Categorization Training Research Articles

Serious Inadequacies in High Alert Medication-Related Knowledge Among Pakistani Nurses: Findings of a Large, Multicenter, Cross-sectional Survey.

IntroductionDeaths-related to medications errors are common in Pakistan but these are not accurately reported. Recently, the death of a 9 months old baby due to abrupt administration of 15% potassium chloride injection sparked the issue of high alert medications (HAMs) related errors in the country. Since drug administration is the prime responsibility of the nurses, it is pivotal that they possess good knowledge of HAMs. Since there is no published data regarding the knowledge of HAMs among Pakistani nurses, we aimed to assess knowledge of HAMs among registered nurses of Pakistan.MethodsA cross-sectional study was conducted among registered nurses, recruited using a convenient sampling technique, from 29 hospitals all over the Punjab Province. Data were collected using a validated self-administered instrument. All data were entered and analyzed using SPSS version 22.ResultsThe study sample was comprised of 2,363 registered nurses (staff nurses = 94.8%, head nurses = 5.2%). Around 63% were working in tertiary hospitals whereas almost 25 and 12% were from district headquarter hospitals and tehsil headquarter hospitals, respectively. Around 84% of the study participants achieved scores <70%, indicating majority of Pakistani nurses having poor knowledge of HAMs administration as well as regulation. There was no significant difference of overall knowledge among age, hospitals, departments, training, designations, qualification, and experience categories. Major obstacles encountered during HAMs administration were “getting uncertain answers from colleagues” (72.9%), “unavailability of suitable person to consult” (61.1%) and “receiving verbal orders” (55.6%).ConclusionOur study revealed the serious inadequacies in HAMs knowledge among Pakistani nurses which may lead to adverse patient outcomes. Nurses should receive comprehensive pharmacology knowledge not only during in-school nursing education but also as hospital-based continuing education. Moreover, it is of immense importance to bridge the gaps between physicians, clinical pharmacists, and nurses through effective communication as this will help reduce medication errors and improve patient care.

Few-shot Food Recognition via Multi-view Representation Learning

This article considers the problem of few-shot learning for food recognition. Automatic food recognition can support various applications, e.g., dietary assessment and food journaling. Most existing works focus on food recognition with large numbers of labelled samples, and fail to recognize food categories with few samples. To address this problem, we propose a Multi-View Few-Shot Learning (MVFSL) framework to explore additional ingredient information for few-shot food recognition. Besides category-oriented deep visual features, we introduce ingredient-supervised deep network to extract ingredient-oriented features. As general and intermediate attributes of food, ingredient-oriented features are informative and complementary to category-oriented features, and thus they play an important role in improving food recognition. Particularly in few-shot food recognition, ingredient information can bridge the gap between disjoint training categories and test categories. To take advantage of ingredient information, we fuse these two kinds of features by first combining their feature maps from their respective deep networks and then convolving combined feature maps. Such convolution is further incorporated into a multi-view relation network, which is capable of comparing pairwise images to enable fine-grained feature learning. MVFSL is trained in an end-to-end fashion for joint optimization on two types of feature learning subnetworks and relation subnetworks. Extensive experiments on different food datasets have consistently demonstrated the advantage of MVFSL in multi-view feature fusion. Furthermore, we extend another two types of networks, namely, Siamese Network and Matching Network, by introducing ingredient information for few-shot food recognition. Experimental results have also shown that introducing ingredient information into these two networks can improve the performance of few-shot food recognition.

Spam Detection Approach for Secure Mobile Message Communication Using Machine Learning Algorithms

The spam detection is a big issue in mobile message communication due to which mobile message communication is insecure. In order to tackle this problem, an accurate and precise method is needed to detect the spam in mobile message communication. We proposed the applications of the machine learning-based spam detection method for accurate detection. In this technique, machine learning classifiers such as Logistic regression (LR), K-nearest neighbor (K-NN), and decision tree (DT) are used for classification of ham and spam messages in mobile device communication. The SMS spam collection data set is used for testing the method. The dataset is split into two categories for training and testing the research. The results of the experiments demonstrated that the classification performance of LR is high as compared with K-NN and DT, and the LR achieved a high accuracy of 99%. Additionally, the proposed method performance is good as compared with the existing state-of-the-art methods.

