Global Performance Research Articles

An experimental study on the influence of wind-wave-current coupling effect on the global performance of a 12 MW semi-submersible floating wind turbine

Investigating the coupling effect between different marine environmental conditions is essential to understand the dynamic response of wind turbines. This paper presents the study findings from the model tests conducted in a wave basin, exploring the coupling effect of wind, waves, and currents on the global performance of a 1:70 scale model 12 MW semi-submersible floating wind turbine (FWT). A multi-blade large-scale wind generation system with a rectifier network was improved and fabricated to provide a reliable wind field for the experiment. The stiffness-matched and geometry-matched tower models were designed and fabricated, achieving an accurate simulation of the tower's flexibility and windward areas and their impact on the global dynamic response. Following the calibration of marine environmental conditions and the characterization of the model FWT system, the study focused on the operational and extreme responses of the FWT concept. The aerodynamic damping effect of wind in pitch and surge motions is remarkable, especially in pitch motion, primarily manifesting by reducing the standard deviation of motions. Mooring load responses are predominantly induced by the hydrodynamic effect, with the wave-frequency response gradually becoming dominant as wave parameters increase. Tower-top load responses are primarily induced by the hydrodynamic effect of waves and the aerodynamic load effect of wind. Furthermore, the phenomenon where the action of current strengthens the aerodynamic damping effect of wind is observed. The coupling effect between wind and current amplifies surge motion while diminishing pitch motion, effectively reducing the tower-top and tower-base load responses.

Accurate multiclassification and segmentation of gastric cancer based on a hybrid cascaded deep learning model with a vision transformer from endoscopic images

Compared with other forms of cancer, gastric cancer has high mortality and incidence rates, making it a major cause of death worldwide. Accurate diagnosis is crucial in the treatment of stomach cancer. Researchers have used deep learning techniques facilitated by developments in artificial intelligence to classify and segment endoscopic images of stomach cancer. Most recentresearch examining endoscopic images of stomach cancer has used a binary classification system, whichis insufficient for practical use. False-positives and computational costs become problematic when segmentation is applied to all the images of healthy patients obtained throughout the evaluation. Hence, the expected level of performance has not been attained in the real-time multiclassification and segmentation of stomach cancer. In this study, we present a deep learning-based technique for multiclassification of endoscopic images by combiningmodified GoogLeNet and vision transformer (ViT) models and identifying invasive areas based on Faster R-CNN. The classification of endoscopic images into three categories, namely, normal, early gastric cancer, and advanced gastric cancer, is accomplished by using a hybrid approach including modified GoogLeNet and vision transformer (ViT) models. Gastric cancer regionsare then identified and segmented in the endoscopic images using the Faster R-CNN method. The Faster R-CNN algorithm is used with an endoscopic image as input, resulting in the generation of a bounding box and label image that accurately represents the gastric cancerous area. The proposed model achieved an accuracy, sensitivity and F1-score of 97.4%, 97.5% and 95.9%, respectively, for the classification of noncancerous, early gastric cancer and advanced gastric cancer. Furthermore, the performance of the segmentation method was also validated based on evaluation metrics and achieved 96.7%, 96.6% and 95.5% accuracy, sensitivity and F1-score, respectively, for the segmentation of noncancerous, early gastric cancer and advanced gastric cancer tissues. In conclusion, the method proposed in this study demonstrates enhanced global classification and detection performance compared to existing state-of-the-art algorithms. This finding underscores the significant potential of the proposed methodin the domain of gastric endoscopic image classification and segmentation.

Statistical and Clustering-Based Assessment of Variable Speed Limits Effects on Motorway Performance from Real-World Observations

Variable Speed Limit (VSL) systems aimed at reducing congestion and improving safety performance have been implemented around the world in previous years. However, field studies have shown controversial results regarding traffic performance improvement. This study integrates statistical testing methods and clustering techniques for assessing the effect of a non-mandatory VSL system on traffic flow performances on a 14-km portion of the Padua–Mestre motorway in Italy. Statistical analysis is conducted on the observed speeds, collected for almost one year, to identify any significant differences provided by VSL activation. The changes in global motorway performances induced by the VSL in typical traffic patterns under recurring congestion are assessed using both statistical tests and two specific clustering algorithms, namely K-means and DBSCAN. The results indicate that the VSL system effectively affects the observed speeds and alleviates congested conditions: the observed reduction in mean travel time ranges is around 4% with the VSL system active across various lanes; the standard deviation of vehicular speeds witnessed a decrease of 12% to 20% in the most congested segments, while no notable distinction is observed in traffic flows.

