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The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors’ prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

Wireless Sensor Networks (WSNs) are widely recognized as a crucial enabling technology for Internet of Things applications. One of the primary challenges in designing WSNs is to ensure efficient energy management, which involves minimizing and uniformly distributing energy consumption among the wireless sensors to extend the network lifetime. In this paper, we propose a set of mathematical tools in the form of fitness functions that can be used to measure, compare, and control how the network manages energy consumption among wireless sensors. Furthermore, we present an Optimized Energy Balanced and Distributed Clustering (OEBDC) protocol for WSNs, which utilizes these fitness functions to manage energy resources more efficiently by promoting uniform energy consumption, minimizing communication overhead, and extending network lifetime. The proposed tools can be integrated with other WSN protocols to manage energy resources according to specific requirements that suited different applications. We have evaluated the performance of the proposed protocol against well-established routing protocols for WSNs and found that OEBDC achieves a notable advantage in extending network lifetime compared to other protocols, while also demonstrating robust control in managing energy resources.

The SPIHT image compression algorithm is characterized by low computational complexity, good performance, and the production of a quality scalable bitstream that can be decoded at several bit-rates with image quality enhancement as more bits are received. However, it suffers from the enormous computer memory consumption due to utilizing linked lists of size of about 2–3 times the image size. In addition, it does not exploit the multi-resolution feature of the wavelet transform to produce a resolution scalable bitstream by which the image can be decoded at numerous resolutions (sizes). The Single List SPIHT (SLS) algorithm resolved the high memory problem of SPIHT by using only one list of fixed size equals to just 1/4 the image size, and state marker bits with an average of 2.25 bits/pixel. This paper introduces two new algorithms that are based on SLS. Like SLS, the first algorithm also produces a quality scalable bitstream. However, it has lower time complexity and better performance than SLS. The second algorithm, which is the major contribution of the work, upgrades the first algorithm to produce a bitstream that is both quality and resolution scalable. As such, the algorithm is very suitable for the modern heterogeneous nature of the internet users to satisfy their different capabilities and desires in terms of image quality and resolution.

This paper explores the integration of social care informatics, pharmacists, and social work to create a holistic approach to healthcare. By focusing on interdisciplinary collaboration and leveraging informatics tools, the study highlights how patient care can be improved by addressing social determinants and encouraging collaborative decision-making among healthcare professionals. The research also emphasizes the need for a patient-centered approach that includes physical, emotional, and social well-being, along with technological advancements that support integrated care.

A Multiple Classifier System (MCS) is designed to combine classification results of an ensemble of different classifiers and consequently to produce the highest possible classification output. MCS has recently drawn growing attention and has become a necessity, especially when a problem involves a large class of noisy data or when using a single pattern classifier that has serious drawbacks in its results. A wide range of pattern recognition applications have benefited from the implementation of MCS, these include areas such as handwriting recognition, incremental learning, data fusion, feature selection, and a large variety of medical applications. To achieve optimal ensemble performance, two design components must be optimized carefully which are diversity and the selection of combining rule. This dissertation is focused on designing an ensemble decision combining rule which leads the MCS to deliver the highest possible accuracy. Several models for decision combining rules, using an ensemble system of N classifiers and M classes, are developed. The proposed system can be considered as a unifying framework that works with any algebraic decision combining rule. While the results affirm that there is no single decision combining rule that can outperform in every classification problem, they clearly present the framework to design an optimum decision combining rule based on the statistics of the classifiers. Based on the predication extracted from the theoretical models, a novel algorithm that achieves optimal classification accuracy is presented in this study. The proposed algorithm is tested on six datasets, the experimental results agree with the trend predicted by theoretical derivations. Results based on the proposed algorithm show that the performance of an ensemble always achieves at least the performance of the best performing individual classifier and evades selecting the least performing classifier. In addition, the results of the proposed algorithm show a comparable performance in classification accuracy compared to the random forest with less computational operations which makes it a good candidate for real time classification problems. Finally, the proposed model serves as an in-depth exploration into the performance of MCS and brings to the forefront of classification research significant insights.

