PLOS ONE: [sortOrder=DATE_NEWEST_FIRST, sort=Date, newest first, filterJournals=PLoSONE, q=subject:"Arithmetic"]PLOShttps://journals.plos.org/plosone/webmaster@plos.orgaccelerating the publication of peer-reviewed sciencehttps://journals.plos.org/plosone/search/feed/atom?sortOrder=DATE_NEWEST_FIRST&unformattedQuery=subject:%22Arithmetic%22&sort=Date,+newest+first&filterJournals=PLoSONEAll PLOS articles are Open Access.https://journals.plos.org/plosone/resource/img/favicon.icohttps://journals.plos.org/plosone/resource/img/favicon.ico2024-03-29T14:21:46ZExamining factors related to low performance of predicting remission in participants with major depressive disorder using neuroimaging data and other clinical featuresJunying WangDavid D. WuChristine DeLorenzoJie Yang10.1371/journal.pone.02996252024-03-28T14:00:00Z2024-03-28T14:00:00Z<p>by Junying Wang, David D. Wu, Christine DeLorenzo, Jie Yang</p>
Major depressive disorder (MDD), a prevalent mental health issue, affects more than 8% of the US population, and almost 17% in the young group of 18–25 years old. Since Covid-19, its prevalence has become even more significant. However, the remission (being free of depression) rates of first-line antidepressant treatments on MDD are only about 30%. To improve treatment outcomes, researchers have built various predictive models for treatment responses and yet none of them have been adopted in clinical use. One reason is that most predictive models are based on data from subjective questionnaires, which are less reliable. Neuroimaging data are promising objective prognostic factors, but they are expensive to obtain and hence predictive models using neuroimaging data are limited and such studies were usually in small scale (N<100). In this paper, we proposed an advanced machine learning (ML) pipeline for small training dataset with large number of features. We implemented multiple imputation for missing data and repeated K-fold cross validation (CV) to robustly estimate predictive performances. Different feature selection methods and stacking methods using 6 general ML models including random forest, gradient boosting decision tree, XGBoost, penalized logistic regression, support vector machine (SVM), and neural network were examined to evaluate the model performances. All predictive models were compared using model performance metrics such as accuracy, balanced accuracy, area under ROC curve (AUC), sensitivity and specificity. Our proposed ML pipeline was applied to a training dataset and obtained an accuracy and AUC above 0.80. But such high performance failed while applying our ML pipeline using an external validation dataset from the EMBARC study which is a multi-center study. We further examined the possible reasons especially the site heterogeneity issue.Electrophysiological correlates of symbolic numerical order processingClemens BrunnerPhilip SchadenbauerNele SchröderRoland H. GrabnerStephan E. Vogel10.1371/journal.pone.03012282024-03-21T14:00:00Z2024-03-21T14:00:00Z<p>by Clemens Brunner, Philip Schadenbauer, Nele Schröder, Roland H. Grabner, Stephan E. Vogel</p>
Determining if a sequence of numbers is ordered or not is one of the fundamental aspects of numerical processing linked to concurrent and future arithmetic skills. While some studies have explored the neural underpinnings of order processing using functional magnetic resonance imaging, our understanding of electrophysiological correlates is comparatively limited. To address this gap, we used a three-item symbolic numerical order verification task (with Arabic numerals from 1 to 9) to study event-related potentials (ERPs) in 73 adult participants in an exploratory approach. We presented three-item sequences and manipulated their order (ordered vs. unordered) as well as their inter-item numerical distance (one vs. two). Participants had to determine if a presented sequence was ordered or not. They also completed a speeded arithmetic fluency test, which measured their arithmetic skills. Our results revealed a significant mean amplitude difference in the grand average ERP waveform between ordered and unordered sequences in a time window of 500–750 ms at left anterior-frontal, left parietal, and central electrodes. We also identified distance-related amplitude differences for both ordered and unordered sequences. While unordered sequences showed an effect in the time window of 500–750 ms at electrode clusters around anterior-frontal and right-frontal regions, ordered sequences differed in an earlier time window (190–275 ms) in frontal and right parieto-occipital regions. Only the mean