Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation and identification of naphthalene degrading bacteria and their degradation characteristics under rainwater environment in heavily polluted areas

Abstract This study is screened for naphthalene degrading strains from a heavily polluted area with high naphthalene concentration in the rainwater for the effective removal of naphthalene from rainwater. Recently, naphthalene biodegradation has been achieved in water. However, the influences of organics and inorganics in the rainwater on the biodegradation of naphthalene remains unclear. The naphthalene degrading strain Klebsiella sp. (WJ-1) was identified from sewage sludge. The effects of temperature, pH, inoculum size, and rotation speed on the degradation ability of WJ-1 were studied. The results showed that the naphthalene degradation rates of WJ-1 in rainwater were higher than those in aqueous solution at different experimental conditions. The optimal conditions were 30 °C, 10% inoculum size, pH 7.0, and a rotation speed of 150 rpm. The substances in rainwater might be important co-metabolites of naphthalene degradation. Based on intermediate metabolites detected by gas chromatography–mass spectrometer (GC-MS), the naphthalene biodegradation pathway was identified, as being similar to the phthalic acid pathway. These results suggest WJ-1 as a good candidate for the efficient bioremediation of naphthalene from rainwater in heavily polluted areas.
Does frequent prayer help reduce alcohol use? Heterogeneity in religious contexts and drinking styles

ABSTRACT Risky alcohol drinking remains a public health issue in many societies. Evidence has shown that religious coping is often associated with less alcohol consumption. This study examines the relationship between frequent prayer and alcohol use in two major denominations in Christianity: Roman Catholicism and Protestantism. Fixed-effects nominal logistic analyses were conducted on a sample (n = 10,666) extracted from the National Longitudinal Survey of Youth 97 (NLSY97). After controlling for unobservable individual characteristics, the present study reveals that frequent prayer could reduce alcohol consumption in the context of Protestantism. This mitigation effect is only significant for moderate alcohol users but not heavy alcohol users. In Roman Catholicism, there is no evidence to support that frequent prayer could reduce alcohol consumption, regardless of one’s drinking style. The present study concludes that not only religious denominations but also drinking styles are factors that need to be considered when implementing intervention programmes for alcohol misuse.
Religiosity, happiness and suicidal behaviour: a cross-sectional comparative study in Iran

ABSTRACT A sample of 102 Iranian psychiatric outpatients was selected. For a comparison group, a sample of 72 Iranian university employees was recruited. Participants responded to the Self-Rating Scale for Religiosity and the Self-Rating Scale of Happiness, and the Suicidal Behaviour Questionnaire-Revised. The correlations of religiosity and happiness with suicidal behaviour for the patients were negative and significant, but the employees were negative and non-significant. The correlations between religiosity and happiness scores for the patients were positive and significant, but the employees were non-significant. Patients obtained significant lower scores for religiosity and happiness than did employees, and higher scores for suicidal behaviour. Sex differences in the scale scores were not significant for the patients, but male employees obtained significant higher scores for religiosity than did females. Religiosity may have a positive impact on the happiness of psychiatric patients and on their suicidal behaviour, indicating that encouraging religious activities for psychiatric patients may improve their subjective well-being.
A slip law for hard-bedded glaciers derived from observed bed topography

Numerical simulations of glacier slip over diverse hard beds indicate Coulomb slip behavior like that of deformable beds. Ice-sheet responses to climate warming and associated sea-level rise depend sensitively on the form of the slip law that relates drag at the beds of glaciers to their slip velocity and basal water pressure. Process-based models of glacier slip over idealized, hard (rigid) beds with water-filled cavities yield slip laws in which drag decreases with increasing slip velocity or water pressure (rate-weakening drag). We present results of a process-based, three-dimensional model of glacier slip applied to measured bed topographies. We find that consideration of actual glacier beds eliminates or makes insignificant rate-weakening drag, thereby uniting process-based models of slip with some ice-sheet model parameterizations. Computed slip laws have the same form as those indicated by experiments with ice dragged over deformable till, the other common bed condition. Thus, these results may point to a universal slip law that would simplify and improve estimations of glacier discharges to the oceans.
De Novo Sequencing Provides Insights Into the Pathogenicity of Foodborne Vibrio parahaemolyticus

