Additional Tasks Research Articles

SHOULD I STAY OR SHOULD I GO-A rapid qualitative study of principal deteriorating factors experienced by nurses relating to their intentions to leave their current position in a medical hospital department.

High nurse turnover in hospitals is a worldwide problem with dire consequences for patient care such as increased mortality and a decrease in patient safety. A specific effort to retain nurses is urgently needed due to the many vacant nursing positions, especially across the Medical hospital departments. To identify the principal deteriorating factors experienced by nurses relating to their intentions to leave the medical department at a university hospital. A rapid qualitative research methodology was used. Participants comprised 22 registered nurses employed in five university hospital medical department units. The Rigorous and Accelerated Data Reduction (RADaR) technique was therefore used for data collection through qualitative semi-structured interviews and analysis. COREQ was used for reporting the study. The RADaR analysis detected a downward spiral of five principal deteriorating factors influencing nurses' intentions to leave their position in the medical department. The factors were resignations from several nursing colleagues combined with too few and inexperienced nurses present during shifts, additional tasks assigned, a management refraining from improving the problematic issues, leading to decisive consequences for patient care and a declining feeling of professional care. The lack of nurses in the department caused missed nursing care, which affected the nurses' job satisfaction and intentions to leave their positions. Future research initiatives must focus on evaluating successful interventions to maintain the nurses in the positions. Further knowledge, is also needed, to investigate how we can change the downward spiral to a story of retention success.

Data Roaming and Quality Assessment for Composed Image Retrieval

The task of Composed Image Retrieval (CoIR) involves queries that combine image and text modalities, allowing users to express their intent more effectively. However, current CoIR datasets are orders of magnitude smaller compared to other vision and language (V&L) datasets. Additionally, some of these datasets have noticeable issues, such as queries containing redundant modalities. To address these shortcomings, we introduce the Large Scale Composed Image Retrieval (LaSCo) dataset, a new CoIR dataset which is ten times larger than existing ones. Pre-training on our LaSCo, shows a noteworthy improvement in performance, even in zero-shot. Furthermore, we propose a new approach for analyzing CoIR datasets and methods, which detects modality redundancy or necessity, in queries. We also introduce a new CoIR baseline, the Cross-Attention driven Shift Encoder (CASE). This baseline allows for early fusion of modalities using a cross-attention module and employs an additional auxiliary task during training. Our experiments demonstrate that this new baseline outperforms the current state-of-the-art methods on established benchmarks like FashionIQ and CIRR.

DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing

Recent advances on deep learning models come at the price of formidable training cost. The increasing model size is one of the root causes, but another less-emphasized fact is that data scale is actually increasing at a similar speed as model scale, and the training cost is proportional to both of them. Compared to the rapidly evolving model architecture, how to efficiently use the training data (especially for the expensive foundation model pretraining) is both less explored and difficult to realize due to the lack of a convenient framework that focus on data efficiency capabilities. To this end, we present DeepSpeed Data Efficiency, a framework that makes better use of data, increases training efficiency, and improves model quality. Specifically, we propose and combine two data efficiency techniques: efficient data sampling via a general curriculum learning library, and efficient data routing via a novel random layerwise token dropping technique. For GPT-3 1.3B language model pretraining, our work achieves 12.5x less data/time/cost ($3.7K if rent on Azure), while still maintaining 95% of model quality compared to baseline with full data and cost ($46.3K). For GPT-3 1.3B and BERT-large pretraining, our work can also achieve the same model quality with up to 2x less data/time/cost, or achieve better model quality under same data/time/cost. DeepSpeed Data Efficiency is easy to use and tune, enabling us to easily apply it and verify its benefit on additional tasks including GPT-3 MoE model pretraining and small-scale GPT-2/ViT finetuning.

