Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Future of Emergency Medical Services: Envisioning and Usability of Electronic Patient Care Report System

Despite the efforts of emerging technologies in the healthcare system, there is still a slower rate of acceleration in prehospital settings compared with the hospitals in digital transformation adaptation. The acknowledgment that digital transformation is significant to healthcare is reflected in planning for the future of digital healthcare. Thus, this study aimed to measure the usability of the electronic patient care report (ePCR) system among emergency medical services (EMS) staff who work in prehospital settings. A descriptive cross-sectional correlation study was used. Two hundred fifty EMS staff who are working in the prehospital setting at Saudi Red Crescent Authority in the Kingdom of Saudi Arabia were surveyed, and the response rate was 79.2% (198). An adapted tool of the Computer System Usability Questionnaire survey was used to collect data. The data were coded numerically and subjected to descriptive and inferential statistical analysis including Pearson’s correlation coefficient using the statistical software (SPSS 21). The majority of the participants rate their ePCR system as “useable” at a high level with a score of 3.41 (SD = 1.021). The overall mean of the ePCR system’s three subscales: system usefulness, information quality, interface quality, and overall satisfaction were 3.39 (SD = 1.152), 3.30 (SD = 1.052), 3.57 (SD = 1.064), and 3.37 (SD = 1.239), respectively. The least liked aspect of ePCR system software was information quality 81 (40.9%). Furthermore, there was a significant correlation between the age of EMS staff and the usability of the ePCR system (r = −0.150 ∗ , P = 0.035 ). The results suggest that healthcare institutions’ policy and decision-makers pay close attention to performing standardized training for the staff on their ePCR system before going to the field to increase efficiency and productivity. Furthermore, the users in this study identified other system features that, if included, could have enhanced usability, and improved functions and capabilities of the design to meet the EMS staff’s expectations.
Effect of Nonsurgical Spinal Decompression on Intensity of Pain and Herniated Disc Volume in Subacute Lumbar Herniated Disc

Objective Nonsurgical spinal decompression therapy (NSDT) is a conservative treatment for the lumbosacral herniated intervertebral disc (L-HIVD). This study aimed to evaluate the clinical effectiveness of the NSDT and change in disc volume through magnetic resonance imaging (MRI) in subacute L-HIVD. Methods Sixty patients with subacute L-HIVD were randomized into either the decompression group (group D, n = 30) or the nondecompression group (group N, n = 30). In group D, NSDT was performed ten times in eight weeks. In group N, pseudodecompression therapy (no force) was performed with the same protocol. Lower back and lower leg pain intensities and functional improvements were measured by the visual analog scale and the Korean Oswestry Disability Index (K-ODI). The change in the lumbosacral disc herniation index (HI) was evaluated through a follow-up MRI three months after the therapy. Results The lower leg pain intensity in group D was lower than that in group N at two months (p=0.028). Additionally, there were significantly lower K-ODI scores in group D at two and three months (p=0.023, 0.019) than in group N. The change in HI after the therapy was −27.6 ± 27.5 (%) in group D and −7.1 ± 24.9 (%) in group N, with a significant difference (p=0.017). Approximately 26.9% of patients in group D and no patients in group N showed over 50% reduction in HI (p=0.031). Conclusion NSDT may be a suitable treatment option for conservative treatment of subacute L-HIVD.
Development of a cross-sectional finite element for the analysis of thin-walled composite beams like wind turbine blades

A method for structural analysis of thin-walled composite beams like wind turbine blades is presented. This method is based on the Nonhomogeneous Anisotropic Beam Section Analysis (NABSA) which consists in discretizing the beam cross section using finite elements. The proposed implementation uses 3-node line cross-sectional finite elements with nodes having rotational degrees of freedom to describe the cross-sectional warping displacements. Solutions obtained using this approach were verified against the corresponding analytical or numerical solutions. Agreement was very good to excellent for the computation of cross-sectional properties and distribution of stresses, strains and warping displacements for a broad range of possible composite beam behaviors including geometric and material couplings, open sections, multicell sections, and arbitrary laminates. For thin-walled layered structures, the proposed method provides models with fewer degrees of freedom than equivalent models based on a two-dimensional discretization of cross sections using triangular or quadrilateral elements such as conventional NABSA or VABS which suggests that computation time could be reduced.
A Hierarchical Ensemble Deep Learning Activity Recognition Approach with Wearable Sensors Based on Focal Loss