Vibrio parahaemolyticus is a common pathogenic marine bacterium that causes gastrointestinal infections and other health complications, which could be life-threatening to immunocompromised patients. For the past two decades, the pathogenicity of environmental V. parahaemolyticus has increased greatly, and the genomic change behind this phenomenon still needs an in-depth exploration. To investigate the difference in pathogenicity at the genomic level, three strains with different hemolysin expression and biofilm formation capacity were screened out of 69 environmental V. parahaemolyticus strains. Subsequently, 16S rDNA analysis, de novo sequencing, pathogenicity test, and antibiotic resistance assays were performed. Comparative genome-scale interpretation showed that various functional region differences in pathogenicity of the selected V. parahaemolyticus strains were due to dissimilarities in the distribution of key genetic elements and in the secretory system compositions. Furthermore, the genomic analysis-based hypothesis of distinct pathogenic effects was verified by the survival rate of mouse models infected with different V. parahaemolyticus strains. Antibiotic resistance results also presented the multi-directional evolutionary potential in environmental V. parahaemolyticus, in agreement with the phylogenetic analysis results. Our study provides a theoretical basis for better understanding of the increasing pathogenicity of environmental V. parahaemolyticus at the genome level. Further, it has a key referential value for the exploration of pathogenicity and prevention of environmental V. parahaemolyticus in the future.
Efficacy of parasternal block to decrease intraoperative opioid use in coronary artery bypass surgery via sternotomy: a randomized controlled trial

Objective This study aims to assess the effect of a preoperative parasternal plane block (PSB) on opioid consumption required to maintain hemodynamic stability during sternotomy for coronary artery bypass graft surgery. Methods This double-blind, randomized, placebo-controlled trial prospectively enrolled 35 patients scheduled for coronary artery bypass graft surgery under general anesthesia with propofol and remifentanil. Patients were randomized to receive preoperative PSB using either ropivacaine (PSB group) or saline solution (placebo group) (1:1 ratio). The primary endpoint was the maximal effect-site concentration of remifentanil required to maintain heart rate and blood pressure within the recommended ranges during sternotomy. Results Median maximum concentration of remifentanil necessary to maintain adequate hemodynamic status during sternotomy was significantly reduced in PSB group (4.2 (2.5–6.0) ng/mL) compared with placebo group (7.0 (5.2–8.0) ng/mL) (p=0.02). Mean maximum concentration of propofol used to control depth of anesthesia was also reduced (3.9±1.1 µg/mL vs 5.0±1.5 µg/mL, PSB vs placebo, respectively; p=0.02). This reduction in propofol consumption during sternotomy enabled a more adequate level of sedation to be maintained in patients (minimum patient state index was 11.7±8.7 in placebo group and 18.3±6.8 in PSB group; p=0.02). PSB reduced postoperative inflammatory response by limiting concentrations of proinflammatory cytokines IL-8, IL-18, IL-23, IL-33 and MCP-1 measured in the first 7-day after surgery (p<0.05). Conclusions Preoperative PSB reduced the maximum concentrations of remifentanil and propofol required to maintain hemodynamic stability and depth of anesthesia during sternotomy. Trial registration number NCT03734159. Sébastien Bloc, M.D.1,2; Brieuc P. Pérot, Ph.D.3; Hadrien Gibert, M.D.1; Jean-Dominique Law Koune, M.D.1; Yannick Burg, M.D.1; Didier Leclerc, M.D.1; Anne-Sophie Vuitton, M.D.1; Christophe De La Jonquière, M.D.1; Marine Luka, L.S. 3; Thierry Waldmann, M.D.4; Nicolas Vistarini, M.D.4; Stéphane Aubert, M.D.4; Mickaël M. Ménager, Ph.D.3; Messaouda Merzoug, Ph.D.2; Cécile Naudin, Ph.D.2; Pierre Squara, M.D.2,5
Early lung ultrasound affords little to the prediction of bronchopulmonary dysplasia

