Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical Students' Competency and Confidence in Interpreting Electrocardiograms at King Faisal University, Al-Ahsa

Introduction Electrocardiography is a crucial emergency tool in the pre-hospital situation. It is a useful non-invasive diagnostic technique for quickly identifying various heart disorders. The clinical value of the electrocardiogram (ECG) depends on the clinician's ability to interpret the result of the ECG accurately. Aims This study aims to assess the competency as well as the confidence in the interpretation of ECG among medical students at King Faisal University, Al Ahsa, Saudi Arabia. Methods This cross-sectional study was conducted among medical students enrolled at King Faisal University. Four hundred and ten (410) medical students from all five years completed an electronic self-administered pre-validated questionnaire. The questionnaire includes basic demographic data and ECG strips to assess medical students' competency and confidence levels in interpreting each case. Results More than half of the medical students were considered to have low competency (56.1%) and confidence (59%) levels. Increased competency and confidence scores were associated with fifth-year medical students and those who learned more about ECG interpretation through teaching during clinical rotations. The majority of medical students correctly interpreted anterior MI (69.3%), ventricular tachycardia (65.6%), and supraventricular tachycardia (61.2%). On the other hand, most students were unable to correctly identify pacemaker ECG (19.8%), long QT (21.2%) and left bundle branch block (33.4%). Conclusion Medical students' competency and confidence in ECG interpretation seems to be lacking. Fifth-year medical students who learned more ECG skills through teaching during clinical rotations tended to be more competent and confident with their ECG interpretation skills.
Oleanolic Acid Complexation with Cyclodextrins Improves Its Cell Bio-Availability and Biological Activities for Cell Migration

Wound healing is a complex process to restore skin. Plant-derived bioactive compounds might be a source of substances for the treatment of wounds stalled in a non-resolving stage of wound healing. Oleanolic acid (OA), a pentacyclic triterpene, has shown favorable wound healing properties both in vitro and in vivo. Unfortunately, OA cannot be solubilized in aqueous media, and it needs to be helped by the use of dimethyl sulfoxide (DMSO). In this paper, we have shown that cyclodextrins (CDs) are a good alternative to DMSO as agents to deliver OA to cells, providing better features than DMSO. Cyclodextrins are natural macromolecules that show a unique tridimensional structure that can encapsulate a wide variety of hydrophobic compounds. We have studied the cyclodextrin-encapsulated form of OA with OA/DMSO, comparing their stability, biological properties for cell migration, and cell viability. In addition, detailed parameters related to cell migration and cytoskeletal reorganization have been measured and compared. Our results show that OA-encapsulateds compound exhibit several advantages when compared to non-encapsulated OA in terms of chemical stability, migration enhancement, and preservation of cell viability.
Pulmonary Cavitary Sarcoidosis: A Case Report

Sarcoidosis is a granulomatous disease characterized by a non-caseating granuloma formation in different organs of the body. However, the presence of cavitary lesions is rare. We present a case report of a 38-year-old male who presented with a three-month history of cough, dyspnea, and weight loss. Computed tomography of the chest demonstrated enlarged mediastinal and bilateral hilar lymphadenopathy with bilateral perihilar consolidation and cavitation in the upper lobes of both lungs. Later, the patient underwent bronchoscopy with bronchoalveolar lavage and endobronchial biopsy which showed well-formed and non-necrotizing granulomas which were also embedded in the dense hyaline sclerosis. This finding is consistent with sarcoidosis. Treatment with systemic corticosteroids was initiated, resulting in significant improvement in the patient’s symptoms. This case report highlights the uncommon manifestation of pulmonary cavitary sarcoidosis and emphasizes the significance of accurate diagnosis and appropriate management of this complex disease.
The timing of deglacial circulation changes in the Atlantic

