Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonality of phytoplankton cell size and the relation between photosynthesis and respiration in the Ría de Vigo (NW Spain)

The Ría de Vigo is a dynamic and productive upwelling ecosystem. We measured 14C incorporation (TO14CP) and gross primary production (GPP), community respiration (DCR), net production (NCP) and size-fractioned chlorophyll a (chl a) fortnightly from May 2012 to May 2013 in the euphotic layer of the Ría. Our aim was to improve the depiction of plankton metabolism in the Ría and to test the general hypothesis that community structure determines the degree of heterotrophy in planktonic ecosystems. Higher primary production was measured after upwelling episodes and during the spring bloom, when the community was dominated by microphytoplankton (>70% chl a>20μm). Lower primary production was observed during summer stratification periods (~65% chl a>20μm), and during the pico- and nanophytoplankton-dominated winter (~25% chl a>20μm). Coupling between phytoplankton photosynthesis and biomass varied seasonally, mainly driven by environmental conditions. DCR was 3 times lower and 8 times less variable than GPP, and its variability was mainly driven by the changes in chl a. The integrated metabolic balance was autotrophic most of the year, despite the negative NCP rates at depth. There was an inverse relationship between the DCR:GPP ratio and the percentage of microphytoplankton (% chl a>20μm) only in the summer. However, DCR:GPP and DCR:chl a ratios were similar in winter and spring, despite the seasonal differences in primary production and size structure. The similar TO14CP:NCP and chl a:DCR relations in spring (>70% chl a>20μm) and winter (~25% chl a>20μm), and the differences in summer (>70% chl a>20μm) confirm cell size independence in trophic functioning. We conclude that respiration variability is relevant for the metabolic balance in the Ría, and that the degree of heterotrophy is not systematically related to phytoplankton size over the scales of our study.
Hydrolysis of aromatic polyurethane in water under high pressure of CO2

We have demonstrated a hydrolysis reaction of polyurethane (PU) under high pressure of carbon dioxide (CO2) in water. We employed the PU sample, poly(methylene bis-(1,4-phenylene)hexamethylene dicarbamate), denoted as M-PU, which was synthesized from 4,4′-diphenyl methane diisocyanate and 1,4-butane diol (BD). The optimum hydrolysis reaction condition was 190 °C under CO2 pressures over 4.1 MPa in water medium, and 93% hydrolysis of M-PU was achieved. After the reaction, the water-soluble parts were obtained, and isolated by column chromatography. The isolated products were 4,4′-methylenedianiline (MDA) and 1,4-butane diol (BD), which were components of repeating unit of M-PU. In addition, the hydrolysis reaction gave no byproduct. This hydrolysis under high pressure of CO2 with water is a reaction by which M-PU is selectively hydrolyzed into MDA and BD by cleaving urethane linkage. Moreover, the resulting hydrolyzed products were easily obtained by evaporation of aqueous layer after the reaction, indicating an efficient chemical recycling of PU was achieved. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2017, 55, 2004–2010
Research on Identification Algorithm Based on ECG Signal and Improved Convolutional Neural Network

In the research of ECG signal identity recognition, most of them adopt the method of feature extraction and recognition model separation, extract the time domain features, transform domain features of the original signal, or combine the features with the cross domain. Then the model is used to complete the recognition and classification. In this paper, an advanced improved convolutional neural network model is proposed, which integrates feature extraction and classification to complete identity recognition. ECG data selected from the ecg-id database and MIT-BIH arrhythmia database are directly sent to the model for automatic sign extraction after hierarchical denoising with wavelet tools and then identified. This method achieves the highest recognition rate of 98.49% on ecg-id database and 99.35% on ECG data of MIT-BIH arrhythmia database. The high reliability of the algorithm and the universality of wireless sensors in mobile devices make this research has high commercial value.
Clinical characteristics and outcomes of mechanically ventilated elderly patients in intensive care units: a Chinese multicentre retrospective study

