Stress among Isfahan medical sciences students

Background: This study was undertaken to determine the prevalence of psychological stress among Isfahan medical sciences students. Methods: Cross-sectional, questionnaire-based survey was carried out among the 387 medical sciences students (medicine, pharmacy, and dentistry) of Isfahan, Iran through census. In academic year 2010-2011, Kessler-10 questionnaire was given to the students a month before semester examinations. Scores �20 were considered as indicative of positive stress symptoms. Results: The overall prevalence of stress among medical sciences students was found to be about 76.1%. The prevalence of stress among medicine students was 22.7% mild, 23% moderate and 21.4% severe while 32.8% showed no stress. The prevalence of stress among pharmacy students was 22.22%, 22.22%, 26.19%, and 29.36% mild, moderate, and severe and no stress, respectively. The prevalence of stress among dentistry students was 25% mild, 27% moderate, and 10% severe while 37.5% showed no stress. The prevalence of stress was higher (70.6%) in pharmacy students when compared with medicine (66.1%) and dentistry (62.5%) students. The odds of student having stress is higher in dentistry students (OR: 1.44, P= 0.33), where as the odds are decreasing in pharmacy student (OR: 1.16, P= 0.66). There is no statistically significant association between gender, ages, and term and having stress symptoms. Conclusions: The high level of stress necessitates interventions like social and psychological support to improve the student's well-being. A prospective study is needed to study the association of psychological morbidity with sources of stress and coping strategies

Medication prescribing errors in a pediatric inpatient tertiary care setting in Saudi Arabia

<p>Abstract</p> <p>Background</p> <p>Medication errors (MEs) are among the most common types of medical errors and one of the most common and preventable causes of iatrogenic injuries. The aims of the present study were; (i) to determine the incidence and types of medication prescribing errors (MPEs), and (ii) to identify some potential risk factors in a pediatric inpatient tertiary care setting in Saudi Arabia.</p> <p>Findings</p> <p>A five-week retrospective cohort study identified medication errors in the general pediatric ward and pediatric intensive care unit (PICU) at King Abdulaziz Medical City (KAMC) through the physical inspection of physician medication orders and reviews of patients' files. Out of the 2,380 orders examined, the overall error rate was 56 per 100 medication orders (95% CI: 54.2%, 57.8%). Dose errors were the most prevalent (22.1%). These were followed by route errors (12.0%), errors in clarity (11.4%) and frequency errors (5.4%). Other types of errors were incompatibility (1.9%), incorrect drug selection (1.7%) and duplicate therapy (1%). The majority of orders (81.8%) had one or more abbreviations. Error rates were highest in prescriptions for electrolytes (17.17%), antibiotics (13.72%) and bronchodilators (12.97%). Medication prescription errors occurred more frequently in males (64.5%), infants (44.5%) and for medications with an intravenous route of administration (50.2%). Approximately one third of the errors occurred in the PICU (33.9%).</p> <p>Conclusions</p> <p>The incidence of MPEs was significantly high. Large-scale prospective studies are recommended to determine the extent and outcome of medication errors in pediatric hospitals in Saudi Arabia.</p

Confident Adaptive Language Modeling

Recent advances in Transformer-based large language models (LLMs) have led to significant performance improvements across many tasks. These gains come with a drastic increase in the models' size, potentially leading to slow and costly use at inference time. In practice, however, the series of generations made by LLMs is composed of varying levels of difficulty. While certain predictions truly benefit from the models' full capacity, other continuations are more trivial and can be solved with reduced compute. In this work, we introduce Confident Adaptive Language Modeling (CALM), a framework for dynamically allocating different amounts of compute per input and generation timestep. Early exit decoding involves several challenges that we address here, such as: (1) what confidence measure to use; (2) connecting sequence-level constraints to local per-token exit decisions; and (3) attending back to missing hidden representations due to early exits in previous tokens. Through theoretical analysis and empirical experiments on three diverse text generation tasks, we demonstrate the efficacy of our framework in reducing compute -- potential speedup of up to $\times 3$ -- while provably maintaining high performance

DSI++: Updating Transformer Memory with New Documents

Differentiable Search Indices (DSIs) encode a corpus of documents in model parameters and use the same model to answer user queries directly. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive because reindexing the corpus requires re-training the model. In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents. Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviate forgetting, so we optimize for flatter loss basins and show that the model stably memorizes more documents ($+12\%$). Next, we introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task. Extensive experiments on novel continual indexing benchmarks based on Natural Questions (NQ) and MS MARCO demonstrate that our proposed solution mitigates forgetting significantly. Concretely, it improves the average Hits@10 by $+21.1\%$ over competitive baselines for NQ and requires $6$ times fewer model updates compared to re-training the DSI model for incrementally indexing five corpora in a sequence.Comment: Accepted at EMNLP 2023 main conferenc

From fidelity to entanglement of entropy of the one-dimensional transverse-field quantum compass model