The uncertainty of economic policies in the United States and its impact on the stock markets of some African countries: An empirical study with VAR modeling

The economic and financial integration that has taken place over the last 20 years has led to a situation where the reaction of financial markets is no longer solely due to local factors, but also to macroeconomic and financial news of regional and international origin. In this regard, we focus on the effect of the international transmission of uncertainty shocks on global stock market performance. Given that the United States is the world’s leading economic power, some studies have highlighted the adverse effects of the international propagation of US uncertainty shocks on international stock markets (Ehrmann and Fratzscher, 2009; Klobner and Sekkel, 2014; Hammoudeh et al., 2016).
 Using VAR modeling, our study examines the specific impact of economic policy uncertainty in the USA on the stock markets of four African countries: Tunisia, Egypt, Morocco, and South Africa. Although geographically distant from the USA, these economies are closely integrated into the global economy and are likely to be affected by international economic and political developments, particularly those originating in the USA, a major economy and a key player on the global stage.

Joint interframe separation and gamma correction for asynchronous optical camera communication systems based on high-order statistics

Optical camera communication (OCC), which is enabled by large-scale light-emitting diodes (LEDs) arrays and image-sensor (IS) based cameras, has garnered significant attention from both researchers and industries. Existing OCC synchronization techniques typically rely on either super-Nyquist sampling or on computationally expensive asynchronous recovery algorithms to relax the required camera frame rate. In this paper, we propose a kurtosis-based asynchronous interference cancellation (K-AIC) algorithm, enabling the estimation for both the asynchronous interframe overlapping ratios and nonlinear Gamma distortion levels for each grayscale frame captured by camera. Through comprehensive numerical simulations, we demonstrate that the K-AIC algorithm exhibits low computational complexity, unique global optimum, high reliability and robust performance in mitigating asynchronous-induced bit errors across diverse scenarios. Short-range OCC experiment shows that the K-AIC scheme can effectively compensate for both interframe overlapping and Gamma distortions in a plesiochronous reception scenario, resulting in a Q-factor enhancement of approximately 12 dB with fluctuations of less than 1 dB. Consequently, the system achieves a net data rate of around 200 kbps.

Synchronous planning of initial and target positions of redundant space manipulator based on deterministic configuration tree

Planning the operating position of redundant space manipulators (RSMs) can effectively improve the performance of manipulators while improving the quality of on-orbit servicing (OOS) is of great significance. For RSM, OOS with synchronous planning of initial and target positions (ITPs) has many combinations of positions or configurations, making it difficult to apply existing methods directly. To address this problem, this study proposes a method for synchronous planning ITPs of RSMs based on a deterministic configuration tree (DCT). First, the planning area is divided into grids, with the global performance analyzed utilizing self-motion manifolds. Then, a DCT is constructed based on the analysis results of global performance. Finally, a position planning strategy of pre-planning, rewiring, and regrowth is proposed to achieve synchronized planning of the ITP of the RSM on the DCT. This method is conducted alternately in operational space and joint space. The gridding and regrowth process in operational space avoids the considerable calculation required by gridding in high-dimensional joint space and improves the efficiency of position planning. In joint space pre-planning and rewiring, the ITP is optimized with the constraint of motion planning results while position planning is realized. The simulation experiment results of the 3-degree-of-freedom (DOF) planar RSM, 4-DOF RSM, and 7-DOF RSM indicate that the proposed method reduces time consumption by 34.81%, 18.15%, and 16.26% compared to conventional methods. Furthermore, energy consumption is reduced by 30.68%, 16.93%, and 15.64%, respectively. A typical ITPs synchronous planning task is also simulated, and the results also show that this planning method can effectively optimize the ITPs and reduce energy consumption.

Multi-layer CNN-LSTM network with self-attention mechanism for robust estimation of nonlinear uncertain systems.