Background In recent years, the number of elderly patients receiving mechanical ventilation (MV) in intensive care units (ICUs) has increased. However, the evidence on the outcomes of elderly mechanically ventilated patients is scant in China. Our objective was to evaluate the characteristics and outcomes in elderly patients (≥65 years) receiving MV in the ICU. Methods We performed a multicentre retrospective study involving adult patients who were admitted to the ICU and received at least 24 hours of MV. Patients were divided into three age groups: under 65, 65–79, and ≥80 years. The primary outcome was hospital mortality. We performed univariate and multivariate logistic regression analysis to identify factors associated with hospital mortality. Results A total of 853 patients were analysed. Of those, 61.5% were ≥65 years of age, and 26.0% were ≥80 years of age. There were significant differences in the principal reason for MV among the three age groups (P<0.001). Advanced age was significantly associated with total duration of MV, ICU length of stay (LOS), and ICU costs (all P<0.001), but not with hospital LOS and hospital costs (P>0.05). In addition, mortality rates in the ICU, hospital, and at 60 days significantly increased with age (all P<0.001). In the age group of 80 years and older, the mortality rates were 47.7%, 49.5%, and 50.0%, respectively. Multivariate logistic regression analysis had found that age, Acute Physiology and Chronic Health Evaluation (APACHE) II score, partial pressure of oxygen in arterial blood/fraction of inspired oxygen (PaO2/FiO2) ratio, total duration of MV, ICU LOS, and the decision to withhold/withdraw life-sustaining treatments were independent influence factors for mortality rates. Conclusions Mechanically ventilated elderly patients (≥65 years) have a higher ICU and hospital mortality, but the hospital LOS and hospital costs are similar to younger patients. Advanced age should be considered as a significant independent risk factor for hospital mortality of mechanically ventilated ICU patients.
Kinetic modeling, thermodynamic, and mass-transfer studies of gas-phase glycerol dehydration to acrolein over supported silicotungstic acid catalyst

The kinetics of gas-phase glycerol dehydration in a packed-bed reactor over a highly active and stable supported silicotungstic acid catalyst with zirconium oxide and nanosized aluminum oxide (30HZ-20A) was investigated. The kinetic study is based on the optimal reaction conditions determined by response surface methodology. The reaction rate followed first-order kinetics with the activation energy and frequency factor, E = 27.5 kJ/mol and A = 5.35 × 105 s–1, respectively. Based on thermodynamic analysis, the values of ΔH° and ΔS° of the endothermic reaction were 14.70 and 0.09 kJ/(mol K), respectively, and ΔG° = −12.12 kJ/mol. The mass-transfer analysis revealed the pellet sizes of dp < 1 μm proceeded under reaction-limiting conditions. Experimental results confirmed high efficacy of the tested catalyst due to unity effectiveness factor and very low Thiele modulus.
Distributionally Robust Chance-Constrained Backscatter Communication-Assisted Computation Offloading in WBANs

Implementing wireless body area networks (WBANs) is very challenging, due to limited power supply, inadequate computation capability, and imperfect channel state information (CSI). In this paper, we propose a hybrid offloading scheme with backscatter communication (BackCom) under imperfect CSI, where each sensor firstly receives radio frequency (RF) energy and then offloads body data task via low-power BackCom to the access point (AP) for edge computing. Aiming to minimize the end-to-end system latency, we jointly optimize the computation speed of AP for processing computation tasks, the power of the signal transmitted by the AP, and the power reflection coefficient under energy and data rate chance constraints. To solve the proposed distributionally robust chance-constrained optimization problem, we approximate chance constraints by the Bernstein-type-inequality (BTI) method and Conditional value-at-risk (CVaR) method in the Gaussian distribution and arbitrary distribution of channel estimation errors, respectively. To tackle the NP-hard problem efficiently, the original problem can be decomposed into two subproblems, which are solved by successive linear programming and iterative algorithm, respectively. Simulation results show that the CVaR method outperforms the other methods for the non-Gaussian CSI mismatch, and the Bernstein method is more suitable for the Gaussian distribution of CSI errors.
A preliminary mapping network model for unsafe acts in maritime accidents