[1] Well-dated benthic foraminifer oxygen isotopic records (δ18O) from different water depths and locations within the Atlantic Ocean exhibit distinct patterns and significant differences in timing over the last deglaciation. This has two implications: on the one hand, it confirms that benthic δ18O cannot be used as a global correlation tool with millennial-scale precision, but on the other hand, the combination of benthic isotopic records with independent dating provides a wealth of information on past circulation changes. Comparing new South Atlantic benthic isotopic data with published benthic isotopic records, we show that (1) circulation changes first affected benthic δ18O in the 1000–2200 m range, with marked decreases in benthic δ18O taking place at ∼17.5 cal. kyr B.P. (ka) due to the southward propagation of brine waters generated in the Nordic Seas during Heinrich Stadial 1 (HS1) cold period; (2) the arrival of δ18O-depleted deglacial meltwater took place later at deeper North Atlantic sites; (3) hydrographic changes recorded in North Atlantic cores below 3000 m during HS1 do not correspond to simple alternations between northern- and southern-sourced water but likely reflect instead the incursion of brine-generated deep water of northern as well as southern origin; and (4) South Atlantic waters at ∼44°S and ∼3800 m depth remained isolated from better-ventilated northern-sourced water masses until after the resumption of North Atlantic Deep Water (NADW) formation at the onset of the Bolling-Allerod, which led to the propagation of NADW into the South Atlantic.
Time and Spatially Resolved Operando Small-Angle X-ray Scattering Measurements during Injection Moulding of Plastics

We recently introduced the possibility of performing operando small-angle X-ray scattering measurements using a novel industrially relevant injection moulding system for plastics. We show that useful time-resolving measurements can be performed with a time-cycle of 1 s and highlight the possible steps to reduce this to 0.5 s. We show how we can use the transmission measurements to provide a time marker when plastic first enters the mould cavity in the region probed by the incident X-ray beam. We show the opportunities provided by this experimental stage mounted on the NCD-SWEET beamline at ALBA to probe the reproducibility of the injection moulding system on different scales. The design of the equipment allowed for the development of the structure and the morphology to be evaluated in different parts of mould cavity, and we evaluated any differences in a rectangular mould cavity. We identified future prospects for this equipment in terms of novel mould heating and cooling systems and the opportunities for quantitatively evaluating radical approaches to injection moulding technology.
Observations of near surface wind speed, temperature and radiative budget at Dome C, Antarctic Plateau during 2005

Abstract The annual and diurnal behaviours of near surface wind speed, temperature, and the radiative budget at Concordia Station (Dome C) in different seasons are shown. The wind speed was lowest in summer when a daily cycle was also observed. The largest mean values were concurrent with boundary layer growth in the afternoon. In winter and spring the wind speed reached the highest mean values. Perturbations in the wind flow were due to warming events which occurred periodically at Dome C. The lowest temperatures were in April and at the end of August. The coreless winter behaviour was perturbed by warming events which in many cases produced an increase in temperature of c. 20°C. The average temperature profiles show permanent thermal inversion, with the exception of a few hours in the afternoons during the summer. The strongest ground-based thermal inversions were observed in the polar winter. The largest potential temperature gradients were limited to a 30–40 m deep layer close to the surface. The net radiation was negative almost all the time with the exception of the period from mid-December to mid-January.
Model comparisons of methane oxidation across a management gradient: Wetlands, rice production systems, and landfill

Through model experiments we quantitatively examined fractional methane oxidation and net methane emissions using three model systems which encompass a management gradient from pristine (wetlands) to managed (rice production) to highly engineered (landfills). Fractional methane oxidation is rarely determined in situ; thus our goal was to cross‐compare modeling results and major drivers to field and laboratory data for this important parameter in both pristine and managed systems. In the models, management factors are typically introduced as combinations of theoretical relationships, empirical functions, or scaling factors which drive net emissions through forcing of water table variations, availability of degradable organic carbon substrates, input and cycling of major nutrients, variability in plant communities, physical properties for gaseous transport, and indigenous capacity of soils for methane oxidation. The net methane emission and fractional oxidation vary by orders of magnitude within and among the three model systems, yet each model was quite consistent in its predictive ability. This study lays the groundwork for a more unified, modular approach to modeling methane emissions from soil sources where both natural (climatic and ecological) and anthropogenic factors are important drivers.
Reply [to “Comments on ‘A limited‐area‐model case study of the effects of sub‐grid scale variations in relative humidity and cloud upon the direct radiative forcing of sulfate aerosol’”]

