Effect of Extensive Solar Ultra-Violet Irradiation on the Durability of High-Density Polyethylene- and Polypropylene-Based Wood-Plastic Composites.

The natural and laboratory-accelerated weathering of wood-plastic composites (WPCs) based on high-density polyethylene (HDPE) and polypropylene (PP) plastics was investigated in this study. Injection molded samples of WPCs with different loadings of wood fiber ranging from 0 to 36 wt.% of wood were subjected to laboratory-accelerated weathering and natural weathering.

Bio-concentration of hazardous metals in migrant shorebirds in a key conservation reserve and adjoining areas on the west coast of India.

Heavy metal pollution is a growing environmental concern as it causes the degradation of wetlands by affecting the organisms at different trophic levels. Shorebirds typically feed on benthic invertebrates including polychaete worms, crustaceans and molluscs. Thus, the assessment of bioconcentration of heavy metals in shorebirds provides an insight into the extent of bioaccumulation of these hazardous metals in the upper trophic levels.

A novel approach to assess acid diversion efficiency in horizontal wells.

Using an acid to stimulate a heterogeneous carbonate reservoir during matrix acidizing may lead to over-treating the high permeability zones, leaving low permeability zones untreated. This is particularly exacerbated in long horizontal sections, necessitating the use of acid diverters for effective acid distribution across the formation. In previous studies, conventional core flooding systems were utilized where single inlet and outlet lines were used or, at best, two outlet lines for dual-core flooding.

Seismic anisotropy prediction using ML methods: A case study on an offshore carbonate oilfield.

Estimating seismic anisotropy parameters, such as Thomson's parameters, is crucial for investigating fractured and finely layered geological media. However, many inversion methods rely on complex physical models with initial assumptions, leading to non-reproducible estimates and subjective fracture interpretation. To address these limitations, this study utilizes machine learning methods: support vector regression, extreme gradient boost, multi-layer perceptron, and a convolutional neural network.

Personality and Attachment Patterns in Patients with Psychogenic Non-Epileptic Seizures in Saudi Arabia.

The purpose of this study was to investigate personality and relationship patterns in patients with psychogenic non-epileptic seizures (PNES) and compare them to patients with epilepsy and healthy controls. A total of 68 participants were recruited (mean age = 29.8 ± 9.

Computer-Aided Diagnosis in Spontaneous Abortion: A Histopathology Dataset and Benchmark for Products of Conception.

Spontaneous abortion, commonly known as miscarriage, is a significant concern during early pregnancy. Histopathological examination of tissue samples is a widely used method to diagnose and classify tissue phenotypes found in products of conception (POC) after spontaneous abortion. Histopathological examination is subjective and dependent on the skill and experience of the examiner.

Facile development of carbon nanotube (CNT)-based flexible thermoelectric materials for energy-harvesting applications.

In this study, we investigate the thermoelectric properties of functionalized multi-walled carbon nanotubes (F-MWCNTs) dispersed over a flexible substrate through a facile vacuum filtration route. To improve their interfacial adhesion and dispersion, F-MWCNTs underwent hot-pressing. The heat-treatment has improved the nanotubes' connections and subsequently reduced porosity as well, which results in an increasing electrical conductivity upon increasing temperature of hot-pressing.

A study on Pufferfish privacy algorithm based on Gaussian mixture models.

In real-world scenarios, mixture models are frequently employed to fit complex data, demonstrating remarkable flexibility and efficacy. This paper introduces an innovative Pufferfish privacy algorithm based on Gaussian priors, specifically designed for Gaussian mixture models. By leveraging a sophisticated masking mechanism, the algorithm effectively safeguards data privacy.

Microbial fuel cells to monitor natural attenuation around groundwater plumes.

This research presents a straightforward and economically efficient design for a microbial fuel cell (MFC) that can be conveniently integrated into a borehole to monitor natural attenuation in groundwater. The design employs conventional, transparent, and reusable PVC bailers with graphite tape and granular activated carbon to create high surface area electrodes. These electrodes are connected across redox environments in nested boreholes through a wire and variable resistor setup.