Abnormal activity in daily life is a relatively common symptom of chronic diseases, such as dementia. There will probably be a variety of repetitive activities in dementia patients’ daily life, such as repeated handling of objects and repeated packing of clothes. It is particularly important to recognize the daily activities of the elderly, which can be further used to predict and monitor chronic diseases. In this paper, we propose a hierarchical ensemble deep learning activity recognition approach with wearable sensors based on focal loss. Seven basic everyday life activities including cooking, keyboarding, reading, brushing teeth, washing one’s face, washing dishes and writing are considered in order to show its performance. Based on hold-out cross-validation results on a dataset collected from elderly volunteers, the average accuracy, precision, recall and F1-score of our approach are 98.69%, 98.05%, 98.01% and 97.99%, respectively, in identifying the activities of daily life for the elderly.
GMM Estimation of Autoregressive Roots Near Unity with Panel Data

This paper investigates a generalized method of moments (GMM) approach to the estimation of autoregressive roots near unity with panel data. The two moment conditions studied are obtained by constructing bias corrections to the score functions under OLS and GLS detrending, respectively. It is shown that the moment condition under GLS detrending corresponds to taking the projected score on the Bhttacharyya basis, linking the approach to recent work on projected score methods for models with infinite numbers of nuisance parameters (Waterman and Lindsay, 1998). Assuming that the localizing parameter takes a non-positive value, we establish consistency of the GMM estimator and find its limiting distribution. A notable new finding is that the GMM estimator is super-consistent (i.e., has convergence rate faster than root n) when the true localizing parameter is zero (i.e., when there is a panel unit root) and the deterministic trends in the panel are linear.
A Secure Batch Authentication Scheme for Multiaccess Edge Computing in 5G-Enabled Intelligent Transportation System

With the introduction of 5G technology in the intelligent transportation system (ITS), data bandwidth over communication channel is significantly increased. In the existing cloud-based ITS, a large-scale data is collected, stored, and analyzed to provide precise services to vehicle user, which creates a burden on the cloud service provider that can cause service delays or the inability to provide complex services. Multi-access edge computing (MEC) technology, which extends cloud capabilities to edge servers, is attracting attention in order to reduce the constraint of the cloud-based infrastructure. However, in ITS constructed on MEC, since vehicles exchange information through a wireless communication channel. It is vulnerable to various security attacks. This paper proposes a secure batch authentication scheme for multi-access edge computing in 5G-enabled ITS. The proposed scheme withstands common security attacks including impersonation, replay, man-in-the-middle, physical capture, offline password guessing attacks, and provides secure batch authentication, anonymity and confidentiality. We also consider the vehicle can cross the boundary between MEC servers for continuity of service. We analyze the security of the proposed scheme to show that the proposed scheme is secure against various security attacks, and performance analysis. Lastly, we conduct comparison with other related works.
A Framework for Mechanistic Flood Inundation Forecasting at the Metropolitan Scale

Urban flooding from extreme precipitation and storm surge is a growing threat to cities, and detailed forecasts of urban inundation are needed for emergency response. We present a mechanistic framework to simulate flood inundation over metropolitan‐wide areas at fine resolution (3 m). A dual‐grid shallow‐water model is used to overcome computational bottlenecks, and an application to Hurricane Harvey focused on pluvial flooding provides a multi‐dimensional assessment of predictive skill. A hindcast model is shown to simulate peak stage across 41 stream gages with a mean absolute error (MAE) of 0.63 m, and hourly stage levels over a 5‐day period with a median MAE and Nash‐Sutcliffe Efficiency (NSE) of 0.74 m and 0.55, respectively. Peak flood level across 228 high water marks (HWMs) were captured with an MAE of 0.69 m. A forecast model forced by Quantitative Precipitation Forecast data is shown to be only marginally less accurate than the hindcast model. Peak stage is simulated with an MAE of 0.86 m, hourly stage is captured with a median MAE and NSE of 0.90 m and 0.41, respectively, and HWMs are captured with an MAE of 0.77 m. The forecast system also achieves hit rates of 90% and 73% predicting distress calls and FEMA damage claims, respectively, based on simulated flood depth. These results demonstrate the potential to operationally forecast pluvial flood inundation in the U.S. with the timeliness and accuracy needed for early warning, and we also highlight future research needs.
Nurses' attitude and barrier toward of performing physical assessment in medical and surgical wards

Background: Physical Assessment is essential in identifying the actual and potential patient problems and evaluating patient response to care. previous studies on the use of physical assessment in a clinical setting found that there are skills sometimes used, and some rarely or never used in the practice which reveals evidence of practice gap. Improper patient assessment rose the concern about the actual nursing practice which fail to recognize patient deterioration. Method: A quantitative descriptive correlation cross-sectional design of 253 nurses recruited from five hospitals in Al Madinah Al Munawarah in Saudi Arabia is used to study the relationship between the nurses' attitudes and the barriers to perform physical assessment and understand the influence of the participants' demographic data on the physical assessment practices. Finding: There was an agreement that physical assessment is an integral part of nursing care and around half of them showed a positive attitude toward performing the physical assessment. There was a positive significant association between attitude and perceived barriers of self-confidence, time limitation during the shift, and reliance on others and technology.
Reconciling Planck results with low redshift astronomical measurements

