Efficient handling of ACL policy change in SDN using reactive and proactive flow rule installation.

Software-defined networking (SDN) is a pioneering network paradigm that strategically decouples the control plane from the data and management planes, thereby streamlining network administration. SDN's centralized network management makes configuring access control list (ACL) policies easier, which is important as these policies frequently change due to network application needs and topology modifications. Consequently, this action may trigger modifications at the SDN controller. In response, the controller performs computational tasks to generate updated flow rules in accordance with modified ACL policies and installs flow rules at the data plane. Existing research has investigated reactive flow rules installation that changes in ACL policies result in packet violations and network inefficiencies. Network management becomes difficult due to deleting inconsistent flow rules and computing new flow rules per modified ACL policies. The proposed solution efficiently handles ACL policy change phenomena by automatically detecting ACL policy change and accordingly detecting and deleting inconsistent flow rules along with the caching at the controller and adding new flow rules at the data plane. A comprehensive analysis of both proactive and reactive mechanisms in SDN is carried out to achieve this. To facilitate the evaluation of these mechanisms, the ACL policies are modeled using a 5-tuple structure comprising Source, Destination, Protocol, Ports, and Action. The resulting policies are then translated into a policy implementation file and transmitted to the controller. Subsequently, the controller utilizes the network topology and the ACL policies to calculate the necessary flow rules and caches these flow rules in hash table in addition to installing them at the switches. The proposed solution is simulated in Mininet Emulator using a set of ACL policies, hosts, and switches. The results are presented by varying the ACL policy at different time instances, inter-packet delay and flow timeout value. The simulation results show that the reactive flow rule installation performs better than the proactive mechanism with respect to network throughput, packet violations, successful packet delivery, normalized overhead, policy change detection time and end-to-end delay. The proposed solution, designed to be directly used on SDN controllers that support the Pyretic language, provides a flexible and efficient approach for flow rule installation. The proposed mechanism can be employed to facilitate network administrators in implementing ACL policies. It may also be integrated with network monitoring and debugging tools to analyze the effectiveness of the policy change mechanism.

Anomaly detection in multivariate time series data using deep ensemble models.

Anomaly detection in time series data is essential for fraud detection and intrusion monitoring applications. However, it poses challenges due to data complexity and high dimensionality. Industrial applications struggle to process high-dimensional, complex data streams in real time despite existing solutions. This study introduces deep ensemble models to improve traditional time series analysis and anomaly detection methods. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks effectively handle variable-length sequences and capture long-term relationships. Convolutional Neural Networks (CNNs) are also investigated, especially for univariate or multivariate time series forecasting. The Transformer, an architecture based on Artificial Neural Networks (ANN), has demonstrated promising results in various applications, including time series prediction and anomaly detection. Graph Neural Networks (GNNs) identify time series anomalies by capturing temporal connections and interdependencies between periods, leveraging the underlying graph structure of time series data. A novel feature selection approach is proposed to address challenges posed by high-dimensional data, improving anomaly detection by selecting different or more critical features from the data. This approach outperforms previous techniques in several aspects. Overall, this research introduces state-of-the-art algorithms for anomaly detection in time series data, offering advancements in real-time processing and decision-making across various industrial sectors.

Development and Characterization of Polymeric-based Biomaterial from Agro-food Waste: A Sustainable and Eco-friendly Approach Towards Plastic Pollution

INTRODUCTION: Commercial plastics are potentially hazardous and can be carcinogenic due to the incorporation of chemical additives along with other additional components utilized as brominated flame retardants and phthalate plasticizers during production that excessively produce large numbers of gases, litter, and toxic components resulting in environmental pollution. METHOD: Biodegradable plastic derived from natural renewable resources is the novel, alternative, and innovative approach considered to be potentially safe as a substitute for traditional synthetic plastic as they decompose easily without causing any harm to the ecosystem and natural habitat. The utilization of undervalued compounds, such as by-products of fruits and vegetables in the production of biodegradable packaging films, is currently a matter of interest because of their accessibility, affordability, ample supply, nontoxicity, physiochemical and nutritional properties. Industrial food waste was processed under controlled conditions with appropriate plasticizers to extract polymeric materials. Biodegradability, solubility, and air test analysis were performed to examine the physical properties of polymers prior to the characterization of the biofilm by Fourier-transformed infrared spectroscopy (FTIR) for the determination of polymeric characteristics. RESULT: The loss of mass examined in each bioplastic film was in the range of 0.01g to 0.20g. The dimension of each bioplastic was recorded in the range of 4.6 mm to 28.7 mm. The existence of -OH, C=C, C=O stretching, and other crucial functional groups that aid in the creation of a solid polymeric material are confirmed by FTIR analysis. This study provides an alternative approach for sustainable and commercially value-added production of polymeric-based biomaterials from agro-industrial waste as they are rich in starch, cellulose, and pectin for the development of bio-plastics. CONCLUSION: The rationale of this project is to achieve a straightforward, economical, and durable method for the production of bio-plastics through effective utilization of industrial and commercial fruit waste, ultimately aiding in revenue generation.

