Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of APACHE II and APACHE IV score as predictors of mortality in patients with septic shock in intensive care unit: A prospective observational study

Background and Aims: Prediction of outcome in intensive care unit (ICU) patients is of imperative importance. Our aim was to assess and compare the performance of Acute Physiology and Chronic Health Evaluation (APACHE) II and APACHE IV scores in predicting mortality in adult patients suffering from septic shock admitted to our ICU. Material and Methods: This was a prospective observational study conducted in a 14-bedded medical ICU of a tertiary care center from January 2019 to March 2020; 128 patients suffering from septic shock were included and APACHE II and IV scores were calculated. We also calculated the predicted and actual mortality rates and standardized mortality ratios. The receiver operating characteristic curves were used to assess discrimination. Results: Out of the 128 patients, 63 patients (49.21%) died. The mean (± standard deviation) admission APACHE II score was 16.7 ± 5.53, while the mean APACHE IV score was 67.25 ± 25.99. The non-survivors had significantly higher APACHE II and IV scores when compared to those who survived (P < 0.001). APACHE II had a slightly better discriminative power (with the area under the Receiver operating characteristic (ROC) curve of 0.78) than APACHE IV (with the area under the ROC curve of 0.74). The mean predicted mortality rate (PMR) of the patient population calculated on the basis of the APACHE II scoring system was 22.46 ± 15.76, and the mean PMR calculated as per the APACHE IV scoring system was 11.64 ± 15.59. Conclusion: Both APACHE II and APACHE IV underestimated mortality in septic shock patients. Both APACHE II and APACHE IV were comparable in differentiating survivors from non-survivors. However, there was a good correlation between the two models.
Exploring a Pathway to Sustainable Organizational Performance of South Korea in the Digital Age: The Effect of Digital Leadership on IT Capabilities and Organizational Learning

In the age of Industry 4.0, the emergence of new technologies is compelling organizations to search for new methods for sustainability. In particular, IT capabilities and organizational learning competencies with digital leadership play crucial roles in managing environmental dynamism, which are profoundly related to sustainable organizational performance in the digital age. This study explored sustainable organizational performance from the perspective of digital leadership (DL) and the role of IT capabilities (IT infrastructure, IT business spanning, IT-proactive stance), as well as organizational learning in sustainable organizational performance. For this research, data from 173 employees from South Korean organizations were collected using an online survey on digital leadership, IT capabilities, organizational learning, and sustainable organizational performance (SOP). Here, an SPSS- and AMOS-based structural equation modeling technique was used to examine the outcomes for analysis. The results confirmed that digital leadership significantly directly affected SOP. Moreover, there was no mediating effect of IT infrastructure and IT business spanning; however, an IT-proactive stance and organizational learning fully mediated the relationship between DL and SOP. This research will aid leadership behavior alongside other knowledge-based studies that empirically tested the role of digital leadership, IT capabilities, organizational learning, and SOP. As digital leadership competencies demand is surging for managing digital challenges alongside the verification of digital leadership behavior and knowledge-based theory, the important role of DL regarding IT capabilities and organizational learning in SOP needs to be prudently considered in the South Korean context.
High Step-Up Three-Port Converter for Renewable Energy Systems

In this paper, a high voltage gain non-isolated three-port converter with a three-winding coupled inductor is proposed for renewable energy system. By implementing a cascoded structure where a photovoltaic (PV) source is stacked on top of a battery voltage source, the high output voltage can be achieved with a reasonable duty cycle, resulting in higher efficiency when both sources provide energy to the load. Furthermore, only three power switches are needed to regulate the energy flows among the three ports. When the low-voltage PV source provides energy to the load, the primary winding and the secondary winding of this coupled inductor are connected in series with opposite polarities to increase the voltage gain of the proposed converter.The use of a clamp circuit allows for the recycling of leakage inductance energy, which helps to reduce voltage spikes on the power switches. This results in the selection of low on-resistance switches and leads to higher converter efficiency. The proposed topology’s operations, steady-state analyses, and key parameter designs are then discussed. To validate the performance of the proposed converter, a 300 W laboratory prototype is implemented.
Microwave-assisted extraction optimization and conventional extraction of phenolic compounds from coriander leaves: UHPLC characterization and antioxidant activity