We show that emerging tension between the direct astronomical measurements at low redshifts and cosmological parameters deduced from the Planck measurements of the CMB anisotropies can be alleviated if the dark matter consists of two fractions, stable part being dominant and a smaller unstable fraction. The latter constitutes $\sim 10$ per cent at the recombination epoch if decays by now.
Climate Legacy Effects Shape Tallgrass Prairie Nitrogen Cycling

Climate change is expected to shift precipitation regimes in the North American Central Plains with likely impacts on ecosystem functioning. In tallgrass prairies, water and nitrogen (N) can co‐limit ecosystem processes, so changes in precipitation may have complex effects on carbon (C) and N cycling. Rates of N supply such as N mineralization and nitrification respond differently to short‐ and long‐term patterns in water availability, and previous climate patterns may exert legacy effects on current N cycling that could alter ecosystem sensitivity to current precipitation regimes. We used a long‐term precipitation manipulation at Konza Prairie (Kansas, USA) to assess how previous and current precipitation influence tallgrass prairie N cycling. Supplemental irrigation was applied across upland and lowland prairie for ∼25 years to reduce water deficits; in 2017, we reversed some of these treatments and added a reduced rainfall treatment across both historic rainfall regimes, allowing us to assess how previous climate and current rainfall patterns interact to shape N cycling. In lowland prairie, previous irrigation doubled N mineralization and nitrification rates the year following cessation of irrigation. Reduced microbial C:N ratio and lower relative investment in N‐acquiring enzymes in previously irrigated lowlands suggested that a wetter climate created a legacy of increased N availability for microbes. Internal plant N resorption increased under short‐term irrigation but recovered to ambient levels following previous irrigation. Together, these results suggest that a history of wetter conditions can create a legacy of accelerated N cycling, with consequences for both plant and microbial functioning.
Molecular orbital ab initio and density functional theoretical study on reaction between PH2 and NO

The theoretical study of reaction between PH2 and NO on the ground state potential energy surface is reported by using molecular orbital ab initio calculation and density function theory (DFT). Equilibrium structural parameters, harmonic vibrational frequencies, total energies and zero point energies of all species during reaction are computed by HF, MP2 (full) and B3LYP theory levels with the medium basis set 6-31G*. Theoretical results indicate that intermediate IM1(H2PNO) is firstly formed by overcoming a small energy barrier TS1, and then two four-membered ring transient states TS2 and TS5, with energy barriers 103.3 and 102.6 kJ/mol respectively, then H-migration and isomerization are completed and the products PN and H2O are formed. The reaction is exothermic one with −189.6 kJ/mol released.
Oral Lichen Planus and Mutated TP53—A Road to Cancer?

The malignant potential of oral lichen planus (OLP) has been discussed and disputed for decades. The lesions are often characterized by strong expression of the TP53 protein in the basal layer of the mucosa. In 2002, we reported the presence of TP53 mutations in nine out of 27 OLP lesions tested. At follow-up in 2009, one case of oral squamous cell cancer (OSCC) had occurred in a different site six years later. In contrast, in another case, TP53 mutation persisted for years without malignant transformation. In a longitudinal study of eight selected patients with OSCC or different pre-malignant lesions, it was concluded that TP53 mutations could occur early or late in the development of OSCC. A follow-up in the present, almost 20 years later, revealed that one further case of OSCC had occurred in a TP53-mutated case of OLP, 21 years after the first sample was taken, again in a different site. With this second case, this small study now points towards a risk of developing OSCC in TP53-mutated OLP lesions. A review of recent literature indicates a growing consensus that OLP should be regarded as a potentially pre-malignant lesion. Several protein markers have been studied, but none proved useful for prediction of malignant progression. The great majority of published studies are retrospective, and it has been suggested that multi-centre prospective studies will be needed to reach a definitive answer on the malignant potential of OLP, and particularly, to identify contributing factors. Screening for TP53 mutations could help to identify the subgroup of OLP patients that is truly at risk of developing oral cancer.
Semantic Segmentation for Mangrove Using Spectral Indices and Self-Attention Mechanism

The segmentation of mangroves based on remote sensing images is of great significance for the protection of mangroves and the ecological environment. Deep learning methods have been widely used in image segmentation, among them, supervised learning methods based on convolutional neural networks can achieve excellent accuracy in image segmentation most of the time. However supervised learning methods often require the production of complete labels, which is a laborious task. In this paper, we propose an innovative mangrove segmentation model based on domain adaptation method, which integrates the self-attention mechanism to make the model focus on more important image channels, and combines the remote sensing spectral indices to solve the problem that the domain adaptation method may lose edge information. Experimental results on Landsat8 dataset show that our proposed model achieves superior performance, and outperforms several popular semantic segmentation models.
Blastocyst Stage Affects the Isolation and Culture of Buffalo Naive/Primed Embryonic Stem-Like Cells