In recent years, revolution of development was exceedingly quick in the Internet. Nevertheless, instead of only linking personal computers, mobiles and wearable equipment's, Internet growths from a web binding to true world physical substances that is indicated to novel connotation, which is labeled as Internet of Things (IoT). This concept is utilized in many scopes like education, health care, agriculture and commerce. IoT devices are presented with batteries to have independence from electric current; consequently, their working time is specified by the total time of the power of these batteries. In many IoT applications, data of IoT devices are extremely critical and should be encrypted. Current encryption approaches are created with a high complexity of an arithmetical process to provide a high level of security. However, these arithmetical processes lead to troubles concerning the efficiency and power consumption. ChaCha cipher is one of these approaches, which recently attracted attention due to its deployment in several applications by Google. In the present study, a new stream cipher procedure is proposed (called Super ChaCha), which performs low duty cycles for securing data on IoT devices. The proposed algorithm represents an improved revision to the standard ChaCha algorithm by increasing resistance to cryptanalysis. The modification focuses on rotation procedure which has been changed from a fixed constant to a variable constant based on random value. Also, the inputs of the cipher are changing in the columns form followed by diagonals form to zigzag form and then by alternate form to provide improved diffusion in comparison with the standard ChaCha. Results regarding the security illustrate that Super ChaCha needs 2512 probable keys to break by brute-force attack. Furthermore, the randomness of Super ChaCha successfully passed the five benchmark and NIST test.

The study aimed to evaluate nursing staff's practices toward oral care of intubated and to determine the effectiveness of an interventional program on nursing staff's practices about oral care at ICU in Al Diwaniya Teaching Hospital, Iraq .Pre experimental (one group, pre/post test) design is used to conduct this study that starting from 26 September, 2020 to 1 May, 2021. According to the American Association of Critical-Care Nurses (2017), efficient oral care practices includes, brushing the tongue, teeth, and gum two times per day, using a soft pediatric toothbrush. Among these barriers are: lack of priority given to oral care, as well as the lack of numbers of nursing staffs, fear of causing trauma or injury in patients' mouth, also fear of dislodging tube, and many nurses incorrectly believe that oral care does not provide significant health benefits. The study started from 26th September, 2020 to 1st May, 2021 at the ICU in Al-Diwaniya Teaching Hospital, to evaluate the nursing staffs' practices toward oral care, and to determine the effectiveness of the interventional program on nursing staff practices regarding oral care of intubated patients.

Gentamicin is a well-known that is drug effective against a wide range of microbes. It is not effective on some bacterial strains. The combination of gentamicin and other bioactive nanostructures can increase the biological properties of the nanocomposite while maintaining its biological properties. Microwave irradiation was utilized to synthesize novel nanocomposites in this study, which consisted of Zn-MOF, gentamicin, and oxidized chitosan. Its structure using EA, XRD, FT-IR, XPS, EDAX, BET, SEM, and TEM were characterized and confirmed. Antimicrobial evaluations were conducted on various bacterial strains according to the Clinical Laboratory Standards Institute’s standards and guidelines, and IZD, MIC, and MBC were reported. The antimicrobial studies resulted in interesting findings. In general, in antimicrobial activity, MIC of synthesized Zn-MOF/gentamicin/oxidized chitosan nanocomposites between 2 and 128 µg/mL, MBC of 4–256 µg/mL and IZD of 15.5–22.7 mm were observed. It was observed that synthesized Zn-MOF/gentamicin/oxidized chitosan nanocomposites were more effective on the strains where gentamicin was effective, and it is also effective on the strains where gentamicin was not effective. These results can be attributed to the loading of gentamicin on the nano substrate as well as the presence of natural bioactive polymer (oxidized chitosan) and the bioactive metal (zinc) in the structure of the final synthesized nanocomposite as well as high specific surface area, and being nano-sized.

This study focuses on China's industrial transformation and urban income inequality. It is shown that between 2011 and 2020, improvements in China's industrial structure have a significant positive influence on lowering income gaps between urban and rural areas when used in conjunction with the empirical research approach. The mechanical study shows that the urban population impacts this causation. Rural-to-urban economic gaps have been reduced through modernisation in different parts of the country. The result remains the same even if the urban-rural consumption gap is used as a proxy for income discrepancy. The mechanism for the industrial structure upgrading model (MISUM) is proposed in this article for the modern circulation industry. Key contributions include: (1) environmental rules in these components have no impact on each other, but the updating of industrial buildings indicates a substantial location-specific dependence; (2) environmental standards have impacts on industrial structures throughout provinces; and (3) environmental standards have a long-term qualifying impact on the industrial structures. This essay focuses on combining environmental regulation with industrial expansion in different regions. In this study, government environmental requirements for industrial structural improvements are shown to be in operation. The test results show the MISUM has been described with high accuracy of 94.2%, carbon emission level of 18%, soil emission level of 11% and efficiency ratio of 97.8% compared to other methods.