With the help of robot technology, intelligent rehabilitation of patients with lower limb motor dysfunction caused by stroke can be realized. A key factor constraining the clinical application of rehabilitation robots is how to realize pattern recognition of human movement intentions by using the surface electromyography (sEMG) sensors to ensure unhindered human-robot interaction. A multilayer CNN-LSTM prediction network incorporating the self-attention mechanism (SAM) is proposed, in this paper, which can extract and learn the periodic and trend characteristics of the sEMG signals, and realize the accurate autoregressive prediction of the human motion information. Firstly, the multilayer CNN-LSTM network utilizes the CNN layer for initial feature extraction of data, and the LSTM network is used to improve the enhancement of the historical time-series features. Then, the SAM is used to improve the global feature extraction performance and parallel computation speed of the network. In comparison with existing test is carried out using actual data from five healthy subjects as well as a clinical hemiplegic patient to verify the superiority and practicality of the proposed algorithm. The results show that most of the model's prediction R > 0.9 for different motion states of healthy subjects; in the experiments oriented to the motion characteristics of patient subjects, the angle prediction results of R > 0.99 for the untrained data on the affected side, which proves that our proposed model also has a better effect on the angle prediction of the affected side. The main contribution of this paper is to realize continuous motion estimation of ankle joint for healthy and hemiplegic individuals under non-ideal conditions (weak sEMG signals, muscle fatigue, high muscle tension, etc.), which improves the pattern recognition accuracy and robustness of the sEMG sensor-based system.

Numerical analysis on global stability performance of fold-fastened multi-cellular steel walls

As a novel cold-formed steel (CFS) built-up closed member, the global stability performance of fold-fastened multi-cellular steel wall (FMSW) under axial compression was numerically investigated in this paper. Firstly, finite element (FE) models of FMSWs that could precisely simulate the global buckling were developed using ABAQUS and validated by existing experiments of CFS built-up members. Then, eight groups of numerical examples were analyzed to explore the effect of different parameters on the global stability performance of FMSWs, including the geometrical dimensions, material property, indented spacing and initial imperfection, where the corresponding global stability coefficients (φ) of FMSWs could be obtained from FE results. The normalized slenderness ratio (λn) of FMSWs was calculated based on the concept of the effective width method (EWM). Finally, the FE results of (φ, λn) were compared with existing stability design curves of steel structures in various codes. It was found that the stability design curve-d specified in Chinese code GB 50017–2017 could achieve a conservative design, and it could provide a valuable reference for designing slender FMSWs in practice.

Pilot study to assess the feasibility of self-administered, low-dose methoxyflurane for cystoscopic procedures.

Methoxyflurane (MEO F) (Penthrox™) is an inhaled, self-administered, non-opioid analgesic approved by Health Canada for the short-term relief of moderate to severe acute pain associated with trauma or interventional medical procedures. In this pilot study, we evaluated the feasibility of using MEOF as an anesthetic agent in 11 patients undergoing outpatient cystoscopic procedures. The average duration of the procedure was 24 (range 20-35) minutes and this included 10 minutes of administration time of the drug and five minutes of wait time before the procedure. The average monitoring time from start to end of the procedure was 23 (range 20-35) minutes and this included 15 minutes of monitoring post-procedure. On a scale of 0-10, patients on average rated the pain 4/10 (standard deviation [SD] 2.6). Global performance was on average 3/4 (SD 1.3) for the patients and 3/4 (SD 1.1) for the operator. Of the 11 patients, four reported adverse events; two experienced euphoria, one experienced dizziness, and one was unable to tolerate the medication. Two patients noted their adverse events to be of moderate intensity, while the other two were of mild intensity. None of the adverse events was deemed serious. Our findings in this pilot study provide proof of principle for the design of a randomized control trial to evaluate MEOF as an anesthetic in an outpatient cystoscopic procedural setting. As more urologic procedures are being performed in an outpatient setting, this may offer significant clinical benefit.

Improved Immune Moth-Flame Optimization Based on Gene Correction for Automatic Reverse Parking.