amplitude difference between ordered and unordered sequences showed an association with arithmetic fluency at the left anterior-frontal electrode. While the earlier time window for ordered sequences is consistent with a more automated and efficient processing of ordered sequential items, distance-related differences in unordered sequences occur later in time.Assessment of indoor radon distribution and seasonal variation within the Kpando Municipality of Volta Region, GhanaAnthony Selorm Kwesi AmableFrancis OtooPaul Kingsley Buah-BassuahAnthony Kwabena Twum10.1371/journal.pone.02990722024-02-27T14:00:00Z2024-02-27T14:00:00Z<p>by Anthony Selorm Kwesi Amable, Francis Otoo, Paul Kingsley Buah-Bassuah, Anthony Kwabena Twum</p>
This study uses CR-39 radon detectors to examine radon distributions, seasonal indoor radon variations, correction factors, and the influence of building materials and characteristics on indoor radon concentration in 120 dwellings. The study also determines the spatial distribution of radon levels using the ArcGIS geostatistical method. Radon detectors were exposed in bedrooms from April to July (R<sub>S</sub>), August to November (D<sub>S</sub>); December to March (H<sub>S</sub>), and January-December (Y<sub>S</sub>) from 2021 to 2022. The result for the radon levels during the weather seasons were; 32.3 to 190.1 Bqm<sup>-3</sup> (80.9 ± 3.2 Bq/m<sup>3</sup>) for (R<sub>S</sub>), 30.8 to 151.4 Bqm<sup>-3</sup> (68.5 ± 2.7 Bqm<sup>-3</sup>) for H<sub>S</sub> and 24.8 to 112.9 Bqm<sup>-3</sup>(61.7 ± 2.1 Bqm<sup>-3</sup>) for D<sub>S</sub>, and 25.2 to 145.2 Bq/m<sup>3</sup> (69.4 ± 2.7 Bqm<sup>-3</sup>). The arithmetic mean for April to July season was greater than August to November. The correction factors associated with this study ranged from 0.9 to 1.2. The annual effective dose (A<sub>E</sub>) associated with radon data was varied from 0.6 to 4.04 mSv/y (1.8 ± 0.1 mSv/y). The April to July period which was characterized by rains recorded the highest correlation coefficient and indoor radon concentration. Distribution and radon mapping revealed radon that the exposure to the occupant is non-uniformly spread across the studied dwellings. 15.4% of the studied data exceeded WHO reference values of 100 Bq/m<sup>3</sup>. The seasonal variation, dwelling age, and building materials were observed to have a substantial impact on the levels of radon concentration within the buildings.Exploring the potential impact of applying web-based training program on nurses’ knowledge, skills, and attitudes regarding evidence-based practice: A quasi-experimental studyRasha A. MohamedMuhanad AlhujailyFaransa A. AhmedWael G. NouhAbeer A. Almowafy10.1371/journal.pone.02970712024-02-08T14:00:00Z2024-02-08T14:00:00Z<p>by Rasha A. Mohamed, Muhanad Alhujaily, Faransa A. Ahmed, Wael G. Nouh, Abeer A. Almowafy</p>
Evidence-based practice (EBP) has gained significant importance in clinical practice worldwide, including in nursing. This study aimed to explore the potential impact of applying a web-based training program on nurses’ knowledge, skills, and attitudes regarding EBP. A quasi-experimental pretest-posttest research design was utilized with a purposive sample of 64 professional nurses who agreed to participate. The study took place in different hospitals and primary healthcare centers in the Bisha Governorate, Aseer region, Saudi Arabia. A four-week standardized web-based training program was implemented using an online learning approach. Nurses were provided with an online self-rated data collection tool through the Google Forms platform. The findings indicated a highly significant difference in the total knowledge and EBP skills mean scores of the post-intervention (53.08±15.9) and (66.03±8.95), respectively compared to pre-intervention (P<0.05). Additionally, there was marked improvement in the mean scores of the positive attitude of the training sessions post-intervention compared to pre-intervention. The program was also well-received by the nurses in terms of quality and usability. The program has the potential to enhance nurses’ knowledge, skills, and attitudes toward EBP. Therefore, healthcare organizations may consider adopting web-based training as a means of continuing professional education to promote EBP competencies among nurses.Numerical stability of DeepGOPlus inferenceInés Gonzalez PepeYohan ChatelainGregory KiarTristan Glatard10.1371/journal.pone.02967252024-01-29T14:00:00Z2024-01-29T14:00:00Z<p>by Inés Gonzalez Pepe, Yohan Chatelain, Gregory Kiar, Tristan Glatard</p>
Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many problems. Originally applied to computer vision tasks, CNNs work well with any data with a spatial relationship, besides images, and have been applied to different fields. However, recent works have highlighted numerical stability challenges in DNNs, which also relates to their known sensitivity to noise injection. These challenges can jeopardise their performance and reliability. This paper investigates DeepGOPlus, a CNN that predicts protein function. DeepGOPlus has achieved state-of-the-art performance and can successfully take advantage and annotate the abounding protein sequences emerging in proteomics. We determine the numerical stability of the model’s inference stage by quantifying the numerical uncertainty resulting from perturbations of the underlying floating-point data. In addition, we explore the opportunity to use reduced-precision floating point formats for DeepGOPlus inference, to reduce memory consumption and latency. This is achieved by instrumenting DeepGOPlus’ execution using Monte Carlo Arithmetic, a technique that experimentally quantifies floating point operation errors and VPREC, a tool that emulates results with customizable floating point precision formats. Focus is placed on the inference stage as it is the primary deliverable of the DeepGOPlus model, widely applicable across different environments. All in all, our results show that although the DeepGOPlus CNN is very stable numerically, it can only be selectively implemented with lower-precision floating-point formats. We conclude that predictions obtained from the pre-trained DeepGOPlus model are very reliable numerically, and use existing floating-point formats efficiently.Optimal controller design for reactor core power stabilization in a pressurized water reactor: Applications of gold rush algorithmH AbdelfattahM EsmailSaid A. kotbMohamed Metwally MahmoudHany S. HusseinDaniel Eutyche Mbadjoun WapetAhmed I. OmarAhmed M. Ewais10.1371/journal.pone.02969872024-01-26T14:00:00Z2024-01-26T14:00:00Z<p>by H Abdelfattah, M Esmail, Said A. kotb, Mohamed Metwally Mahmoud, Hany S. Hussein, Daniel Eutyche Mbadjoun Wapet, Ahmed I. Omar, Ahmed M. Ewais</p>
Nuclear energy (NE) is seen as a reliable choice for ensuring the security of the world’s energy supply, and it has only lately begun to be advocated as a strategy for reducing climate change in order to meet low-carbon energy transition goals. To achieve flexible operation across a wide operating range when it participates in peak regulation in the power systems, the pressurised water reactor (PWR) NE systems must overcome the nonlinearity problem induced by the substantial variation. In light of this viewpoint, the objective of this work is to evaluate the reactor core (main component) of the NE system via different recent optimization techniques. The PWR, which is the most common form, is the reactor under investigation. For controlling the movement of control rods that correspond with reactivity for power regulation the PWR, PID controller is employed. This study presents a dynamic model of the PWR, which includes the reactor core, the upper and lower plenums, and the piping that connects the reactor core to the steam alternator is analyzed and investigated. The PWR dynamic model is controlled by a PID controller optimized by the gold rush optimizer (GRO) built on the integration of the time-weighted square error performance indicator. Additionally, to exhibit the efficacy of the presented GRO, the dragonfly approach, Arithmetic algorithm, and planet optimization algorithm are used to adjust the PID controller parameters. Furthermore, a comparison among the optimized PID gains with the applied algorithms shows great accuracy, efficacy, and effectiveness of the proposed GRO. MATLAB\ Simulink program is used to model and simulate the system components and the applied algorithms. The simulation findings demonstrate that the suggested optimized PID control strategy has superior efficiency and resilience in terms of less overshoot and settling time.Economic policy uncertainty, intra-industry trade, and China’s mechanical and electrical product exportsDajun LiuXiugang ZhuHuiru Yu10.1371/journal.pone.02908662024-01-18T14:00:00Z2024-01-18T14:00:00Z<p>by Dajun Liu, Xiugang Zhu, Huiru Yu</p>
