Utilising date palm fibres as a permeable reactive barrier to remove methylene blue dye from groundwater: a batch and continuous adsorption study.

This study aimed to utilise cheap and abundantly available date palm fibre (DPF) wastes for the remediation of methylene blue (MLB) dye-contaminated groundwater. The DPF adsorbents were first prepared, followed by various characterisation analyses, including surface morphology, functional groups, and material structure. Subsequently, the DPF adsorbents were applied in the batch and continuous adsorption studies to assess the MLB dye removal from aqueous environments.

Reimagining the NICU: a human-centered design approach to healthcare innovation.

Design charettes have been utilized in architectural and design practice to generate innovative ideas. The Reimagining Workshop is a version that combines practical and blue-sky thinking to improve healthcare facility design. The workshop engages diverse stakeholders who follow a human-centered design framework.

Avascular necrosis of femoral head following COVID-19 infection.

Introduction: It is crucial to be aware of post-COVID-19 non-pulmonary complications. Avascular necrosis (AVN) is one of these complications. It should be noted that the risk of AVN persists in individuals who have recovered from the COVID-19 infection.

Rice husk coated with copper oxide nanoparticles for 17α-ethinylestradiol removal from an aqueous solution: adsorption mechanisms and kinetics.

The 17 α-ethinylestradiol (EE2) adsorption from aqueous solution was examined using a novel adsorbent made from rice husk powder coated with CuO nanoparticles (CRH). Advanced analyses of FTIR, XRD, SEM, and EDSwere used to identify the classification parameters of a CRH-like surface morphology, configuration, and functional groups. The rice husk was coated with CuO nanoparticles, allowing it to create large surface area materials with significantly improved textural qualities with regard to functional use and adsorption performance, according to a detailed characterization of the synthesized materials.

Medical Workflow Design and Planning Using Node-RED Data Fusion.

The space of clinical planning requires a complex arrangement of information, often not capable of being captured in a singular dataset. As a result, data fusion techniques can be used to combine multiple data sources as a method of enriching data to mimic and compliment the nature of clinical planning. These techniques are capable of aiding healthcare providers to produce higher quality clinical plans and better progression monitoring techniques.

The use of non-living animals as simulation models for cranial neurosurgical procedures: a literature review.

Simulation plays a pivotal role in neurosurgical training by allowing trainees to develop the requisite expertise to enhance patient safety. Several models have been used for simulation purposes. Non-living animal models offer a range of benefits, including affordability, availability, biological texture, and a comparable similarity to human anatomy.

White learning methodology: A case study of cancer-related disease factors analysis in real-time PACS environment.

Background And Objective: Bayesian network is a probabilistic model of which the prediction accuracy may not be one of the highest in the machine learning family. Deep learning (DL) on the other hand possess of higher predictive power than many other models. How reliable the result is, how it is deduced, how interpretable the prediction by DL mean to users, remain obscure.

Pathogen-Host Analysis Tool (PHAT): an integrative platform to analyze next-generation sequencing data.

Summary: The Pathogen-Host Analysis Tool (PHAT) is an application for processing and analyzing next-generation sequencing (NGS) data as it relates to relationships between pathogens and their hosts. Unlike custom scripts and tedious pipeline programming, PHAT provides an integrative platform encompassing raw and aligned sequence and reference file input, quality control (QC) reporting, alignment and variant calling, linear and circular alignment viewing, and graphical and tabular output. This novel tool aims to be user-friendly for life scientists studying diverse pathogen-host relationships.

Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data.

Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority.

Robust High-dimensional Bioinformatics Data Streams Mining by ODR-ioVFDT.

Outlier detection in bioinformatics data streaming mining has received significant attention by research communities in recent years. The problems of how to distinguish noise from an exception and deciding whether to discard it or to devise an extra decision path for accommodating it are causing dilemma. In this paper, we propose a novel algorithm called ODR with incrementally Optimized Very Fast Decision Tree (ODR-ioVFDT) for taking care of outliers in the progress of continuous data learning.

WITHDRAWN: Improving classification of protein binders for virtual drug screening by novel swarm-based feature selection techniques.

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.

WITHDRAWN: Clinical pathways inference from decision rules by hybrid stream mining and fuzzy unordered rule induction strategy.

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.

WITHDRAWN: Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data.

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.

Evaluation of stream mining classifiers for real-time clinical decision support system: a case study of blood glucose prediction in diabetes therapy.

Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT.

Real-time clinical decision support system with data stream mining.

This research aims to describe a new design of data stream mining system that can analyze medical data stream and make real-time prediction. The motivation of the research is due to a growing concern of combining software technology and medical functions for the development of software application that can be used in medical field of chronic disease prognosis and diagnosis, children healthcare, diabetes diagnosis, and so forth. Most of the existing software technologies are case-based data mining systems.