Evaluating the effects of the CERES-Rice model to simulate upland rice (Oryza sativa L.) yield under different plant density and nitrogen management strategies in Fogera Plain, Northwest Ethiopia.

This study assessed the optimal nitrogen (N) fertilizer rate and planting density for the well-adapted upland rice cultivar NERICA_4 on the Fogera Plain. The primary objective was to evaluate the effects of varied planting densities and N-fertilizer rates on upland rice yield and other agronomic parameters. A two-year field study (2020 and 2021) was conducted at the Fogera Rice Research Field Station, testing nine plant densities (75, 87, and 98; 72, 82, and 91; 70, 79, and 89 plants per m2 and two N rates (115 and 138 kg N ha-1). The Crop Simulation Model Crop Environment Resource Synthesis (CSM-CERES-Rice) within the Decision Support System for Agrotechnology Transfer (DSSAT) framework was calibrated and validated using site-specific weather, soil, crop, and agronomic management data from the experiment. Results on the subsequent RMSE, RMSEn, and d index values during the calibration phase were 0.074 t ha-1, 1.82 %, and 0.86 of grain yield; 0.307 t ha-1, 3.36 %, and 0.87 of by-product yield; 0.489 t ha-1, 3.74 %, and 0.79 of top dry biomass yield; and 0.28, 8.24 %, and 0.63 of leaf area index values, respectively. Whereas results on the corresponding RMSE, RMSEn, and d index values during the evaluation phase were: 0.58 t ha-1, 1.33 %, and 0.90 of grain yield; 0.69 t ha-1, 0.58 %, and 0.99 of by-product yield; 0.678 t ha-1, 4.36 %, and 0.67 of top dry biomass yield; and 0.75, 13.92 %, and 0.74 of leaf area index, respectively. The findings of the long-term simulation showed that a 23 % increase in grain yield was achieved with 138 kg N ha-1 and 87 plants per m2 of planting density, as compared to 115 kg N ha-1 and 75 plants per m2 of plant density. The recommended optimum plant density and N fertilizer rate were 138 kg N ha-1 with PD2 of plant density for upland rice production in the Fogera Plain.

Evaluation of enhanced external counterpulsation for diabetic foot based on a patient-specific 0D-1D cardiovascular system model.

BACKGROUND AND OBJECTIVE: Diabetic foot (DF) complications often lead to severe vascular issues. This study investigated the effectiveness of enhanced external counterpulsation (EECP) and its derived innovative compression strategies in addressing poor perfusion in DF. Although developing non-invasive and efficient treatment methods for DF is critical, the hemodynamic alterations during EECP remain underexplored despite promising outcomes in microcirculation. This research sought to address this gap by developing a patient-specific 0D-1D model based on clinical ultrasound data to identify potentially superior compression strategies that could substantially enhance blood flow in patients with DF complications. METHODS: Data were gathered from 10 patients with DF utilizing ultrasound for blood flow rate and computed tomography angiography (CTA) to identify lower limb conditions. Clinical measurements during standard EECP, with varying cuff pressures, facilitated the creation of a patient-specific 0D-1D model through a two-step parameter estimation process. The accuracy of this model was verified via comparison with the clinical measurements. Four compression strategies were proposed and rigorously evaluated using this model: EECP-Simp-I (removing hip cuffs), EECP-Simp-II (further removing the cuffs around the lower leg), EECP-Impr-I (removing all cuffs around the affected side), and EECP-Impr-II (building a loop circulation from the healthy side to the affected side). RESULTS: The predicted results under the rest and standard EECP states were generally closely aligned with clinical measurements. The patient-specific 0D-1D model demonstrated that EECP-Simp-I and EECP-Impr-I contributed similar enhancement to perfusion in the dorsal artery (DA) and were comparable to standard EECP, while EECP-Simp-II had the least effect and EECP-Impr-II displayed the most significant enhancement. Pressure at the aortic root (AO) remained consistent across strategies. CONCLUSIONS: EECP-Simp-I is recommended for patients with DF, emphasizing device simplification. However, EECP-Simp-II is discouraged as it significantly diminished blood perfusion in this study, except in cases of limb fragility. EECP-Impr-II showed superior enhancement of blood perfusion in DA to all other strategies but required a more complex EECP device. Despite increased AO pressure in all the proposed compression strategies, safety could be guaranteed as the pressue remained within a safe range.