The effect of high-frequency rTMS over left DLPFC and fluid abilities on goal neglect

Goal neglect refers to when an aspect of task instructions is not utilised due to increased competition between goal representations, an attentional limit theoretically linked to working memory. In an attempt to alleviate goal neglect and to investigate the association between dorsolateral prefrontal cortex (DLPFC)-supported working memory and goal neglect, we used high-frequency repetitive transcranial magnetic stimulation to the left DLPFC whilst participants completed the letter-monitoring task, a measure of goal neglect, and an N3-back task, a working memory task known to be affected by rTMS of the left DLPFC, following 20 min of active and sham stimulation (run on separate days). We found increased accuracy on the N3-back task in addition to decreased goal neglect in the active compared to sham condition when controlling for age and fluid abilities (as assessed by matrix reasoning performance). Furthermore, analysis showed that active stimulation improvements on both the N3-back and letter-monitoring tasks were greater for those with higher fluid abilities. These findings provide support for the link between the DLPFC-support working memory and goal neglect. Increased performance on the N3-back task also supports the literature reporting a link between left DLPFC and verbal working memory. Results are evaluated in the context of potential use to alleviate symptoms of disorders related to goal neglect.

Physical activity, eating behaviour, and sleep quality as predictors of Body Mass Index in adolescents? A correlational study on high school students in East Kalimantan

Background and Study Aim. Obesity is a metabolic disease characterised by excessive fat accumulation. Indonesia ranks second after Singapore with the largest number of obese adolescents. The study aimed to assess the impact of physical activity, eating behavior, and sleep quality on Body Mass Index (BMI) in adolescents. Materials and Methods. This study was quantitative, with an ex post facto design. The study included 231 students (136 males, 95 females) aged 16-19 years, each with a Body Mass Index (BMI) ≥ 25.1. Participants were healthy and consented to the study by completing a screening questionnaire. The analysis was performed using regression techniques in the Statistical Package for Social Sciences (SPSS) version 21 software. Results. The selected regression model is feasible and demonstrates that physical activity, diet, and sleep quality collectively influence students' BMI (p-value < 0.001). Furthermore, the analysis shows that physical activity (p-value < 0.001), diet (p-value = 0.019), and sleep quality (p-value < 0.001) independently affect students' BMI. Conclusions. We suggest that schools need to hold counseling programs in collaboration with relevant health workers to conduct counseling with the prevention of malnutrition, especially obesity. Provide additional tasks in the form of physical activity that students must do at home with parental supervision and the results will be reported to the teacher as an additional task value. Parents should also provide supervision of eating behavior and sleep patterns. For future researchers to be able to reveal other variables that can affect student BMI, because our report found only 49.90%.

Enhancing Emergency Department Patient Experience and Flow Through Real-Time Text-Messaging Updates

Surging patient volumes in emergency departments require innovative approaches to improve patients’ experiences, given the expectations they have formed by interacting with industries such as travel, retail, and dining, where they receive personalized text messaging or in-application updates on their order or the status of their flight. This technology has not been widely adopted in the health care industry, and it presents an opportunity to meet and exceed consumer expectations. At Inova Health System, a diverse team comprising physicians, nurses, patient engagement leaders, and informaticists developed a text-messaging application for delivering real-time updates to emergency department patients and their family members during their visit. This process occurs almost entirely in the background without burdening clinical and nursing staff with additional administrative tasks. The team demonstrated substantial improvement in key patient experience metrics and revealed the broader operational utilities of automated messaging to enhance departmental efficiency.

Configurable in-memory computing architecture based on dual-port SRAM

In the emerging field of in-memory computing (IMC), this study proposes a dual-port static random access memory (SRAM) IMC architecture with the distinct capability of realizing XOR encryption (XORE), thus serving as a potential solution for the Von Neumann bottleneck. Beyond providing traditional SRAM read and write operations, the proposed architecture carries out additional tasks such as multi-bit multiply and accumulate (MAC) and XOR accumulation (XORA). The architecture was simulated using a 28-nm Complementary Metal Oxide Semiconductor Process, demonstrating a minor standard deviation of 9.41 mV in bit line voltage at the SS process corner, as evidenced by Monte Carlo simulation. Energy expenditure for the MAC, XORA, and XORE, was found to be 1.65, 1.46, and 9.02 fJ/ops respectively at the TT process corner. Furthermore, the presented architecture showed considerable energy efficiency, with MAC, XORA, and XORE operations achieving energy efficiency values of 604.9, 682.7, and 110.8 TOPS/W respectively, at a supply voltage of 0.9 V at the TT process corner.

