Background: Neonatal hyperbilirubinemia is a prevalent condition commonly treated with phototherapy. While effective, phototherapy can cause adverse effects such as dehydration and maternal separation, necessitating complementary care strategies. Objectives: This systematic review aims to evaluate the efficacy of foot reflexology and related tactile interventions on bilirubin levels and physiological parameters in neonates undergoing treatment for hyperbilirubinemia. Methods: A systematic search was conducted across five electronic databases (PubMed, Scopus, Web of Science, Google Scholar, and Cochrane Library) for studies published between 2019 and 2025. The review followed the PRISMA guidelines and PICOS framework. Twenty studies,

Pathology reports are necessary for specialists to make an appropriate diagnosis of diseases in general and blood diseases in particular. Therefore, specialists check blood cells and other blood details. Thus, to diagnose a disease, specialists must analyze the factors of the patient’s blood and medical history. Generally, doctors have tended to use intelligent agents to help them with CBC analysis. However, these agents need analytical tools to extract the parameters (CBC parameters) employed in the prediction of the development of life-threatening bacteremia and offer prognostic data. Therefore, this paper proposes an enhancement to the Rabin–Karp algorithm and then mixes it with the fuzzy ratio to make this algorithm suitable

The deficiency of potable water resources and energy supply is emerging as a significant and concerning obstacle to sustainable development. Solar and waste heat-powered humidification dehumidification (HDH) desalination systems become essential due to the severe impacts of global warming and water shortages. This problem highlights the need to apply boosted water desalination solutions. Desalination is a capital-intensive process that demands considerable energy, predominantly sourced from fossil fuels worldwide, posing a significant carbon footprint risk. HDH is a very efficient desalination method suitable for remote areas with moderate freshwater requirements for domestic and agricultural usage. Several operational and maintenance conce

The present study reports Morchella conica Pers.1818, which belongs to the family, Morchellaceae as a new record of Iraqi macromycota based on the morphological and molecular methods. During their short and often sporadic fruiting season, this fungal species was found in mixed forest unburned areas in Branan ranges (Suliamaniya Province, Northeast Iraq). Currently, M. conica is the second Morchella species reported from Iraq. The current study aimed to introduce this new record, which is poorly studied in the Middle East. M. conica is morphologically described and phylogenetically confirmed. The relationship between this species and other species within the genus was studied using the nrDNA ITS sequences from different species and divers

This review highlights recent advances in the development and use of eco-friendly polymeric membranes made from polymer blends for treating oily wastewater, a major source of environmental pollution. Among treatment options, membrane separation stands out because of its high efficiency and ability to selectively remove oil from water using semi-permeable barriers. The focus of this review is on polymeric membranes derived from polymer blends, which show improved mechanical strength, thermal stability, and resistance to fouling. Additionally, the role of nanomaterials such as titanium dioxide, graphene oxide, and iron nanoparticles is discussed for their ability to boost membrane porosity, permeability, and oil separation efficiency.

Cybersecurity involves protecting computer networks, systems, and data from unauthorized access and disruptions using advanced technologies. The purpose of this research is to establish a novel cyber security framework for strengthening cloud data protection. In this paper, we propose a novel Dung Beetle optimization-redefined Intelligent Random Forest (DB-IRF) for accurate detection of intrusions in a cloud environment. We obtained a dataset that includes cloud system logs and network traffic data, including normal and malicious activities, to train our proposed model. We utilized z-score normalization to pre-process the gathered raw data. Our suggested model enhances classification accuracy by integrating DB optimization with the

Illegal web platforms known as phishing websites adopted high-risk threats to mimic legitimate online platforms in order to steal important data of users, like login credentials and financial information. These forged platforms often involve internet tactics to attract unsuspecting victims through URL manipulation. Many recent machine learning techniques are proposed for detecting web attackers. However, under an increasing number of internet users, they struggle to reveal recent phishing strategies for stealing sensitive information covered by fake websites. This study presents a comprehensive methodology for detecting phishing websites through a proposed hybrid technique (RF_PSO) by involving the Random Forest (RF) classification algorith