Information System Model and Key Technologies of High-Definition Maps in Autonomous Driving Scenarios.

Background: High-definition maps can provide necessary prior data for autonomous driving, as well as the corresponding beyond-line-of-sight perception, verification and positioning, dynamic planning, and decision control. It is a necessary element to achieve L4/L5 unmanned driving at the current stage. However, currently, high-definition maps still have problems such as a large amount of data, a lot of data redundancy, and weak data correlation, which make autonomous driving fall into difficulties such as high data query difficulty and low timeliness. In order to optimize the data quality of high-definition maps, enhance the degree of data correlation, and ensure that they better assist vehicles in safe driving and efficient passage in the autonomous driving scenario, it is necessary to clarify the information system thinking of high-definition maps, propose a complete and accurate model, determine the content and functions of each level of the model, and continuously improve the information system model. Objective: The study aimed to put forward a complete and accurate high-definition map information system model and elaborate in detail the content and functions of each component in the data logic structure of the system model. Methods: Through research methods such as the modeling method and literature research method, we studied the high-definition map information system model in the autonomous driving scenario and explored the key technologies therein. Results: We put forward a four-layer integrated high-definition map information system model, elaborated in detail the content and functions of each component (map, road, vehicle, and user) in the data logic structure of the model, and also elaborated on the mechanism of the combined information of each level of the model to provide services in perception, positioning, decision making, and control for autonomous driving vehicles. This article also discussed two key technologies that can support autonomous driving vehicles to complete path planning, navigation decision making, and vehicle control in different autonomous driving scenarios. Conclusions: The four-layer integrated high-definition map information model proposed by this research institute has certain application feasibility and can provide references for the standardized production of high-definition maps, the unification of information interaction relationships, and the standardization of map data associations.

Assessing Transition Pathways of Hydrogen Production in China with a Probabilistic Framework.

The transition of China's hydrogen production system to meeting carbon neutrality is considerably uncertain. This study uses a probabilistic framework to assess the transition pathways of hydrogen production in China to meet the goal of carbon neutrality and reveals the key technology selection mechanism. Three strategies for hydrogen production transition were considered: delayed, orderly, and radical, corresponding to the green hydrogen shares between 70 and 95% in 2060. More ambitious strategies tended to result in greater uncertainty of green hydrogen production and introduce higher system costs and cost uncertainty. The different strategies showed notable differences in carbon dioxide (CO2) reduction pathways. The cumulative CO2 emissions of the delayed strategy may reach 3 times that of the radical strategy, and the CO2 reduction uncertainty of the orderly strategy may be twice that of the other strategies. Alkaline electrolyzers were predicted to dominate green hydrogen production until being surpassed by proton exchange membrane electrolyzers (PEM) after 2060. The synergy of the solar-energy storage-PEM technology combination was notable because expensive electrolyzers tended to increase utilization, thereby diluting fixed costs. Our results underscore the importance of studying the impact of uncertainty and technology selection mechanisms on transition pathways.

Responses of precipitation and water vapor budget on the Chinese Loess Plateau to global land cover change forcing.

There have been notable changes in precipitation patterns on the Loess Plateau (LP) of China in recent decades, and numerous attribution studies have focused on sea surface temperature anomalies and atmospheric circulation changes induced by aerosols and greenhouse gases emission. However, the influences of global land use and land cover change (LULCC) as an important forcing factor in the climate system on regional precipitation remains poorly understood. In this study, we quantified the impacts of LULCC on precipitation and the water vapor budget in the LP region, utilizing data from LULCC forcing experiments conducted by the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Although global LULCC forcing exerted a negative effect on long-term mean precipitation on the LP region from 1850 to 2014, the different response characteristics were detected during different time periods. The global LULCC caused a decrease of 14 mm in annual precipitation during the period of 1850-1960. Conversely, from 1961 to 2014, it led to an increase of 6.4 mm, which is largely attributed to the enhanced water vapor transport along the southern boundary and westerly belt of the LP region. Moreover, from the perspective of the net water vapor balance of the entire LP, although LULCC caused net water vapor export during both periods 1850-1960 and 1961-2014, the export during the latter period (0.20 × 104 kg s-1) was smaller than that during the former period (0.28 × 104 kg s-1), indicating that the global expansion of grassland and cropland, along with the continuous rise in the leaf area index from 1961 to 2014, contributed to retaining more water vapor within the LP, which in turn was more favorable for precipitation. These findings provide valuable insights into the reasons behind precipitation variations in the LP region, emphasizing that global vegetation restoration and greening play a significant role in improving precipitation in ecologically fragile areas.

Inverse stimulation enables ultrasonic binary coding for NDE using a custom linear testing system.

Ultrasonic testing is an established method of non-destructive evaluation. The increasing complexity of material systems requires an extension of conventional methods. In related fields such as radar and medical ultrasound, signal optimisation and coded stimulation are successfully used and offer great potential for optimising state-of-the-art measurements and extending applications. In our work, we highlight the difference between using a coded sequence to stimulate an ultrasonic testing system and the actual performance of the digital code to motivate the exploration of inverse stimulation. In order to study inverse stimulation, a custom-built ultrasonic system was designed. As a first step, the transfer function was obtained by testing pulse and chirp stimulation. In the next step, inverse stimulation was performed based on the linear transfer function to engineer the ultrasonic echoes to have shapes similar to the target code. Finally, the auto-correlation function of the ultrasonic echoes resulting from the inverse stimulation is compared with the function of the original code sequence and the agreement of the recorded ultrasonic echo with the spectrally limited code sequence. With this work we propose an integrated, low-voltage, fully linear ultrasonic testing system where the recording of a linear transfer function allows echo engineering even for a binary coded excitation sequence. We have demonstrated that inverse stimulation enables the generation of binary ultrasonic echoes with performance equal to the digital code.

Addressing future food demand in The Gambia: can increased crop productivity and climate change adaptation close the supply-demand gap?

With rising demand for food and the threats posed by climate change, The Gambia faces significant challenges in ensuring sufficient and nutritious food for its population. To address these challenges, there is a need to increase domestic food production while limiting deforestation and land degradation. In this study, we modified the FABLE Calculator, a food and land-use system model, to focus on The Gambia to simulate scenarios for future food demand and increasing domestic food production. We considered the impacts of climate change on crops, the adoption of climate change adaptation techniques, as well as the potential of enhanced fertiliser use and irrigation to boost crop productivity, and assessed whether these measures would be sufficient to meet the projected increase in food demand. Our results indicate that domestic food production on existing cropland will not be sufficient to meet national food demand by 2050, leading to a significant supply-demand gap. However, investments in fertiliser availability and the development of sustainable irrigation infrastructure, coupled with climate change adaptation strategies like the adoption of climate-resilient crop varieties and optimised planting dates, could halve this gap. Addressing the remaining gap will require additional strategies, such as increasing imports, expanding cropland, or prioritising the production of domestic food crops over export crops. Given the critical role imports play in The Gambia's food supply, it is essential to ensure a robust flow of food imports by diversifying partners and addressing regional trade barriers. Our study highlights the urgent need for sustained investment and policy support to enhance domestic food production and food imports to secure sufficient and healthy food supplies amidst growing demand and climate change challenges. Supplementary Information: The online version contains supplementary material available at 10.1007/s12571-024-01444-1.