A limited-area non-hydrostatic model with a horizontal spatial resolution of 2km by 2km is used to assess the importance of sub-grid scale variations in relative humidity and cloud upon the direct radiative forcing (DRF) by tropospheric sulfate aerosols. The DRF from the limited-area model for both clear and cloudy regions is analyzed and the results compared against those obtained using general circulation model (GCM) parameterizations that perform the computations over coarse horizontal grids. In this idealized model study, the GCM calculations underestimate the clear sky DRF by approximately 73% and the cloudy sky DRF by approximately 60%. These results indicate that, for areas where the relative humidity is high and where there is substantial spatial variability in relative humidity and cloud, GCM calculations may considerably underestimate the DRF.
Nitrogen-Activated CLV3/ESR-Related 4 (CLE4) Regulates Shoot, Root, and Stolon Growth in Potato

In potato, high levels of nitrogen (N) can lead to excessive vegetative growth at the expense of tuber development, resulting in lower yield and poor-quality tubers. We found that Solanum tuberosum CLE4 (StCLE4) is expressed most strongly in the roots grown in N-rich media, and it positively regulates potato root growth under N-deficient conditions. We noted that StCLE4 functions as a negative regulator of normal shoot apex development similar to CLV3 in Arabidopsis. Transcriptomic analysis revealed that overexpression of StCLE4 resulted in the repression of the StIT1 gene, a regulator of potato tuber initiation. StCLE4-overexpressing stolons were converted into branches, that were similar to a mild phenotype of the it1 (identity of tuber 1) mutant. We also found that NIN-like proteins, key regulators of nitrate signaling bind to the regulatory sequence of StIT1 in a yeast one-hybrid assay. Taken together, our findings suggest that StCLE4 regulates shoot, root, and stolon growth in potato.
Crustal anisotropy from tectonic tremor under Washington State in the Cascadia

We present new observations of crustal shear wave anisotropy extracted from nonvolcanic tremor in Cascadia under Washington State. Measurements of crustal anisotropy are extremely sparse and limited in this area mainly due to low level of seismicity. Abundance of tremor activity during slow earthquakes offers a unique opportunity to measure anisotropy parameters of the continental crust using tremor signal. To accomplish this, polarization and splitting analyses of nonvolcanic tremor are performed using three‐component broadband seismic stations. Splitting times measurements range between 0.08 and 0.17 s and similar to the splitting magnitude typically observed in the continental crust. Fast direction of shear wave anisotropy generally trends ESE‐WNW. Fast polarization directions are, in general, perpendicular to the prevailing maximum compressive stress field but tend to be parallel to several mapped EW and ESE‐WNW trending faults in this area. The observed spatial pattern of anisotropy is likely controlled by faulting that accommodates NS compression resulting from the tectonic movement of the Oregon block toward north. Existence of several EW trending crustal faults and source parameters of crustal earthquakes at depth, consistent with the regional stress regime, indicate that these faults may be the dominant factor causing the observed pattern of shear wave anisotropy.
Shifts in flood and low-flow regimes in New Zealand due to interdecadal climate variations