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.

Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of depression and therefore, has the potential to contribute to the diagnosis of depression effectively. This study presents an EEG-based mental depressive disorder detection mechanism using a publicly available EEG dataset called Multi-modal Open Dataset for Mental-disorder Analysis (MODMA). This study uses EEG data acquired from 55 participants using 3 electrodes in the resting-state condition. Twelve temporal domain features are extracted from the EEG data by creating a non-overlapping window of 10 seconds, which is presented to a novel feature selection mechanism. The feature selection algorithm selects the optimum chunk of attributes with the highest discriminative power to classify the mental depressive disorders patients and healthy controls. The selected EEG attributes are classified using three different classification algorithms i.e., Best- First (BF) Tree, k-nearest neighbor (KNN), and AdaBoost. The highest classification accuracy of 96.36% is achieved using BF-Tree using a feature vector length of 12. The proposed mental depressive classification scheme outperforms the existing state-of-the-art depression classification schemes in terms of the number of electrodes used for EEG recording, feature vector length, and the achieved classification accuracy. The proposed framework could be used in psychiatric settings, providing valuable support to psychiatrists.

A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning.

Phishing attacks are evolving with more sophisticated techniques, posing significant threats. Considering the potential of machine-learning-based approaches, our research presents a similar modern approach for web phishing detection by applying powerful machine learning algorithms. An efficient layered classification model is proposed to detect websites based on their URL structure, text, and image features. Previously, similar studies have used machine learning techniques for URL features with a limited dataset. In our research, we have used a large dataset of 20,000 website URLs, and 22 salient features from each URL are extracted to prepare a comprehensive dataset. Along with this, another dataset containing website text is also prepared for NLP-based text evaluation. It is seen that many phishing websites contain text as images, and to handle this, the text from images is extracted to classify it as spam or legitimate. The experimental evaluation demonstrated efficient and accurate phishing detection. Our layered classification model uses support vector machine (SVM), XGBoost, random forest, multilayer perceptron, linear regression, decision tree, naïve Bayes, and SVC algorithms. The performance evaluation revealed that the XGBoost algorithm outperformed other applied models with maximum accuracy and precision of 94% in the training phase and 91% in the testing phase. Multilayer perceptron also worked well with an accuracy of 91% in the testing phase. The accuracy results for random forest and decision tree were 91% and 90%, respectively. Logistic regression and SVM algorithms were used in the text-based classification, and the accuracy was found to be 87% and 88%, respectively. With these precision values, the models classified phishing and legitimate websites very well, based on URL, text, and image features. This research contributes to early detection of sophisticated phishing attacks, enhancing internet user security.

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features.

One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is critical for the patient's health. Many automated artificial intelligence (AI) methods have recently been developed to diagnose tumors. These approaches, however, result in poor performance; hence, there is a need for an efficient technique to perform precise diagnoses. This paper suggests a novel approach for brain tumor detection via an ensemble of deep and hand-crafted feature vectors (FV). The novel FV is an ensemble of hand-crafted features based on the GLCM (gray level co-occurrence matrix) and in-depth features based on VGG16. The novel FV contains robust features compared to independent vectors, which improve the suggested method's discriminating capabilities. The proposed FV is then classified using SVM or support vector machines and the k-nearest neighbor classifier (KNN). The framework achieved the highest accuracy of 99% on the ensemble FV. The results indicate the reliability and efficacy of the proposed methodology; hence, radiologists can use it to detect brain tumors through MRI (magnetic resonance imaging). The results show the robustness of the proposed method and can be deployed in the real environment to detect brain tumors from MRI images accurately. In addition, the performance of our model was validated via cross-tabulated data.

Deep fake detection and classification using error-level analysis and deep learning.