Background: Qualitative and quantitative investigations of bioactive compounds in plant materials are heavily based on the selection of an accurate extraction method. Aims: That’s why; this work consists of a comparative study between Microwave Assisted Extraction (MAE) and Conventional Extraction (CE), based on the total phenolic compounds (TPC) yield, phenolic profile, and antioxidant activity of coriander leaves powder (Coriandrum sativum L.). Material and Methods: MAE was optimized and performed using the Response Surface Methodology (RSM), and was modeled by using a second-order regression equation. While CE was done using the classic water bath method. Results: Under the optimal conditions, the recovery of TPC yield obtained was 37.94 ± 2.06 mg (MAE) vs 44.47 ± 0.57 mg GAE/g DW (CE). The UHPLC characterization showed a close phenolic composition of the two extracts, mainly represented by quercetin glucosides and by dimethoxycinnamoyl hexoside. No significant difference (p>0.05) was recorded in terms of the antioxidant activity of both extracts, as estimated by Ferric reducing antioxidant power (FRAP), Nitric oxide (NO•), and superoxide anion (O2•-) scavenging tests. Conclusions: Hence, the exploitation of MAE has many valuable advantages, as the processing time is brief and the antioxidant activities and phenolic composition were not affected by the extraction process.
An Algorithm to Detect Community by Geodesic Line in Social Networks

Communities in networks usually can be defined as sub-graphs in which nodes are more densely linked with each other and the rest are comparatively sparse in the network. In this paper, connective relationship of a network is defined, k-walks in (1+A) matrix is proposed, and then we normalize the walker vector of each node into unit n-dimension unit spherical surface, and then we compute geodesic lines between two nodes on the spherical surface, and get the best partition of the communities by k-means clustering method. The algorithm is proved to be highly effective in detecting communities in real-world network data, such as Zachary karate club and football game network etc.
The lifetime of contact binaries

Abstract The lifetime of contact binaries is an important parameter for studying the evolution of contact binaries. By reproducing the relative frequency of occurrence for contact binaries, we found that the lifetime of contact binaries decreases with increasing mass of the primaries. The lifetime of contact binaries is about 4%–10% of the main sequence lifetime of the primaries, and is about 0.006–0.65 Gyr. This suggests that the lifetime of contact binaries strongly depends on the mass of the primaries.
Classification of JET Neutron and Gamma Emissivity Profiles

In thermonuclear plasmas, emission tomography uses integrated measurements along lines of sight (LOS) to determine the two-dimensional (2-D) spatial distribution of the volume emission intensity. Due to the availability of only a limited number views and to the coarse sampling of the LOS, the tomographic inversion is a limited data set problem. Several techniques have been developed for tomographic reconstruction of the 2-D gamma and neutron emissivity on JET. In specific experimental conditions the availability of LOSs is restricted to a single view. In this case an explicit reconstruction of the emissivity profile is no longer possible. However, machine learning classification methods can be used in order to derive the type of the distribution. In the present approach the classification is developed using the theory of belief functions which provide the support to fuse the results of independent clustering and supervised classification. The method allows to represent the uncertainty of the results provided by different independent techniques, to combine them and to manage possible conflicts.
Conditions on sub-extraction

The goal of this paper is to address CED effects, building on the mechanics of Chomsky’s (2000; 2001) Agree mechanism. In so doing, we will critically review Chomsky’s (2005) proposal that phase edges create locality difficulties, restricting ourselves to the predictions this idea makes with elements merged in SPEC-v* (arguably, subjects and shifted objects; see Chomsky 2001). We will eventually argue that the relevant blocking effect has nothing to do with phase edges per se, but rather with details pertaining to Chomsky’s (2000; 2001) Activity Condition:
Repercussions of the COVID-19 pandemic on Nursing training: A Scoping Review

Objective: to map the knowledge produced about the repercussions imposed by the COVID-19 pandemic on Nursing training. Method: this is a Scoping Review, guided by the recommendations set forth in the Joanna Briggs Institute Reviewer’s Manual and carried out in 15 electronic databases and theses and dissertations repositories. The protocol was registered at the Open Science Framework. The data were analyzed and synthesized into two pre-established analysis categories: positive and negative repercussions; and descriptive statistics. Results: 33 publications identified, the most cited positive aspects were the development of new teaching strategies adapted to the virtual environment and the training of future professionals in clinical practice in the context of a health crisis. The negative repercussions are related to psychological issues such as increase in the cases of anxiety, stress and loneliness among the students. Conclusion: the diverse evidence suggests that remote teaching was a timely emergency way out for the continuity of academic training; however, this educational modality presented positive and negative aspects that need to be rethought for a better systematization of teaching-learning in other contexts that resemble the COVID-19 pandemic.
ElectrodeNet—A Deep-Learning-Based Sound Coding Strategy for Cochlear Implants

