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Climate models predict substantial summer precipitation reductions in Europe and the Mediterranean region in the twenty-first century, but the extent to which these models correctly represent the mechanisms of summertime precipitation in this region is uncertain. Here an analysis is conducted to compare the observed and simulated impacts of the dominant large-scale driver of summer rainfall variability in Europe and the Mediterranean, the summer North Atlantic Oscillation (SNAO). The SNAO is defined as the leading mode of July–August sea level pressure variability in the North Atlantic sector. Although the SNAO is weaker and confined to northern latitudes compared to its winter counterpart, with a southern lobe located over the UK, it significantly affects precipitation in the Mediterranean, particularly Italy and the Balkans (correlations of up to 0.6). During high SNAO summers, when strong anticyclonic conditions and suppressed precipitation prevail over the UK, the Mediterranean region instead is anomalously wet. This enhanced precipitation is related to the presence of a strong upper-level trough over the Balkans—part of a hemispheric pattern of anomalies that develops in association with the SNAO—that leads to mid-level cooling and increased potential instability. Neither this downstream extension nor the surface influence of the SNAO is captured in the two CMIP3 models examined (HadCM3 and GFDL-CM2.1), with weak or non-existent correlations between the SNAO and Mediterranean precipitation. Because these models also predict a strong upward SNAO trend in the future, the error in their representation of the SNAO surface signature impacts the projected precipitation trends. In particular, the attendant increase in precipitation that, based on observations, should occur in the Mediterranean and offset some of the non-SNAO related drying does not occur. Furthermore, the fact that neither the observed SNAO nor summer precipitation in Europe/Mediterranean region exhibits any significant trend so far (for either the full century or the recent half of the record) does not increase our confidence in these model projections.

From early 2003 to mid-2013, the total mass of ice in Greenland declined at a progressively increasing rate. In mid-2013, an abrupt reversal occurred, and very little net ice loss occurred in the next 12–18 months. Gravity Recovery and Climate Experiment (GRACE) and global positioning system (GPS) observations reveal that the spatial patterns of the sustained acceleration and the abrupt deceleration in mass loss are similar. The strongest accelerations tracked the phase of the North Atlantic Oscillation (NAO). The negative phase of the NAO enhances summertime warming and insolation while reducing snowfall, especially in west Greenland, driving surface mass balance (SMB) more negative, as illustrated using the regional climate model MAR. The spatial pattern of accelerating mass changes reflects the geography of NAO-driven shifts in atmospheric forcing and the ice sheet’s sensitivity to that forcing. We infer that southwest Greenland will become a major future contributor to sea level rise.

Thua-nao, or Thai fermented soybeans, is a traditional Lanna fermented food in Northern Thailand. It is produced by using a specific bacterial species called Bacillus subtilis var. Thua-nao. We investigated the antioxidant activity and cytotoxic effect of isoflavones from Thua-nao. The phenolic compound contents and total flavonoid contents were determined by spectrophotometry. The antioxidant activity was examined using the ABTS, FRAP, and DPPH assays. The isoflavone contents and phenolic compositions were examined by the high-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS) techniques. The ability of isoflavones to inhibit human cancer cell growth was assessed by the MTT assay. The total phenolic content, total flavonoid content, and antioxidant activities of the isoflavones were 49.00 ± 0.51 mg GAE/g of dry extract (DE), 10.76 ± 0.82 mg QE/g of DE, 61.03 ± 0.97 µmol Trolox/g of DE, 66.54 ± 3.97 µM FeSO4/g of DE, and 22.47 ± 1.92% of DPPH inhibition, respectively. Additionally, the isoflavone extracts from Thua-nao had high isoflavone contents and polyphenolic compound compositions, especially daidzein and genistein. The isoflavone demonstrated a weak inhibition of MCF-7 and HEK293 cancer cell growth. It has a high antioxidant component, which is beneficial and can be developed for new therapeutic uses. However, further studies on the benefits of Thua-nao should be performed for realizing better and more effective uses soon.

In recent years, robotics has emerged as a powerful and appealing instrument for autistic children, who struggle with communication. This study aims to analyze the improvement in social communication and interaction derived from the intervention with the NAO robot. A quantitative methodological approach has been adopted together with a quasi-experimental design based on a paired pre-test–post-test model. The participants were sixteen autistic children at the curriculum competence level of kindergarten (three-year-olds) and second grade and classified as ASD levels 1 and 3. The intervention consisted of eleven sessions, in which activities such as identifying the cause of a mood in a social context were proposed. The main instruments used for data collection were the modified checklist from the Early Start Denver Model (ESDM) and a field journal. The results showed substantial improvements in expressive communication, joint attention behavior, and social skills during interactions with both adults and peers. Cohen's d values, close to or greater than 0.8, support these differences, indicating that the improvements observed are both visible and meaningful. Therefore, it can be concluded that the NAO robot may be a valuable tool for developing expressive communication, joint attention behavior, and social skills in interactions with both adults and peers. Consequently, it is recommended to progressively incorporate it into school environments to address these dimensions where autistic children require support. For future studies, it would be advisable to increase the sample size to potentially achieve more substantial improvements in other dimensions where no significant improvements were found.