Optimization of nonlinear properties of COLi-doped alkalides group I/III doping for unprecedented charge transfer and advancements in optoelectronics.

The design and synthesis of nonlinear optical (NLO) materials are rapidly growing fields in optoelectronics. Considering the high demand for newly designed materials with superior optoelectronic characteristics, we investigated the doping process of Group-IIIA elements (namely, B, Al and Ga) onto alkali metal (AM = Li, Na and K)-supported COLi (AM@COLi) complexes to enhance their NLO response. The AM-COLi complexes retained their structural features following interaction with the Group-IIIA elements.

Modelling Euphrates river water quality index based on field measured data in Al-Diwaniyah City, Iraq.

Pollution monitoring in surface water using field observational procedure is a challenging matter as it is time consuming, and needs a lot of efforts. This study addresses the challenge of efficiently monitoring and predicting water pollution using a GIS-based artificial neural network (ANN) to detect heavy metal (HM) pollution in surface water and effect of wastewater required discharge on the Euphrates River in Al-Diwaniyah City, Iraq. The study established using 40 water sampling stations and incorporates Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-OES) to assess HM levels.

Albizia Procera-Derived Nitrogen-Doped Carbon: A Versatile Material for Energy Conversion, Storage, and Environmental Applications.

This review explores the diverse applications of nitrogen-doped carbon derived from Albizia procera, known as white siris. Native to the Indian subcontinent and tropical Asia, this species thrives in varied conditions, contributing to sustainable development. The nitrogen-rich leaves of Albizia procera are an excellent source for synthesizing nitrogen-doped carbon, which possesses remarkable properties for advanced technologies.

Catalytic and Noncatalytic in Situ Hydrogen Production from Heavy Oil: A Review of Experimental Studies.

Hydrogen (H) offers a less carbon-intensive energy production method than natural gas. The potential of utilizing hydrogen at a large scale within the future energy mix to fuel the world opens the door to investigating hydrogen production from heavy and extra-heavy oil reservoirs. Various reaction mechanisms are involved in the in situ combustion gasification of heavy oil to produce sustainable and low carbon intensive hydrogen.

Enhancing lesion detection in liver and kidney CT scans via lesion mask selection from two models: A main model and a model focused on small lesions.

Automated segmentation and detection of tumors in CT scans of the liver and kidney have a significant potential in assisting clinicians with cancer diagnosis and treatment planning. However, current approaches, including state-of-the-art deep learning ones, still face many challenges. Many tumors are not detected by these approaches when tested on public datasets for tumor detection and segmentation such as the Kidney Tumor Segmentation Challenge (KiTS) and the Liver tumor segmentation challenge (LiTS).

Predicting student self-efficacy in Muslim societies using machine learning algorithms.

Introduction: Self-efficacy is a critical determinant of students' academic success and overall life outcomes. Despite its recognized importance, research on predictors of self-efficacy using machine learning models remains limited, particularly within Muslim societies. This study addresses this gap by leveraging advanced machine learning techniques to analyze key factors influencing students' self-efficacy.

Temporal trends and predictive modeling of air pollutants in Delhi: a comparative study of artificial intelligence models.

Air pollution monitoring and modeling are the most important focus of climate and environment decision-making organizations. The development of new methods for air quality prediction is one of the best strategies for understanding weather contamination. In this research, different air quality parameters were forecasted, including Carbon Monoxide (CO), Nitrogen Monoxide (NO), Nitrogen Dioxide (NO), Ozone (O), Sulphur Dioxide (SO), Fine Particles Matter (PM), Coarse Particles Matter (PM), and Ammonia (NH).

Global big data laboratory experiment, integrated with kernel-based algorithm with an improved nonlinear ensemble for compressive strength modeling.