Unsafe acts are a critical issue causing maritime accidents. In order to explore the correlations between the crew’s unsafe acts and the induced maritime accidents, this paper proposes a preliminary mapping network model. Its first part is a hierarchical model, making crews’ unsafe acts as the top layer, skill-based failures, rule- and knowledge-based mistakes and violations acts as criteria, and basic unsafe acts (BUA) as the bottom layer. Therein, the BUAs are identified from 413 marine accident/incident investigation reports and 91 crews’ questionnaires. The second part is composed of several maritime accident types. The connection links between these two parts are aiming to reveal the correlations between each BUA and the specific maritime accident type. The proposed mapping network model can not only support unsafe acts analysis for one and multiple maritime accidents but also present the common BUAs for safety management.
Evaluating the Performance of Collimated Light for near Infrared Analysis of Minced Beef Samples

I n the last few years, near infrared (NIR) spectroscopy has found its place as a fast and valuable tool for quality monitoring in the food industry. NIR spectroscopy provides many advantages such as being fast, suitable for on/in-line monitoring, requiring little or no sample preparation, nondestructive, non-contact and more in comparison to traditional methods (proximate analysis, high-performance liquid chromatography, gas chromatography). Current NIR systems essentially use a tungsten halogen lamp as a light source. Acquiring a good NIR reflectance spectrum requires proper illumination of the sample and a sufficient amount of diffusively reflected light reaching the detector. In the case of NIR spectrophotometers employing fibreoptic probes, it is important to minimise the loss of incident and reflected light travelling through the fibres in order to attain a good signal. Loss of light when it travels through one end of the fibre to the other is termed attenuation. Attenuation is affected by various factors such as absorption, scattering, dispersion and bending. Absorption is largely related to the fibre material, while bending occurs due to physical stress on the fibre. Temperature and humidity also affect the fibre performance depending on the material. The amount of diffusively reflected light from the sample going back to the detector largely depends on the distance between the sample and the probe (stand-off) and the fibre-optic material. A typical fibre-optic reflectance probe works at stand-off distances in the range of millimetres. The use of collimators allows stand-off distances in the range of centimetres and thus enhances the non-contact nature of the technology. The current study aims at evaluating the performance of a collimator–probe setup in terms of the reflected light signal received at different stand-off distances in relation to spectral signatures obtained for a calibration standard and minced beef samples.
The Association Between Locus of Control and Psychopathology: A Cross-Cohort Comparison Between a UK (Avon Longitudinal Study of Parents and Children) and a Japanese (Tokyo Teen Cohort) Cohort

Background: An external locus of control (externality) is associated with poorer psychopathology in individualist cultures, but associations are reported to be weaker in collectivist cultures where an external style is less maladaptive. We investigated the prospective association between externality and psychotic-like experiences (PLE) and depressive symptoms (DS) and compared the strength of associations between a UK and a Japanese cohort. Method: Cross-cultural cohort study of a UK (Avon Longitudinal Study of Parents and Children) and a Japanese cohort (Tokyo Teen Cohort). Externality was assessed using the Children's Nowicki and Strickland Internal, External Scale and DS using the Short Moods and Feelings Questionnaire in both cohorts, PLE were assessed with the Psychosis-Like Experiences Questionnaire (ALSPAC), and the Adolescent Psychotic-Like Symptom Screener (TTC). Associations were investigated using multivariable regression models and bivariate regression models to compare the strength of associations. Results: Mean externality in both childhood and adolescence was higher in ALSPAC than in the TTC. Childhood externality was associated with PLE in late childhood and adolescence in both cohorts and adolescent externality was associated with PLE in young adulthood in the ALSPAC cohort. There was a more mixed pattern of association between externality and DS scores. There was little evidence of any differences in the strength of associations between externality and different psychopathologies, or between cohorts. In ALSPAC adolescent externality and early adult psychopathology were more strongly associated than childhood externality and adolescent and early adult psychopathology. There was no evidence that change in externality between childhood and adolescence was associated with new onset PLE or DS in early adulthood. Conclusion: An external locus of control is associated with poor mental health regardless of cultural context.
Analysis of point cloud data of tunnel cross-section using cubic B-spline curve