ElectrodeNet, a deep-learning-based sound coding strategy for the cochlear implant (CI), is proposed to emulate the advanced combination encoder (ACE) strategy by replacing the conventional envelope detection using various artificial neural networks. The extended ElectrodeNet-CS strategy further incorporates the channel selection (CS). Network models of deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) were trained using the fast Fourier transformed bins and channel envelopes obtained from the processing of clean speech by the ACE strategy. Objective speech understanding using short-time objective intelligibility (STOI) and normalized covariance metric (NCM) was estimated for ElectrodeNet using CI simulations. Sentence recognition tests for vocoded Mandarin speech were conducted with normal-hearing listeners. DNN, CNN, and LSTM-based ElectrodeNets exhibited strong correlations to ACE in objective and subjective scores using mean squared error (MSE), linear correlation coefficient (LCC), and Spearman’s rank correlation coefficient (SRCC). The ElectrodeNet-CS strategy was capable of producing N-of-M compatible electrode patterns using a modified DNN network to embed maxima selection, and to perform in similar or even slightly higher average in STOI and sentence recognition compared to ACE. The methods and findings demonstrated the feasibility and potential of using deep learning in the CI coding strategy.
Disturbance rejection control based on acceleration projection method for walking robots

This paper presents a disturbance rejection scheme for walking robots under unknown external forces and moments. The disturbance rejection strategy, which combines the inverse dynamics control with the acceleration projection onto the ZMP (zero moment point)-plane, can ensure the overall dynamic stability of the robot during tracking the pre-computed trajectories. Under normal conditions, i.e., the system is dynamically balanced, a primary inverse dynamics control is utilized. In the case that the system becomes unbalanced due to external disturbances, the acceleration projection control (APC) loop, will be activated to keep the dynamic stability of the walking robot through modifying the input torques. The preliminary experimental results on a robot leg demonstrate that the proposed method can actually make the robot keep a stable motion under unknown external perturbations.
Multiple Bioimaging Applications Based on the Excellent Properties of Nanodiamond: A Review

Nanodiamonds (NDs) are emerging as a promising candidate for multimodal bioimaging on account of their optical and spectroscopic properties. NDs are extensively utilized for bioimaging probes due to their defects and admixtures in their crystal lattice. There are many optically active defects presented in NDs called color centers, which are highly photostable, extremely sensitive to bioimaging, and capable of electron leap in the forbidden band; further, they absorb or emit light when leaping, enabling the nanodiamond to fluoresce. Fluorescent imaging plays a significant role in bioscience research, but traditional fluorescent dyes have some drawbacks in physical, optical and toxicity aspects. As a novel fluorescent labeling tool, NDs have become the focus of research in the field of biomarkers in recent years because of their various irreplaceable advantages. This review primarily focuses on the recent application progress of nanodiamonds in the field of bioimaging. In this paper, we will summarize the progress of ND research from the following aspects (including fluorescence imaging, Raman imaging, X-ray imaging, magnetic modulation fluorescence imaging, magnetic resonance imaging, cathodoluminescence imaging, and optical coherence tomography imaging) and expect to supply an outlook contribution for future nanodiamond exploration in bioimaging.
Pilates to Improve Core Muscle Activation in Chronic Low Back Pain: A Systematic Review

Low back pain is prevalent in the community and associated with deficits in core muscle strength and activation. Pilates is argued to improve movement and reduce pain, yet there is a limited understanding of the specific effects of Pilates training on core muscle strength or activity. A systematic search of databases (CINAHL, Embase, Ovid MEDLINE) used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methods to evaluate randomised controlled trials (RCTs) on the effects of Pilates to improve core muscle activation. Methodological quality was assessed using the Physiotherapy Evidence Database scale (PEDro). The certainty of findings was determined using the Grading of Recommendations Assessment, Development and Evaluation tool. Of the initial yield of 563 articles, eight RCTs met the inclusion criteria. A diverse range of Pilates interventions and outcome measures were utilised to assess effects on core muscle activation and strength. The main finding was that Pilates is not inferior to equivalently dosed exercises, and can be superior to non-equivalent or no exercise, for improving core muscle strength as indicated by muscle thickness. There was emerging evidence that Pilates positively impacts core muscle strength and can be an effective intervention for people living with chronic low back pain.
Ray Tracing Simulation of X-ray Microdiffraction Beamline on the Inverse Compton Source

This paper deals with the simulation of output beam parameters of the inverse Compton X-ray Source (ICS). The simulation takes into account the main parameters of the laser and electron beams, such as their pulse shapes, polarization properties as well as the angular and energy dispersion of the electrons. The layout of the presented ICS and X-ray optical beamline is dedicated to X-ray micro-diffraction studies. To maintain the axial symmetry of output X-ray beams at different photon energies, the beamline monochromator is based on a single crystal installed in the Borrmann transparency condition.
Laser-Synthesized 2D-MoS2 Nanostructured Photoconductors

