Parametric study of coagulant recovery from water treatment sludge towards water circular economy.

This study aims to recover the used coagulants from two water treatment plants via acidification technique. The water treatment sludge (WTS) was acidified with sulfuric acid (H2SO-4) at variable normalities (0.5, 1, 1.

Evaluation on microalgae for the production of bio-chemicals and electricity.

Recent advancement in biophotovoltaic systems using microalgae, coupled with biorefinery approach, would improve economy-feasibility in production. The major concern is its commercial strength in terms of scalability, strain selection and extraction procedure cost. It must compete with conventional feedstocks such as fossil fuels.

High-efficiency electromagnetic energy harvesting using double-elliptical metasurface resonators.

This study introduces a metasurface (MS) based electrically small resonator for ambient electromagnetic (EM) energy harvesting. It is an array of novel resonators comprising double-elliptical cylinders. The harvester's input impedance is designed to match free space, allowing incident EM power to be efficiently absorbed and then maximally channelled to a single load through optimally positioned vias.

Numerical investigation of heat transfer and fluid flow characteristics of ternary nanofluids through convergent and divergent channels.

The characteristics of nanomaterials have garnered significant attention in recent research on natural and forced convection. This study focuses on the forced convection characteristics of ternary nanofluids within convergent and divergent channels. The ternary nanofluid comprises titanium oxide (TiO), zinc oxide (ZnO), and silver suspended in water, which serves as the base fluid.

MHD blood flow effects of Casson fluid with Caputo-Fabrizio fractional derivatives through an inclined blood vessels with thermal radiation.

This study investigates a fractional-order time derivative model of non-Newtonian magnetic blood flow in the presence of thermal radiation and body acceleration through an inclined artery. The blood flow is formulated using the Casson fluid model under the control of a uniformly distributed magnetic field and an oscillating pressure gradient. Caputo-Fabrizio's fractional derivative mathematical model was used, along with Laplace transform and the finite Hankel transform technique.

The nexus between circular economy innovation, market competitiveness, and triple bottom lines efficiencies among SMEs: evidence from emerging economies.

Recently, the trend of circular economy innovation (CEI) drive SMEs to initiate more sustainable practices to attain market competitiveness but rare attention has been paid in emerging economies. Hence, this study plans to explore the impacts of CEI on the triple bottom lines (TBL) efficiencies in the light of market competitiveness among the production SMEs in emerging economies. The study complied data by using a survey-based technique in Pakistan, Malaysia, and China.

Complete genome sequences of D1_2, a bacterium with antimicrobial properties isolated from peat soil.

is a beneficial lactic acid bacterium commonly studied for its probiotic properties and role in dairy production. Here, we present a complete genome of D1_2, isolated from peat swamp forests. To discover the potential antimicrobial properties, the complete genome of the strain was sequenced and analyzed.

A refactoring categorization model for software quality improvement.

Refactoring, a widely adopted technique, has proven effective in facilitating and reducing maintenance activities and costs. Nonetheless, the effects of applying refactoring techniques on software quality exhibit inconsistencies and contradictions, leading to conflicting evidence on their overall benefit. Consequently, software developers face challenges in leveraging these techniques to improve software quality.

Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream.

Dragon fruit () is a tropical and subtropical fruit that undergoes multiple ripening cycles throughout the year. Accurate monitoring of the flower and fruit quantities at various stages is crucial for growers to estimate yields, plan orders, and implement effective management strategies. However, traditional manual counting methods are labor-intensive and inefficient.

Eco-friendly alkali lignin-assisted water-based graphene oxide ink and its application as a resistive temperature sensor.

Inkjet-printable ink formulated with graphene oxide (GO) offers several advantages, including aqueous dispersion, low cost, and environmentally friendly production. However, water-based GO ink encounters challenges such as high surface tension, low wetting properties, and reduced ink stability over prolonged storage time. Alkali lignin, a natural surfactant, is promising in improving GO ink's stability, wettability, and printing characteristics.

Implementing circularity measurements in industry 4.0-based manufacturing metrology using MQTT protocol and Open CV: A case study.

In the context of Industry 4.0, manufacturing metrology is crucial for inspecting and measuring machines. The Internet of Things (IoT) technology enables seamless communication between advanced industrial devices through local and cloud computing servers.

Investigation of a two-dimensional photovoltaic thermal system using hybrid nanofluids and a rotating cylinder.

This article focuses on a numerical investigation aimed at enhancing the electrical performance of a two-dimensional photovoltaic thermal system (PV/T) through the application of cooling using hybrid nanofluids. The hybrid nanofluids consist of titanium oxide and silver nanoparticles suspended in water, while the PV/T system is based on polycrystalline silicon, copper, and a flow channel with a rotating cylinder. PV/T devices generate electricity from sunlight, but their performance degrades over time due to the heat generated by solar radiation.

Ensemble learning for multi-class COVID-19 detection from big data.

Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19.

Visible and near-infrared spectroscopic determination of sugarcane chlorophyll content using a modified wavelength selection method for multivariate calibration.

Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis.