How to predict the year‐to‐year variation of the east Asian summer monsoon (EASM) is one of the most challenging and important tasks in climate prediction. It has been recognized that the EASM variations are intimately but not exclusively linked to the development and decay of El Niño or La Niña. Here we present observed evidence and numerical experiment results to show that anomalous North Atlantic Oscillation (NAO) in spring (April–May) can induce a tripole sea surface temperature pattern in the North Atlantic that persists into ensuing summer and excite downstream development of subpolar teleconnections across the northern Eurasia, which raises (or lowers) the pressure over the Ural Mountain and the Okhotsk Sea. The latter strengthens (or weakens) the east Asian subtropical front (Meiyu‐Baiu‐Changma), leading to a strong (or weak) EASM. An empirical model is established to predict the EASM strength by combination of the El Niño–Southern Oscillation (ENSO) and spring NAO. Hindcast is performed for the 1979–2006 period, which shows a hindcast prediction skill that is comparable to the 14 state‐of‐the‐art multimodel ensemble hindcast. Since all these predictors can be readily monitored in real time, this empirical model provides a real time forecast tool.

There is evidence that the observed changes in winter North Atlantic Oscillation (NAO) drive a significant portion of Atlantic Multi Decadal Variability (AMV). However, whether the observed decadal NAO changes can be forced by the ocean is controversial. There is also evidence that artificially imposed multi-decadal stratospheric changes can impact the troposphere in winter. But the origins of such stratospheric changes are still unclear, especially in early to mid winter, where the radiative ozone-impact is negligible. Here we show, through observational analysis and atmospheric model experiments, that large-scale Atlantic warming associated with AMV drives high-latitude precursory stratospheric warming in early to mid winter that propagates downward resulting in a negative tropospheric NAO in late winter. The mechanism involves stratosphere/troposphere dynamical coupling, and can be simulated to a large extent, but only with a stratosphere resolving model (i.e., high-top). Further analysis shows that this precursory stratospheric response can be explained by the shift of the daily extremes toward more major stratospheric warming events. This shift cannot be simulated with the atmospheric (low-top) model configuration that poorly resolves the stratosphere and implements a sponge layer in upper model levels. While the potential role of the stratosphere in multi-decadal NAO and Atlantic meridional overturning circulation changes has been recognised, our results show that the stratosphere is an essential element of extra-tropical atmospheric response to ocean variability. Our findings suggest that the use of stratosphere resolving models should improve the simulation, prediction, and projection of extra-tropical climate, and lead to a better understanding of natural and anthropogenic climate change.

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change 1 – 3 . Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain 4 . This leads to low confidence in regional projections, especially for precipitation, over the coming decades 5 , 6 . The chaotic nature of the climate system 7 – 9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models 10 , and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade. Current models are too noisy to predict climate usefully on decadal timescales, but two-stage post-processing of model outputs greatly improves predictions of decadal variations in North Atlantic winter climate.

The interaction between humans and an NAO robot using deep convolutional neural networks (CNN) is presented in this paper based on an innovative end-to-end pipeline method that applies two optimized CNNs, one for face recognition (FR) and another one for the facial expression recognition (FER) in order to obtain real-time inference speed for the entire process. Two different models for FR are considered, one known to be very accurate, but has low inference speed (faster region-based convolutional neural network), and one that is not as accurate but has high inference speed (single shot detector convolutional neural network). For emotion recognition transfer learning and fine-tuning of three CNN models (VGG, Inception V3 and ResNet) has been used. The overall results show that single shot detector convolutional neural network (SSD CNN) and faster region-based convolutional neural network (Faster R-CNN) models for face detection share almost the same accuracy: 97.8% for Faster R-CNN on PASCAL visual object classes (PASCAL VOCs) evaluation metrics and 97.42% for SSD Inception. In terms of FER, ResNet obtained the highest training accuracy (90.14%), while the visual geometry group (VGG) network had 87% accuracy and Inception V3 reached 81%. The results show improvements over 10% when using two serialized CNN, instead of using only the FER CNN, while the recent optimization model, called rectified adaptive moment optimization (RAdam), lead to a better generalization and accuracy improvement of 3%-4% on each emotion recognition CNN.

Two intense heatwaves of July and early August 2018 are found to be associated with a European blocking (EB) event accompanied by a series of consecutive positive North Atlantic Oscillation (NAO+) events. Further analyses show that the collaborative role of an EB event and its upstream NAO+ pattern could increase the frequency, persistence, magnitude and scale of heatwaves over Europe. Compared with NAO+-unrelated EB events, NAO+-related EB events are less movable (quasi-stationary) and more persistent over Europe, which could contribute to an increase in the intensity and persistence of heatwaves. In addition, the blocking high of this type has a northeast-southwest orientation with stronger warm airflow and less precipitation in northern and western Europe, where large scopes of higher temperatures tend to occur. In contrast, NAO+-unrelated EB events without orientation correspond to a trough in the south, which results in increased precipitation and cold air in the southern part of Europe, and thus high temperatures contract to the northern part of Europe. Moreover, considering that the NAO+ pattern leads the formation of an EB event, the NAO+ pattern might serve as a potential predictor for European heatwaves. Our conclusions are strongly supported by the analysis of CMIP6 historical simulations which also capture the differences of high temperatures and atmospheric circulations between NAO+-related EB events and NAO+-unrelated EB events.

A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications.