Calculation of Intake Oxygen Concentration through Intake CO2 Measurement and Evaluation of Its Effect on Nitrogen Oxide Prediction Accuracy in a Heavy-Duty Diesel Engine

A new procedure, based on measurement of intake CO2 concentration and ambient humidity was developed and assessed in this study for different diesel engines in order to evaluate the oxygen concentration in the intake manifold. Steady-state and transient datasets were used for this purpose. The method is very fast to implement since it does not require any tuning procedure and it involves just one engine-related input quantity. Moreover, its accuracy is very high since it was found that the absolute error between the measured and predicted intake O2 levels is in the ±0.15% range. The method was applied to verify the performance of a previously developed NOx model under transient operating conditions. This model had previously been adopted by the authors during the IMPERIUM H2020 EU project to set up a model-based controller for a heavy-duty diesel engine. The performance of the NOx model was evaluated considering two cases in which the intake O2 concentration is either derived from engine-control unit sub-models or from the newly developed method. It was found that a significant improvement in NOx model accuracy is obtained in the latter case, and this allowed the previously developed NOx model to be further validated under transient operating conditions.

A neural network approach based on more input neurons to predict nuclear mass

The study of nuclear mass is very important, and the neural network(NN) approach can be used to improve the prediction of nuclear mass for various models. Considering the number of valence nucleons of protons and neutrons separately in the input quantity of the NN model, the root-mean-square deviation of binding energy between data from AME2016 and liquid drop model calculations for 2314 nuclei was reduced from 2.385 MeV to 0.203 MeV. In addition, some defects in the Weizs\"{a}cker-Skyrme(WS)-type model were repaired, which well reproduced the two neutron separation energy of the nucleus synthesized recently by RIKEN RI Beam Factory [Phys. Rev. Lett 125 (2020) 122501]. The masses of some of the new nucleus appearing in the latest atomic mass evaluation(AME2020) are also well reproduced. However, the results of neural network methods for predicting the description of regions far from known atomic nuclei need to be further improved The study shows that such a statistical model can be a possible tool for searching in systematic of nuclei beyond existing experimental data.

Input Quantity Decision and Coordination Mechanism with Two Production Chances and Yield Uncertainty

Conducting a second production run can improve the company’s capability of meeting the market demand. Few works examine optimal input quantity decisions under the mode with two production chances considering demand and yield uncertainty. We propose a vendor-managed inventory (VMI) supply chain with one supplier and one retailer. The supplier has two production chances and faces yield uncertainty in each production run. It is necessary for the supplier to make trade-offs between the cost and benefit of the second production run, then decide whether to conduct the second production run. We investigate the supplier’s optimal input quantity decision in each production run and obtain the supply chain members’ expected profits. As a comparison, the mode with one production chance is also developed. We find that two production chances can help improve the performance of the supply chain under yield uncertainty. A revenue-sharing contract is introduced to coordinate the supply chain with two production chances, and efficient profit allocation is achieved through adjusting the revenue-sharing ratio and the wholesale price. An extension is conducted for a sensitivity analysis of unit punishment cost on the supplier’s input quantity decisions.

The Design of Directions and Shot Sound Distance Using Fuzzy Logic Method Based on Raspberry Pi

- The rise of a shooting incident that occurred in the border areas of the Republic of Indonesia is a big loss for the state in terms of personnel. Technological developments can be used as an alternative in the military world to help the role of soldiers so as to reduce personnel losses. This study aims to create a system for detecting the direction and distance of gunshots. This study uses an experimental method. This gunshot detection system also applies the Fuzzy Logic Method which is applied to the Raspberry Pi 4 and Microphone Max 4466 which is expected to detect the direction and distance of gunshots. This Fuzzy Logic method is used as an inference system or decision maker according to the input given. Fuzzy Logic broadly consists of fuzzification, rule base, and defuzzification. Fuzzification is useful for input normalization, so that the input quantity is in accordance with the fuzzy magnitude, namely the value in the range 0 to 1. After that, enter the rule base where in this step, the input set is compared with the rules or provisions of sound decibels so that it can be classified whether the distance and the direction of the captured sound is in the data range that has been programmed, in this step the signal is analyzed how much decibel sound SS2-V1 is by the MAX 4466 sensor. The conclusion is done by defuzzification, so the final result is that the closest distance to a gunshot at 1 meter is 250 Decibels.

