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BackgroundWorkflow interruptions are common in the emergency department (ED) of the hospitals for physicians, leading to an increased risk of errors.PurposeThis study aims to understand the baseline activities of the ED doctors and how these are affected by workflow interruptions.MethodsThe study was conducted in two phases to collect the doctor’s perspective (through questionnaire survey) and observer’s perspective (through workflow observation study) about ED doctors’ baseline activities and workflow interruptions. Two different perspectives were obtained to make the insights clearer and more valuable. The point of view of the 223 doctors working in ED of the hospitals was recorded through a questionnaire survey. In the second phase, the observer’s point of view (authors) was obtained through a workflow observation study, and 13 doctors were observed for 160 hours.ResultsDirect communication with patients (37.1%) and ‘documentation and prescription’ (22.7%) were found to be the most frequent activities. The most common interruptions were visual and auditory distractions, rumination (mind-wandering) and intrusion (by co-workers). Also, the time consumed on indirect patient care (6.6%) was higher than direct patient care (4. 2%). Interruptions increase the chances of errors by making it hard for a doctor to resume a primary task after facing interruptions.ConclusionInterruptions increase the chances of errors and make it difficult for the doctors to resume primary tasks (after facing such incidents).

The Internet revolution has resulted in abundant data from various sources, including social media, traditional media, etcetera. Although the availability of data is no longer an issue, data labelling for exploiting it in supervised machine learning is still an expensive process and involves tedious human efforts. The overall purpose of this study is to propose a strategy to automatically label the unlabeled textual data with the support of active learning in combination with deep learning. More specifically, this study assesses the performance of different active learning strategies in automatic labelling of the textual dataset at sentence and document levels. To achieve this objective, different experiments have been performed on the publicly available dataset. In first set of experiments, we randomly choose a subset of instances from training dataset and train a deep neural network to assess performance on test set. In the second set of experiments, we replace the random selection with different active learning strategies to choose a subset of the training dataset to train the same model and reassess its performance on test set. The experimental results suggest that different active learning strategies yield performance improvement of 7% on document level datasets and 3% on sentence level datasets for auto labelling.

Readability is an active field of research in the late nineteenth century and vigorously persuaded to date. The recent boom in data-driven machine learning has created a viable path forward for readability classification and ranking. The evaluation of text readability is a time-honoured issue with even more relevance in today’s information-rich world. This paper addresses the task of readability assessment for the English language. Given the input sentences, the objective is to predict its level of readability, which corresponds to the level of literacy anticipated from the target readers. This readability aspect plays a crucial role in drafting and comprehending processes of English language learning. Selecting and presenting a suitable collection of sentences for English Language Learners may play a vital role in enhancing their learning curve. In this research, we have used 30,000 English sentences for experimentation. Additionally, they have been annotated into seven different readability levels using Flesch Kincaid. Later, various experiments were conducted using five Machine Learning algorithms, i.e ., KNN, SVM, LR, NB, and ANN. The classification models render excellent and stable results. The ANN model obtained an F-score of 0.95% on the test set. The developed model may be used in education setup for tasks such as language learning, assessing the reading and writing abilities of a learner.

Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning.

In last two decades a promising performance improvement in the electrical insulation materials has been reported by many researchers using different nano fillers (NFs). In recent years another type of NFs called core-shell type nano fillers have shown even more attention than ordinary NFs. The core-shell NFs are a combination of nano particle core coated with outer nano layer of different material. The core-shell NFs combine the beneficial properties of two material with in one NF. Recently a few studies have reported a considerable improvement in the dielectric properties of epoxy by utilising TiO2@SiO2 core-shell NFs. The TiO2@SiO2 particles have a core of TiO2 coated with an out layer of SiO2 at nano level. This study investigates the improvement in tracking performance of silicone rubber (SiR) using TiO2@SiO2 core-shell nano particles which has not been reported previously. The tracking immunity of low (below 1%) and high (above 1%) filler concentrations of nano TiO2@SiO2 incorporated into SiR was investigated according to IEC 60587. The results showed that an optimum percentage of 0.6 wt% of TiO2@SiO2 imparts best immunity to silicone rubber against tracking.

PurposeCloud manufacturing (CMfg) has emerged as a service-oriented paradigm that enables modularization and on-demand servitization of resources in the context of manufacturing. The plethora of studies on CMfg has led the authors to investigate its implementation, as most of the literature is theoretical or simulation-based. Therefore, the purpose of this study is to investigate the reality of the CMfg concept in terms of adoption.Design/methodology/approachA tri-theoretic model is developed using the technology adoption model, diffusion of innovation and technology-organization-environment for hypotheses development. Data are collected from 218 US manufacturers. The data analysis approaches are partial least squares structural equation modeling, while data visualization is done to further analysis.FindingsThe study shows that most of the US manufacturers are reluctant to adopt the CMfg. Further, the statistical findings imply that competitive pressure, top management support, compatibility and trialability play a vital role in its adoption. The success of the CMfg adoption relies on the implementation of the pre-installation stage and the top management decisions.Practical implicationsFor practitioners, the study provides insight on how to supervise the CMfg platform implementation to improve the adoption process. For researchers and academicians, the significance of trialability provides a wide range of research topics on developing the CMfg trials and models.Originality/valueThis paper highlights the concerns of manufacturers about the pros and cons of the CMfg adoption, as this topic has not been given due attention in the literature. This will help to align future research directions according to market concerns and mitigating the factors that are hindering its adoption.