In order to overcome the problem of extraction and deformation information analysis of tunnel cross-section by 3D laser scanning technology in tunnel engineering application, a method of extraction and fitting of tunnel cross-section based on cubic B-spline curve was proposed. First, obtain the initial cross-sectional point cloud by using the cross-sectional intercept method based on the spatial point cloud subset;Then, the cross-section was fitted with a cubic B-spline curve, and the missing part of the point cloud was repaired automatically.In the end, specific points are extracted from the fitted cross-section point cloud to realize cross-section deformation analysis.And cross-section analysis of point cloud data of a section of subway tunnel shows that this method can quickly and accurately analyze the deformation information of the tunnel cross-section.
A Machine Learning Approach for Expression Detection in Healthcare Monitoring Systems

: Expression detection plays a vital role to determine the patient’s con-dition in healthcare systems. It helps the monitoring teams to respond swiftly in case of emergency. Due to the lack of suitable methods, results are often compromised in an unconstrained environment because of pose, scale, occlusion and illuminationvariations in the image of the face of the patient. A novel patch-based multiple local binary patterns (LBP) feature extraction technique is proposed for analyzing human behavior using facial expression recognition. It consists of three-patch [TPLBP] and four-patch LBPs [FPLBP] based feature engineering respectively. Image representation is encoded from local patch statistics using these descriptors. TPLBP and FPLBP capture information that is encoded to find likenesses between adjacent patches of pixels by using short bit strings contrary to pixel-based methods. Coded images are transformed into the frequency domain using a discrete cosine transform (DCT). Most discriminant features extracted from coded DCT images are combined to generate a feature vector. Support vector machine (SVM), k-nearest neighbor (KNN), and Naïve Bayes (NB) are used for the classification of facial expressions using selected features. Extensive experimentation is performed to analyze human behavior by considering standard extended Cohn Kanade (CK + ) and Oulu–CASIA datasets. Results demonstrate that the proposed methodology outperforms the other techniques used for comparison.
A numerical investigation on a capsule-intake of the electrical submersible pump in skid

The Electrical Submersible Pumping (ESP) system is one of the most commonly used artificial lift methods in the petroleum industry and one recent breakthrough in this system is enclosed in a capsule and positioned on the seabed in a skid. As it is a recent technology, with only a small amount of equipment currently in operation, there is a clear need for a greater understanding of the flow within this geometry with the objective of perfecting the design of this promising submarine boosting system. This paper presents a numerical investigation of single-phase flow within the scaled capsule-intake of an ESP system in the Skid considering a model with geometric and dynamic similarities in relation to a real equipment in operation in the Espadarte field, located in the Campos Basin, Brazil. The tridimensional and transient simulation for a case for one mass flow rate and inclination angles are investigated. While neglecting the effects of the diffuser and impeller in the system, the flow field features, axial and radial velocity profiles in the intake region were computed. The numerical results show that the flow in the intake region is dominated by the secondary flow, leading to the formation of bathtub vortex. It is expected that the vortices influence the flow pattern in the intake region, breaking the larger bubbles into smaller bubbles, making the transition from the slug flow pattern to the dispersed bubbles or bubbly pattern in which it would be more difficult to be dragged into the intake.
Pore‐scale modeling of microbial activity: What we have and what we need