Due to the wide availability of easy-to-access content on social media, along with the advanced tools and inexpensive computing infrastructure, has made it very easy for people to produce deep fakes that can cause to spread disinformation and hoaxes. This rapid advancement can cause panic and chaos as anyone can easily create propaganda using these technologies. Hence, a robust system to differentiate between real and fake content has become crucial in this age of social media. This paper proposes an automated method to classify deep fake images by employing Deep Learning and Machine Learning based methodologies. Traditional Machine Learning (ML) based systems employing handcrafted feature extraction fail to capture more complex patterns that are poorly understood or easily represented using simple features. These systems cannot generalize well to unseen data. Moreover, these systems are sensitive to noise or variations in the data, which can reduce their performance. Hence, these problems can limit their usefulness in real-world applications where the data constantly evolves. The proposed framework initially performs an Error Level Analysis of the image to determine if the image has been modified. This image is then supplied to Convolutional Neural Networks for deep feature extraction. The resultant feature vectors are then classified via Support Vector Machines and K-Nearest Neighbors by performing hyper-parameter optimization. The proposed method achieved the highest accuracy of 89.5% via Residual Network and K-Nearest Neighbor. The results prove the efficiency and robustness of the proposed technique; hence, it can be used to detect deep fake images and reduce the potential threat of slander and propaganda.

Cloud-based email phishing attack using machine and deep learning algorithm.

Cloud computing refers to the on-demand availability of personal computer system assets, specifically data storage and processing power, without the client's input. Emails are commonly used to send and receive data for individuals or groups. Financial data, credit reports, and other sensitive data are often sent via the Internet. Phishing is a fraudster's technique used to get sensitive data from users by seeming to come from trusted sources. The sender can persuade you to give secret data by misdirecting in a phished email. The main problem is email phishing attacks while sending and receiving the email. The attacker sends spam data using email and receives your data when you open and read the email. In recent years, it has been a big problem for everyone. This paper uses different legitimate and phishing data sizes, detects new emails, and uses different features and algorithms for classification. A modified dataset is created after measuring the existing approaches. We created a feature extracted comma-separated values (CSV) file and label file, applied the support vector machine (SVM), Naive Bayes (NB), and long short-term memory (LSTM) algorithm. This experimentation considers the recognition of a phished email as a classification issue. According to the comparison and implementation, SVM, NB and LSTM performance is better and more accurate to detect email phishing attacks. The classification of email attacks using SVM, NB, and LSTM classifiers achieve the highest accuracy of 99.62%, 97% and 98%, respectively.

A residual network-based framework for COVID-19 detection from CXR images.

In late 2019, a new Coronavirus disease (COVID-19) appeared in Wuhan, Hubei Province, China. The virus began to spread throughout many countries, affecting a large population. Polymerase chain reaction is currently being utilized to diagnose COVID-19 in suspected patients; however, its sensitivity is quite low. The researchers also developed automated approaches for reliably and timely identifying COVID-19 from X-ray images. However, traditional machine learning-based image classification algorithms necessitate manual image segmentation and feature extraction, which is a time-consuming task. Due to promising results and robust performance, Convolutional Neural Network (CNN)-based techniques are being used widely to classify COVID-19 from Chest X-rays (CXR). This study explores CNN-based COVID-19 classification methods. A series of experiments aimed at COVID-19 detection and classification validates the viability of our proposed framework. Initially, the dataset is preprocessed and then fed into two Residual Network (ResNet) architectures for deep feature extraction, such as ResNet18 and ResNet50, whereas support vector machines with its multiple kernels, including Quadratic, Linear, Gaussian and Cubic, are used to classify these features. The experimental results suggest that the proposed framework efficiently detects COVID-19 from CXR images. The proposed framework obtained the best accuracy of 97.3% using ResNet50.

Face mask detection and social distance monitoring system for COVID-19 pandemic.

Coronavirus triggers several respirational infections such as sneezing, coughing, and pneumonia, which transmit humans to humans through airborne droplets. According to the guidelines of the World Health Organization, the spread of COVID-19 can be mitigated by avoiding public interactions in proximity and following standard operating procedures (SOPs) including wearing a face mask and maintaining social distancing in schools, shopping malls, and crowded areas. However, enforcing the adaptation of these SOPs on a larger scale is still a challenging task. With the emergence of deep learning-based visual object detection networks, numerous methods have been proposed to perform face mask detection on public spots. However, these methods require a huge amount of data to ensure robustness in real-time applications. Also, to the best of our knowledge, there is no standard outdoor surveillance-based dataset available to ensure the efficacy of face mask detection and social distancing methods in public spots. To this end, we present a large-scale dataset comprising of 10,000 outdoor images categorized into a binary class labeling i.e., face mask, and non-face masked people to accelerate the development of automated face mask detection and social distance measurement on public spots. Alongside, we also present an end-to-end pipeline to perform real-time face mask detection and social distance measurement in an outdoor environment. Initially, existing state-of-the-art single and multi-stage object detection networks are fine-tuned on the proposed dataset to evaluate their performance in terms of accuracy and inference time. Based on better performance, YOLO-v3 architecture is further optimized by tuning its feature extraction and region proposal generation layers to improve the performance in real-time applications. Our results indicate that the presented pipeline performed better than the baseline version, showing an improvement of 5.3% in terms of accuracy.

