Wireless Sensor Networks (WSNs) have been around for over a decade and have been used in many important applications. Energy and reliability are two of the major problems with these kinds of applications. Reliable data delivery is an important issue in WSNs because it is a key part of how well data are sent. At the same time, energy consumption in battery-based sensors is another challenge. Therefore, efficient clustering and routing are techniques that can be used to save sensors energy and guarantee reliable message delivery. With this in mind, this paper develops an energy-efficient and reliable clustering protocol (ERCP) for WSNs. First, an efficient clustering technique is proposed for sensor nodes’ energy savings considering different clustering parameters, including the link quality metric, the energy, the distance to neighbors, the distance to the sink node, and the cluster load metric. The proposed routing protocol works based on the concept of a reliable inter-cluster routing technique that saves energy. The routing decisions are made based on different parameters, such as the energy balance metric, the distance to the sink node, and the wireless link quality. Many experiments and analyses are examined to determine how well the ERCP performs. The experiment results showed that the ECRP protocol performs much better than some of the recent algorithms in both homogeneous and heterogeneous networks.

This paper presents a visualization tool that works as a debugger to learn the minimal spanning tree. The tool allows the user to enter the graph as a matrix and then allow the user to visualize the execution of the algorithm step by step. During the visualization, the tool can handle and debug the errors that occurred by the user. Also the tool gives the user a feedback from the execution of the algorithm by storing the errors that occurred by the user. The tool can be used by the teacher and students inside and outside the class. The tool was evaluated by the students and the results show that the tool enhances the understanding of algorithms.

Imagine you are walking in your company or a hospital and instead of trying to connect to an access point to reach the internet or downloading a file, the data is coming to you through the hospital or your company lights. Also, imaging that the data transfer rate is increased multiple times. Isn't great to have such technology? It seems that the dream comes true with the appearance of Li-Fi technology. In this paper, we will overview the concept of Li-Fi, applications, its advantages, and the suitable types of modulations. By the end of this research, some of the disadvantages and open problems are presented.

Blood flow abnormalities are central to cardiovascular and cerebrovascular diseases, yet early detection remains challenging due to invasive or resource‑intensive traditional methods. Deep neural networks (DNNs) offer a potential non‑invasive alternative by inferring hemodynamic states from medical images and physiological signals. In this critical review, we examine DNN methods for early prediction of problematic hemodynamics—defined as detecting dysfunction before irreversible damage or clinical symptoms appear. We focus on architectures, data modalities, validation practices, and clinical applicability. Our combined database and manual search yielded 192 studies. Over 70% of relevant studies were identified through manual reference screening, not primary database queries—revealing severe terminological fragmentation. Among studies that specified their architecture, convolutional neural networks (CNNs) predominated (image‑based tasks). Recurrent networks, transformers, and physics‑informed models appeared less often despite their theoretical fit for temporal flow data. Most studies lacked external validation, and many failed to report architecture or key metrics. Physics‑informed networks offer interpretability promise, but clinical translation is hindered by heterogeneous datasets, inconsistent reporting, and black‑box models. Because no validated quality tool exists for DNN‑hemodynamic studies, we developed a custom eight‑domain checklist. The field has grown rapidly (most studies since 2020) but has major weaknesses: external validation was rare, prospective evaluations exceptional, and statistical rigor often inadequate. We conclude that DNNs hold promise for non‑invasive hemodynamic prediction, but clinical readiness is limited. Future research should prioritize multi‑center prospective studies, standardized reporting (e.g., TRIPOD‑AI), explainable AI, and open datasets. This review is a critical synthesis with transparent search limitations, not a full systematic review.

The geometric‐arithmetic (GA) index of a graph G is the sum of the ratios of geometric and arithmetic means of end‐vertex degrees of edges of G. Similarly, the arithmetic‐geometric (AG) index of G is defined. Recently, Vujošević et al. conjectured that a tree attaining the maximum value of the addition AG + GA or difference AG − GA of the AG and GA indices in the class of all n‐vertex molecular trees must contain at most one vertex of degree 2 and at most one vertex of degree 3, but not both, for every fixed integer n ≥ 11. In this paper, the aforementioned conjecture is p.