Stash: Flexible Energy Storage for Intermittent Sensors

Batteryless sensors promise a sustainable future for sensing, but they face significant challenges when storing and using environmental energy. Incoming energy can fluctuate unpredictably between periods of scarcity and abundance, and device performance depends on both incoming energy and how much a device can store. Existing batteryless devices have used fixed or run-time selectable front-end capacitor banks to meet the energy needs of different tasks. Neither approach adapts well to rapidly changing energy harvesting conditions, nor does it allow devices to store excess energy during times of abundance without sacrificing performance. This article presents Stash, a hardware back-end energy storage technique that allows batteryless devices to charge quickly and store excess energy when it is abundant, extending their operating time and carrying out additional tasks without compromising the main ones. Stash performs like a small capacitor device when small capacitors excel and like a large capacitor device when large capacitors excel, with no additional software complexity and negligible power overhead. We evaluate Stash using two applications—temperature sensing and wearable activity monitoring—under both synthetic solar energy and recorded solar and thermal traces from various human activities. Our results show that Stash increased sensor coverage by up to 15% under variable energy-harvesting conditions when compared to competitor configurations that used fixed small, large, and reconfigurable front-end energy storage.

Does dual‐tasking affect the ability to generate anticipatory postural adjustments in young adults?

AbstractThe aim of this study was to investigate how additional cognitive tasks (Stroop test and counting backwards task) influence young adults' ability to generate appropriate postural responses while standing on a continuously oscillating platform. Twenty young adults (25.95 ± 2.97 years) stood on a moving platform which was translated in the anterior–posterior direction at three different frequencies (0.10, 0.25 and 0.50 Hz) in three dual‐task conditions (counting backwards task, a Stroop task or no additional cognitive task). Postural muscle onset latencies and tonic activity levels of the leg muscles were measured through surface electromyography; the number of steps taken and cognitive errors made were recorded. Results showed no significant differences in muscle activity between dual and single‐tasking conditions nor between the two dual tasking conditions. Cognitive errors were made in the counting backwards task but not the Stroop task. A frequency effect was identified with participants showing greater tonic activity in rectus femoris (p = 0.012), gastrocnemius medialis (p = 0.016) and bicep femoris (p = 0.043) at 0.5 Hz, as well as earlier muscle activation in tibialis anterior, gastrocnemius medialis and bicep femoris (p < 0.001) at 0.50 Hz. Transition and steady state muscle onset latencies were only significantly different for gastrocnemius medialis at 0.25 Hz (p = 0.001). Dual tasking did not seem to influence anticipatory postural adjustments in young adults; however, perturbation intensities did. The differences observed in the number of cognitive errors made could be indicative of the regional cortical activations and overlapping demand for resources interfering with balance control, though cortical activation was not recorded. Future research should include detailed cognitive behavior, including cortical activations and task reaction times to better understand the allocation of attentional resources during perturbed balance dual tasking.

Part-Of-Speech Tagging for Balochi Language: A Data driven application of Conditional Random Fields

Parts-of-Speech (POS) tagging involves the assignment of the correct part of speech or lexical category to individual words within a sentence in a natural language. This procedure holds significant in the field of Natural Language Processing (NLP) and find utility across a variety of NLP applications. Commonly, it constitutes the initial phase of natural language processing. Subsequent stages may encompass additional tasks such as chunking, parsing and more. Balochi stands as the predominant language in Balochistan,, ranking as the fourth most prevalent language in Pakistan. The field of natural language processing for Balochi is still in its nascent stages. In this research, we introduce an algorithm for Balochi part-of-speech tagging, leveraging machine learning techniques. The core of our approach relies on a Conditional Random Field model as the machine learning component. Careful consideration is given to selecting appropriate features for the CRF, taking into account the linguistic characteristics of Balochi. Balochi is currently considered a resource poor language, and thus, the available manually tagged data consists of only approximately 1500 sentences. The tagset used in this study created for research purpose, consisting of 16 different tags. The learning process incorporates tagged data. The algorithm demonstrates a high accuracy rate of 86.78% when applied to Balochi texts. The training corpus comprises 40000 words, while the test corpus contains 10000 words.