Multifunctional Bioactive Scaffolds from ARX-g-(Zn@rGO)-HAp for Bone Tissue Engineering: In Vitro Antibacterial, Antitumor, and Biocompatibility Evaluations.

Advanced biomaterials are required with enhanced antibacterial and anticancer activities to obtain desirable biocompatibility during and after scaffold implantation in tissue engineering. Here, we report the development of a nanosystem by the hydrothermal method using different zinc (Zn) amounts and reduced graphene oxide (GO). Arabinoxylan, the nanosystem (Zn@rGO), and nanohydroxyapatite polymeric nanocomposites ARX-g-(Zn@rGO)/HAp were prepared by the free radical polymerization method, and porous bioactive scaffolds were fabricated via the freeze-drying technique. The structural, morphological, and elemental analyses of the bioactive scaffolds were conducted using Fourier transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, and energy-dispersive X-ray analysis. The wetting behavior was studied by a water contact meter and swelling in aqueous and phosphate-buffered saline solutions at 37 °C. The degradation was also studied in the phosphate-buffered saline solution at 37 °C. The increase in Zn content increased the pore size, and hydrophobic behavior shifted to hydrophilic (AGZ-1 = 131.40° at 0 s and 120.60° at 10 s to AGZ-1 = 81.30° at 0 s and 69.20° at 10 s) with the increase in contact time. Maximum swelling was observed in deionized water (AGZ-1 = 52.87%, AGZ-4 = 90.20%), followed by phosphate-buffered saline (PBS; AGZ-1 = 44.80%, AGZ-4 = 67.90%) and electrolyte (AGZ-1 = 32.40%, AGZ-4 = 63.47%), and biodegradation in PBS media increased (AGZ-1 = 36.80%, AGZ-4 = 55.92%). Antimicrobial activities against severe infection-causing pathogens and antitumor activity against U87 cell lines showed exceptional results. Cell viability and cell proliferation studies were conducted against preosteoblast cell lines, and increased cell viability and proliferation were observed from AGZ-1 to AGZ-4. Antimicrobial and anticancer activities were enhanced with the increase of Zn content in the Zn@rGO system. The bioactive scaffolds with different formulations could be potential biomaterials to treat and regenerate defected bone tissue.

Software-Defined Networking: Categories, Analysis, and Future Directions.

Software-defined networking (SDN) is an innovative network architecture that splits the control and management planes from the data plane. It helps in simplifying network manageability and programmability, along with several other benefits. Due to the programmability features, SDN is gaining popularity in both academia and industry. However, this emerging paradigm has been facing diverse kinds of challenges during the SDN implementation process and with respect to adoption of existing technologies. This paper evaluates several existing approaches in SDN and compares and analyzes the findings. The paper is organized into seven categories, namely network testing and verification, flow rule installation mechanisms, network security and management issues related to SDN implementation, memory management studies, SDN simulators and emulators, SDN programming languages, and SDN controller platforms. Each category has significance in the implementation of SDN networks. During the implementation process, network testing and verification is very important to avoid packet violations and network inefficiencies. Similarly, consistent flow rule installation, especially in the case of policy change at the controller, needs to be carefully implemented. Effective network security and memory management, at both the network control and data planes, play a vital role in SDN. Furthermore, SDN simulation tools, controller platforms, and programming languages help academia and industry to implement and test their developed network applications. We also compare the existing SDN studies in detail in terms of classification and discuss their benefits and limitations. Finally, future research guidelines are provided, and the paper is concluded.

Point prevalence of delirium among critically ill patients in Saudi Arabia: A multicenter observational study.

