Background :Thalassemia is an autosomal
disease of the haemoglobin. Two types of
thalassemia are recognized: thalassemia major
and thalassemia intermedia.
The most serious cardiac complication in
thalassemia major is due to multiple blood
transfusions rather than the disease itself, which
is due to iron overload.
Cardiomyopathy is the most common cardiac
defect that occurs with iron overload. Pricarditis,
congestive heart failure and arrhythmias are due
to hemosidrosis and chronic aneamia.
Aim of the study: to demonstrate the prevalence
and types of electrocardiographic changes among
thalassemic patients with aged over ten years old.

In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo

The modification of hydrophobic rock surfaces to the water-wet state via nanofluid treatment has shown promise in enhancing their geological storage capabilities and the efficiency of carbon dioxide (CO2) and hydrogen (H2) containment. Despite this, the specific influence of silica (SiO2) nanoparticles on the interactions between H2, brine, and rock within basaltic formations remains underexplored. The present study focuses on the effect of SiO2 nanoparticles on the wettability of Saudi Arabian basalt (SAB) under downhole conditions (323 K and pressures ranging from 1 to 20 MPa) by using the tilted plate technique to measure the contact angles between H2/brine and the rock surfaces. The findings reveal that the SAB's hydrophobicity intensif

Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

Software-Defined Networking (SDN) has evolved network management by detaching the control plane from the data forwarding plane, resulting in unparalleled flexibility and efficiency in network administration. However, the heterogeneity of traffic in SDN presents issues in achieving Quality of Service (QoS) demands and efficiently managing network resources. SDN traffic flows are often divided into elephant flows (EFs) and mice flows (MFs). EFs, which are distinguished by their huge packet sizes and long durations, account for a small amount of total traffic but require disproportionate network resources, thus causing congestion and delays for smaller MFs. MFs, on the other hand, have a short lifetime and are latency-sensitive, but they accou