The direct laser synthesis of periodically nanostructured 2D transition metal dichalcogenide (2D-TMD) films, from single source precursors, is presented here. Laser synthesis of MoS2 and WS2 tracks is achieved by localized thermal dissociation of Mo and W thiosalts, caused by the strong absorption of continuous wave (c.w.) visible laser radiation by the precursor film. Moreover, within a range of irradiation conditions we have observed occurrence of 1D and 2D spontaneous periodic modulation in the thickness of the laser-synthesized TMD films, which in some cases is so extreme that it results in the formation of isolated nanoribbons with a width of ~200 nm and a length of several micrometers. The formation of these nanostructures is attributed to the effect that is known as laser-induced periodic surface structures (LIPSS), which is caused by self-organized modulation of the incident laser intensity distribution due to optical feedback from surface roughness. We have fabricated two terminal photoconductive detectors based on nanostructured and continuous films and we show that the nanostructured TMD films exhibit enhanced photo-response, with photocurrent yield increased by three orders of magnitude as compared to their continuous counterparts.
Research on trusted Modbus/TCP protocol of SCADA system based on digital envelope technology

Considering the issues of lack of integrity, authentication and confidentiality of Modbus/TCP protocol broadly utilized in SCADA system, this study suggests a trusted Modbus/TCP protocol based on digital envelope technology on the basis of trusted cryptography module (TCM). To the basic Modbus/TCP protocol, the protocol adds identity authentication sub-protocol and communication sub-protocol. Utilizing the SM2, SM3 and SM4 algorithms provided by TCM, the security of sub-protocol communication procedure and communication information is guaranteed by digital envelope technology and hash calculation. The security of the sub-protocol is verified by the SPAN tool, as well as the time overhead determined by the sub-protocol is tested. Experiments illustrates that the proposed trusted Modbus/TCP protocol can meet the high real-time prerequisites of industrial control systems and various business performance criteria while ensuring integrity, authentication and confidentiality.
Dissipative fuzzy filtering for nonlinear systems with multiple packet dropouts

The problem of designing a dissipative fuzzy filter is addressed for a class of nonlinear systems with multiple packet dropouts. The Takagi and Sugeno(T-S) fuzzy model is employed to represent a discrete-time nonlinear system, and the multiple packet dropouts in measurements are described by a binary switching sequence satisfying a conditional probability distribution. Based on a linear matrix inequality(LMI) approach, we develop a fuzzy dissipative filter which guarantees that the filter-error systems are exponentially mean-square stable and with strict dissipativeness. A numerical example is provided to illustrate the design developed in this paper.
Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems

The problem of energy optimization for Internet of Things (IoT) devices is crucial for two reasons. Firstly, IoT devices powered by renewable energy sources have limited energy resources. Secondly, the aggregate energy requirement for these small and low-powered devices is translated into significant energy consumption. Existing works show that a significant portion of an IoT device’s energy is consumed by the radio sub-system. With the emerging sixth generation (6G), energy efficiency is a major design criterion for significantly increasing the IoT network’s performance. To solve this issue, this paper focuses on maximizing the energy efficiency of the radio sub-system. In wireless communications, the channel plays a major role in determining energy requirements. Therefore, a mixed-integer nonlinear programming problem is formulated to jointly optimize power allocation, sub-channel allocation, user selection, and the activated remote radio units (RRUs) in a combinatorial approach according to the channel conditions. Although it is an NP-hard problem, the optimization problem is solved through fractional programming properties, converting it into an equivalent tractable and parametric form. The resulting problem is then solved optimally by using the Lagrangian decomposition method and an improved Kuhn–Munkres algorithm. The results show that the proposed technique significantly improves the energy efficiency of IoT systems as compared to the state-of-the-art work.
A behavioural investigation of the substitution phenomenon between tax-sheltered savings plans

We provide behavioral insights into the economic substitution phenomenon among front-loaded and back-loaded tax-sheltered savings plans. We conduct three behavioral studies with adult Canadian participants to show experimentally that substitution can occur and to explain why substitution can occur. The first study shows that participants transfer savings from a front-loaded plan to a back-loaded plan when the latter becomes available, consistent with a substitution effect. The second study examines how participants trade-off two unique features of back-loaded and front-loaded savings plans. Our results indicate that participants favor the back-loaded tax feature and a variable contribution limit (offered in a front-loaded plan). As participants prefer one feature from each type of plan, this finding can help explain why substitution occurs. The third study provides participants a categorization task with various household budgeting items, including savings items. Results show that 68.1% of participants categorize multiple tax-sheltered savings plans in the same mental account, again consistent with a substitution effect under a budget constraint. As both tax-sheltered savings plans in Canada are used for different purposes, this finding shows that participants tend not to distinguish between the purpose of saving in each account, consistent with a substitution phenomenon.