During the process of reverse parking, it is difficult to achieve the ideal reference trajectory while avoiding collision. In this study, with the aim of establishing reference trajectory optimization for automatic reverse parking that smooths and shortens the trajectory length and ensures the berthing inclination angle is small enough, an improved immune moth-flame optimization method based on gene correction is proposed. Specifically, based on the standard automatic parking plane system, a reasonable high-quality reference trajectory optimization model for automatic parking is constructed by combining the cubic spline-fitting method and a boundary-crossing solution based on gene correction integrated into moth-flame optimization. To enhance the model's global optimization performance, nonlinear decline strategies, including crossover and variation probability and weight coefficient, and a high-quality solution-set maintenance mechanism based on fusion distance are also designed. Taking garage No.160 of the Dalian Shell Museum located in Dalian, Xinghai Square, as the experimental site, experiments on automatic parking reference trajectory optimization and tracking control were carried out. The results show that the proposed optimization algorithm provides higher accuracy for reference trajectory optimization and can achieve better tracking control of the reference trajectory.

Hybrid Metaheuristic Algorithms for Optimization of Countrywide Primary Energy: Analysing Estimation and Year-Ahead Prediction

In the present work, India’s primary energy use is analysed in terms of four socio-economic variables, including Gross Domestic Product, population, and the amounts of exports and imports. Historical data were obtained from the World Bank database for 44 years as annual values (1971–2014). Energy use is analysed as an optimisation problem, where a unique ensemble of two metaheuristic algorithms, Grammatical Evolution (GE), and Differential Evolution (DE), is applied. The energy optimisation problem has been investigated in two ways: estimation and a year-ahead prediction. Models are compared using RMSE (objective function) and further ranked using the Global Performance Index (GPI). For the estimation problem, RMSE values are found to be as low as 0.0078 and 0.0103 on training and test datasets, respectively. The average estimated energy use is found in good agreement with the data (RMSE = 6.3749 kgoe/capita), and the best model (E10) has an RMSE of 5.8183 kgoe/capita, with a GPI of 1.7249. For the prediction problem, RMSE is found to be 0.0096 and 0.0122 on training and test datasets, respectively. The average predicted energy use has RMSE of 7.8857 (kgoe/capita), while Model P20 has the best value of RMSE (7.9201 kgoe/capita) and a GPI of 1.8836.

A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain

The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with the extreme gradient boosting (XGBoost) model to propose a novel NGO-XGBoost model. The performance of this model was evaluated using meteorological data from 30 stations in the North China Plain and compared with XGBoost, random forest (RF), and k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method combined with the results from RF, XGBoost, adaptive boosting (AdaBoost), and categorical boosting (CatBoost) models is used to obtain the importance of meteorological factors in estimating ETo, and thereby determine the optimal combination of inputs to the model. The results indicated that by using the top 3, 4, and 5 important factors as input combinations, all models achieved high ETo estimation accuracy. It is worth noting that there were significant spatial differences in the estimation precisions of the four models, but the NGO-XGBoost model exhibited consistently high estimation precisions, with global performance indicator (GPI) rankings of 1st, and the range of coefficient of determination (R2), nash efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) were 0.920–0.998, 0.902–0.998, 0.078–0.623 mm d−1, 0.058–0.430 mm d−1, and −0.254–0.062 mm d−1, respectively. Furthermore, the accuracy of the NGO-XGBoost model in estimating ETo varied across different seasons, which was more significantly affected by humidity and wind speed in winter. When the target station data was insufficient, the NGO-XGBoost model was trained by using the historical data from neighboring stations and still maintained a high precision. Overall, this study recommends a reliable method for estimating ETo, which provides a reference for accurately calculating ETo in the North China Plain in the absence of meteorological data.

Entirely Coupled Recurrent Neural Network-Based Backstepping Control for Global Stability of Power System Networks