We study fidelity and fidelity susceptibility by addition of entanglement of entropy in the one-dimensional quantum compass model in a transverse magnetic field numerically. The whole four recognized gapped regions in the ground state phase diagram are in the range of our investigation. Power-law divergence at criticality accompanied by finite size scaling indicates the field induced quantum phase transitions are of second order as well as from the scaling behavior of the extremum of fidelity susceptibility is shown the quantum critical exponents are different in the various regions of phase diagram. We further calculate a recently proposed quantum information theoretic measure, von-Neumann entropy, and show that this measure provide appropriate signatures of the quantum phase transitions (QPT)s occurring at the critical fields. Von-Neumann entropy indicates a measure of entanglement between some-particle block and the rest of the system. We show the value of entanglement between a two-particle block with the rest of the system is more dependent on the power of exchange couplings connecting the block with the rest of the system than the power of exchange coupling between two particles in the block

Small size boundary effects on two-pion interferometry

The Bose-Einstein correlations of two identically charged pions are derived when these particles, the most abundantly produced in relativistic heavy ion collisions, are confined in finite volumes. Boundary effects on single pion spectrum are also studied. Numerical results emphasize that conventional formulation usually adopted to describe two-pion interferometry should not be used when the source size is small, since this is the most sensitive case to boundary effects. Specific examples are considered for better illustration.Comment: more discussion on Figure4 and diffuse boundar

Structural build-up of cementitious paste under external magnetic fields

Engineering application processes of fresh concrete include transporting, pumping, formwork casting, etc. Each process is a significant factor influencing properties of fresh and hardened concrete. However, many contradicting requirements of fresh concrete performances (such as structuration rate) exist in these operation processes. Therefore, advanced techniques need to be proposed to satisfy future challenges. Actively controlling the stiffness by applying external magnetic fields would be a potential solution for the contradicting requirements, and could make the pumping and casting processes smarter and more reliable. In the present paper, the effects of magnetic field strength and magnetizing time on structural build-up of cementitious paste are discussed. The results show that higher magnetic field strengths result in higher percolation time and lower phase angle at equilibrium state. However, the application of external magnetic fields with low flux density has little effects on the viscoelastic behaviour of cementitious paste. Under high magnetic field strengths, the viscousliquid behaviour dominates the elastic-solid behaviour at early stage, while the solid-like behaviour becomes more dominant with magnetizing time

An Adaptive Multi-Level Quantization-Based Reinforcement Learning Model for Enhancing UAV Landing on Moving Targets

The autonomous landing of an unmanned aerial vehicle (UAV) on a moving platform is an essential functionality in various UAV-based applications. It can be added to a teleoperation UAV system or part of an autonomous UAV control system. Various robust and predictive control systems based on the traditional control theory are used for operating a UAV. Recently, some attempts were made to land a UAV on a moving target using reinforcement learning (RL). Vision is used as a typical way of sensing and detecting the moving target. Mainly, the related works have deployed a deep-neural network (DNN) for RL, which takes the image as input and provides the optimal navigation action as output. However, the delay of the multi-layer topology of the deep neural network affects the real-time aspect of such control. This paper proposes an adaptive multi-level quantization-based reinforcement learning (AMLQ) model. The AMLQ model quantizes the continuous actions and states to directly incorporate simple Q-learning to resolve the delay issue. This solution makes the training faster and enables simple knowledge representation without needing the DNN. For evaluation, the AMLQ model was compared with state-of-art approaches and was found to be superior in terms of root mean square error (RMSE), which was 8.7052 compared with the proportional-integral-derivative (PID) controller, which achieved an RMSE of 10.0592

Anomalous lattice dynamics and thermal properties of supported size- and shape-selected Pt nanoparticles

Anomalous lattice dynamics and thermal behavior have been observed for ligand-free, size-, and shape-selected Pt nanoparticles (NPs) supported on nanocrystalline gamma-Al(2)O(3) via extended x-ray absorption fine-structure spectroscopy. Several major differences were observed for the NPs with respect to bulk Pt: (i) a contraction in the interatomic distances, (ii) a reduction in the dynamic (temperature-dependent) bond-length disorder and associated increase in the Debye temperature (theta(D)), and (iii) an overall decrease in the bond-length expansion coefficient coupled with NP stiffening. The increase in the Debye temperature is explained in terms of the NP size, shape, support interactions, and adsorbate effects. For a similar average size, we observe a striking correlation between the shapes of the NPs and their theta(D) values

Exploring the molecular mechanisms of MSC-derived exosomes in Alzheimer's disease : Autophagy, insulin and the PI3K/Akt/mTOR signaling pathway

The authors thank you for acknowledging technical and financial support from the Ministry of Education and the University of Hafr Al Batin, Saudi Arabia. The authors gratefully acknowledge all mothers’ volunteers in the community around the Faculty of Agriculture, Benha University, for their cooperationPeer reviewe