Ertapenem's therapeutic potential for Mycobacterium avium lung disease in the hollow fibre model.

INTRODUCTION: Guideline-based therapy for Mycobacterium avium complex (MAC) pulmonary disease achieves sustained sputum conversion rates in only 43-53% of patients. Repurposing of ß-lactam antibiotics such as ertapenem could expedite design of more efficacious regimens, compared to developing new drugs. METHODS: We performed an ertapenem exposure-response study in the hollow fibre system model of intracellular MAC (HFS-MAC). We recapitulated human-like intrapulmonary concentration-time profiles of eight once-daily intravenous doses of ertapenem over 28 days and performed repetitive sampling for drug concentration-time profiles and MAC burden. The % of time concentration persisted above MIC (%TMIC) mediating either 50% or 80% of maximal effect (E50, EC80) were identified. The EC80 was used as target exposure in a 10 000 subject Monte Carlo experiments for ertapenem doses of 1G, 2G, or 4G administered once versus twice daily. RESULTS: The ertapenem MIC ranged from 0.5 to 2 mg/L on three occasions. Ertapenem achieved a half-life of 4.04 ± 0.80 h in the HFS-MAC and killed a maximum of 2.17 log10 CFU/mL below day 0. The EC50 was %TMIC of 75.9% (95% confidence interval: 68.43%-86.54%) and the EC80 was %TMIC of 100%. Target attainment probability was >90% for 1G twice daily up to an MIC of 2 mg/L, while for 2G twice daily the susceptibility MIC breakpoint was 4-8 mg/L. CONCLUSIONS: Ertapenem microbial kill below day 0 burden was better than guideline-based therapy drugs in the HFS-MAC in the past. Ertapenem is a promising drug for novel combination therapies for MAC lung disease.

SNS citizenship behavior based on D&M information system success model and social capital theory: Mediating role of subjective well-being.

Social media has become integral to contemporary society, with online behaviors impacting individual experiences and the wider community. In Bangladesh, a developing country, SNS have played a pivotal role in the nation's digitalization efforts. This study explores the relationship between social capital theory, D&M Information System Model, subjective well-being, and SNS Citizenship Behavior (SCB) among active social media users in Bangladesh. Data was collected from 418 participants through an online survey, and hypotheses were tested using structural equation modeling. The findings indicate that the items of the D&M model positively influenced the aspects of social capital theory, excluding service quality. In contrast, social interaction ties and shared values were positively associated with SCB, although social trust did not exhibit a significant relationship. Additionally, subjective well-being mediated the connection between social capital and SCB. This research offers valuable insights into the factors influencing online prosocial behavior and provides practical implications for cultivating a positive communication culture in the digital era. The model proposed in this study holds significant implications for Bangladesh's policymakers and social networking site authorities, guiding their efforts in implementing technology-based initiatives.

Functional alterations in overweight/obesity: focusing on the reward and executive control network.

Overweight (OW) and obesity (OB) have become prevalent issues in the global public health arena. Serving as a prominent risk factor for various chronic diseases, overweight/obesity not only poses serious threats to people's physical and mental health but also imposes significant medical and economic burdens on society as a whole. In recent years, there has been a growing focus on basic scientific research dedicated to seeking the neural evidence underlying overweight/obesity, aiming to elucidate its causes and effects by revealing functional alterations in brain networks. Among them, dysfunction in the reward network (RN) and executive control network (ECN) during both resting state and task conditions is considered pivotal in neuroscience research on overweight/obesity. Their aberrations contribute to explaining why persons with overweight/obesity exhibit heightened sensitivity to food rewards and eating disinhibition. This review centers on the reward and executive control network by analyzing and organizing the resting-state and task-based fMRI studies of functional brain network alterations in overweight/obesity. Building upon this foundation, the authors further summarize a reward-inhibition dual-system model, with a view to establishing a theoretical framework for future exploration in this field.

Topsoil porosity prediction across habitats at large scales using environmental variables.