Economic policy uncertainty has had an important impact on trade and sustainable economic development. Especially in some specific industries, uncertainty has increased dramatically. The extant related literature mainly analyzes the nexus between uncertainty and trade across different industries and focuses less on a specific industry. Using Chinese customs data on HS 8-digit products over the period of 2000–2013, this paper first investigates the impact of both foreign economic policy uncertainty (EPU) and domestic intra-industry trade on China’s mechanical and electrical product exports to 23 trading partners and applies pooled OLS regressions to conduct an empirical study. This paper finds that EPU has a significant inhibition effect on mechanical and electrical product exports; conversely, intra-industry trade can both significantly promote exports and alleviate the inhibition effect of EPU. In addition, the export impact of EPU varied with different trade patterns. It can significantly inhibit processing exports, while it has no effect on ordinary exports. The results of this paper indicate that in the context of increasing uncertainty, our findings could have far-reaching policy implications for China to build a new development pattern of domestic and international dual circulation.Easier comparison of bets in evaluation does not reduce classical preference reversals: Evidence against a context-dependent explanationRaúl López-PérezEli Spiegelman10.1371/journal.pone.02920112024-01-03T14:00:00Z2024-01-03T14:00:00Z<p>by Raúl López-Pérez, Eli Spiegelman</p>
In preference reversals, subjects express different rankings over a set of alternatives depending on how preferences are elicited. In classical reversal tasks, for instance, subjects often select a safe bet over a risky one when given a choice between the two in a pair, but then assign a higher monetary evaluation to the risky bet. Motivated by a rich literature on context-dependent preferences, we conjecture that comparisons across bets in a pair can influence <i>both</i> Choice and Evaluation. Yet deciders are less likely to mentally compare the bets in the latter case, as bets are typically evaluated in isolation. This asymmetry between Choice and Evaluation is, we surmise, one cause of the reversals. If we further assume that memory decay affects mental comparisons in Evaluation, the account predicts order and timing effects on the reversal probability. We run several treatments designed to facilitate or hinder the retrieval from memory of the alternative bet during evaluation of a bet. However, the reversal rate does not vary across treatments in the predicted direction, and we find no systematic order or timing effects. We conclude that reversals are not influenced by the ease with which subjects recall the alternative bet during the evaluations, which suggests in turn that a relatively smaller frequency of comparisons across bets during the (typically isolated) evaluations is not a significant cause of reversals.Investigation of the neural correlation with task performance and its effect on cognitive load level classificationFarzana KhanamMohiuddin AhmadA. B. M. Aowlad Hossain10.1371/journal.pone.02915762023-12-21T14:00:00Z2023-12-21T14:00:00Z<p>by Farzana Khanam, Mohiuddin Ahmad, A. B. M. Aowlad Hossain</p>
Electroencephalogram (EEG)-based cognitive load assessment is now an important assignment in psychological research. This type of research work is conducted by providing some mental task to the participants and their responses are counted through their EEG signal. In general assumption, it is considered that during different tasks, the cognitive workload is increased. This paper has investigated this specific idea and showed that the conventional hypothesis is not correct always. This paper showed that cognitive load can be varied according to the performance of the participants. In this paper, EEG data of 36 participants are taken against their resting and task (mental arithmetic) conditions. The features of the signal were extracted using the empirical mode decomposition (EMD) method and classified using the support vector machine (SVM) model. Based on the classification accuracy, some hypotheses are built upon the impact of subjects’ performance on cognitive load. Based on some statistical consideration and graphical justification, it has been shown how the hypotheses are valid. This result will help to construct the machine learning-based model in predicting the cognitive load assessment more appropriately in a subject-independent approach.The establishment and application of quality gain-loss function when the loss of primary and cubic term is not ignored and the compensation quantity is constantBo WangXiaojuan LiJianyou ShiQikai LiXiangtian Nie10.1371/journal.pone.02959492023-12-18T14:00:00Z2023-12-18T14:00:00Z<p>by Bo Wang, Xiaojuan Li, Jianyou Shi, Qikai Li, Xiangtian Nie</p>
