaching Management Ethics

The sitcom The Good Place can be used by management instructors to teach ethical frameworks and concepts. This series, familiar to many undergraduate students in the US, features a Professor of Ethics and Moral Philosophy who gives mini lectures applied to what the characters are experiencing. These mini lectures can be shown to undergraduates studying ethics in a full course or as a subsection of courses such as management, organizational behavior, or leadership. This article provides information on particular clips that management instructors can use, recommendations for discussion topics with prompts provided, and a comparative pedagogical analysis of using this resource.