In an interconnected power system network, global performance is affected by various unknown power system parameters subjected to system uncertainties. These unknown parameters in the control expression deteriorate the performance of the power system network. Therefore, these terms associated with the control expression must be estimated precisely. This paper proposes an adaptive backstepping scheme using an entirely coupled recurrent neural network (ECRNN) to estimate these control terms. Three continuous differentiable functions are used to design control input, virtual signals, and adaptive control laws to achieve global performance. The objective of ECRNN is to reduce the control complexity by estimating the associated nonlinear term in the control expression. It will facilitate the complex formulation of the controller and improve system performance. The robust functional estimation ability of ECRNN will make the system immune to uncertainties that may appear due to external disturbances. In ECRNN, feedback loops are added to each neuron layer to achieve additional estimation accuracy. The proposed adaptive law enhances the online weight update speed and accuracy. Verification of the proposed scheme is carried out in MATLAB/Simulink. It is also verified in the real-time digital simulator (RTDS) platform using the IEEE standard New England 39-bus, 10-machine power system model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the existing limitations in the global performance of a multimachine power system network due to uncertainty in the system parameters. Uncertainties in the power system network primarily appeared due to penetration of renewable power sources, load uncertainty, or occurrence of an uncertain fault in the network. As power system networks are complex nonlinear interconnected systems, these perturbations in the generator states may cause economic losses, load demand uncertainty, or electric scarcity. Thus, a control scheme is required to address the global performance of such networks. This article proposes uniform ultimate bounded stability of synchronous generators in a multimachine power system network with a robust ECRNN-based backstepping control design. The other available technique has not adequately addressed the global performance under uncertainties. The efficacy of the proposed scheme is verified in a real-time environment via a real-time digital simulator which may be a feasible solution for designing excitation control for the synchronous machine in an extensive industrial network.

Experimental and Modeling Analyses of the Correlation between Local 3D Heterogeneities and the Macroscopic Observers of a Proton Exchange Membrane Fuel Cell Stack

In this study, we offer a complete investigation of a high-performing Proton Exchange Membrane Fuel Cell stack customized for automotive use. Our approach goes beyond traditional global electrochemical performance metrics such as polarization curves, ohmic resistance. Instead, we utilize specialized segmented high-surface sensors to measure current density and temperature in the active area plane, along with neutron imaging to determine liquid water distributions. Employing a pseudo three-dimensional two-phase flow model that integrates electrochemical and transport phenomena, we gain insight into the intricate relationships among these observables. The model proves particularly valuable in elucidating the operation of the anode and cathode sides, aspects challenging to capture solely through experimental mean. Our findings emphasize the substantial impact of fluid flow directions and current density on the distribution of liquid water. It is noteworthy that despite fluid flow direction, there is a consistent decrease in overall liquid water content with an increase in current density. This results in voltage instability within the cell, attributed to flooding phenomena, especially at low current densities. However, this is not observed in conditions representative of those encountered in on-field systems. We conduct a thorough analysis of this failure scenario to improve the fuel cell system’s control mechanisms.

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics.

A New Comprehensive Cloud Macrophysics Scheme With a Prognostic Dual‐Triangular PDF

AbstractTo improve cloud simulation in a general circulation model, we develop a new cloud macrophysics scheme that treats detrained cumulus generated by convective detrainment process separately from pure stratus; prognoses two symmetric triangular probability density functions of total specific humidity for each detrained cumulus and pure stratus; and diagnoses both cloud fraction and cloud condensate in all liquid, ice, and mixed‐phases in a consistent and unified way without any adjustment to remove empty or very dense cloud. Supersaturation is allowed within ice cloud. The new scheme (“NEW”) is compared with the previous model (“OLD”) using single‐column simulations for subtropical marine stratocumulus (DYCOMS2) and continental deep convection (ARM97) cases. In DYCOMS2, both NEW and OLD produce vertical profiles of grid‐mean cloud condensate similar to large‐eddy simulation. In ARM97, compared with OLD, NEW simulates less sporadic vertical profiles of in‐cloud condensates, due to consistent diagnosis of cloud fraction and cloud condensate; more continuously‐varying detrained cumulus with time, due to prognostic treatment of detrained cumulus; and ice cloud fraction and ice condensate similar to those of OLD, in spite of completely different treatment of ice cloud processes. The global performance of NEW is similar to OLD with improved relative humidity. Compared to OLD, NEW simulates more and improved cloud condensate, but less and degraded cloud fraction, particularly, in the lower troposphere. Detrained cumulus is moister and colder and has a larger moisture variance than pure stratus. Overall, NEW simulates stronger condensation‐deposition rates than OLD, due in part to the separate treatment of detrained cumulus and pure stratus.