Abstract Since embryonic stem cells (ESCs) were first identified, significant progress has been achieved. However, the establishment of buffalo ESCs (bESCs) is still unclear. This study was undertaken to explore the effect of the blastocyst stage on the isolation of bESCs. Firstly, our results indicated that the pluripotent genes were mainly expressed at the early stages of blastocyst, and the attachment and colony formation rates of bESCs derived from expanded blastocyst and hatched blastocyst were significantly higher than early blastocyst and blastocyst. In the meantime, bESCs showed positive alkaline phosphatase activity and expressed genes like OCT4, NANOG, SOX2, c-MYC, CDH1, KLF4, and TBX3. Immunofluorescence also confirmed the expression of OCT4, SOX2. Embryoid bodies expressing three marker genes were generated from the differentiation experiment, and fibroblast, epithelial, and neuron-like cells were induced. Moreover, naive-related genes KLF4, TBX3, primed-related genes FGF5, ACTA2 were expressed in the cells, but not REX-1. Immunofluorescence and western blot confirmed the FGF5 expression. Furthermore, bESCs could maintain pluripotency with the signal of LIF and bFGF. In summary, our results indicated that expanded blastocyst and hatched blastocyst are more suitable for bESCs isolation.
Model Independent Extraction of S-Matrix Poles from Experimental Data

By separating data points close to a resonance into intervals, and fitting all possible intervals to a simple pole with constant coherently added background, we obtained a substantial number of convergent fits. After a carefully chosen set of statistical constraints was imposed, we calculated the average of a resonance pole position from the statistically acceptable results. We used this method to find pole positions of Z and N(1440) resonances, and to show that the strong discrepancy between the old and new measurements of the Upsilon(11020) mass stems from specious comparison of the Upsilon(11020) pole with its Breit-Wigner mass.
A Healthcare Digital Twin for Diagnosis of Stroke

Neurological impairment is a common disorder observed in stroke population and Electroencephalography (EEG) monitoring is considered a significant marker for diagnostics stroke onset. This study aims to propose a proof-of-concept of a healthcare “digital twin” and utilize EEG data and machine-learning models to build a digital twin for the stroke patients. We examined 48 stroke patients admitted to a rehabilitation clinic and 75 healthy persons. Portable EEG devices were used to capture EEG using frontal, central, temporal, and occipital cortical electrodes. The statistical analysis revealed that the revised brain-symmetry index, theta, and delta activities are relevant characteristics for classifying stroke patients and healthy individuals in motor and cognitive states. Using the machine learning approach, Support vector machine (SVM) classified the EEG feature dataset with 76% accuracy (AUC: 0.84) for classifying the stroke and the control group. This healthcare digital twin framework may assist in clinical decision-making for stroke preventive measures and post-stroke treatment.
Organizational Autonomy: A Review and Agenda for Future Research

Organizational autonomy is a fundamental organizational design choice that holds a central position in management theories and practice. To date, this construct has suffered from definitional vagueness and conceptual fragmentation in its academic study across different management subfields. Drawing from a review of 87 articles appearing in top academic management journals, we had four objectives. We sought to establish clarity and consensus on the construct of organizational autonomy, to review the fragmented fields of studies on its determinants and outcomes, to identify unresolved or neglected debates, and to provide an organizing template for guiding future research. In a first section, we discuss the development of the organizational autonomy construct and review its diverse definitions, primary determinants, and outcomes. In a second section, we provide a set of recommendations spanning methodological directions and conceptual opportunities on the overlooked dynamics of organizational autonomy. Overall, our review provides a unified framework and direction for enhancing the understanding of one of the management field’s fundamental concepts.
Acoustics in Bose-Einstein condensates as an example of broken Lorentz symmetry

To help focus ideas regarding possible routes to the breakdown of Lorentz invariance, it is extremely useful to explore concrete physical models that exhibit similar phenomena. In particular, acoustics in Bose--Einstein condensates has the interesting property that at low-momentum the phonon dispersion relation can be written in a ``relativistic'' form exhibiting an approximate ``Lorentz invariance''. Indeed all of low-momentum phonon physics in this system can be reformulated in terms of relativistic curved-space quantum field theory. In contrast, high-momentum phonon physics probes regions where the dispersion relation departs from the relativistic form and thus violates Lorentz invariance. This model provides a road-map of at least one route to broken Lorentz invariance. Since the underlying theory is manifestly physical this type of breaking automatically avoids unphysical features such as causality violations. This model hints at the type of dispersion relation that might be expected at ultra-high energies, close to the Planck scale, where quantum gravity effects are suspected to possibly break ordinary Lorentz invariance.