Soil porosity and its reciprocal bulk density are important environmental state variables that enable modelers to represent hydraulic function and carbon storage. Biotic effects and their 'dynamic' influence on such state variables remain largely unknown for larger scales and may result in important, yet poorly quantified environmental feedbacks. Existing representation of hydraulic function is often invariant to environmental change and may be poor in some systems, particularly non-arable soils. Here we assess predictors of total porosity across two comprehensive national topsoil (0-15 cm) data sets, covering the full range of soil organic matter (SOM) and habitats (n = 1385 & n = 2570), using generalized additive mixed models and machine learning. Novel aspects of this work include the testing of metrics on aggregate size and livestock density alongside a range of different particle size distribution metrics. We demonstrate that porosity trends in Great Britain are dominated by biotic metrics, soil carbon and land use. Incorporating these variables into porosity prediction improves performance, paving the way for new dynamic calculation of porosity using surrogate measures with remote sensing, which may help improve prediction in data sparse regions of the world. Moreover, dynamic calculation of porosity could support representation of feedbacks in environmental and Earth System Models. Representing the hydrological feedbacks from changes in structural porosity also requires data and models at appropriate spatial scales to capture conditions leading to near-saturated soil conditions. Classification. Environmental Sciences.

Scalable musculoskeletal model for dynamic simulations of lower body movement.

A musculoskeletal (MSK) model is an important tool for analysing human motions, calculating joint torques during movement, enhancing sports activity, and developing exoskeletons and prostheses. To enable biomechanical investigation of human motion, this work presents an open-source lower body MSK model. The MSK model of the lower body consists of 7 body segments (pelvis, left/right thigh, left/right leg, and left/right foot). The model has 20 degrees of freedom (DoFs) and 28 muscle torque generators (MTGs), which are developed from experimental data. The model can be modified for different anthropometric measurements and subject body characteristics, including sex, age, body mass, height, physical activity, and skin temperature. The model is validated by simulating the torque within the range of motion (ROM) of isolated movements; all simulation findings exhibit a good level of agreement with the literature.

Fifty years of environmental progress for United States dairy farms.

Dairy farms in the United States have changed in many ways over the past 50 yr. Milk production efficiency has increased greatly, with ∼30% fewer cows producing about twice the amount of milk today. Other improvements include increases in crop yields, fuel efficiency of farm equipment, and efficiency in producing most resources used on farms (e.g. electricity, fuel, fertilizer). These improvements have led to changes in the environmental impact of farms. Through simulation of representative dairy farms in 1971 and 2020, changes in nutrient losses and farmgate life cycle assessments of greenhouse gas (GHG) emissions, fossil energy use, and blue (ground and surface) water use were determined for 6 regions and the United States. For all environmental metrics studied, intensities expressed per unit of fat- and protein-corrected milk produced were reduced, but the total effects over all farms or milk produced increased for 5 of the 13 environmental metrics. Reductions in the impacts of dairy farms in the eastern United States were offset by large increases in western regions because of a major increase in cow numbers in the West. The national average intensity of GHG emissions decreased by 42%, which gave just a 14% increase in the total GHG emissions of all dairy farms over the 50-yr period. The intensity of fossil energy use decreased by 54%, with the total for all farms decreasing by 9%. Water use related to milk production decreased in intensity by 28%, but due to the large increase in dairy production in the dry western regions that have a greater dependence on irrigated feed crops, total blue water use increased by 42%. Major pathways of nitrogen loss included ammonia volatilization, leaching, and denitrification, where total ammonia emissions related to US dairy farms increased by 29%, while leaching losses decreased by 39%, with little change in nitrous oxide emissions. Simulated nitrogen and phosphorus runoff losses totaled for all dairy farms decreased by 27% to 51% through more efficient fertilizer use, reduced tillage, and greater use of cover crops. Emissions of methane and reactive non-methane volatile organic compounds increased by 32% and 53%, respectively, due to greater use of long-term manure storage and silage stored in bunkers and piles. Although much progress has been made in improving production efficiency, continued improvements with new strategies and technologies are needed to meet the demand for dairy products and mitigate total environmental impacts, particularly in view of projected climate variability.