Aspects of physical training related with upper respiratory tract infections: a review

The upper respiratory tract infections (URTI) are characterized by symptoms present, mainly, in the nose and pharynx. Physical exercise is one of the factors that can lead to development of URTI, causing a state of immunosuppression during a small-time interval, making the athlete susceptible to development of acute infectious states. Besides its influence in athlete’s quality of life and healthy, the progression of URTI seems to be associated with overtraining state, inducing reduction on physical performance. Several authors have been discussing how the variables of exercise training can alter the incidence and prevalence of URTI, and the present study was developed to present the current knowledge about this tissue, discussing how the modulation in the exercise training variables may interfere in the URTI. Methods: This is a review study. Results: Athletes with different levels of training and different categories in most diverse modalities seem to suffer from symptoms of URTI in training and competitive periods. Increasing of URTI symptoms seems to be related to internal training load and evidence has pointed out that suppression of serum and salivary immunoglobulin concentrations appear to be a factor which influences appearance of these symptoms and may even be used as markers of immune status of athlete. Conclusion: In point of view of reviewed articles, athletes with different levels of training and different categories in most diverse modalities seem to suffer from symptoms of URTI in training and competitive periods. Increasing of URTI symptoms seems to be related to internal training load and evidence has pointed out that suppression of serum and salivary immunoglobulin concentrations appear to be a factor which influences appearance of these symptoms and may even be used as markers of immune status of athlete. However, studies that verify and provide “cutoff points” that indicate a higher risk for URTI onset is still needed.

Perceptions of disaster management knowledge and skills among advanced practice registered nurses.

Disaster management experts around the world agree that nurses play a vital role in disaster preparation, response, and recovery. The Joint Commission on Accreditation of Health care Organizations requires biannual disaster drills for accredited facilities, and there are numerous training resources and educational materials on disaster management topics. Multiple federal and state laws govern health care provider activities during a disaster. However, there is a lack of guidance and evidence surrounding the specific roles and responsibilities of advanced practice registered nurses (APRNs) in a disaster setting. This project assesses APRNs knowledge of disaster management roles and responsibilities and the training for these roles. Using a cross-sectional survey research design, a 68-item survey was distributed via e-mail and social media to members of Texas APRN professional organizations. A convenience sample of those responding and meeting inclusion criteria (Texas-licensed APRN) was analyzed using SPSS to perform descriptive and nonparametric statistics. Thirty-one percent of responding APRNs considered themselves prepared for the management of disasters. A majority of statements (29/47, 61.7%) evaluating APRN knowledge and skills were significantly different (p < .05) across three training and experience categories: post-APRN program education, workplace/community drills or exercises, and planning or disaster response. More clearly defined roles and readily available training and resources will improve APRNs preparation for disaster situations. Recurrent training and professional experience increase knowledge and confidence related to advanced practice disaster management competencies.

Rescheduling Traffic on a Partially Blocked Segment of Railway with a Siding

The paper presents a polynomial-time algorithm for rescheduling traffic when one track of a double-track railway becomes unavailable, the remaining track has a siding, and there are two categories of trains—priority trains such as passenger trains and ordinary trains such as the majority of freight trains. The presented algorithm minimises the negative effect, caused by the track blockage, first for the priority trains and then for the ordinary trains on the set of all schedules optimal for the priority trains.

Attribute-Guided Feature Learning for Few-Shot Image Recognition

Few-shot image recognition has become an essential problem in the field of machine learning and image recognition, and has attracted more and more research attention. Typically, most few-shot image recognition methods are trained across tasks. However, these methods are apt to learn an embedding network for discriminative representations of training categories, and thus could not distinguish well for novel categories. To establish connections between training and novel categories, we use attribute-related representations for few-shot image recognition and propose an attribute-guided two-layer learning framework, which is capable of learning general feature representations. Specifically, few-shot image recognition trained over tasks and attribute learning trained over images share the same network in a multi-task learning framework. In this way, few-shot image recognition learns feature representations guided by attributes, and is thus less sensitive to novel categories compared with feature representations only using category supervision. Meanwhile, the multi-layer features associated with attributes are aligned with category learning on multiple levels respectively. Therefore we establish a two-layer learning mechanism guided by attributes to capture more discriminative representations, which are complementary compared with a single-layer learning mechanism. Experimental results on CUB-200, AWA and MiniImageNet datasets demonstrate our method effectively improves the performance.

Strengthening spatial reasoning: elucidating the attentional and neural mechanisms associated with mental rotation skill development

Spatial reasoning is a critical skill in many everyday tasks and in science, technology, engineering, and mathematics disciplines. The current study examined how training on mental rotation (a spatial reasoning task) impacts the completeness of an encoded representation and the ability to rotate the representation. We used a multisession, multimethod design with an active control group to determine how mental rotation ability impacts performance for a trained stimulus category and an untrained stimulus category. Participants in the experimental group (n = 18) showed greater improvement than the active control group (n = 18) on the mental rotation tasks. The number of saccades between objects decreased and saccade amplitude increased after training, suggesting that participants in the experimental group encoded more of the object and possibly had more complete mental representations after training. Functional magnetic resonance imaging data revealed distinct neural activation associated with mental rotation, notably in the right motor cortex and right lateral occipital cortex. These brain areas are often associated with rotation and encoding complete representations, respectively. Furthermore, logistic regression revealed that activation in these brain regions during the post-training scan significantly predicted training group assignment. Overall, the current study suggests that effective mental rotation training protocols should aim to improve the encoding and manipulation of mental representations.

Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling

Accurate prediction of building’s response subjected to earthquakes makes possible to evaluate building performance. To this end, we leverage the recent advances in deep learning and develop a physics-guided convolutional neural network (PhyCNN) for data-driven structural seismic response modeling. The concept is to train a deep PhyCNN model based on limited seismic input–output datasets (e.g., from simulation or sensing) and physics constraints, and thus establish a surrogate model for structural response prediction. Available physics (e.g., the law of dynamics) can provide constraints to the network outputs, alleviate overfitting issues, reduce the need of big training datasets, and thus improve the robustness of the trained model for more reliable prediction. The surrogate model is then utilized for fragility analysis given certain limit state criteria. In addition, an unsupervised learning algorithm based on K-means clustering is also proposed to partition the datasets to training, validation and prediction categories, so as to maximize the use of limited datasets. The performance of PhyCNN is demonstrated through both numerical and experimental examples. Convincing results illustrate that PhyCNN is capable of accurately predicting building’s seismic response in a data-driven fashion without the need of a physics-based analytical/numerical model. The PhyCNN paradigm also outperforms non-physics-guided neural networks.

Training and Competition Load in Female Basketball: A Systematic Review.

The scientific literature on women’s basketball is still limited, mainly in performance parameters. The purpose of this study was to analyse the state of art on the internal and external loads supported by female basketball players during sports practice. The design of this research is theoretical. The most relevant databases were searched for pertinent published studies according to the following keywords: “basketball”, “female” or “woman”, “training” or “competition”, “load” or “demand”. Of the 644 studies initially identified, 26 were selected for a complete review. These investigations were characterised by having as an objective an individualization of training for this type of population. Of the selected studies, it was evaluated: (i) goal (training, competition or both); (ii) category (stages: U14, U16, U18 and senior; level: state, national or international); (iii) type of load (Internal, External or both); (iv) instruments used and (v) variables analysed. The most studied goal was competition, mainly in senior national level, carrying out an analysis of the external and internal load together. Depending on the instruments and the variables used, the subjective load analysis was recurrent and important in the publication of articles in this topic. The quality of the studies was good, but for a better description of women’s basketball, there is a need to jointly investigate sports training and competition, to study the training categories and to use micro technology that guarantees obtaining objective and reliable data.

Contemporary Training Practices of Norwegian Powerlifters.

Shaw, MP, Andersen, V, Sæterbakken, AH, Paulsen, G, Samnøy, LE, and Solstad, TEJ. Contemporary training practices of Norwegian powerlifters. J Strength Cond Res 36(9): 2544-2551, 2022-The aim of this study was to explore the contemporary training practices of Norwegian powerlifters. One hundred twenty-four Norwegian powerlifters completed an electronic questionnaire that surveyed their current training practices with a focus on 2 areas: (a) training content and (b) training design and monitoring. One hundred seventeen respondents met the inclusion criteria, and the sample included World, European, and Norwegian champions. Where data were dichotomized, chi-square tests were used. The most frequently reported (58.1%) category of training was 5-6 times per week, with no statistically significant associations between levels of competitors (international vs. noninternational) (X 2 (1) = 0.414, p = 0.52). The most frequently reported load used in training was 71-80% 1 repetition maximum. The majority of Norwegian (76.9%) powerlifters train with variable resistance, with those competing internationally more likely to use elastic bands (X 2 (1) = 4.473, p = 0.034). 32.5% of respondents reported that they included strength training exercises in their training. Norwegian powerlifters' training differs from practices previously identified in the literature, with a higher prevalence of elastic resistance, particularly for those competing internationally, and a decreased use of strength training exercises at all levels. Norwegian powerlifters train frequently (5 or more times per week) and with submaximal loads.

Semantic-k-NN algorithm: An enhanced version of traditional k-NN algorithm

The k-NN algorithm is one of the most renowned ML algorithms widely used in the area of data classification research. With the emergence of big data, the performance and the efficiency of the traditional k-NN algorithm is fast becoming a critical issue. The traditional k-NN algorithm is inefficient to solve the high volume multi-categorical training datasets Traditional k-NN algorithm has a constraint in filtering the training dataset to yield training data that are most relevant to the intended or the targeted test dataset/file. It has to scan through all the training datasets categories to classify the intended/targeted data. As such, traditional k-NN is considered not intelligent and consequently is suffering poor accuracy performance with high computational complexity. A Semantic-kNN (Sk-NN) algorithm for ML is thus proposed in this paper to address the limitations in the traditional k-NN. The proposed Sk-NN deploys a process by leveraging on the semantic itemization and bigram model to filter the training dataset in accordance with the relevant information engaged in the test dataset. It is aimed for general security applications such as finding (the confidentiality level of the data when the algorithm is trained with multiple training categories during the data classification phase. Ultimately, Sk-NN is to elevate the ML performance in pattern extraction and labeling in the big data context.

Emergency and critical care services in Malawi: Findings from a nationwide survey of health facilities.

Globally, critical illness causes up to 45 million deaths every year. The burden is highest in low-income countries such as Malawi. Critically ill patients require good quality, essential care in emergency departments and in hospital wards to avoid negative outcomes such as death. Little is known about the quality of care or the availability of necessary resources for emergency and critical care in Malawi. The aim of this study was to assess the availability of resources for emergency and critical care in Malawi using data from the Service Provision Assessment (SPA). We conducted a secondary data analysis of the SPA - a nationwide survey of all health facilities. We assessed the availability of resources for emergency and critical care using previously developed standards for hospitals in low-income countries. Each health facility received an availability score, calculated as the proportion of resources that were present. Resource availability was sub-divided into the seven a-priori defined categories of drugs, equipment, support services, emergency guidelines, infrastructure, training and routines. Of the 254 indicators in the standards necessary for assessing the quality of emergency and critical care, SPA collected data for 102 (40.6%). Hospitals had a median resource availability score of 51.6% IQR (42.2-67.2) and smaller health facilities had a median of 37.5% (IQR 28.1-45.3). For the category of drugs, the hospitals' median score was 62.0% IQR (52.4-81.0), for equipment 51.9% IQR (40.7-66.7), support services 33.3% IQR (22.2-77.8) and emergency guidelines 33.3% IQR (0-66.7). SPA did not collect any data for resources in the categories of infrastructure, training or routines. Hospitals in Malawi lack resources for providing emergency and critical care. Increasing data about the availability of resources for emergency and critical care and improving the hospital systems for the care of critically ill patients in Malawi should be prioritized.

Effects of specific-level versus broad-level training for broad-level category learning in a complex natural science domain.