Quality of L2 Input and Cognitive Skills Predict L2 Grammar Comprehension in Instructed SLA Independently

Input is considered one of the most important factors in the acquisition of lexical and grammatical skills. Input has been found to interact with other factors, such as learner cognitive skills and the circumstances where language is heard. Language learning itself has sometimes been found to enhance cognitive skills. Indeed, intensive contact with another language has been found to sometimes boost cognitive skills, even in intensive instructed settings, such as immersion programs (bilingual advantage hypothesis). In this paper, we report a cross-sectional study to assess grammar learning of 79 fourth grade German students learning L2 English in two immersion schools. Verbal teacher input was assessed using the Teacher Input Observation Scheme (TIOS, Items 14–25), and the learners’ L2 grammar comprehension was tested with the ELIAS Grammar Test II. Cognitive skills, including phonological awareness, working memory, and non-verbal intelligence, were determined using standardized assessment procedures. The results show that verbal input quantity and quality correlated significantly with the learners’ L2 grammar comprehension. None of the cognitive skills moderated the effect of input on grammar comprehension but all predicted it independently. The combination of L2 input and phonological awareness was found to be the most robust predictor of L2 grammar comprehension.

THE EFFECTS OF TALKER VARIABILITY AND FREQUENCY OF EXPOSURE ON THE ACQUISITION OF SPOKEN WORD KNOWLEDGE

Eighty Japanese learners of English as a foreign language encountered 40 target words in one of four experimental conditions (three encounters, six encounters, three encounters with talker variability, and six encounters with talker variability). A picture-naming test was conducted three times (pretest, immediate posttest, and delayed posttest) and elicited speech samples were scored in terms of form-meaning connection (spoken form recall) and word stress accuracy (stress placement accuracy and vowel duration ratio). Results suggested that frequency of exposure consistently promoted the recall of spoken forms, whereas talker variability was more closely related to the enhancement of word stress accuracy. These findings shed light on how input quantity (frequency) and quality (variability) affect different stages of lexical development and provide implications for vocabulary teaching.

Change the Things You Can: Modifiable Parent Characteristics Predict High-Quality Early Language Interaction Within Socioeconomic Status

Purpose Understanding variability sources in early language interaction is critical to identifying children whose development is at risk and designing interventions. Variability across socioeconomic status (SES) groups has been extensively explored. However, SES is a limited individual clinical indicator. For example, it is not generally directly modifiable. The purpose of this study was to examine if child language ability, input quantity and quality, and dyadic interaction were associated with modifiable caregiver characteristics—self-efficacy and developmental knowledge. Method We conducted secondary analyses using the baseline data ( n = 41 dyads enrolled, n = 30 analyzed) from a longitudinal study. Mothers and children (1;0–2;3 [years;months]) in low-income households completed demographic questionnaires, self-efficacy and developmental knowledge measures, child language assessments, and interaction samples. We used linear regression models to examine the relationship between self-efficacy, developmental knowledge, and outcomes. Results Child receptive and expressive language scores were significantly associated with mothers' self-efficacy, knowledge, and Efficacy × Knowledge interaction. Specifically, maternal self-efficacy was positively associated with child language only in the context of high developmental knowledge. Neither self-efficacy nor developmental knowledge was significantly associated with the number of total or different words mothers produced. However, self-efficacy was significantly and positively associated with the rate of child-initiated conversational turns per minute, controlling for the number of child utterances. Mothers with higher self-efficacy responded more readily to their children than those with lower self-efficacy. Conclusions Child language ability and interaction quality vary based on modifiable parent characteristics. Modifiable individual characteristics should be considered in early language interaction within and across SES groups.

Towards improved N2O budgets estimation from 10 site-years measurement and analysis of key drivers

