Evaluating the dynamic response and phase angle behavior of SBS-modified asphalt mixtures for enhanced pavement performance.

Bitumen exhibits viscoelastic properties, showcasing both viscous and elastic behaviors, which are characterized by the phase angle and dynamic modulus. Issues like early fatigue fractures, rutting, and permanent deformations in bituminous asphalt pavements arise due to moisture susceptibility, high-temperature deformation, low-temperature cracking, and overloading. These distresses result in potholes, alligator cracks, and specific deformations that lead to early pavement failure, increasing rehabilitation and maintenance costs.

Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources.

Functional excitation-inhibition ratio for social anxiety analysis and severity assessment.

Introduction: Social anxiety disorder (SAD) is a prevalent psychiatric condition characterized by an intense fear of and avoidance of social situations. Traditional assessment methods for SAD primarily rely on subjective self-report questionnaires and clinical interviews, which can be prone to biases and inaccuracies. This study aims to explore the functional excitation-inhibition (fEI) ratio derived from EEG data as a potential objective biomarker for assessing SAD severity.

Evaluating and optimizing NBR-modified bituminous mixes: a rheological and RSM-based study.

Bitumen shows visco-elastic behavior, exhibiting both elastic and viscous properties as predicted by dynamic response and phase angle. Modern asphalt bituminous pavements face issues such as early-stage fatigue cracks, rutting, and permanent deformations due to low-temperature cracking, high-temperature deformation, moisture susceptibility, and overloading. These pavement distresses result in the formation of potholes, alligator cracks, and various deformations, which accelerate the need for rehabilitation and maintenance.

Multifaceted Impact of Lipid Extraction on the Characteristics of Polymer-Based Sewage Sludge towards Sustainable Sludge Management.

Dewatered sludge (DS) is a sewage sludge with a unique property due to extracellular polymeric substances (EPSs) and polymer flocculants. These components form a stable 3D polymer network to increase dewatering efficiency, leaving behind valuable materials such as lipids. This article explored the influences of DS particle size on lipid yield and the effects of extraction on the chemical, morphological, and thermal properties of the residual dewatered sludge (RDS).

Response surface methodology and machine learning optimisations comparisons of recycled AA6061-BC-ZrO hybrid metal matrix composites via hot forging forming process.

The optimal conditions of applied factors to reuse Aluminium AA6061 scraps are (450, 500, and 550) ⁰C preheating temperature, (1-15) % Boron Carbide (BC), and Zirconium (ZrO) hybrid reinforced particles at 120 min forging time via Hot Forging (HF) process. The response surface methodology (RSM) and machine learning (ML) were established for the optimisations and comparisons towards materials strength structure. The Ultimate Tensile Strength (UTS) strength and Microhardness (MH) were significantly increased by increasing the processed temperature and reinforced particles because of the material dispersion strengthening.

MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach.

Background: Major depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine learning (ML) has been implemented in previous studies to study the alteration of microRNA (miRNA) levels in MDD cases, clinical translation has not been feasible due to the lack of interpretability (i.

Using an Interpretable Amino Acid-Based Machine Learning Method to Enhance the Diagnosis of Major Depressive Disorder.

Major depressive disorder (MDD) is a leading cause of disability worldwide. At present, however, there are no established biomarkers that have been validated for diagnosing and treating MDD. This study sought to assess the diagnostic and predictive potential of the differences in serum amino acid concentration levels between MDD patients and healthy controls (HCs), integrating them into interpretable machine learning models.

Synthesis and Characterization of a Novel Nanosized Polyaniline.

Polyaniline (PANI) is a conductive polymer easily converted into a conducting state. However, its limited mechanical properties have generated interest in fabricating PANI composites with other polymeric materials. In this study, a PANI-prevulcanized latex composite film was synthesized and fabricated in two phases following chronological steps.

Robustness evaluations of pathway activity inference methods on gene expression data.

Background: With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches.

An enhanced gain non-isolated quadratic boost DC-DC converter with continuous source current.

In this paper, a non-isolated quadratic boost DC-DC converter has been proposed. The proposed converter provides high output voltage gain with a lower component count on the structure. In addition, the input side inductor provides continuous source current and the output voltage is positive.

Green Synthesis of ZnO and Black TiO Materials and Their Application in Photodegradation of Organic Pollutants.

ZnO and black TiO have been selected as the most efficient materials for organic pollution abatement due to their increased efficiency when compared to other materials. However, the concept of green chemistry makes it desirable to design green synthesis approaches for their production. In this study, black TiO was synthesized using an environmentally safe synthetic technique with glycerol as a reductant.

Co-anaerobic digestion of sawdust and chicken manure with plant herbs: Biogas generation and kinetic study.

