Browse Articles

The visual communication using generative artificial intelligence in the context of new media

Superconductivity in bcc-selenium under megabar pressure

Green synthesis of superhydrophobic cotton filters using Pistacia atlantica gum for efficient oil and water separation

Safety assessment of proteasome inhibitors real world adverse event analysis from the FAERS database

A fine-tuned convolutional neural network model for accurate Alzheimer’s disease classification

Abstract Alzheimer’s disease (AD) is one of the primary causes of dementia in the older population, affecting memories, cognitive levels, and the ability to accomplish simple activities gradually. Timely intervention and efficient control of the disease prove to be possible through early diagnosis. The conventional machine learning models designed for AD detection work well only up to a certain point. They usually require a lot of labeled data and do not transfer well to new datasets. Additionally, they incur long periods of retraining. Relatively powerful models of deep learning, however, also are very demanding in computational resources and data. In light of these, we put forward a new way of diagnosing AD using magnetic resonance imaging (MRI) scans and transfer learned convolutional neural networks (CNN). Transfer learning makes it easier to reduce the costs involved in training and improves performance because it allows the use of models which have been trained previously and which generalize very well even when there is very little training data available. In this research, we used three different pre-trained CNN based architectures (AlexNet, GoogleNet, and MobileNetV2) each implemented with several solvers (e.g. Adam, Stochastic Gradient Descent or SGD, and Root Mean Square Propagation or RMSprop). Our model achieved impressive classification results of 99.4% on the Kaggle MRI dataset as well as 98.2% on the Open Access Series of Imaging Studies (OASIS) database. Such results serve to demonstrate how transfer learning is an effective solution to the issues related to conventional models that limits the accuracy of diagnosis of AD, thus enabling their earlier and more accurate diagnosis. This would in turn benefit the patients by improving the treatment management and providing insights on the disease progression.

A three-stage machine learning and inference approach for educational data

Abstract A central task in educational studies is to uncover factors that drive a student’s academic performance. While existing studies have utilized meticulous regression designs, it is challenging to select appropriate controls. Machine learning, however, offers a solution whereby the entire variable set can be inspected and filtered by different optimization schemes. In that light, this paper adopts a three-stage framework to analyze and discover potentially latent causal relationships from an open dataset from UCI. In the first stage, machine learning methods are employed to select candidate variables that are closely associated with student grades, and then a “post-double-selection” process is implemented to select the set of control variables. In the final stage, three case studies are conducted to illustrate the effectiveness of the three-stage design. The model pipeline is suitable for situations where there is only minimal prior knowledge available to address a potentially causal research question.

A muscle synergy-based method to improve robot-assisted movements

An IPv6 address fast scanning method based on local domain name association

Abstract With the increase of security issues in IPv6 networks, conducting address scanning in IPv6 networks proves beneficial for identifying potential security risks and vulnerabilities. To enhance the privacy of users’ IPv6 addresses, mainstream OS (Operating System) nodes currently employ randomized interface identifiers and temporary IPv6 addresses. Additionally, since most existing IPv6 address scanning methods rely on active scanning, which makes current on-link IPv6 address scanning methods face the challenges of incomplete scan results, poor coverage across different OSs, significant impact on network performance, and the inability to promptly detect subsequently joined hosts. To this end, An IPv6 address fast scanning method based on local domain name association (FScan6), which combines active scanning and passive listening, is proposed. The active scanning module targets different OSs using distinct protocols (Browser and DNS-SD) to obtain local domain names of on-link hosts. Meanwhile, the passive listening module monitors traffic to extract local domain names of on-link hosts. Then, it employs mDNS protocol to retrieve IPv6 addresses associated with these local domain names. A typical on-link IPv6 network environment was constructed, comprising 26 versions of Windows, Apple, and Linux OSs, and FScan6 was compared with 9 IPv6 address scanning methods. The experimental results show that FScan6 outperforms existing IPv6 address scanning methods in terms of OS coverage and scanning result completeness. Specifically, regarding OS coverage, FScan6 successfully detected all IPv6 addresses across 26 different OS versions, which outperformed 9 address scanning tools and scripts by a factor of 2.89 times at most. Regarding scanning result completeness, FScan6 identified up to 54 additional IPv6 addresses at most compared to these tools and scripts. Additionally, FScan6 has a minimal impact on network performance, with the packet loss rate induced by the tool consistently remaining at 0%.