Tongue reflex for speech posture control

Although there is no doubt from an empirical viewpoint that reflex mechanisms can contribute to tongue motor control in humans, there is limited neurophysiological evidence to support this idea. Previous results failing to observe any tonic stretch reflex in the tongue had reduced the likelihood of a reflex contribution in tongue motor control. The current study presents experimental evidence of a human tongue reflex in response to a sudden stretch while holding a posture for speech. The latency was relatively long (50 ms), which is possibly mediated through cortical-arc. The activation peak in a speech task was greater than in a non-speech task while background activation levels were similar in both tasks, and the peak amplitude in a speech task was not modulated by the additional task to react voluntarily to the perturbation. Computer simulations with a simplified linear mass-spring-damper model showed that the recorded muscle activation response is suited for the generation of tongue movement responses that were observed in a previous study with the appropriate timing when taking into account a possible physiological delay between reflex muscle activation and the corresponding force. Our results evidenced clearly that reflex mechanisms contribute to tongue posture stabilization for speech production.

Ecological validity of common behavioral decision making tasks: evidence across two samples

ABSTRACT Background Clinicians and scholars routinely use behavioral decision tasks to assess real-world decision making capabilities. However, many common behavioral decision making tasks lack data on the extent to which they predict real-world risky behaviors. Across two pre-registered studies, and two timepoints, we assessed decision making abilities using common behavioral tasks and predicted participants’ real-world risky decision making from task performance. Method In Study 1, 918 Mechanical Turk (MTurk) workers completed three decision making tasks in addition to assessments of real-world risk behavior: preventive health behaviors, COVID-19 vaccination status, and virtual social distancing task performance. In Study 2, 221 college student participants completed the Study 1 tasks plus additional assessments of decision making and real world risk and protective behaviors. Results Across both studies, the selected behavioral decision tasks rarely predicted real world behavior and, when they did, the relationship was weak at best. Conclusions These data suggest that these behavioral decision making tasks may not be good predictors of real world risky behavior at present, with some evidence that the specificity of the behavior being assessed matters (i.e. the closer the task was to the specific behavior being predicted), calling for additional ecological validity research, with a greater variety of tasks in the future.

Personal development of gifted students in chemistry education

Relevance. The level of innovative development of the state depends on the achievements of Kazakh scientists in the field of natural sciences. The question of ensuring the quality training of gifted students and allowing them to express their scientific and research potential in the field of chemistry is gaining relevance. Purpose. The purpose of the study was to reveal the specifics of the activity and personality formation of gifted children in regular classes during the study of chemistry. Methodology. The methods of analysis, synthesis, comparison, generalization, and deduction were used. Results. As a result, the priority role of such personal qualities as independence and discipline among gifted students in the process of learning chemistry in general educational institutions was revealed. It was established that the level of their chemical giftedness affects their activity during the performance of additional and complex educational tasks. Attention was paid to the peculiarities of the interaction between the gifted student and the teacher, based on which it was stated that it is based on interactive approaches and tools. It has been established that an important role is played by providing students with appropriate conditions for conducting laboratory work in chemistry as well as for the child�s investigation of problems related to teaching the content of chemistry. Conclusions. It was noted that gifted students can memorize educational material faster and interpret complex chemical concepts. It was established that for the high-quality organization of the educational process with gifted children, it is expedient for chemistry teachers to constantly improve their knowledge level to attract more creative and innovative pedagogical tools. The results obtained in the study should be used in the process of developing educational and methodological materials for improving the competence of chemistry teachers to qualitatively meet the academic and educational needs of gifted students. Keywords: natural sciences; additional classes; pedagogical competence; increased interest in classes; high school

Role of the supervision of the school principal in improving the quality of learning at state elementary school’s post-pandemic COVID-19

This research aims to evaluate the role of principal supervision in improving teacher performance, identify obstacles related to academic supervision analysis, and design strategies to overcome these obstacles. This research was conducted at a public elementary school in Blora Regency. Qualitative data analysis techniques were used in this research. Sampling used purposive sampling technique. Data collection through observation and in-depth interviews. The informants taken included the Principal, Teachers, Supervisors, Chair of the School Committee, and Students. The results of the study show that academic supervision is carried out by the principles of academic supervision by going through the stages of planning, implementing, evaluating, and following up. The techniques and models of academic supervision that are applied vary according to the situation and condition of the school, as well as the principles of academic supervision that are applied. The obstacles found include the role of the principal as a supervisor who is often given additional tasks by the department, teachers as the party being supervised have various activities and competencies, as well as a lack of facilities and infrastructure. Another obstacle is the limited study time and the limited communication skills of students. Efforts have been made, among others, by rearranging schedules, involving teachers in training, allocating budgets for new facilities and infrastructure through the School Operational Assistance program, and building good cooperation with school committees.

PvFed: Personalized Vertical Federated learning for Client‐Specific Tasks

Federated Learning (FL) is a distributed machine learning paradigm that enables multiple data holders to collaborate on building machine learning models while preserving the privacy of their data. FL can be categorized as horizontal or vertical, depending on the distribution characteristics of the data. Specifically, horizontal FL uses data partitioned in the sample space, whereas vertical FL uses data partitioned in the feature space. Traditional vertical FL methods aim to facilitate collaboration among clients to infer a single global target. However, these methods may be impractical because each client often has a unique target to be inferred. In this paper, we propose a novel vertical FL method, called personalized vertical federated learning (pvFed), which addresses this limitation by allowing each client to perform inferences specific to their individual task. To the best of our knowledge, no existing method currently addresses this limitation. The objective of pvFed is to construct a global model that generates a representation vector to support client inference. The global model, constructed using distillation and dimensionality reduction, takes a sample ID common to all clients as input and outputs a sample‐specific representation vector. Clients utilize the intermediate representation of their own model and the representation vectors output by the global model for inference. Because these vectors are not dependent on client‐specific tasks, clients can repurpose them for any additional tasks. Our experiments, conducted on two distinct data types—image and tabular data sets, under a vertical partitioning where each client had its own specific task, demonstrated the efficacy of vectors generated by the global model in pvFed for client inference. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Roles of Histone H2A Variants in Cancer Development, Prognosis, and Treatment.

Histones are nuclear proteins essential for packaging genomic DNA and epigenetic gene regulation. Paralogs that can substitute core histones (H2A, H2B, H3, and H4), named histone variants, are constitutively expressed in a replication-independent manner throughout the cell cycle. With specific chaperones, they can be incorporated to chromatin to modify nucleosome stability by modulating interactions with nucleosomal DNA. This allows the regulation of essential fundamental cellular processes for instance, DNA damage repair, chromosomal segregation, and transcriptional regulation. Among all the histone families, histone H2A family has the largest number of histone variants reported to date. Each H2A variant has multiple functions apart from their primary role and some, even be further specialized to perform additional tasks in distinct lineages, such as testis specific shortH2A (sH2A). In the past decades, the discoveries of genetic alterations and mutations in genes encoding H2A variants in cancer had revealed variants' potentiality in driving carcinogenesis. In addition, there is growing evidence that H2A variants may act as novel prognostic indicators or biomarkers for both early cancer detection and therapeutic treatments. Nevertheless, no studies have ever concluded all identified variants in a single report. Here, in this review, we summarize the respective functions for all the 19 mammalian H2A variants and their roles in cancer biology whilst potentiality being used in clinical setting.

Physics-Guided Dual Self-Supervised Learning for Structure-Based Material Property Prediction.