Objective To test the hypothesis that lung ultrasound (LUS) performed in the first week of life would predict bronchopulmonary dysplasia (BPD). Secondary outcomes included the utility of LUS in predicting interim respiratory interventions. Design A prospective observational cohort study in preterm infants born <28 weeks’ gestation in the single tertiary statewide neonatal intensive care unit in Western Australia. Methods A rigorous protocol for LUS acquisition on day 1, day 3–4, day 7, day 28 and 36 weeks’ postmenstrual age (PMA) was implemented with blinded analysis using a modified, previously validated LUS score. BPD was defined by both recent National Institute of Child Health and Human Development categorical criteria and a continuous physiological variable using a modified Shift test. Results Of the 100 infants studies, primary outcome data were available for the 96 infants, surviving to 36 weeks’ PMA. In a univariate logistic regression analysis, LUS on days 3–4 and day 7 accurately predicted BPD (day 3–4 OR (95% CI)=1.54 (1.03 to 2.42), p=0.044; day 7 OR (95% CI)=1.66 (1.07 to 2.70), p=0.031). The predictive value of LUS was insignificant in a multivariate model in which gestational age was the dominant predictor. LUS accurately predicted interim respiratory outcomes including surfactant administration, duration of intubation and extubation to non-invasive support at 48 hours. Conclusions LUS in the first week of life predicted BPD. However, LUS offers little additive accuracy to current gestational age-based models. Trial registration number ACTRN12617000208303.
Electrochemical kinetics and mechanisms of iron oxidation in CO2-containing aqueous monoethanolamine

Concerns about the impact of anthropogenic CO2 emissions on global climate change are necessitating the development of low-carbon technologies. Despite its energy requirements, carbon capture and sequestration (CCS) provides a promising approach to limiting carbon dioxide emissions, in addition to renewable energy and nuclear power. Due to the depth of technological experience and retrofitting capabilities associated with the process, postcombustion CO2 capture based on absorption in alkanolamine solutions is presently the most feasible technology available for mitigating the soaring levels of atmospheric CO2 and achieving the ambitious targets set by the Intergovernmental Panel for Climate Change (IPCC). Whilst the process of CO2 capture by alkanolamines involves a technologically mature process, it is still faced with numerous challenges, such as high energy requirements and operating costs, as well as operational difficulties. Monoethanolamine (MEA), the benchmark amine for the process, frequently becomes contaminated with degradation products and, when CO2-loaded, is corrosive to the carbon steel process equipment. Hence, this thesis aimed to determine the oxidation and reduction reaction kinetics and mechanisms of iron in aqueous MEA-CO2 systems as function of experimental variables, aiming to predict iron corrosion rates under process conditions. The behaviour of iron in aqueous MEA solutions was characterized by voltammetry with a rotating disc electrode (RDE) and an electrochemical quartz crystal microbalance (EQCM), as functions of temperature (25-80C), CO2 loading (0-0.6 mol CO2 (mol amine)), pH (8.1012.55), MEA concentration (5-60 wt%) and oxygen concentration. Electrode potential-pH and activity-pH diagrams of iron-water–CO2 systems were used to assist with reaction assignments.
Numerical Model of Chirped Pulse Amplification in Pulsed Synchronously Pumped Low-Repetition-Rate Fiber Amplifiers

We propose a numerical model to simulate amplification in a pulsed synchronously pumped chirped pulse amplifier (CPA) operating at a low repetition rate. The generalized nonlinear Schrödinger equation and dynamic rate equations are employed to facilitate an accurate treatment of time-space-dependent-gain, wavelength-dependent saturation energy, nonlinearity, and dispersion. Because of an accurate instantaneous gain term, the model can adequately explain pulse distortion and spectral redshift during amplification. The bidirectional amplified spontaneous emission, final output energy, and evolution of temporal and spectral intensities are also investigated through simulations. In addition, a pulsed synchronously pumped CPA fiber system is estbalished to validate the model. The experimental results for the wave shape, spectrum and energy show excellent agreement with those of simulations.
Execution of Hybrid NOMA Schemes Concerning Outage Performance and Sum Rate Interplay

The steadily developing number of users has achieved the test to distinguish an advantageous strategy for different access in upcoming wireless communication. It has been seen that non-orthogonal multiple access (NOMA) method gives far superior execution than orthogonal multiple access (OMA) procedure while exploiting the accessible assets. Interferences can be limited enough using successive interference cancellation (SIC) when actualized broadly, however it is normal situation in NOMA. But enormous usage increments the complexity of the framework and as a result energy effectiveness is decreased. To balance the detriments of NOMA procedure, interest in hybrid NOMA strategies has developed. Numerous new papers have proposed hybrid NOMA schemes as a reasonable alternative to moderate the disservices of NOMA. This paper plays out an investigation of the exhibition between hybrid NOMA systems such as CR-NOMA, U2X-NOMA, RIS-NOMA, HS-UAV NOMA, SCMA-NOMA and GFDM-NOMA. An examination of every one of these methods is led to recognize the best hybrid NOMA procedure for 5G and beyond wireless communication. The articulations for sum rate and outage probability have been determined for all the procedures. Through examination and reenactment results it is found that U2X-NOMA gives the best exhibition regarding sum rate and outage probability among the six discussed schemes.
MLP for Spatio-Temporal Traffic Volume Forecasting

Avoiding traffic congestion phenomena is an important aspect of efficient transportation infrastructure (e.g. toll roads) management. Traffic congestion phenomena can be avoided by forecasting volume traffic data. This paper aims to analyze how specific factors and parameters affect the behavior of traffic volume, like vehicle category, date, and weather data at a given timestamp and place. Moreover, the procedure of data prepossessing is presented to produce a cleaner data set that gives more fundamental information. Subsequently, spatio-temporal toll road prediction is achieved through a multi-layer perceptron. Finally, the proposed low-cost method is evaluated using real-life data from a toll plaza, while the experimental results show the efficiency of the proposed method.
Federated-Learning-Based Client Scheduling for Low-Latency Wireless Communications

Motivated by the ever-increasing demands for massive data processing and intelligent data analysis at the network edge, federated learning (FL), a distributed architecture for machine learning, has been introduced to enhance edge intelligence without compromising data privacy. Nonetheless, due to the large number of edge devices (referred to as clients in FL) with only limited wireless resources, client scheduling, which chooses only a subset of devices to participate in each round of FL, becomes a more feasible option. Unfortunately, the training latency can be intolerable in the iterative process of FL. To tackle the challenge, this article introduces update-importance-based client scheduling schemes to reduce the required number of rounds. Then latency-based client scheduling schemes are proposed to shorten the time interval for each round. We consider the scenario where no prior information regarding the channel state and the resource usage of the devices is available, and propose a scheme based on the multi-armed bandit theory to strike a balance between exploration and exploitation. Finally, we propose a latency-based technique that exploits update importance to reduce the training time. Computer simulation results are presented to evaluate the convergence rate with respect to the rounds and wall-clock time consumption.
Forecasting the Early Market Movement in Bitcoin Using Twitter's Sentiment Analysis: An Ensemble-based Prediction Model

Data collected from social media such as tweets, posts, and blogs can assist in an early indication of market sentiment in the financial field. This has frequently been conducted on Twitter data in particular. Using data mining techniques, opinion mining, machine learning, natural language processing (NLP), and knowledge management, the underlying public mood states and sentiment can be uncovered. As cryptocurrencies play an increasingly significant role in global economies, there is an evident relationship between Twitter sentiment and future price fluctuations in Bitcoin. This paper assesses Tweets' collection, manipulation, and interpretation to predict early market movements of cryptocurrency. More specifically, sentiment analysis and text mining methods, including Logistic Regressions, Binary Classified Vector Prediction, Support Vector Mechanism, and Naive Bayes, were considered. Each model was evaluated on their ability to predict public mood states as measured by ‘tweets' from Twitter during the era of covid-19. An XGBoost-Composite ensemble model is constructed, which achieved higher performance than the state-of-the-art prediction models.
Few-Shot Learning for Palmprint Recognition via Meta-Siamese Network

