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Modifying graphene oxide with magnetic nanoparticles and Mg-Al LDHs and its application as an efficient catalyst in organic reactions
Housing structure shapes dengue transmission dynamics in a rapidly urbanizing Malaysian district
A bio inspired hybrid optimization framework for efficient real time malware detection
SBTM: epileptic seizure prediction from EEG signal using deep learning in blockchain-enabled smart healthcare monitoring with IoT networking
Abstract Epileptic Seizure prediction is highly significant for the identification and reduction of high risks related to serious brain injuries, strokes, and brain tumors. Early and accurate diagnosis is vital for providing intervention measures for enhancing the quality of life of the affected individuals. Numerous techniques have been developed based on Machine vision techniques to predict epileptic seizures. Nonetheless, the acquisition of precise epileptic seizure detection with low false positive rates is challenging. Moreover, the emergence of the Internet of Things (IoT) revolutionized healthcare monitoring with technological improvements, aiming to handle the concerns related to data interoperability, scalability, as well as privacy issues. Hence, this research proposes the Smart Healthcare Monitoring Framework, namely Spizella Optimization-based Bidirectional Short Term Memory Network (SBTM), for determining the seizure states, thereby allowing the provision of remote care. Specifically, the proposed model exploits the Bi-LSTM architecture that captures the temporal dependencies and nonlinear dynamics of EEG signals, making the model highly efficient for predicting the seizure patterns. Besides, the Spizella Optimization is applied for fine-tuning the hyperparameters of the classifier, thereby leading to accurate prediction. Experimental results demonstrate that the proposed SBTM model accomplishes superior results by achieving high accuracy, sensitivity, and specificity equivalent to 97.52%, 97.51% and 98.51% with 90% training, outperforming the state-of-the-art techniques. Moreover, the presented approach significantly improves the remote monitoring, guaranteeing on-time medical care, ensuring data security, and enhancing the overall performance of applications in tech-aided healthcare systems.
Cybersickness-induced EEG responses in curved monitor and head-mounted display
Geochemical cycling of arsenic in magmatic systems across supercontinent cycles
Experimental investigation of energy and exergy characteristics of a novel solar collector with swirling reversed circular flow jet impingement
Comparative entropy analysis of 2D transition metal tetrahydroxyquinones via machine learning approaches
Machine learning prediction of live birth after IVF using the morphological uterus sonographic assessment group features of adenomyosis
Abstract Predicting live birth after the first IVF/ICSI treatment is challenging, as many factors may interact to affect IVF/ICSI outcomes. Adenomyosis is one factor that impacts live birth rates. Machine learning algorithms have been shown valuable for detecting complex dependencies and predicting outcomes in different clinical settings. We aimed to develop a prediction model for live birth after IVF/ICSI treatment, using the Extreme Gradient Boosting (XGBoost) algoritm and incorporating the revised Morphological Uterus Sonographic Assessment (MUSA) group features of adenomyosis. We used a machine learning model based on data from 1037 women undergoing their first IVF/ICSI treatment between January 2019 and October 2022. The importance of each variable on the model was illustrated with the Shapley additive explanations algorithm (SHAP) variable importance. The prediction model was presented with the area under receiver operating characteristics curve (ROC). The proposed XGBoost model had a test AUC of 0.66 and accuracy of 0.59. S-AMH was the best variable for predicting live birth with a mean SHAP of 0.21, followed by a regular junctional zone as the best ultrasonographic variable, mean SHAP 0.13. The predictive ability of MUSA features in relation to live birth was limited. Additional variables should be included in future prediction models.