Decoding the language of fear: Unveiling objective and subjective indicators in rodent models through a systematic review and meta-analysis.

While rodent models are vital for studying mental disorders, the underestimation of construct validity of fear indicators has led to limitations in translating to effective clinical treatments. Addressing this gap, we systematically reviewed 5054 articles from the 1960 s, understanding underlying theoretical advancement, and selected 68 articles with at least two fear indicators for a three-level meta-analysis. We hypothesized correlations between different indicators would elucidate similar functions, while magnitude differences could reveal distinct neural or behavioral mechanisms. Our findings reveal a shift towards using freezing behavior as the primary fear indicator in rodent models, and strong, moderate, and weak correlations between freezing and conditioned suppression ratios, 22-kHz ultrasonic vocalizations, and autonomic nervous system responses, respectively. Using freezing as a reference, moderator analysis shows treatment types and fear stages significantly influenced differences in magnitudes between two indicators. Our analysis supports a two-system model of fear in rodents, where objective and subjective fears could operate on a threshold-based mechanism.

Scalable musculoskeletal model for dynamic simulations of upper body movement.

A musculoskeletal (MSK) model is a valuable tool for assessing complex biomechanical problems, estimating joint torques during motion, optimizing motion in sports, and designing exoskeletons and prostheses. This study proposes an open-source upper body MSK model that supports biomechanical analysis of human motion. The MSK model of the upper body consists of 8 body segments (torso, head, left/right upper arm, left/right forearm, and left/right hand). The model has 20 degrees of freedom (DoFs) and 40 muscle torque generators (MTGs), which are constructed using experimental data. The model is adjustable for different anthropometric measurements and subject body characteristics: sex, age, body mass, height, dominant side, and physical activity. Joint limits are modeled using experimental dynamometer data within the proposed multi-DoF MTG model. The model equations are verified by simulating the joint range of motion (ROM) and torque; all simulation results have a good agreement with previously published research.

Progress, implications, and challenges in using humanized immune system mice in CAR-T therapy-Application evaluation and improvement.

In recent years, humanized immune system (HIS) mice have been gradually used as models for preclinical research in pharmacotherapies and cell therapies with major breakthroughs in tumor and other fields, better mimicking the human immune system and the tumor immune microenvironment, compared to traditional immunodeficient mice. To better promote the application of HIS mice in preclinical research, we selectively summarize the current prevalent and breakthrough research and evaluation of chimeric antigen receptor (CAR) -T cells in various antiviral and antitumor treatments. By exploring its application in preclinical research, we find that it can better reflect the actual clinical patient condition, with the advantages of providing high-efficiency detection indicators, even for progressive research and development. We believe that it has better clinical patient simulation and promotion for the updated design of CAR-T cell therapy than directly transplanted immunodeficient mice. The characteristics of the main models are proposed to improve the use defects of the existing models by reducing the limitation of antihost reaction, combining multiple models, and unifying sources and organoid substitution. Strategy study of relapse and toxicity after CAR-T treatment also provides more possibilities for application and development.

Enhanced growth resistance but no decline in growth resilience under long-term extreme droughts.