Palmprint is one of the discriminant biometric modalities of humans. Recently, deep learning-based palmprint recognition algorithms have improved the accuracy and robustness of recognition results to a new level. Most of them require a large amount of labeled training samples to guarantee satisfactory performance. However, getting enough labeled data is difficult due to time consumption and privacy issues. Therefore, in this article, a novel meta-Siamese network (MSN) is proposed to exploit few-shot learning for small-sample palmprint recognition. During each episode-based training iteration, a few images are selected as sample and query sets to simulate the support and testing sets in the test set. Specifically, the model is trained episodically with a flexible framework to learn both the feature embedding and deep similarity metric function. In addition, two distance-based losses are introduced to assist the optimization. After training, the model can learn the ability to get similarity scores between two images for few-shot testing. Adequate experiments conducted on several constrained and unconstrained benchmark palmprint databases show that MSN can obtain competitive improvements compared with baseline methods, where the best accuracy can be up to 100%.
Cellular network bandwidth prediction in consumer applications

The quality of experience that uses the application for reproduction in movement depends on bandwidth fluctuation during driving. The popularity of technologies that require the delivery of bandwidth information along a defined trajectory has heightened the need to predict bandwidth and examine its behavior. Knowing achievable bandwidth along a defined trajectory, we can assist and adaptively affect video reproduction (bit rate and video resolution). Many solutions used today for estimating bandwidth directly depend on the sequence of previous measurements. Because of fluctuation and other factors that affect, we cannot rely only on previous measurements. In this paper, we propose predicting communication bandwidth using signal strength parameters and other physical factors that can affect the exactness of achievable prediction using machine learning methods.
Using Sampling Techniques and Machine Learning Algorithms to Improve Big Five Personality Traits Recognition from Non-verbal Cues

Automatic personality recognition using the Big Five dimensions (OCEAN: extraversion, agreeableness, conscientiousness, neuroticism and openness) is capturing the attention of researchers. Personality recognition is expected to have encouraging future in Human- computer and Robot Interaction applications. Human speech conveys rich information that can be derived to recognize speaker traits. However, our focus is on the rich content of non-verbal features in human speech. We focus on how humans talk, not what they talk about. The focus in this paper is to experiment with four different machine learning techniques, and their performance in recognizing personality traits, we report our results in this regard. We use the Speaker Personality Corpus provided by the Interspeech 2012 challenge. First, we recognize three issues affecting the system’s low performance: dimensionality, judges’ agreement, and imbalanced data. Next, we address each issue and provide a solution to improve the system’s performance. Finally, we compare our results with the baseline showing better classification results.
Mem-Elements Emulator Design With Experimental Validation and Its Application

An emulator circuit of Memristor, Memcapacitor, and Meminductor commonly termed as mem-elements has been demonstrated in this article. The circuit has been realized using the technique of current mode, which provides better performance over voltage mode counterparts. The current mode analog building blocks, along with a few passive components, have been used in the presented circuit implementation. The fingerprint characteristics have been observed in both simulation and experimental results, validating the theoretical analysis. The robustness of the presented design has been supported by performing different types of analysis like process corner, temperature, and non-volatility behavior. The mem-elements emulator design has been simulated using <inline-formula> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> TSMC process parameter, and ±1.2 V power supply has been used. The commercial ICs AD844 and CA3080 are used for the experimental demonstration of the proposed mem-elements design by making a prototype on a breadboard. A layout area of <inline-formula> <tex-math notation="LaTeX">$4829~\mu \text{m}^{2}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$8098~\mu \text{m}^{2}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$8061~\mu \text{m}^{2}$ </tex-math></inline-formula> respectively is required for the Memristor, Memcapacitor, and meminductor circuit. The power consumed by the mem-elements circuit is also provided. A chaotic has been implemented using mem-elements to show the usefulness of the emulator design.