In the recent years the research on the activated carbon preparation from agro-waste and byproducts have been increased due to their potency for agro-waste elimination. This paper presents a literature review on the synthesis of activated carbon from agro-waste using microwave irradiation method for heating. The applicable approach is highlighted, as well as the effects of activation conditions including carbonization temperature, retention period, and impregnation ratio. The review reveals that the agricultural wastes heated using a chemical process and microwave energy can produce activated carbon with a surface area that is significantly higher than that using the conventional heating method.

One of the challenges faced by traditional intrusion detection systems based on machine learning or deep learning is instability when dealing with unbalanced network traffic, leading to failure in detecting certain attacks (minority classifications). Additionally, they struggle with multi-stage attacks, resulting in an increase in false alarms. This paper presents a hierarchical intrusion detection system supported by a Prediction Verification Layer (PVL) and a Feature Inheritance Mechanism (FIM). Where PVL contributes to documenting the system’s final decision and increasing sensitivity to minority attacks, FIM also helps in inheriting features from previous layers and correcting errors as much as possible. Additionally, it allows for ad

Centralization and decentralization, planning and development, and community participation in the management of its affairs and to activate all the abilities that multiple methods aimed at creating the proper environment for the growth and development of society in the place where he lives. As long as the overall trend in Iraq, represented by the Permanent Constitution of decentralization to regions and provinces, the solutions to the obstacles that may face this transition in some respects presents ways of coordination and integration between multiple levels of planning which can be exercised by the schematic in the future the organization. In this paper some of the visions and ideas that can  contribute to the organization

It is not available for us to go back in time and see plays old and it plays giants tragedy ancient Greek (Aeschylus, Sofokls, and yourbedes) through the eyes of a generation ago, and if we were able to go back to Ntegathm play it is certain that we will not taste or Nstassig for much of what we see from these offers will not afford the traditional religious rituals, which was accompanied also dance and music in the style of ancient Greek play was representing a large part of the theater see manifestations Can our eyes and ears we twentieth century audience to accept those appearances, and it was then?
Inevitably it will look like a museum bycollection not only. So we find ourselves in the light of the foregoing forced When you do rem

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

Osteoporosis is a global health concern with bone frailty and high fracture risk. Existing diagnostic paradigms largely rely on bone scanning and bone mineral density evaluation which are hindered by the delayed prediction of fractures, especially in high-risk groups. This review assesses existing and novel Osteoporosis biomarkers, their mechanisms, clinical efficacy, drawbacks, and discusses the best biomarkers in Osteoporosis risk stratification and management, to convert them into better patient care. An online search was conducted, including PubMed, Web of Science, Embase, and Google Scholar up to June 2026. Passed studies were reviewed critically and organized biomarkers into five different panels: traditional and bone turnover

Renewable energy sources - realities of the present and future options

    Many of the directories indicate that the global energy system begin with a period of transition from total dependence on fossil energy sources, particularly oil, Into a new era in which renewable energy sources play an important role in meeting the growing needs of energy demand. There are many factors that will contribute to the strengthening of this trend towards transformation, which also will decide how quickly this transformation of renewable energy systems effectively in the global system of energy demand.

   These factors, In brief: the size of environmental pollution and cl

In recent years, literary studies have witnessed a remarkable shift towards employing digital technologies, particularly artificial intelligence tools, in analyzing literary texts and exploring their linguistic and semantic structures. This trend has provided researchers with new possibilities for understanding texts in quantitative and qualitative ways that transcend traditional methods based solely on critical reading. The current research aims to introduce professors and students of Arabic to artificial intelligence tools that contribute to the analysis of literary texts, focusing on exploring their mechanisms for studying style, meaning, structure, and emotion. It also seeks to highlight the most prominent challenges facing researchers

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct