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Selection of optimal fabrication parameters of an innovative pressure sensor using fuzzy-AHP method based on sensor characteristics for robotic gripper
Deep transfer learning approach for the classification of single and multiple power quality disturbances
An ensemble model based on transfer learning for the early detection of Alzheimer’s disease
Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the gradual decline in cognitive functions, particularly memory and reasoning. Early detection, especially during cognitive impairment (MCI) stage, is crucial for timely intervention and management. Enhanced diagnostic methods are essential for facilitating early identification and improving patient outcomes. This study presents a robust deep learning framework for the early detection of Alzheimer’s disease. It employs transfer learning and hyperparameter-tuning of InceptionResnetV2, InceptionV3, Xception architectures to enhance feature extraction by leveraging their pre-trained capabilities. An ensemble voting mechanism has been integrated to combine predictions from different models, optimizing both accuracy and robustness. The proposed ensemble voting approach demonstrated exceptional performance, achieving 98.96% accuracy and 100% precision for predicting classes Mildly Demented and Moderately Demented. It outperformed baseline and state-of-the-art models, highlighting its potential as a reliable tool for early diagnosis and intervention.
Sociodemographic determinants and temporal variability of blood lead levels (2003–2019) in a pooled analysis of nine studies in four European countries
Comparing hierarchical and inductive methods reveals fundamental differences in social vulnerability rankings
Evaluation of high-nicotine oral products shows potential to reduce tobacco-related harm by offering satisfying alternatives
A nile red fluorescence assay for LC3 autophagy protein binding to lipid bilayers
Explainability and importance estimate of time series classifier via embedded neural network
Abstract Time series is common across disciplines, however the analysis of time series is not trivial due to inter- and intra-relationships between ordered data sequences. This imposes limitation upon the interpretation and importance estimate of the features within a time series. In the case of multivariate time series, these features are the individual time series and the time steps, which are intertwined. There exist many time series analyses, such as Autocorrelation and Granger Causality, which are based on statistic or econometric approaches. However analyses that can inform the importance of features within a time series are uncommon, especially with methods that utilise embedded methods of neural network (NN). We approach this problem by expanding upon our previous work, Pairwise Importance Estimate Extension (PIEE). We made adaptations toward the existing method to make it compatible with time series. This led to the formulation of aggregated Hadamard product, which can produce an importance estimate for each time point within a multivariate time series. This subsequently allows each time series within a multivariate time series to be interpreted as well. Within this work, we conducted an empirical study with univariate and multivariate time series, where we compared interpretation and importance estimate of features from existing embedded NN approaches, an explainable AI (xAI) approach, and our adapted PIEE approach. We verified interpretation and importance estimate via ground truth or existing domain knowledge when it is available. Otherwise, we conducted an ablation study by retraining the model with Leave-One-Out and Singleton feature subsets to see their contribution towards model performance. Our adapted PIEE method was able to produce various feature importance heatmaps and rankings inline with the ground truth, the existing domain knowledge or the ablation study.
Cortical modulation through robotic gait training with motor imagery brain-computer interface enhances bladder function in individuals with spinal cord injury
Abstract Neurogenic bladder (NB) dysfunction in individuals with complete spinal cord injury (SCI) is a condition that significantly affects quality of life. Despite the prevalence of interventions, there is a substantial gap in effective treatments for this dysfunction. This study proposes robotic-assisted gait training combined with motor imagery (MI)-based brain-computer interface (BCI) to induce improved cortical modulation, and consequently improve bladder function in patients with SCI. The study involved seven men with complete and chronic SCI in a protocol comprising 24 sessions of robotic-assisted walking with BCI and MI. This regimen was designed to teach both mu (µ, 8–12 Hz) and beta (β, 15–20 Hz) modulation through MI practices using multi-channel EEG neurofeedback (NFB), focusing on sensorimotor rhythm (SMR) activation. Clinical outcomes were measured using the neurogenic bladder symptom score (NBSS), which revealed substantial improvements in bladder control among participants. EEG analysis confirmed a significant correlation between modulation of µ and β rhythms with decreased NBSS scores. Our findings support that robotic-assisted gait training combined with MI-based BCI effectively modulates with more precision the cortical µ and β rhythms and improves NB dysfunction in SCI individuals.
Transfer learning-enhanced CNN model for integrative ultrasound and biomarker-based diagnosis of polycystic ovarian disease
Preparation of high nutritional value biscuits from germinated chickpea, carrot, and turmeric rhizomes powder
Effects of compression pants on hip proprioception and dynamic balance during intermittent half-marathon running
On the solution for a nonlinear wave equation with variable exponent nonlinearity and a varying delay
Antipsychotics and other risk factors for mortality among people with schizophrenia during an extreme heat event: a population-based case-control study
Abstract Schizophrenia is associated with increased mortality during extreme heat events (EHEs), including the unprecedented 2021 Western North America EHE in British Columbia, Canada. We sought to examine the association between mortality during the 2021 EHE and multiple risk factors among people with schizophrenia, with a focus on antipsychotic medications. We identified all individuals with schizophrenia in British Columbia using an administrative chronic disease registry and linked them with antipsychotic dispensations for the 30 days prior to the EHE. We compared 137 individuals who died during the EHE with 57,394 who survived using multiple logistic regression. Analyses also included age, sex, income assistance, comorbidities, and indicators of schizophrenia severity. Dispensation of any antipsychotic was associated with increased EHE mortality [OR 2.43, 95% CI 1.52, 4.01], which was driven by haloperidol, clozapine, and zuclopenthixol. The risk was increased for dispensation of two or more antipsychotics in combination [OR 4.05, 95% CI 2.41, 6.98]. Other variables associated with EHE mortality included being on income assistance, age, sex, having a mental health-related emergency department visit, and time since disease registry entry. Being dispensed an antipsychotic prior to the EHE was associated with increased odds of mortality among people with schizophrenia. Some antipsychotics had larger effects, and risk increased with combination therapy. There were also significant non-pharmaceutical risk factors. People with schizophrenia are at-risk during EHEs due to multiple overlapping factors, including antipsychotic medications.
Experimental investigation on slotted Koch snowflake fractal patch rectenna
Abstract Radio frequency energy harvesting, a sustainable power source for IoT devices, faces limitations due to its low power density. However, existing slotted fractal antenna designs often lack systematic optimization and do not emphasize the 2 GHz band, which is crucial for urban RF energy harvesting. This article proposes a rectenna design featuring a Koch snowflake fractal patch antenna with optimized rectangular slots to enhance performance. A comparative study of single-slot and double-slot configurations revealed that double slots improve return loss peak depth and introduce additional resonance modes. The antenna was optimized using a multivariate parametric modelling-based approach with particle swarm optimization for the 2 GHz band. The 102 mm × 102 mm fabricated antenna exhibited − 26.8 dB return loss at 2.07 GHz, validated by experiments. Hence, the work demonstrated the feasibility of incorporating rectangular slots onto the Koch snowflake fractal patch. Furthermore, a half-wave rectifier circuit with a Pi matching network was fabricated. Initial discrepancies between the fabricated and simulated rectifier circuit resonant frequencies were observed. To address this, a microstrip line was introduced for impedance matching, and a capacitor was replaced with that of a higher operating frequency to minimize the parasitic effect due to the self-resonant frequency, thereby improving RF power transfer. The improved fabricated rectifier achieved over 20% RF-DC efficiency from an input power range of − 16 to 16 dBm at 1.88 GHz. Future work will focus on antenna size optimization and refined rectifier circuit simulations to develop a fully functional rectenna system for efficient energy harvesting.