<p>Agriculture represents 14% of global anthropogenic greenhous gases (GHG) emissions, 46% of this amount being due to N<sub>2</sub>O emissions from soils (UNEP, 2012). N<sub>2</sub>O is a powerful GHG (IPCC, 2013) and its emissions from agricultural soils are related to physical-chemical parameters which depend on climate (temperature, rain…), soil properties (Robertson et al., 1989) and farming practices (irrigation, tillage, fertilization…) (Tellez-Rio et al., 2015). The IPCC Tier 1 emission factor remains widely used to estimate annual N<sub>2</sub>O budgets from agricultural soils by taking into account the annual amount of N input only. However, not taking into account the environmental controlling factors may introduce high uncertainty in N<sub>2</sub>O budget estimation. Our study aims at highlighting the key drivers of N<sub>2</sub>O emissions from two agricultural sites in the South West of France and at proposing an improved, simple and accessible methodology to estimate N<sub>2</sub>O budget at crop plot and seasonal scale. For this purpose, we benefited from a unique long time series of daily N<sub>2</sub>O fluxes (from 2011 to 2016) measured with 6 closed automated chambers on two ICOS sites with contrasted agricultural management (FR-Lam and FR-Aur).</p><p>N<sub>2</sub>O annual budget vary from 1.04 to 7.96 kgN ha<sup>-1</sup> yr<sup>-1 </sup>for winter wheat and maize crop, respectively. The effects of fertilization, rain and irrigation, plant development, spring mineralization and deep tillage on N<sub>2</sub>O emissions were investigated. Significant correlations between rain combined with fertilization and plant development, deep tillage or spring mineralisation was found with R² of 0.91, 0.99 and 0.85, respectively.  We took advantage of these results to develop an empirical model, including N input quantity, residual N, leaf area index and water input in order to estimate seasonal and annual N<sub>2</sub>O budget. At the seasonal scale, the model output matched well with the observed budget, with a R² and a RMSE of 0.87 and 0.33 kgN ha<sup>-1</sup> at FR-Lam and of 0.92 and 0.12 kgN ha<sup>-1</sup> at FR-Aur, respectively.  It also gave good statistical scores at the crop year scale with a R² of 0.96 and a low RMSE of 0.43 kgN ha<sup>-1</sup> when binding data from both sites. Using the IPCC Tiers 1 methodology gave lower and more scattered results with a R² of 0.46 and a RMSE of 1.46 kgN ha<sup>-1</sup>. For sites where N<sub>2</sub>O fluxes are not monitored,  that new methodology may be an alternative and a more precise methodology than the IPCC Tiers 1 approach. It has also the advantage to require only few and accessible input variables.</p><p> </p><p>REFERENCES</p><p>IPCC, 2013. Climate Change 2013: The Physical Science Basis. Cambridge University Press, Cambridge.</p><p>Robertson et al., 1989. Aerobic denitrification in various heterotrophic nitrifiers. Antonie van Leeuwenhock., 56, 289-299.</p><p>Tellez-Rio et al., 2015. N2O and CH4 Emissions from a Fallow–wheat Rotation with Low N Input in Conservation and Conventional Tillage under a Mediterranean Agroecosystem. Sci. Total Environ., 508, 85–94.</p><p>UNEP, 2012. Growing greenhouse gas emissions due to meat production.</p>

Multilingual toddlers’ vocabulary development in two languages: Comparing bilinguals and trilinguals

Many children grow up hearing multiple languages, learning words in each. How does the number of languages being learned affect multilinguals’ vocabulary development? In a pre-registered study, we compared productive vocabularies of bilingual (n = 170) and trilingual (n = 20) toddlers aged 17–33 months growing up in a bilingual community where both French and English are spoken. We hypothesized that because trilinguals have reduced input in French and English due to time spent hearing their third language, they would have smaller French–English vocabulary sizes than bilinguals. Trilinguals produced on average 2/3 of the number of words in these languages that bilinguals did: however, this difference was not statistically robust due to large levels of variability. Follow-up analyses did, however, indicate a relationship between input quantity and vocabulary size. Our results indicate that similar factors contribute to vocabulary development across toddlers regardless of the number of languages being acquired.

A Four-Year Longitudinal Comparative Study on the Lexicon Development of Russian and Turkish Heritage Speakers in Germany

Russian and Turkish are the most frequently spoken and intensively investigated heritage languages in Germany, but contrastive research on their development in early childhood is still missing. This longitudinal study compares the trajectories of expressive lexicon development in Russian (n = 70) and Turkish (n = 79) heritage speakers and identifies predictors for their lexicon size at preschool age. Heritage lexicon size was tested with two comparable tests assessing the expressive lexicon at four test points between the mean ages of 3.3 (range: 25–49 months) and 5.6 (range: 54–78 months) years. The influence of language-related factors, such as input quantity, parents’ heritage language proficiency and age of onset (AoO) of German, and other potential predictors, i.e., intelligence and socio-economic status, is evaluated. Results show that the Turkish group’s abilities grow slower but are similar at the last test point. Common predictors for lexicon size are input quantity from siblings and AoO. Group-specific influences are parental input quantity in the Russian group and siblings’ proficiency in the Turkish group. Our findings emphasize the interplay of input quantity and society language AoO for heritage lexicon development. The relevance of our results for the usage-based theory of language acquisition is discussed.