A hybrid XGBoost–SVM ensemble framework for robust cyber-attack detection in the internet of medical things (IoMT)
Identification of RBX1 as a regulator of LIPT1 transcription and its role in copper-induced cell death in GBM cells
A novel optimized fuzzy neural network for enhanced topology control in k-connected mobile adhoc networks
Mango peels-assisted synthesis of carbon quantum dots for potential optical sensing of diazinon
Dynamic chain for scheduling of the multi-AGV systems with load-aware motion profiling
Development of a nomogram to predict in-hospital mortality of trauma patients in the ICU: an analysis of the MIMIC-IV database
Distance-amplified power-law distributions better characterize human long-distance travel
Abstract Human mobility patterns have been the subject of research for many decades. Understanding long-distance trips is critical in our globalized world, for example, to model the spread of diseases. Traditional models generally assume that trip lengths follow a power-law distribution. We analyze over one million long-distance trips using three datasets: two survey-based (from Germany and the U.S.) and one from mobile network data in the U.K. We find that the observed trip length distributions deviate from typical power-law behavior, motivating a new approach. In addition, we examine COVID-19 spreading patterns in Germany and identify mobility dynamics that traditional power-law models fail to capture. To address these limitations, we introduce a model that extends the power-law framework by amplifying long-distance trips – based on the intuition that once a journey exceeds a certain length, the remaining distance is also likely to be substantial. Our experiments underscore the need for advanced models of long-distance travel and demonstrate that distance amplification can enhance the accuracy of conventional models.
Evaluating the prognostic significance of HIST1H4C in breast cancer: implications for neoadjuvant therapy
Energy, exergy, and environmental performance of a solar dryer for orange slices across tray levels and thicknesses
Abstract This research introduces the development of an automated forced and natural solar dryer (AFNSD) equipped with a photovoltaic-powered IoT technology, temperature-responsive control system that seamlessly alternates between natural and forced convection to improve efficiency and minimize energy consumption. In contrast to traditional fixed systems, it avoids both over-drying and product spoilage. The affordable, solar-driven design makes it ideal for off-grid communities. By combining drying kinetics analysis with economic and environmental evaluations, the system aligns with and promotes sustainability objectives. The thermodynamic performance and sustainability indicators were also evaluated. The developed AFNSD was used for drying orange slices at different tray positions (lower, middle, and upper), and three slice thicknesses (4, 6, and 8 mm). the obtained results showed that thinner orange slices (4 mm) placed on the lower trays reached the equilibrium moisture content more quickly, with an average drying time of about 13 h. In contrast, thicker slices (8 mm) positioned on the upper trays required the longest drying time, averaging around 25 h to reach the equilibrium moisture content. The thermodynamic analysis showed that the maximum energy efficiency of the solar collector (SC) ( $$\:{\eta\:}_{en,\:\:SC})\:$$ was about 70.98%. And the maximum exergy efficiency of the SC ( $$\:{\eta\:}_{ex,\:\:SC})\:$$ and the drying chamber (DCh) ( $$\:{\eta\:}_{ex,\:\:DCh}$$ ) were about 21.93% and 43.64%, respectively. additionally, the sustainable indicators of both SC and DCh of the developed AFNSD, showed that the improved potential (IP) was in the range of 2.03 to 12.61 W in the SC and from 0.03 to 1.85 W in the DCh. The average waste energy ratio (WER) was 0.9 for the SC and 0.7 for the DCh. And the sustainability index (SI) ranged from 1.02 to 1.28 in the SC and from 1.2 to 1.77 in the DCh.
Peptidomics and pharmacological profiling of Odontobuthus doriae (Buthidae) scorpion venom at the kappa opioid receptor
Abstract Scorpion venoms are rich in bioactive peptides, many of which act on ion channels and neurotransmitter systems, yet their capacity to interact with G protein-coupled receptors (GPCRs) has been largely unexplored. Here, we profiled the venom peptides of five species in the family Buthidae and evaluated their activity at the kappa opioid receptor (KOR). Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) revealed species-specific peptide fingerprints in the mass range between 2.5 and 4 kDa, underscoring interspecies peptide toxin variation. Following pre-purification by solid-phase extraction, radioligand displacement assays demonstrated that fractions from Odontobuthus doriae bound to KOR, with Od-e (36% acetonitrile) and Od-f (45% acetonitrile) displacing ~ 35–40% of [³H]-diprenorphine. However, BRET-based functional assays demonstrated only weak receptor activation, suggesting that these peptides may function as partial agonists or antagonists rather than full agonists. Collectively, these findings highlight scorpion venoms as a previously underexplored source of opioid receptor-interacting peptides. Systematic investigation of their structural diversity and pharmacological profiles in the future may not only expand our understanding of venom evolution but also provide novel scaffolds for GPCR ligand discovery and potential analgesic development.