Category learning is a core component of course curricula in science education. For instance, geology courses teach categorization of rock types. Using the educationally authentic rock categories, the current project examined whether category learning at a broad-level (BL; igneous, sedimentary, and metamorphic rocks) could be enhanced by learning category information at a more specific-level (SL; e.g., diorite under igneous, breccia under sedimentary, etc.). Experiments 1 and 2 showed that SL training was inferior to BL training when participants were required to respond at the BL regardless of whether BL and SL category labels were presented simultaneously during classification training or SL categories were learned initially followed by training on the specific-broad level name associations. However, Experiments 3 and 4 showed that SL training was as good as BL training when the training was more extensive and participants were allowed to respond at the trained level. By considering confusion matrices (i.e., probabilities that instances in a given category was erroneously classified as belonging to other categories), we conjectured that between-SL category similarity, specifically the degree to which similar-looking SL categories belong to the same BL category, is an important factor in determining the efficacy of SL training. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

UNIFIED: Understanding New Information from Emergency Departments Involved in the San Bernardino Terrorist Attack.

IntroductionEmergency departments (ED) are on the front line for treating victims of multi-casualty incidents. The primary objective of this study was to gather and detail the common experiences from those hospital-based health professionals directly involved in the response to the San Bernardino terrorism attack on December 2, 2015. Secondary objectives included gathering information on experiences participants found were best practices.MethodsWe undertook a qualitative study using Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines by performing semi-structured interviews with physicians, nurses, and incident management staff from multiple institutions responding to the San Bernardino terrorist attack. We coded transcripts using qualitative analysis techniques and we delineated and agreed upon a refined list with code definitions using a negotiated group process. Final themes were developed and analyzed.ResultsA total of 26 interviews were completed; 1172 excerpts were coded and categorized into 66 initial themes. Six final categories of communication, training, unexpected help, process bypassed, personal impact/emotions, and practical advice resulted.ConclusionOur study provides context regarding the response of healthcare personnel from multiple institutions to a singular terrorist attack in the United States. It elucidates several themes to help other institutions prepare for similar events. Understanding these common experiences provides opportunity to prepare for future incidents and develop questions to study in future events.

Producing consistent visually interpreted land cover reference data: learning from feedback

ABSTRACT Reference data for large-scale land cover map are commonly acquired by visual interpretation of remotely sensed data. To assure consistency, multiple images are used, interpreters are trained, sites are interpreted by several individuals, or the procedure includes a review. But little is known about important factors influencing the quality of visually interpreted data. We assessed the effect of multiple variables on land cover class agreement between interpreters and reviewers. Our analyses concerned data collected for validation of a global land cover map within the Copernicus Global Land Service project. Four cycles of visual interpretation were conducted, each was followed by review and feedback. Each interpreted site element was labelled according to dominant land cover type. We assessed relationships between the number of interpretation updates following feedback and the variables grouped in personal, training, and environmental categories. Variable importance was assessed using random forest regression. Personal variable interpreter identifier and training variable timestamp were found the strongest predictors of update counts, while the environmental variables complexity and image availability had least impact. Feedback loops reduced updating and hence improved consistency of the interpretations. Implementing feedback loops into the visually interpreted data collection increases the consistency of acquired land cover reference data.

Effects of semantic categorization strategy training on episodic memory in children and adolescents.

Episodic memory is the ability to learn, store and recall new information. The prefrontal cortex (PFC) is a crucial area engaged in this ability. Cognitive training has been demonstrated to improve episodic memory in adults and older subjects. However, there are no studies examining the effects of cognitive training on episodic memory encoding in typically developing children and adolescents. This study investigated the behavioral effects and neural correlates of semantic categorization strategy training in children and adolescents during verbal episodic memory encoding using functional magnetic resonance imaging (fMRI). Participants with age range: 7–18 years were scanned before and after semantic categorization training during encoding of word lists. Results showed improved memory performance in adolescents, but not in children. Deactivation of the anterior medial PFC/anterior cingulate and higher activation of the right anterior and lateral orbital gyri, right frontal pole and right middle frontal gyrus activation were found after training in adolescents when compared to children. These findings suggest different maturational paths of brain regions, especially in the PFC, and deactivation of default mode network areas, which are involved in successful memory and executive processes in the developing brain.

Distance and Direction Codes Underlie Navigation of a Novel Semantic Space in the Human Brain.