With the continuous clamor for a reduction in embodied carbon in cement, rapid solution to climate change, and reduction to resource depletion, studies into substitute binders become crucial. These cementitious binders can potentially lessen our reliance on cement as the only concrete binder while also improving concrete functional properties. Finer particles used in cement microstructure densify the pore structure of concrete and enhance its performance properties.

CO Foamed Viscoelastic Gel-Based Seawater Fracturing Fluid for High-Temperature Wells.

This study investigates the development of a novel CO-foamed viscoelastic gel-based fracturing fluid to address the challenges of high-temperature formations. The influence of various parameters, including surfactant type and concentration, gas fraction, shear rate, water salinity, temperature, and pressure, on foam viscosity was systematically explored. Rheological experiments were conducted using a high-pressure/high-temperature (HPHT) rheometer at 150 °C and pressures ranging from 6.

A proposed framework to address metric inflation in research publications.

Background: Since the advent of online research metrics, which began with Web of Science in 1997, these metrics have been increasingly used to rank researchers and universities. Over the last two decades, the easy access to research metrics has greatly benefitted the academic community and beyond by providing quantitative measures for ranking researchers, universities and departments. However, this accessibility, accompanied by a tendency to quantitatively evaluate research quality and impact, has also shifted the focus toward practices aimed at enhancing research metrics rather than pursuing high-quality, potentially path-breaking research.

Integrative bioinformatic analysis to identify potential phytochemical candidates for glioblastoma.

Glioblastoma (GBM) is one of the most malignant forms of cancer with the lowest survival ratio. Our study aims to utilize an integrated bioinformatic analysis to identify hub genes against GBM and explore the active phytochemicals with drug-like properties in treating GBM. The study employed databases of DisGenet, GeneCards, and Gene Expression Omnibus to retrieve GBM-associated genes, revealing 142 overlapping genes.

Electrochemical Selective Nitrate Reduction: Pathways to Nitrogen and Ammonia Production.

Nitrate (NO ) contamination from industrial, agricultural, and anthropogenic activities poses significant risks to human health and ecosystems. While traditional NO remediation methods are effective, they often generate secondary pollutants and incur high costs. Electrochemical NO reduction (ECNR) offers a sustainable alternative, converting NO into environmentally benign nitrogen (N) or valuable ammonia (NH).

Tunning the Wettability of the PVDF Membrane using the PVA-Stabilized TA-UiO-66-NH MOF Membranes to Separate Layered Oil-Water Mixture and Surfactant-Stabilized Emulsion.

This study introduces a UiO-66-NH/Tannic acid/Polyvinylidene fluoride (UTP) composite membrane for efficient oil-water separation. Pristine polyvinylidene fluoride (PVDF) membranes, due to their hydrophobic nature, tend to foul during oil-in-water emulsion separation. By incorporating the metal-organic framework (MOF) UiO-66-NH and stabilizing it with tannic acid (TA) and polyvinyl alcohol (PVA), the membrane's hydrophilicity and antifouling properties were significantly enhanced.

Predicting the Rate of Penetration while Horizontal Drilling through Unconventional Reservoirs Using Artificial Intelligence.

Estimating the rate of penetration (ROP) is one of most critical tasks for evaluating the efficiency and profitability of drilling operation, which will aim in decision-making related to well planning, time estimation, cost estimation, bit selection, operational troubles, and logistics in drilling operation. The rise in unconventional resource development underscores the need for accurate ROP prediction to optimize drilling operations in these valuable reserves. ROP prediction and optimization in unconventional hydrocarbon reservoirs are challenging due to the formations' heterogeneity, high strength, and brittleness.

Insights into Rock Wettability Influencing Factors: A Review.

Wetting characteristics of a hydrocarbon reservoir are generally quantified for cost-effective field development. The wetting process of rock by oil is a complex process involving reactions among compounds (rock, oil, and brine), the impact of environmental conditions (temperature, pressure, etc.), and treatment history (coring, transportation, etc.