Eye-tracking technology applications for supporting individuals with autism spectrum disorder: insights, challenges, and opportunities.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterised by deficits in social communication and restricted, repetitive behaviours, with significant impacts on individuals and their families. In recent years, eye-tracking technology has emerged as a valuable tool for examining the cognitive and perceptual differences associated with ASD. Despite technological advancements, the diversity and complexity of this technology may pose challenges for researchers, particularly in fields such as psychology and special education.

Multi-fidelity neural network-based prediction of tensile strength of high-entropy alloy (FeNiCoCrCu) using molecular dynamics data.

Context: High-entropy alloys (HEAs) represent a class of advanced materials with superior mechanical, thermal, and chemical properties. FeNiCoCrCu HEA has been of particular interest due to its excellent tensile strength, corrosion resistance, and thermal stability. However, it is a significant challenge to understand and optimize the mechanical properties of such alloys due to the complex structure.

The association between women's decision-making roles in sanitation and mental well-being in urban Bangladesh.

Research indicates that women and girls face gender-specific stressors related to sanitation that affect their mental health, and women's decision-making agency may play a role in this relationship. This study aimed to quantitatively assess the association between women's sanitation-related decision-making - overall, within the household, and within the community - and well-being in two urban municipalities in Bangladesh. This paper is a secondary analysis of cross-sectional survey data collected from 1449 women in Meherpur and Saidpur, Bangladesh from March-April 2022.

A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques.

Classifying emotions based on EEG signals is really important for enhancing our interactions with computers, monitoring mental health and creating applications in affective computing field. This study explores improving emotion recognition performance by applying traditional machine learning classifiers and boosting techniques to EEG data from the DEAP dataset. To categorize emotional states, we used four classifiers: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XGBoost and Gradient Boosting.

Facile synthesis of C-TiO-MoS nanocomposite based on commercial TiO nanopowder for photodegradation of methylene blue.

We present a facile synthesis method for a C-TiO-MoS-based photocatalytic nanocomposite designed for the efficient degradation of pollutant dyes, specifically methylene blue (MB) and rhodamine B (RhB). Few-layered MoS nanosheets were exfoliated from natural bulk MoS urea-assisted ball milling. These nanosheets were then mixed with TiO nanopowder and sodium deoxycholate (SDC) surfactant, followed by thermal annealing at 600 °C in argon (Ar) gas.

Design, Synthesis, In Vitro, and In Silico Studies of 5-(Diethylamino)-2-Formylphenyl Naphthalene-2-Sulfonate Based Thiosemicarbazones as Potent Anti-Alzheimer Agents.

Alzheimer's disease (AD) is known as one of the more devastating neurodegenerative diseases diagnosed in older people. Cholinesterase inhibitors (ChEI) can be used as an effective palliative treatment for AD. An extensive range of new biologically active 4-(diethylamino) salicylaldehyde-based thiosemicarbazone derivatives 5(a-u) was synthesized and evaluated as inhibitors of cholinesterase (ChE) and monoamine oxidase (MAO) enzymes.

Design of a bio-hybrid solar quadricycle for sustainable urban delivery service.

The current study intends to provide a sustainable substitute for conventional motorbike-based delivery systems in Sydney, Australia, by designing a novel bio-hybrid solar quadricycle powered by plug-in, pedal, and solar energy. The current study exclusively integrates structural material analysis through ANSYS with powertrain simulation in Simulink to ascertain performance and feasibility. Among the tested materials, the low alloy steel AISI 4140 exhibits exceptional structural integrity with a minimal total deformation of 0.

Monitoring and Health Risks Assessment of Primordial Radionuclides in Vegetables and Agricultural Soils in Some Hilly Regions of Southeastern Bangladesh.

This study demonstrates the naturally occurring radioactive materials (NORMs: Ra, Th, and K) in vegetables and their corresponding agricultural soil samples collected from southeastern Bangladesh (Chittagong). Elemental abundances measured by neutron activation analysis have been converted into associated radioactivities. Mean specific radioactivities of Ra, Th, and K in soil were 43.