A recent proposal posits that humans might use the same neuronal machinery to support the representation of both spatial and nonspatial information, organizing concepts and memories using spatial codes. This view predicts that the same neuronal coding schemes characterizing navigation in the physical space (tuned to distance and direction) should underlie navigation of abstract semantic spaces, even if they are categorical and labeled by symbols. We constructed an artificial semantic environment by parsing a bidimensional audiovisual object space into four labeled categories. Before and after a nonspatial symbolic categorization training, 25 adults (15 females) were presented with pseudorandom sequences of objects and words during a functional MRI session. We reasoned that subsequent presentations of stimuli (either objects or words) referring to different categories implied implicit movements in the novel semantic space, and that such movements subtended specific distances and directions. Using whole-brain fMRI adaptation and searchlight model-based representational similarity analysis, we found evidence of both distance-based and direction-based responses in brain regions typically involved in spatial navigation: the medial prefrontal cortex and the right entorhinal cortex (EHC). After training, both regions encoded the distances between concepts, making it possible to recover a faithful bidimensional representation of the semantic space directly from their multivariate activity patterns, whereas the right EHC also exhibited a periodic modulation as a function of traveled direction. Our results indicate that the brain regions and coding schemes supporting relations and movements between spatial locations in mammals are "recycled" in humans to represent a bidimensional multisensory conceptual space during a symbolic categorization task.SIGNIFICANCE STATEMENT The hippocampal formation and the medial prefrontal cortex of mammals represent the surrounding physical space by encoding distances and directions between locations. Recent works suggested that humans use the same neural machinery to organize their memories as points of an internal map of experiences. We asked whether the same brain regions and neural codes supporting spatial navigation are recruited when humans use language to organize their knowledge of the world in categorical semantic representations. Using fMRI, we show that the medial prefrontal cortex and the entorhinal portion of the hippocampal formation represent the distances and the movement directions between concepts of a novel audiovisual semantic space, and that it was possible to reconstruct, from neural data, their relationships in memory.

Physical and Physiological Profiles of Aerobic and Anaerobic Capacities in Young Basketball Players.

Current trends in the analysis of the physical fitness of athletes are based on subjecting the athlete to requirements similar to those found in competition. Regarding physical fitness, a thorough study of the capacities that affect the development of team sports in different ages and gender is required since the demands are not equivalent. The objective of this paper was to characterize the physical-physiological demands of athletes in an aerobic and anaerobic test specific to basketball players, as well as the evolution of the variables according to age and gender. The research was carried out in 149 players from different training categories (n = 103 male; n = 46 female). The athletes performed two field tests that evaluated both aerobic capacity and lactic anaerobic capacity. Each athlete was equipped with an inertial device during the tests. Sixteen variables (equal in both tests) were analyzed. Three of them evaluated technical-tactical aspects, four variables of objective internal load, six kinematic variables of objective external load (two related to distance and four related to accelerometry) and three neuromuscular variables of objective external load. The obtained results show significant differences in the variables analyzed according to the age and gender of the athletes. They are mainly due to factors related to the anthropometric maturation and development inherent in age and have an impact on the efficiency and technical and tactical requirements of the tests carried out and, therefore, on the obtained results in the tests.

Could regular practice of volleyball modulate salivary secretory immunity in children? Cross-sectional and longitudinal studies

Introduction The aim of these studies was to evaluate the effects of regular volleyball training on salivary SIgA and alpha-amylase in female children and adolescents. Material and method In the cross-sectional study, 115 female children (12.6±2.2 years) participating in a Volleyball training program were classified as beginners, intermediate, and competitive level. The children were evaluated regarding caries index, body mass index (BMI), cardiorespiratory fitness, and countermovement jump. In the longitudinal study: 54 girls (intermediate and competitive groups) trained for 8 weeks and were re-evaluated at weeks 4 and 8. Results The SIgA secretion rate and alpha-amylase activity did not present correlations with training category, age, caries index, or training status. A weak positive correlation was detected between IgA secretion rate and BMI (r=0.20, p<0.05). After 8 weeks of training, VO2max (p<0.005) and jump height (p<0.005) improved in the competitive girls. No differences were observed in salivary secretion rate, SIgA concentration and secretion rate, or alpha-amylase activity at weeks 4 and 8. Conclusion We concluded that despite improving physical fitness, 8-weeks of recreational volleyball training are not able to improve salivary SIgA secretion or alter alpha-amylase activity in female children and adolescents.