Deep learning models have been widely used for high-performance material property prediction. However, training such models usually requires a large amount of labeled data, which are usually unavailable. Self-supervised learning (SSL) methods have been proposed to address this data scarcity issue. Herein, we present DSSL, a physics-guided dual SSL framework, for graph neural network-based material property prediction, which combines node masking-based generative SSL with atomic coordinate perturbation-based contrastive SSL strategies to capture local and global information about input crystals. Moreover, we achieve physics-guided pretraining by using the macroproperty (e.g., elasticity)-related microproperty prediction of atomic stiffness as an additional pretext task. We pretrain our DSSL model on the Materials Project database and fine-tune it with 10 material property data sets. The experimental results demonstrate that teaching neural networks some physics using the SSL strategy can afford ≤26.89% performance improvement compared to that of the baseline models.

Listening effort under different rates of speech

Listening effort, the cognitive resources allocated to understand spoken language, is a critical issue in communication under adverse listening environments. The present study investigated how listening effort would be affected by differing speech tempos. The experiment involved 23 native Korean adults with normal hearing, who were presented with linguistically complex Korean sentences at five different speeds (35%∼200% of original time). The cognitive load associated with listening under various speech rate conditions was quantified employing a dual-task paradigm, where the increased listening effort would impair the performance on an additional cognitive task. Participants listened to the sentences while performing an n-back task and then repeated back the sentence they heard. The results found a significant effect of speech rate on the n-back task, with lower accuracies in the faster speech conditions. The additional task also increased the overall cognitive load of sentence recognition, leading to various speech errors, such as the omission of key content words, unnecessary additions, and substitutions with similar words. This demonstrates that increased cognitive load during listening impairs speech processing at multiple levels (e.g., semantic processing, retention of words in working memory), emphasizing the need to adjust speech tempos for better understanding and comfort in listeners.

Assessing multi-domain impact of IAQ and noise on productivity with portable air cleaners through physiological signals

The office environment's indoor air quality (IAQ) and noise levels are critical factors that affect productivity and overall health. However, a research gap still exists on multi-domain impact of IAQ and noise on human physiological signals. The research aims to establish a comprehensive understanding of the multi-domain impact by analyzing physiological signals such as Electroencephalogram (EEG), Electrodermal Activity (EDA), eye blinks, jaw clenching, and heart rate. 30 human subjects participated in a series of tests in various indoor environments with portable air cleaner configurations. The study incorporated four scenarios with baseline condition and two noise levels. By comparing the different scenarios, the separate and combined impacts of IAQ and noise on office productivity were assessed. Meanwhile, physiological signals were collected and analyzed alongside productivity tests, which included typing and addition tasks, to determine the relationship between IAQ, noise, and office productivity. We discovered a strong relationship between EEG power and productivity, indicating that EEG power was a valuable indicator for evaluating indoor environmental quality, especially the multi-domain effects of IAQ and noise. When using the air cleaner, the relative power in the Theta, Alpha, and Beta bands indicated increased concentration and activity levels in the brain. Additionally, the increasing number of jaw clenching suggested that participants became more anxious and nervous in noisy environments. We also found that there were no gender differences for the analysis of EEG power, providing insights for future research. These findings have implications for smart control and built environment aimed at enhancing office workers' well-being and productivity.

Effects of speech production training on memory across short and long delays in 5- and 6-year-olds: A pre-registered study

AbstractStudies on the role of speech production on learning have found a memory benefit from production labeled the “Production Effect.” While research with adults has generally shown a robust memory advantage for produced words, children show more mixed results, and the advantage is affected by age, cognitive, and linguistic factors. With adults, the Production Effect is not restricted to the immediate context but is also found after a delay. So far, no studies have investigated the effect of delayed recall on the Production Effect with children. Children aged 5 and 6 years old (n = 60) participated in two sessions. Children were trained on familiar words and images, which were heard (Listen) or produced aloud (Say). Children then performed a free recall task. One week later, children repeated the recall task and an additional recognition task. At immediate testing, there was a recency effect on words recalled from the different training conditions and a recall advantage for words produced over words heard; however, this no longer held after a 1-week delay in either the recall or recognition task. Exploratory analysis showed that vocabulary did not predict the Production Effect. Findings indicate that unlike adults, the Production Effect is not as robust in children after a delay.