The traditional quality gain-loss function(QGLF) considers the case that the primary term loss cannot be ignored, does not consider the cubic term loss, but in practice the cubic term loss should not be ignored. In this paper, based on the existing QGLF model, the Taylor expansion is reserved to the third order, the determination of the quality loss coefficient is discussed and analyzed under the condition that the compensation quantity is constant, and the asymmetric cubic QGLF model is established, and uses an example of concrete mixture out of the machine slump during the dam concrete construction to analyze and discuss the relationship between the proposed model and the traditional quadratic QGLF, which verifies the rationality of the proposed model.Effects of a 3-factor field intervention on numerical and geometric knowledge in preschool childrenHernando Taborda-OsorioYenny Otálora10.1371/journal.pone.02909562023-11-16T14:00:00Z2023-11-16T14:00:00Z<p>by Hernando Taborda-Osorio, Yenny Otálora</p>
The main aim of this study was to develop and test the effects of a field math intervention program on both number and geometry knowledge. The intervention was developed based on three basic skills previously associated with mathematical performance: symbolic number knowledge, mapping processes and spatial reasoning. The participants were 117 preschoolers from six schools in Cali and Bogotá. The children were assigned to an intervention group (N = 55) or a control group (N = 62). The intervention lasted 11 weeks with 3 sessions per week where the children participated in different game-based activities. Tests of numerical and geometric knowledge were administered before and after the intervention. The effects of the intervention were tested twice, immediately after the program ended and six months later. The results show that the children in the intervention group improved more than the control group in both number and geometry. The second posttest revealed a significant intervention effect for geometry, but not for numerical knowledge. The implications of these mixed patterns of results are discussed in the paper.Need for cognition moderates the relief of avoiding cognitive effortDavide GhezaWouter KoolGilles Pourtois10.1371/journal.pone.02879542023-11-16T14:00:00Z2023-11-16T14:00:00Z<p>by Davide Gheza, Wouter Kool, Gilles Pourtois</p>
When making decisions, humans aim to maximize rewards while minimizing costs. The exertion of mental or physical effort has been proposed to be one those costs, translating into avoidance of behaviors carrying effort demands. This motivational framework also predicts that people should experience positive affect when anticipating demand that is subsequently avoided (i.e., a “relief effect”), but evidence for this prediction is scarce. Here, we follow up on a previous study [1] that provided some initial evidence that people more positively evaluated outcomes if it meant they could avoid performing an additional demanding task. However, the results from this study did not provide conclusive evidence that this effect was driven by effort avoidance. Here, we report two experiments that are able to do this. Participants performed a gambling task, and if they did not receive reward they would have to perform an orthogonal effort task. Prior to the gamble, a cue indicated whether this effort task would be easy or hard. We probed hedonic responses to the reward-related feedback, as well as after the subsequent effort task feedback. Participants reported lower hedonic responses for no-reward outcomes when high vs. low effort was anticipated (and later exerted). They also reported higher hedonic responses for reward outcomes when high vs. low effort was anticipated (and avoided). Importantly, this relief effect was smaller in participants with high need for cognition. These results suggest that avoidance of high effort tasks is rewarding, but that the size off this effect depends on the individual disposition to engage with and expend cognitive effort. They also raise the important question of whether this disposition alters the cost of effort per se, or rather offset this cost during cost-benefit analyses.Higher level domain specific skills in mathematics; The relationship between algebra, geometry, executive function skills and mathematics achievementJayne SpillerSarah ClaytonLucy CraggSamantha JohnsonVictoria SimmsCamilla Gilmore10.1371/journal.pone.02917962023-11-06T14:00:00Z2023-11-06T14:00:00Z<p>by Jayne Spiller, Sarah Clayton, Lucy Cragg, Samantha Johnson, Victoria Simms, Camilla Gilmore</p>