Contrasting genetic and morphological differentiation among geographical lineages of a stenotopic miniature rasborine, Boraras maculatus, in Peninsular Malaysia.

The variability in the stenotopic miniature rasborine Boraras maculatus (Cypriniformes: Danionidae: Rasborinae) across acidic-water habitats of Peninsular Malaysia (PM) was investigated using two molecular markers (the mitochondrial cytochrome c oxidase subunit I [COI] gene and the nuclear rhodopsin gene), as well as morphological evidence. Molecular phylogenetic analyses revealed differentiation among populations of B. maculatus in PM with the distinction of four allopatric lineages.

Artificial dragonfly algorithm in the Hopfield neural network for optimal Exact Boolean k satisfiability representation.

This study proposes a novel hybrid computational approach that integrates the artificial dragonfly algorithm (ADA) with the Hopfield neural network (HNN) to achieve an optimal representation of the Exact Boolean kSatisfiability (EBkSAT) logical rule. The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EBkSAT logic representation. To assess the performance of the proposed hybrid computational model, a specific Exact Boolean kSatisfiability problem is constructed, and simulated data sets are generated.

Modelling the utilization rates of pedestrian crosswalks.

A zebra crossing is a form of pedestrian crossing provision point on roads that have significant volumes of traffic. It is one of the safety measures employed to reduce avoidable pedestrian-motorist conflicts and accidents on such roads. In the past several studies have been conducted on the behaviours of road users (pedestrians and motorists) especially on non-signal intersections.

Sustainable Resource Allocation and Reduce Latency Based on Federated-Learning-Enabled Digital Twin in IoT Devices.

In this article, we utilize Digital Twins (DT) with edge networks using blockchain technology for reliable real-time data processing and provide a secure, scalable solution to bridge the gap between physical edge networks and digital systems. Then, we suggest a Federated Learning (FL) framework for collaborative computing that runs on a blockchain and is powered by the DT edge network. This framework increases data privacy while enhancing system security and reliability.

Progress on Improved Fouling Resistance-Nanofibrous Membrane for Membrane Distillation: A Mini-Review.

Nanofibrous membranes for membrane distillation (MD) have demonstrated promising results in treating various water and wastewater streams. Significant progress has been made in recent decades because of the development of sophisticated membrane materials, such as superhydrophobic, omniphobic and Janus membranes. However, fouling and wetting remain crucial issues for long-term operation.

Optimization of total phenolic content, total flavonoid content and anti-gout properties of polyherbal formulation.

Objectives: An increase in gout prevalence has drawn attention among society and this situation drives the exploration of more favourable treatment using traditional medicinal plants which are rich in phenolic and flavonoid to avoid the side effects of modern medication. However, there are only few studies regarding the optimization of phytochemicals and anti-gout properties of medicinal plants and their combinations. The objectives of this study were to determine the optimal formulation of , Benth and with maximum total phenolic and flavonoid contents as well as minimum IC of xanthine oxidase inhibitory activity and to examine their correlations among the formulations.

Evaluation of convolutional neural network for non-destructive detection of imidacloprid and acetamiprid residues in chili pepper (Capsicum frutescens L.) based on visible near-infrared spectroscopy.

Consumption of agricultural products with pesticide residue is risky and can negatively affect health. This study proposed a nondestructive method of detecting pesticide residues in chili pepper based on the combination of visible and near-infrared (VIS/NIR) spectroscopy (400-2498 nm) and deep learning modeling. The obtained spectra of chili peppers with two types of pesticide residues (acetamiprid and imidacloprid) were analyzed using a one-dimensional convolutional neural network (1D-CNN).

Using of reciprocal teaching to enhance academic achievement: A systematic literature review.

Reciprocal Teaching a type of cooperative teaching and learning strategy involving learners with similar academic backgrounds sharing teacher and student roles. Its use was not explored to the same degree as other types of peer-assisted strategy, including students at different levels. The goal of the systematic review was to investigate the impact of Reciprocal Teaching on existing literature in the field of education which include the use of Reciprocal Teaching for all student's level to enhancing academic achievement and ROSES (Reporting Standards for Systematic Evidence Syntheses) was carried out as a review protocol to conduct Systematic Literature Review (SLR).

Attention Aware Deep Learning Approaches for an Efficient Stress Classification Model.

In today's world, stress is a major factor for various diseases in modern societies which affects the day-to-day activities of human beings. The measurement of stress is a contributing factor for governments and societies that impacts the quality of daily lives. The strategy of stress monitoring systems requires an accurate stress classification technique which is identified via the reactions of the body to regulate itself to changes within the environment through mental and emotional responses.

Induction of Apoptosis and Modulation of Caspase Activity on MCF-7 Human Breast Cancer Cells by Bioactive Fractionated Cocoa Leaf Extract.

Background: The objective of this study was to investigate the potential anti-proliferative activities of a methanolic extract of cocoa leaves (CL) obtained through sequential partition and fractionation against MCF-7 breast cancer cells.  Methods: The methanolic extract of CL was partitioned in three separated solvents (hexane, dichloromethane, and methanol). Hexane partition was the most potent against MCF-7 cells growth with the lowest IC50 value.