In last two decades a promising performance improvement in the electrical insulation materials has been reported by many researchers using different nano fillers (NFs). In recent years another type of NFs called core–shell type nano fillers have shown even more attention than ordinary NFs. The core–shell NFs are a combination of nano particle core coated with outer nano layer of different material. The core–shell NFs combine the beneficial properties of two material with in one NF. Recently a few studies have reported a considerable improvement in the dielectric properties of epoxy by utilising TiO 2 @SiO 2 core–shell NFs. The TiO 2 @SiO 2 particles have a core of TiO 2 coated with an out layer of SiO 2 at nano level. This study investigates the improvement in tracking performance of silicone rubber (SiR) using TiO 2 @SiO 2 core–shell nano particles which has not been reported previously. The tracking immunity of low (below 1%) and high (above 1%) filler concentrations of nano TiO 2 @SiO 2 incorporated into SiR was investigated according to IEC 60587. The results showed that an optimum percentage of 0.6 wt% of TiO 2 @SiO 2 imparts best immunity to silicone rubber against tracking.

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

Dental caries and lesions are difficult to treat during cement repairs. A remarkable antimicrobial therapeutic biomaterial is needed to fight dental caries and recurrent necrotic lesions. This study used Mentha longifolia extract to synthesize Copper nanoparticles (CuNPs) with distinctive properties at room temperature (22–25 °C). These CuNPs were supplemented with cephalosporin antibiotics that act as a capping agent to explore their synergistic antibacterial potency. These nanoparticles were subjected to FTIR, XRD, UV-Vis spectrophotometry, and SEM for characterisation. These CuNPs capped with antibiotics were added to glass ionomer (GIC) cement. These GIC samples were divided into pure GIC and modified GIC samples. Antibiotic-supplemented CuNPs, conjugated with GIC, showed good effect against Methicillin-resistant Staphylococcus aureus , Enterococcus faecalis , Klebsiella pneumoniae and Pseudomonas aeruginosa as compared to conventional GIC, tested through a modified direct contact test. Among them, GIC enriched with cefotaxime-supplemented CuNPs exhibited excellent antibacterial effects, followed by Cefepime and Ceftriaxone-supplemented CuNPs, respectively. Pure GIC has the most negligible antibacterial effect. Further, the interaction of these modified GICs with the selected bacterial strains was studied using the extended Derjaguin–Landau–Verwey–Overbeek (XDLVO) approach. The results show that the modified GIC effectively inhibited biofilm formation on dental implants.

Pakistan's highest diabetes prevalence necessitates equitable access to anti-diabetic medicines. This study evaluated the access to Oral antidiabetics (OADs) and the effect of Pakistan's recently launched price deregulation policy-applicable to medicines not included on the National Essential Medicines List (non-NEML)-on their prices and affordability by comparing NEML and non-NEML OADs. A WHO/HAI methodology-based survey in 30 private pharmacies across six regions gathered prices and availability data of 30 OADs, including the Lowest Price Generic (LPG), Highest Price Generic (HPG), and originator brand (OB). These selected OADs consisted of 11 products from NEML and 19 non-NEML products, comprising 17 single-active ingredient and 13 multi-active ingredient formulations. Published and surveyed retail prices of OADs (in Pakistani Rupees, PKR) before and after deregulation were compared, and the policy's effect was determined by difference-in-differences (DiD) analysis. Affordability for the lowest-paid employee and medicine availability in percentages were calculated. The DiD analysis revealed that the unit prices of OADs were significantly increased by PKR 15.08 (OB), PKR 5.89 (HPG), and PKR 2.81 (LPG) ( < 0.05) within just 6 months of the policy's introduction. Medicines listed on the NEML remained consistently cheaper than non-NEML, with differences of -30.20 for OBs, -9.83 for HPGs, and -7.51 for LPGs in PKR ( < 0.001). As per DiD interaction terms (NEML enlistment status × deregulation), a greater increase in prices of non-NEML OBs was observed compared to NEML counterparts (PKR -10.85, ≈ 0.05), while differences observed for LPGs (PKR 0.77, = 0.73) and HPGs (PKR -0.20, = 0.95) were insignificant. Prices of both single and multi-active ingredient formulations also increased significantly ( < 0.05). Although most OADs had fair availability from 47% to 97% after deregulation, seven out of 30 OADs remained unaffordable at both time points, and the overall affordability declined significantly post-deregulation ( < 0.05). The study revealed significant price escalations for most OADs, particularly those not enlisted on NEML, highlighting access challenges for diabetic patients and necessitating targeted policy reforms that address key market-related factors to ensure equitable access to OADs.