ABSTRACT
The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed to optimize performance. For example, solar trackers follow the sun's path to increase the generation capacity of photovoltaic plants. However, several factors need consideration to further optimize this process. Important variables include the distance between panels, surface reflectivity, bifacial panels, and climate variations throughout the day. Thus, this paper proposes an artificial intelligence-based algorithm for solar trackers that takes all these factors into account-mainly weather variations and the distance between solar panels. The methodology can be replicated anywhere in the world, and its effectiveness has been validated in a real solar plant with bifacial panels located in northeastern Brazil. The algorithm achieved gains of up to 7.83% on a cloudy day and obtained an average energy gain of approximately 1.2% when compared to a commercial solar tracker algorithm.
ABSTRACT
Background: Transcranial direct current stimulation (tDCS) is a promising treatment for Alzheimer's Disease (AD). However, identifying objective biomarkers that can predict brain stimulation efficacy, remains a challenge. The primary aim of this investigation is to delineate the cerebral regions implicated in AD, taking into account the existing lacuna in comprehension of these regions. In pursuit of this objective, we have employed a supervised machine learning algorithm to prognosticate the neurophysiological outcomes resultant from the confluence of tDCS therapy plus cognitive intervention within both the cohort of responders and non-responders to antecedent tDCS treatment, stratified on the basis of antecedent cognitive outcomes. Methods: The data were obtained through an interventional trial. The study recorded high-resolution electroencephalography (EEG) in 70 AD patients and analyzed spectral power density during a 6 min resting period with eyes open focusing on a fixed point. The cognitive response was assessed using the AD Assessment Scale-Cognitive Subscale. The training process was carried out through a Random Forest classifier, and the dataset was partitioned into K equally-partitioned subsamples. The model was iterated k times using K-1 subsamples as the training bench and the remaining subsample as validation data for testing the model. Results: A clinical discriminating EEG biomarkers (features) was found. The ML model identified four brain regions that best predict the response to tDCS associated with cognitive intervention in AD patients. These regions included the channels: FC1, F8, CP5, Oz, and F7. Conclusion: These findings suggest that resting-state EEG features can provide valuable information on the likelihood of cognitive response to tDCS plus cognitive intervention in AD patients. The identified brain regions may serve as potential biomarkers for predicting treatment response and maybe guide a patient-centered strategy. Clinical Trial Registration: https://classic.clinicaltrials.gov/ct2/show/NCT02772185?term=NCT02772185&draw=2&rank=1, identifier ID: NCT02772185.
ABSTRACT
OBJECTIVE: This systematic review with meta-analysis aimed to evaluate the effectiveness of tDCS on lower limb function, balance and quality of life in stroke patients. METHODS: The search included PubMed, CENTRAL, PEDro, Web of Science, SCOPUS, PsycINFO Ovid, CINAHL EBSCO, EMBASE, ScienceDirect, reference lists of relevant reviews, clinical trials registries and academic google, in June and July 2021. Randomized controlled trials were selected, which present the effect of tDCS on lower limb motor function recovery in stroke patients, comparing any type of active tDCS versus sham; parallel or crossover study design; adult patients; stimulation on the primary motor cortex; articles published in any language; without restriction of publication period. RESULTS: Nineteen studies were included. The treatment with active tDCS did not improve motor function (Chi2 = 32,87, I2 = 76%, SMD = 0,36 e 95% CI -0,18-0,90). Subgroup analyzes showed a significant effect favorable to tDCS, in relation to motor function, in the acute and subacute post stroke phases. However, the quality of evidence for this outcome was very low. Regarding balance outcome, a meta-analysis showed a significant difference in favor of active tDCS, but the quality of the evidence was considered very low. As for the quality of life outcome, no statistically significant difference was found in favor of tDCS. DISCUSSION: There is a lack of evidence in recommending the use of tDCS in isolation in the treatment of patients after stroke, aiming at improving motor function, balance and quality of life. However, it is possible that tDCS can be beneficial when associated with other therapies or interventions.