Abstract Abstrac Thirty-one of the longest available streamflow records for New Zealand were analysed to see whether shifts in flood and low-flow regime occurred in 1977/1978 corresponding to a shift in phase of the Interdecadal Pacific Oscillation. Annual peak flows, and annual minimum flows averaged over 28 days, for two periods, 1947–1977 and 1978–1999, were compared using box plots and statistical tests. The plots and the tests show that a decrease of flood size has occurred since 1978 in the Bay of Plenty region of the North Island, and that increases in flood size and low-flow magnitude have occurred in the South Island for most rivers with headwaters draining from the main divide of the Southern Alps and Southland. For much of the North Island apart from the Bay of Plenty, and the north of the South Island, no consistent pattern of shifts was identified.
Feature-based classification fusion of vehicles in high-resolution SAR and optical imagery

An effective feature fusion strategy applied in vehicles classification is devised, which takes advantage of the complementary vehicle features in Synthetic aperture radar (SAR) and Optical images. With high spatial resolution SAR images, it is easy to detect vehicles fast and accurately because of the strong radar cross sections (RCS) of them compared to background. However, the classification of vehicles in SAR images can carry a significant amount of error (misclassification) since the radar scattering from a vehicle is often highly dependent on the target-sensor orientation. In contrast, optical images usually provide good classification performance exploiting the aspect dependent information. Therefore, the proposed method maps the detection results of SAR image into the co-registered optical image, and then combines the target's features from SAR and Optical images together to feature-fusion classification using the Fuzzy C-means (FCM) algorithm.
OfpCNN: On-Demand Fine-Grained Partitioning for CNN Inference Acceleration in Heterogeneous Devices

Collaborative inference is a promising method for balancing the limited computational power of Internet of Things (IoT) devices with the huge computational demands of convolutional neural networks (CNNs). In this approach, a CNN is divided into multiple partitions and placed on multiple devices to run simultaneously. However, two major challenges are raised. (1) Computational latencies vary when the central processing unit (CPU) loads of devices are different. However, no suitable methods are available for accurately determining computation latencies on the basis of CPU utilization. (2) Existing methods partition a CNN model either vertically or horizontally. The granularity of these methods is extremely coarse and their accuracy is low. To address the aforementioned issues, this study proposes a distributed collaborative inference framework that supports a fine-grained partitioning scheme for CNN in heterogeneous devices (hereafter referred to as OfpCNN). First, the framework uses the layer latency prediction model based on floating-point operations and CPU load (FCPM) to accurately predict the computation latency of each layer of CNN in different devices. Subsequently, OfpCNN uses horizontal and vertical partitioning methods (HVPM) to partition the input feature maps and the structure of CNN respectively in accordance with network conditions and computing capacity, then assigns them to multiple devices for execution. The HVPM solution overall considers the execution position of the layer, parallelism, and location of devices responsible for data aggregation and distribution, which can consequently obtain more fine-grained partition schemes. Experimental results show that FCPM can achieve a minimum accuracy of 88% and HVPM can improve the inference speed by 1–2.54 times compared with other state-of-the-art methods.
Lateral Earth Pressure Coefficients for Anchored Sheet Pile Walls

AbstractConventional methods used for the design of anchored sheet pile walls are based on the lateral force and moment equilibrium of active and passive earth pressures and anchor force. Although it has been known for decades that the stress concentration occurs around the anchor level because of the restricted wall movements, this stress concentration is not considered in determining the lateral earth pressures. A parametric study using conventional and numerical methods was performed to investigate the behavior of single-level anchored sheet pile walls, and the lateral earth pressures, wall bending moments, and anchor forces were analyzed. The study results indicate that the conventional methods for the conditions considered and cases studied overestimate the wall bending moments, whereas the anchor forces are underestimated. New lateral earth pressure coefficients that consider the stress concentration around the anchor level were developed and proposed to be used in the design of single-level anchore.
Court-ordered inpatient psychiatric care in Switzerland: determinants of length of stay and treatment outcome