Plant herbs specifically (SW) and peppermint (PPM) are selected for its insect repellent properties as the use of chicken manure (CM) in anaerobic digestion (AD) potentially attract flies due to the digestate produced. Hence, the addition of SW and PPM in the AD system of CM could deter flies' infestation while producing biogas. Previous work has shown that AD of sawdust (SD) and CM with these plant herbs were able to produce biogas and reduce the flies attraction towards the digestate.

Fabrication and Characterization of Magnetic Cellulose-Chitosan-Alginate Composite Hydrogel Bead Bio-Sorbent.

The implementation of inorganic adsorbents for the removal of heavy metals from industrial effluents generates secondary waste. Therefore, scientists and environmentalists are looking for environmentally friendly adsorbents isolated from biobased materials for the efficient removal of heavy metals from industrial effluents. This study aimed to fabricate and characterize an environmentally friendly composite bio-sorbent as an initiative toward greener environmental remediation technology.

Nylon 6,6 Waste Nanofiber Membrane for Produced Water Filtration: Experimental, Performance Modelling, Optimization and Techno-Economic Analysis.

Produced water (PW) is a by-product of oil and gas extraction, of which it is deemed as the primary contributor of wastewater stream in oil production. Conventional treatment such as membrane separation is favoured due to its sustainability and cost effectiveness. On the other hand, oceanic litters such as abandoned fishing nets endangered the marine life ecosystem, despite of its potential to be raw material for fabrication of nanofiber membrane (NFM).

Electrochemical biosensor detection on respiratory and flaviviruses.

Viruses have spread throughout the world and cause acute illness or death among millions of people. There is a growing concern about methods to control and combat early-stage viral infections to prevent the significant public health problem. However, conventional detection methods like polymerase chain reaction (PCR) requires sample purification and are time-consuming for further clinical diagnosis.

An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity.

The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity.

ReSTiNet: An Efficient Deep Learning Approach to Improve Human Detection Accuracy.

Human detection is an important task in computer vision. It is one of the most important tasks in global security and safety monitoring. In recent days, Deep Learning has improved human detection technology.

Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data.

Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.

Optimization of CO Sorption onto Spent Shale with Diethylenetriamine (DETA) and Ethylenediamine (EDA).

A novel technique was employed to optimize the CO sorption performance of spent shale at elevated pressure-temperature (PT) conditions. Four samples of spent shale prepared from the pyrolysis of oil shale under an anoxic condition were further modified with diethylenetriamine (DETA) and ethylenediamine (EDA) through the impregnation technique to investigate the variations in their physicochemical characteristics and sorption performance. The textural and structural properties of the DETA- and EDA- modified samples revealed a decrease in the surface area from tens of m/g to a unit of m/g due to the amine group dispersing into the available pores, but the pore sizes drastically increased to macropores and led to the creation of micropores.

Altering substrate properties of thin film nanocomposite membrane by AlO nanoparticles for engineered osmosis process.

The scarcity of energy and water resources is a major challenge for humanity in the twenty-first century. Engineered osmosis (EO) technologies are extensively researched as a means of producing sustainable water and energy. This study focuses on the modification of substrate properties of thin film nanocomposite (TFN) membrane using aluminium oxide (AlO) nanoparticles and further evaluates the performance of resultant membranes for EO process.

GO/TiO-Related Nanocomposites as Photocatalysts for Pollutant Removal in Wastewater Treatment.

Water pollution has been a prevalent issue globally for some time. Some pollutants are released into the water system without treatment, making the water not suitable for consumption. This problem may lead to more grave problems in the future including the destruction of the ecosystem along with the organisms inhabiting it, and illness and diseases endangering human health.

Thermal Expansion of 3C-SiC Obtained from In-Situ X-ray Diffraction at High Temperature and First-Principal Calculations.

In situ X-ray crystallography powder diffraction studies on beta silicon carbide (3C-SiC) in the temperature range 25-800 °C at the maximum peak (111) are reported. At 25 °C, it was found that the lattice parameter is 4.596 Å, and coefficient thermal expansion (CTE) is 2.

Updated Perceptions on Polymer-Based Enhanced Oil Recovery toward High-Temperature High-Salinity Tolerance for Successful Field Applications in Carbonate Reservoirs.

The aging of the existing reservoirs makes the hydrocarbon extraction shift toward newer reserves, and harsh conditioned carbonates, which possess high temperature and high salinity (HTHS). Conventional polymer-flooding fails in these HTHS carbonates, due to precipitation, viscosity loss, and polymer adsorption. Therefore, to counteract these challenges, novel polymer-based cEOR alternatives employ optimized polymers, polymer-surfactant, and alkali-surfactant-polymer solutions along with hybrid methods, which have shown a potential to target the residual or remaining oils in carbonates.

Translating medical image to radiological report: Adaptive multilevel multi-attention approach.

Background And Objective: Medical imaging techniques are widely employed in disease diagnosis and treatment. A readily available medical report can be a useful tool in assisting an expert for investigating the patient's health. A radiologist can benefit from an automatic medical image to radiological report translation system while preparing a final report.