A Markov Chain Approach for the Evaluation of Global Performance of Universities of South Africa

A Markov Chain Approach for the Evaluation of Global Performance of Universities of South Africa

Global Performance, Trends, and Challenges for Assessment and Management of Per- and Polyfluoroalkyl Substances (PFASs): A Critical Review

Per- and polyfluoroalkyl substances (PFASs) are a large and complex group of thousands of synthetic chemicals with different properties such as strong acidity, high surface activity at very low concentrations, high chemical stability, and water and oil repellency. Many PFASs have been used in various industrial and consumer applications based on the desired functionality and production capability, and have consequently emerged as pervasive environmental contaminants of major concern. Recognition that several PFASs are transported globally, bioaccumulate, and exert multiple adverse effects on environmental quality and health has led to their regulation and ultimate phase-out. Despite decades of global management and research on PFAS, fundamental obstacles still remain in addressing worldwide contamination by these chemicals and their associated impacts on environmental quality and human health. Here, we reviewed the global issues, major barriers, and challenges that must be addressed to remedy the “PFAS problem” from scientific, technological, and policy perspectives. A comprehensive review of the latest scientific research and recent legislative developments regarding PFAS will improve the assessment and management of the numerous PFAS on the market in the near future.

WOFOST-N: An improved WOFOST model with nitrogen module for simulation of Korla Fragrant pear tree growth and nitrogen dynamics

Few studies have focused on simulating fruit tree growth under fertilization constraints and evaluating nitrogen utilization efficiency. This study aimed to improve the WOFOST (World Food Studies) model by embedding the nitrogen dynamics module into the original WOFOST model (WOFOST-N) to simulate the Korla Fragrant pear tree growth, nitrogen transport, nitrogen use efficiency and nitrogen stress. In addition, we used the BBCH scale to provide a more detailed description of the phenological development process of pear trees to improve phenology simulation performance. Field trials were conducted in 2021 and 2022 in two pear orchards in the Alar region of China. The results showed that the simulated total dry weight (leaves and fruits, TAGPl,f) and leaf area index (LAI) growth dynamics agreed well with the measured values in each treatment in 2021 and 2022. The R2 values of the simulated TAGPl,f was between 0.87 and 0.97, and the R2 values of the simulated LAI were between 0.80 and 0.95. The improved model also showed relatively high simulation accuracy (8.2 % ≤ NRMSE ≤ 20.5 % for TAGPl,f, 7.1 % ≤ NRMSE ≤ 20.1 % for LAI). The improved model had good global simulation performance for soil nitrogen content in six treatments with a good consistency (R2 = 0.87 in 2021 and 0.89 in 2022) and low error (NRMSE = 12.4 % in 2021 and 11.8 in 2022). The simulated yields and nitrogen use efficiencies (NUE) in the two orchards also showed good performance, with R2 = 0.94 and 0.93, NRMSE = 5.2 % and 7.5 % for yields, R2 = 0.93 and 0.96, NRMSE = 10.7 % and 9.6 % for NUE, respectively. The proposed method provides a promising method to simulate fruit trees' growth and nitrogen dynamic effects.

Cognition in older age bipolar disorder: An analysis of archival data across the globe

BackgroundCognitive deficits in bipolar disorder (BD) impact functioning and are main contributors to disability in older age BD (OABD). We investigated the difference between OABD and age-comparable healthy comparison (HC) participants and, among those with BD, the associations between age, global cognitive performance, symptom severity and functioning using a large, cross-sectional, archival dataset harmonized from 7 international OABD studies. MethodsData from the Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE-BD) database, spanning various standardized measures of cognition, functioning and clinical characteristics, were analyzed. The sample included 662 euthymic to mildly symptomatic participants aged minimum 50years (509 BD, 153 HC), able to undergo extensive cognitive testing. Linear mixed models estimated associations between diagnosis and global cognitive performance (g-score, harmonized across studies), and within OABD between g-score and severity of mania and depressive symptoms, duration of illness and lithium use and of global functioning. ResultsAfter adjustment for study cohort, age, gender and employment status, there was no significant difference in g-score between OABD and HC, while a significant interaction emerged between employment status and diagnostic group (better global cognition associated with working) in BD. Within OABD, better g-scores were associated with fewer manic symptoms, higher education and better functioning. LimitationsCross-sectional design and loss of granularity due to harmonization. ConclusionMore research is needed to understand heterogenous longitudinal patterns of cognitive change in BD and understand whether particular cognitive domains might be affected in OABD in order to develop new therapeutic efforts for cognitive dysfunction OABD.