Secular trends in heat related illness and excess sun exposure rates across climatic zones in the United States from 2017 to 2022

Abstract Heat waves are a major public health challenge, yet the link between heat-related illness (HRI) and regional climate and geography is underexplored. We examined HRI and excess sun exposure incidence rates (IR) [95% confidence interval (CI) per 100,000 person-years], and their correlation with regional maximum temperatures across 9 US climatic zones 33,603,572 individuals were followed from 2017 to 2022. We observed 10,652 individuals with HRI diagnosis (median age: 49 years, 62.3% male). Seasonal peaks occurred during summer: highest overall IR (130.97 [119.93–142.75]) was recorded in July 2019, highest regional IR was reported in the South (186.04 [117.93–279.15]) during 2020. Strongest correlations between monthly maximum temperature and incidence of HRI were observed in the West (Pearson Correlation Coefficient (cor) = 0.854) and Southwest (cor = 0.832). In contrast, we observed 131,204 individuals with excess sun exposure (predominantly older adults [median age: 67 years], 52.3% female, 30% with history of cancer). Overall IR for sun exposure peaked in March 2021 (664.31 [644.84–684.21]) and lacked a consistent seasonal pattern. Sun exposure exhibited weaker correlations with regional temperatures, even in high-temperature regions like the West (cor = 0.305). These data indicate regional variations in HRI. With distinct at-risk groups for HRI and sun exposure, targeted regional interventions may be beneficial, such as heat safety protocols to reduce HRI risk and sun protection campaigns for older adults to mitigate sun exposure risk.

Enhancing the anticancer effects of rosmarinic acid in PC3 and LNCaP prostate cancer cells using titanium oxide and selenium-doped graphene oxide nanoparticles

Development of a stream DTM generation method using vegetation and morphology composite filters with SfM point clouds

Dynamic monitoring of M-protein quantification by immunotyping using capillary zone electrophoresis during the chemotherapy of patients with multiple myeloma

Modeling and performance evaluation of hybrid photovoltaic thermal, wind, and battery microgrids using optimization and dynamic simulation

Abstract This study aims to comprehensively develop a modeling framework to evaluate the dynamic performance of a photovoltaic/thermal (PV/T) system integrated with a hybrid off-grid microgrid. The advancements made by this research in investigating the optimal design of the PV/T system and dynamic performance assessment of the proposed hybrid microgrid are twofold. First, a nonlinear mathematical problem is formulated to determine the optimal system design that maximizes power, taking into account the thermo-electrical constraints. Secondly, the research highlights the development of a component mask with a user-defined functionality in MATLAB/Simulink using the optimal design parameters obtained from the optimization model. The developed PV/T component is then integrated with a wind turbine/ battery system. The resultant integrated energy system is then compared with a conventional PV/wind/battery microgrid system based on a 72-hour simulation. The outcomes showed that under cloudy, rainy, sunny, and windy conditions, the extra cumulative electricity generation from the PV/T system-based microgrid is 2.12, 2.74, 1.72, and 0.31% compared to the PV system-based microgrid. Additionally, improved battery system operation of nearly 1.8Wh was realized, signifying PV/T contribution to efficient microgrid operation.