Synthesis, characterization, antimicrobial activity and study of pyridinium based ionic liquids.

A series of twenty-one pyridinium-based ionic liquids (ILs) was synthesized and thoroughly characterized using FTIR, NMR, UV-Vis spectroscopy, and melting point analysis. The ILs' antibacterial activity was assessed against and a panel of bacteria, both Gram-positive and Gram-negative. Notable antibacterial and antifungal properties were demonstrated by IL-10, IL-11, and IL-14 specifically.

Ce-doped nano-Fe-TiO composites for photocatalytic degradation of naphthol blue black in aqueous medium.

In this study, the effectiveness of Ce-doped nano-Fe-TiO composites as photocatalysts for the degradation of Naphthol Blue Black (NBB) dye present in water was investigated. Several TiO nano-photocatalysts were synthesized using the sol-gel method, including undoped TiO, TiO doped with Fe (TF), and co-doped with FeO-CeO (TFC), FeO-AgO (TFA), and FeO-CeO-AgO (TFCA). Characterizations of the synthesized catalysts were performed using Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), and Scanning Electron Microscopy (SEM) to understand their structural and morphological properties.

A multifaceted approach to drug-drug interaction extraction with fusion strategies.

Objective: Drug-drug interactions (DDIs) occur when one medication affects the efficacy of another, potentially leading to unforeseen patient outcomes. Existing studies primarily focus on textual data, but overlook a wealth of the drug's multimodal information. This study aims to enhance DDI extraction by integrating diverse data modalities and evaluating various fusion strategies.

Impact of Brownian motion on the optical soliton solutions for the three component nonlinear Schrödinger equation.

In this manuscript, the three-component nonlinear stochastic Schrö dinger equation under the effects of Brownian motion in the Stratonovich sense is examined here. The different types of exact optical soliton solutions are explored under the noise effects. The propagation of an optical pulse in a birefringent optical fiber is described by the three nonlinear complex models.

A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization.

With human guidance, computers now use machine learning (ML) in artificial intelligence (AI) to learn from data, detect trends, and make predictions. Software can adapt and improve with new information. Imaging scans leverage pattern recognition to predict outcomes, diagnose disorders, and suggest treatments.

Attomolar Detection of HIV-1 with Label-Free RCA-rCRISPR on Smartphone.

Human Immunodeficiency Virus-1 (HIV) remains a major global public health challenge, having led to over 42.3 million deaths since its discovery in the early 1980s. Despite progress in prevention and treatment, around 60% of people with HIV (PWH) remain undiagnosed in resource-limited regions, disproportionately affecting vulnerable populations and underserved communities across the world.

Developing smart phone-based image scanning densitometric method for on-site analysis of Cr(VI), Ni(II), and Fe(II) in aqueous medium for monitoring environmental and biological samples.

The main objective of this work is to introduce "image scanning densitometry" for the elimination of drawbacks of the spectrometric methods, which are conventionally used for analysis of metal ions in aqueous samples. This method is beneficial because a small amount of analyte was used for toxicological chemical analysis in medical diagnostics and environmental monitoring along with its portable nature for on-spot analysis by integrating AI technology with conventional methods. By using this method, colorimetric reactions are performed on TLC plates and results are quantified by digitizing the area of spots by using image scanning devices and specially developed software.

A Topsoil Salinity Observatory for Arable Lands in Coastal Southwest Bangladesh.

The research assesses soil salinity in the southwest coastal region of Bangladesh, collecting a total of 162 topsoil samples between March 1 and March 9, 2024, and processing them following the standard operating procedure for soil electrical conductivity (soil/water, 1:5). Electrical conductivity (EC) measurements obtained using a HI-6321 advanced conductivity benchtop meter were visualized spatially using bubble density mapping. The map demonstrates that soil salinity in the region of interest ranges from 0.

A meta fusion model combining geographic data and twitter sentiment analysis for predicting accident severity.

In recent years, advancements in deep learning and real-time data processing have significantly enhanced traffic management and accident prediction capabilities. Building on these developments, this study introduces an innovative approach ConvoseqNet to improve traffic accident prediction by integrating traditional traffic data with real-time social media insights, specifically using geographic data and Twitter sentiment analysis. ConvoseqNet combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks in a sequential architecture, enabling it to effectively capture complex spatiotemporal patterns in traffic data.