The frequency, intensity, and duration of extreme droughts, with devastating impacts on tree growth and survival, have increased with climate change over the past decades. Assessing growth resistance and resilience to drought is a crucial prerequisite for understanding the responses of forest functioning to drought events. However, the responses of growth resistance and resilience to extreme droughts with different durations across different climatic zones remain unclear. Here, we investigated the spatiotemporal patterns in growth resistance and resilience in response to extreme droughts with different durations during 1901-2015, relying on tree-ring chronologies from 2389 forest stands over the mid- and high-latitudinal Northern Hemisphere, species-specific plant functional traits, and diverse climatic factors. The findings revealed that growth resistance and resilience under 1-year droughts were higher in humid regions than in arid regions. Significant higher growth resistance was observed under 2-year droughts than under 1-year droughts in both arid and humid regions, while growth resilience did not show a significant difference. Temporally, tree growth became less resistant and resilient to 1-year droughts in 1980-2015 than in 1901-1979 in both arid and humid regions. As drought duration lengthened, the predominant impacts of climatic factors on growth resistance and resilience weakened and instead foliar economic traits, plant hydraulic traits, and soil properties became much more important in both climatic regions; in addition, such trends were also observed temporally. Finally, we found that most of the Earth system models (ESMs) used in this study overestimated growth resistance and underestimated growth resilience under both 1-year and 2-year droughts. A comprehensive ecophysiological understanding of tree growth responses to longer and intensified drought events is urgently needed, and a specific emphasis should be placed on improving the performance of ESMs.

Direct retrieval as a theory of involuntary autobiographical memories: evaluation and future directions.

I evaluate the conception of direct retrieval as originally formulated in the Self-Memory System model (Conway & Pleydell-Pearce [2000]. The construction of autobiographical memories in the self-memory system. Psychological Review, 107(2), 261-288. https://doi.org/10.1037/0033-295X.107.2.261). In the hierarchical memory organisation proposed in the Self-Memory System model, direct retrieval is described as a bottom-up associative process. While its theoretical role within this model is clear, systematic empirical examination of direct retrieval, viewed as a natural and observable phenomenon, has been hampered by inconsistent operationalisations. Here, I suggest that direct retrieval should be treated as a theoretical concept, aiming at explaining the phenomenon of involuntary (spontaneously arising) autobiographical memories. I evaluate predictions derived from the concept of direct retrieval against findings on involuntary autobiographical memories obtained over the past 25 years. Most of these predictions are consistent with the evidence, notably, the enhanced episodic specificity and constructive nature of involuntary autobiographical memories. However, the theory also has critical limitations. It did not predict the frequent occurrence of involuntary recollections in daily life, exceeding the prevalence of voluntary memories. Additionally, it overlooked the early emergence of spontaneously arising event memories in ontogenesis and their presence in other species, such as great apes. Future advancements of the Self-Memory System model should integrate evolutionary perspectives to address these limitations.

Proposal of neural network model for neurocognitive rehabilitation and its comparison with fuzzy expert system model.

This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic.

Seasonality of coastal upwelling trends in the Mauritania-Senegalese region under RCP8.5 climate change scenario.

The Mauritania-Senegalese upwelling region (MSUR), the southernmost region of the Canary current upwelling system, is well-known for its coastal productivity and the key role it plays in enriching the oligotrophic open ocean through the offshore transport of the upwelled coastal waters. The great ecological and socio-economic importance makes it necessary to evaluate the impact of climate change on this region. Hence, our main objective is to examine the climate change signal over the MSUR with a high resolution regional climate system model (RCSM) forced by the Earth system model MPI-ESM-LR under RCP8.5 scenario. This RCSM has a regional atmosphere model (REMO) coupled to a global ocean model (MPIOM) with high-resolution in the MSUR, which allows us to evaluate the wind pattern, the ocean stratification, as well as the upwelling source water depth, while maintaining an ocean global domain. Under RCP8.5 scenario, our results show that the upwelling favourable winds of the northern MSUR are year-round intensified, while the southern MSUR presents a strengthening in winter and a weakening in March-April. Along with changes in the wind pattern, we found increased ocean stratification in the spring months. In those months southern MSUR presents a shallowing of the upwelling source water depth associated to changes in both mechanisms. However, in winter the whole MSUR shows a deepening of the upwelling source water depth due to the intensification of the upwelling favourable winds, with the increased ocean stratification playing a secondary role. Our results demonstrate the need to evaluate the future evolution of coastal upwelling systems taking into account their latitudinal and seasonal variability and the joint contribution of both mechanisms.