Algebra and geometry are important components of mathematics that are often considered gatekeepers for future success. However, most studies that have researched the cognitive skills required for success in mathematics have only considered the domain of arithmetic. We extended models of mathematical skills to consider how executive function skills play both a direct role in secondary-school-level mathematical achievement as well as an indirect role via algebra and geometry, alongside arithmetic. We found that verbal and visuospatial working memory were indirectly associated with mathematical achievement via number fact knowledge, calculation skills, algebra and geometry. Inhibition was also indirectly associated with mathematical achievement via number fact knowledge and calculation skills. These findings highlight that there are multiple mechanisms by which executive function skills may be involved in mathematics outcomes. Therefore, using specific measures of mathematical processes as well as context-rich assessments of mathematical achievement is important to understand these mechanisms.High-speed and energy-efficient asynchronous carry look-ahead adderPadmanabhan BalasubramanianWeichen Liu10.1371/journal.pone.02895692023-10-05T14:00:00Z2023-10-05T14:00:00Z<p>by Padmanabhan Balasubramanian, Weichen Liu</p>
Addition is a fundamental computer arithmetic operation that is widely performed in microprocessors, digital signal processors, and application-specific processors. The design of a high-speed and energy-efficient adder is thus useful and important for practical applications. In this context, this paper presents the designs of novel asynchronous carry look-ahead adders (CLAs) viz. a standard CLA (SCLA) and a block CLA (BCLA). The proposed CLAs are monotonic, dual-rail encoded, and are realized according to return-to-zero handshake (RZH) and return-to-one handshake (ROH) protocols using a 28-nm CMOS process technology. The proposed BCLA has a slight edge over the proposed SCLA, and the proposed BCLA reports the following optimizations in design metrics such as cycle time (delay), area, and power compared to a recently presented state-of-the-art asynchronous CLA for a 32-bit addition: (i) 32.6% reduction in cycle time, 29% reduction in area, 4.3% reduction in power, and 35.5% reduction in energy for RZH, and (ii) 31.4% reduction in cycle time, 28.9% reduction in area, 4.4% reduction in power, and 34.4% reduction in energy for ROH. Also, the proposed BCLA reports reductions in cycle time and power/energy compared to many other asynchronous adders.The importance of spatial language for early numerical development in preschool: Going beyond verbal number skillsCarrie GeorgesVéronique CornuChristine Schiltz10.1371/journal.pone.02922912023-09-29T14:00:00Z2023-09-29T14:00:00Z<p>by Carrie Georges, Véronique Cornu, Christine Schiltz</p>
Recent evidence suggests that spatial language in preschool positively affects the development of verbal number skills, as indexed by aggregated performances on counting and number naming tasks. We firstly aimed to specify whether spatial language (the knowledge of locative prepositions) significantly relates to both of these measures. In addition, we assessed whether the predictive value of spatial language extends beyond verbal number skills to numerical subdomains without explicit verbal component, such as number writing, symbolic magnitude classifications, ordinal judgments and numerosity comparisons. To determine the unique contributions of spatial language to these numerical skills, we controlled in our regression analyses for intrinsic and extrinsic spatial abilities, phonological awareness as well as age, socioeconomic status and home language. With respect to verbal number skills, it appeared that spatial language uniquely predicted forward and backward counting but not number naming, which was significantly affected only by phonological awareness. Regarding numerical tasks that do not contain explicit verbal components, spatial language did not relate to number writing or numerosity comparisons. Conversely, it explained unique variance in symbolic magnitude classifications and was the only predictor of ordinal judgments. These findings thus highlight the importance of spatial language for early numerical development beyond verbal number skills and suggest that the knowledge of spatial terms is especially relevant for processing cardinal and ordinal relations between symbolic numbers. Promoting spatial language in preschool might thus be an interesting avenue for fostering the acquisition of these symbolic numerical skills prior to formal schooling.