Fusion of Personalized Federated Learning (PFL) with Differential Privacy (DP) Learning for Diagnosis of Arrhythmia Disease.

This paper presents a novel privacy-preserving architecture, a fusion of Federated Learning with Personalized Models and Differential Privacy (FLPMDP), for diagnosing arrhythmia from 12-lead electrocardiogram (ECG) signals. The architecture supports collaborative training in decentralized healthcare institutions without exposing sensitive patient information. By employing gated recurrent units (GRUs) for temporal sequence modeling along with feature fusion techniques and local differential privacy enforcement, FLPMDP ensures robust classification performance with data confidentiality.

A compact design of MIMO patch antenna with high gain and symmetrical radiation pattern.

This paper presents a compact design of multiple-input multiple-output (MIMO) microstrip patch antenna with high gain and symmetrical radiation pattern. The proposed antenna consists of dual-polarized crossed patches and T-junction power dividers. This combination makes the proposed design achieve high gain and high isolation with compact size and not requiring any additional decoupling structure.

An interpretable dynamic ensemble selection multiclass imbalance approach with ensemble imbalance learning for predicting road crash injury severity.

Accurate prediction of crash injury severity and understanding the seriousness of multi-classification injuries is vital for informing authorities and the public. This Knowledge is crucial for enhancing road safety and reducing congestion, as different levels of injury necessitate distinct interventions, policies and responses to support sustainable transportation. Existing ML techniques often face class imbalance issues, resulting in suboptimal performance.

Effect of gramicidin A on the constant tension-induced rupture of giant unilamellar vesicles and the underlying mechanisms.

Gramicidin A (GrA), a well-known ionophore, plays a significant role in modifying the biophysical properties of membranes. However, its mechanism of action in influencing rupture kinetics of vesicles and the stability of membranes under constant mechanical tension remains unclear. To investigate this, giant unilamellar vesicles (GUVs) composed of DOPG and DOPC phospholipids, with varying molar fractions of GrA (ranging from 0 % to 5 %), were synthesized using the natural swelling method.

Optimizing tomato plant and insect disease control using a fractional model and deep neural networks.

Plants are vital as a primary source of food and provide essential contributions to the production of medicines, fibers, and the preservation of ecological equilibrium. Nevertheless, they are often endangered by viral infections, primarily transmitted by insect vectors that transmit the virus from host plants to susceptible plants. This work aims to investigate a biological control approach by integrating predators into a plant-virus propagation model controlled by delay differential equations.

Dynamics of computational waveform: A study of bifurcation, chaos, and sensitivity analysis.

This study delves into the extraction of solutions for the fractional-order that describes wave circulation in space-time fractional low-pass electrical transmission (LPET) lines and Drinfel'd-Sokolov-Wilson (DSW) equations. Leveraging the Sardar-subequation scheme, by applying a simple linear fractional transformation, the model equations are transformed into an ordinary differential equation. The use of the Sardar-subequation technique produces a diverse range of traveling waveform for the governing equations.

StackGlyEmbed: prediction of N-linked glycosylation sites using protein language models.

Motivation: N-linked glycosylation is one of the most basic post-translational modifications (PTMs) where oligosaccharides covalently bond with Asparagine (N). These are found in the conserved regions like N-X-S or N-X-T where X can be any residue except Proline (P). Prediction of N-linked glycosylation sites has great importance as these PTMs play a vital role in many biological processes and functionalities.

Mechanical and environmental performance of sugarcane Bagasse Ash from Khyber Pakhtunkhwa in sustainable concrete.

In recent decades, the partial substitution of cement with sugarcane bagasse ash (SCBA) has received attention for construction applications because of its pozzolanic characteristics. However, regional-scale studies are encouraged to increase the use of SCBA at the industrial level. Limited literature is available on the effect of SCBA on concrete Alkali-silica reactivity (ASR), CO emissions and economic feasibility.