Under water‐unsaturated conditions, environmental factors (e.g., substrate, nutrients, O2, water activity, and porous structure) controlling microbial activity are highly variable in space and time. The physical structure of porous media (pore size distribution and pore arrangements) has been considered to have a significant role in nutrient (either substrate and/or its reaction partners like O2) fluxes. Understanding what eventually controls microbial activity thus requires a sound description of the physical and chemical conditions in the pore space down to the microscale, as well as knowledge on how microorganisms respond to the energy and matter fluxes in their microscale environment. Microscale modeling has the advantage to resolve the processes down to their fundamental level. In recent years, microscale models describing the physical setting in unsaturated porous media such as soils, as well as growth and functional performance of microorganisms, have become increasingly established and are supported by new experimental techniques resolving the distribution of solid, liquid, and gaseous phases at microscale resolution. Still, integration of these components for simulation of microbial activities in a soil system at the microscale is scarce. This mini‐review highlights the available approaches for pore‐scale modeling of flow and transport processes in both saturated and unsaturated porous media, and for modeling the dynamics of microbial activity constrained by different soil boundary conditions. Further, it is discussed what is required in terms of model development and improved modeling concepts to achieve a holistic reactive transport modeling approach describing microbial activity at the pore scale.
Flipped Learning, Self-Regulated Learning and Learning Retention of Students with Internal/External Locus of Control

This article investigates the effect of the flipped learning model on university students with internal/external locus of control regarding their self-regulated learning skills and learning retention in Saudi Arabia. The sample was divided into two groups according to learning style (internal/external locus of control). Both groups were taught through a flipped learning model. A self-regulated learning skills scale and a learning retention instrument were used. Results indicate significant differences between the experimental groups in favor of the internal locus control. The study highlights the necessity of identifying students’ learning styles and finding the best e-learning environments and strategies that meet their learning preferences. self-regulated learning skills of students who enroll in a MOOC. Their study identified differences in behaviour associated with five self-regulated learning sub-processes including motivation and goals setting, self-efficacy, task interest value, task strategies, and self-satisfaction and evaluation, in favour of the students with higher self-regulated learning.
A Broadband Model-Based Parameter Estimation Method for Analyzing Multilayer Periodic Structures

A new model-based parameter estimation (MBPE) method for multilayer periodic EM surfaces is discussed in this article. By applying the proposed method to predict the generalized scattering matrix (GSM) of each unit layer section, the response of a composite structure can be determined by cascading GSMs while allowing the salient behavior of each section to be accurately captured. Based on the spectral properties of Green’s functions with high-order wavenumbers and chosen basis functions, an advanced interpolation model is developed to estimate the broadband response of periodic structures in a robust and efficient manner. It needs fewer samples in the interpolation compared to the state of the art, and no restriction for the sample selection is required. Numerical analysis has been carried out for two types of multilayer structures, and a good agreement between the conventional periodic method of moments (PMM) and the proposed method has been obtained.
Bid Evaluation and Assessment of Innovation in Road Construction Industry: A Systematic Literature Review

Objectives: This paper present a thorough understanding of bid evaluation process and assessment of innovation in road construction industry. Methods/Analysis: The research articles reviewed are based on topics of bid evaluation process, evaluation of bids in road construction industry, the indicators and techniques of bids evaluation, process of Bid evaluation in countries, innovation in road industry, and indicators of innovation in the context of road construction industry. Findings: It is widely accepted that projects related to the road construction must be accomplished in an organized manner. Literature indicates that the road constructing demands from customers and competition of assessing and evaluating tenders have been increasing rapidly. Novelty /Improvement: Challenges in developing suitable roads contribute to the inconsistency in the industry and directly demands towards physical work on a road construction project, nevertheless, a failure to accurately assessment of tenders can lead to complications for the whole project and road Construction Company. The selection of a suitable construction contractor’s surges likelihoods of successful achievement of a road construction project. This may also achieve the customer goals, and retain the project schedule on time, within the budget and achieve high quality project. Doi: 10.28991/cej-2021-03091646 Full Text: PDF