A high-throughput, fully automated competition assay to evaluate SARS-CoV-2 neutralizing responses and epitope specificity in clinical samples

Abstract Coronavirus disease-2019 (COVID-19) remains a critical global health concern. We developed a fully automated, high-throughput competition immunoassay to elucidate how epitope recognition on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor-binding domain (RBD) correlates with neutralizing activity. Analysis of clinical samples from both SARS-CoV-2-infected and vaccinated individuals revealed that vaccination elicits significantly higher antibody titers across multiple S1 subunit epitopes compared to natural infection. Notably, median antibody levels against the receptor-binding motif (RBM) exceeded 50% in both cohorts, highlighting the RBM as a key target for antibody induction irrespective of immune origin. Furthermore, the strongest correlation with neutralizing activity was observed for antibodies directed against the broader S1 subunit, indicating that epitopes outside the RBM also contribute to neutralization. These findings underscore the importance of both RBM- and non-RBM-directed antibodies in effective immune defense against SARS-CoV-2. Our assay enables large-scale, reliable quantification of neutralizing antibodies and provides critical insights for developing improved diagnostic antigens and vaccine strategies aimed at eliciting robust, multi-epitope immune responses.

Detection of pathogens associated with acute febrile illness in children under five years of age in rural Tanzania

Abstract Acute febrile illness (AFI) investigations are crucial for public health. They can provide data on disease prevalence, morbidity, and mortality, and improve treatment, management, control, and detection of outbreaks in areas with limited diagnostic tests. Current understanding of multiple causes of AFI in the paediatric population in Tanzania is limited. This study aimed to simultaneously detect 33 pathogens using TaqMan Array Card based real-time PCR. Whole blood samples were collected from a total of 247 children (2–59 months old) who presented with febrile illness at Dareda and Haydom hospitals in north-eastern Tanzania between November 2015 and March 2016. Overall, 50 (20.2%) and 8 (3.2%) of 247 children had at least one and more than one pathogen detected respectively. Bacterial zoonoses were frequently detected including Brucella spp. (n = 18, 7.3%), C. burnetii (n = 4, 1.6%), Bartonella spp. (n = 3, 1.2%), Rickettsia spp. (n = 3, 1.2%) and Leptospira spp. (n = 1, 0.4%). Dengue virus was detected in 14 (5.7%) individuals and Plasmodium spp. in 12 (4.9%) individuals. These findings reveal the potential clinical importance of zoonoses and arboviruses in febrile children in Tanzania and highlight the need to consider a broad range of pathogens in febrile illness diagnosis.

Blended nutrition education with real-life scenarios enhances learning and nutritional counseling capabilities in nursing students

HIF-1 regulates mitochondrial function in bone marrow-derived macrophages but not in tissue-resident alveolar macrophages

Author Correction: An open-access dashboard to interrogate the genetic diversity of Mycobacterium tuberculosis clinical isolates

Tire-road surface characteristics estimation for skid-steered wheeled vehicle

A quantum inspired machine learning approach for multimodal Parkinson’s disease screening

Abstract Parkinson’s disease, currently the fastest-growing neurodegenerative disorder globally, has seen a 50% increase in cases within just two years. As disease progression impairs speech, memory, and motor functions over time, early diagnosis is crucial for preserving patients’ quality of life. Although machine-learning-based detection has shown promise for detecting Parkinson’s disease, most studies rely on a single feature for classification and can be error-prone due to the variability of symptoms between patients. To address this limitation we utilized the mPower dataset, which includes 150,000 samples across four key biomarkers: voice, gait, tapping, and demographic data. From these measurements, we extracted 64 features and trained a baseline Random Forest model to select the features above the 80th percentile. For classification, we designed a simulatable quantum support vector machine (qSVM) that detects high-dimensional patterns, leveraging recent advancements in quantum machine learning. With this novel and simulatable architecture that can be run on standard hardware rather than resource-intensive quantum computers, our model achieves an accuracy of 90%, F-1 score of 0.90, and an AUC of 0.98—surpassing benchmark models. Utilizing an innovative classification framework built on a diverse set of features, our model offers a pathway for accessible global Parkinson’s screening.