In several European countries, offenders with decreased or abolished responsibility and high risk of recidivism due to long-lasting mental disorders are compulsory admitted for court-ordered treatments (COT) that take place in high and medium-security hospitals. As a rule, length of stay in these structures is very long implying major restrictions for the inmate and high societal cost. Despite intensive research, the predictors of length of stay and treatment outcome in long stay forensic services is still matter of debate. We report here a detailed analysis of the demographic, psychiatric and offense predictors of length of stay and discharge locations of 204 mentally disordered offenders convicted to COT in a new medium-security forensic psychiatry clinic in Geneva, Switzerland. Kaplan-Meier survival estimates were performed to determine time to release. Length of stay was predicted by Cox regressions, and discharge locations were predicted by multinomial logistic regressions. The typical inpatient was a 35-age single male re-offender, submitted to COT after a conviction for physical violence (78.9%) or property violation (64.2%), with drug trafficking (52.9%), in relation to psychotic (67.2%), antisocial or borderline personality disorder (35.8%) with comorbid substance use disorders (60.3%). Sex offenses were found in 24.5% of cases and were associated with Cluster B personality disorders. The median length of stay was of 2.5 years and was independent of demographic variables, severity of crime recidivism and psychiatric diagnosis. Longer COT at admission, and type of offense (in particular drug traffic and sexual violence) predicted longer stays. At discharge, 32.8% of cases were transferred to sheltered educational housing, 23.1% to open low-security wards, while 30.6% returned to regular prisons and 9.7% to their country of origin. Younger age and conviction for property violation rather than physical violence increased the chances to be discharged to sheltered educational housing. Longer COT at admission, personality disorders, and conviction for sexual offense increased the risk to return to prison. These data suggest that sex offenses determine not only longer stays under COT but also drastically decreases the chance of freedom for inmates with Cluster B personality disorders.
Multi-Scale CNN: An Explainable AI-Integrated Unique Deep Learning Framework for Lung-Affected Disease Classification

Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial to save lives, but the availability of testing equipment remains a challenge, often coupled with issues of reliability. Recent research has highlighted the potential of Chest X-Ray (CXR) images in identifying various lung diseases, including COVID-19, fibrosis, pneumonia, and more. In this comprehensive study, four publicly accessible datasets have been combined to create a robust dataset comprising 6650 CXR images, categorized into seven distinct disease groups. To effectively distinguish between normal and six different lung-related diseases (namely, bacterial pneumonia, COVID-19, fibrosis, lung opacity, tuberculosis, and viral pneumonia), a Deep Learning (DL) architecture called a Multi-Scale Convolutional Neural Network (MS-CNN) is introduced. The model is adapted to classify multiple numbers of lung disease classes, which is considered to be a persistent challenge in the field. While prior studies have demonstrated high accuracy in binary and limited-class scenarios, the proposed framework maintains this accuracy across a diverse range of lung conditions. The innovative model harnesses the power of combining predictions from multiple feature maps at different resolution scales, significantly enhancing disease classification accuracy. The approach aims to shorten testing duration compared to the state-of-the-art models, offering a potential solution toward expediting medical interventions for patients with lung-related diseases and integrating explainable AI (XAI) for enhancing prediction capability. The results demonstrated an impressive accuracy of 96.05%, with average values for precision, recall, F1-score, and AUC at 0.97, 0.95, 0.95, and 0.94, respectively, for the seven-class classification. The model exhibited exceptional performance across multi-class classifications, achieving accuracy rates of 100%, 99.65%, 99.21%, 98.67%, and 97.47% for two, three, four, five, and six-class scenarios, respectively. The novel approach not only surpasses many pre-existing state-of-the-art (SOTA) methodologies but also sets a new standard for the diagnosis of lung-affected diseases using multi-class CXR data. Furthermore, the integration of XAI techniques such as SHAP and Grad-CAM enhanced the transparency and interpretability of the model’s predictions. The findings hold immense promise for accelerating and improving the accuracy and confidence of diagnostic decisions in the field of lung disease identification.