Background: Delirium in critically ill patients is independently associated with poor clinical outcomes. There is a scarcity of published data on the prevalence of delirium among critically ill patients in Saudi Arabia. Therefore, we sought to determine, in a multicenter fashion, the prevalence of delirium in critically ill patients in Saudi Arabia and explore associated risk factors. Methods: A cross-sectional point prevalence study was conducted on January 28, 2020, at 14 intensive care units (ICUs) across 3 universities and 11 other tertiary care hospitals in Saudi Arabia. Delirium was screened once using the Intensive Care Delirium Screening Checklist. We excluded patients who were unable to participate in a valid delirium assessment, patients admitted with traumatic brain injury, and patients with documented dementia in their medical charts. Results: Of the 407 screened ICU patients, 233 patients were enrolled and 45.9% were diagnosed with delirium. The prevalence was higher in mechanically ventilated patients compared to patients not mechanically ventilated (57.5% vs. 33.6%; P < 0.001). In a multivariate model, risk factors independently associated with delirium included age (adjusted odds ratio [AOR], 1.021; 95% confidence interval [CI], 1.01-1.04; P = 0.008), mechanical ventilation (AOR, 2.39; 95% CI, 1.34-4.28; P = 0.003), and higher severity of illness (AOR, 1.01; 95% CI, 1.001-1.021; P = 0.026). Conclusion: In our study, delirium remains a prevalent complication, with distinct risk factors. Further studies are necessary to investigate long-term outcomes of delirium in critically ill patients in Saudi Arabia.

Stress-Relieving Video Game and Its Effects: A POMS Case Study.

Stress is the response or a change in our bodies to environmental factors like challenges or demands that are physical and emotional. The main cause of stress is illnesses and it is gaining more interest, a hot topic for many researchers. Stress can be brought about by a wide range of normal life occasions that are hard to avoid. Stress generally refers to two things: first, the psychological perception of pressure and the body's response to it. On the other hand, it involves multiple systems, from metabolism to muscles to memory. Many methods and tools are being developed to reduce stress in humans. Stress can be a short-term issue or a long-term problem, depending on what changes in your life. The emphasis of this article is to reduce the effects of stress by developing a stress-releasing game and verifying its results through the Profile of Mood States (POMS) and POMS-2 survey. Games are associated with stress levels; hence, parameters like sounds, visuals, and colors associated with reducing stress are used to develop a game for the stress reduction in the players. The survey research aims to determine that the purpose-built game will affect the player's stress level using a reliable psychological survey paper. The survey collected a variety of information from its participants over six months. Different aspects of a person's psychology and reactions are recorded in this scenario by calculating the mean, standard deviation, degree of freedom, zero-error, and probability-value%. The POMS and POMS-2 results are obtained from the custom-built game, and these are found to be effective in reducing stress.

Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition.

The most often reported danger to computer security is malware. Antivirus company AV-Test Institute reports that more than 5 million malware samples are created each day. A malware classification method is frequently required to prioritize these occurrences because security teams cannot address all of that malware at once. Malware's variety, volume, and sophistication are all growing at an alarming rate. Hackers and attackers routinely design systems that can automatically rearrange and encrypt their code to escape discovery. Traditional machine learning approaches, in which classifiers learn based on a hand-crafted feature vector, are ineffective for classifying malware. Recently, deep convolutional neural networks (CNNs) successfully identified and classified malware. To categorize malware, a smart system has been suggested in this research. A novel model of deep learning is introduced to categorize malware families and multiclassification. The malware file is converted to a grayscale picture, and the image is then classified using a convolutional neural network. To evaluate the performance of our technique, we used a Microsoft malware dataset of 10,000 samples with nine distinct classifications. The findings stood out among the deep learning models with 99.97% accuracy for nine malware types.

A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers.

Brain tumors are difficult to treat and cause substantial fatalities worldwide. Medical professionals visually analyze the images and mark out the tumor regions to identify brain tumors, which is time-consuming and prone to error. Researchers have proposed automated methods in recent years to detect brain tumors early. These approaches, however, encounter difficulties due to their low accuracy and large false-positive values. An efficient tumor identification and classification approach is required to extract robust features and perform accurate disease classification. This paper proposes a novel multiclass brain tumor classification method based on deep feature fusion. The MR images are preprocessed using min-max normalization, and then extensive data augmentation is applied to MR images to overcome the lack of data problem. The deep CNN features obtained from transfer learned architectures such as AlexNet, GoogLeNet, and ResNet18 are fused to build a single feature vector and then loaded into Support Vector Machine (SVM) and K-nearest neighbor (KNN) to predict the final output. The novel feature vector contains more information than the independent vectors, boosting the proposed method's classification performance. The proposed framework is trained and evaluated on 15,320 Magnetic Resonance Images (MRIs). The study shows that the fused feature vector performs better than the individual vectors. Moreover, the proposed technique performed better than the existing systems and achieved accuracy of 99.7%; hence, it can be used in clinical setup to classify brain tumors from MRIs.

Classification of COVID-19 and Influenza Patients Using Deep Learning.

Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019-22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.

Antibacterial and Hemocompatible pH-Responsive Hydrogel for Skin Wound Healing Application: In Vitro Drug Release.

The treatment of successive skin wounds necessitates meticulous medical procedures. In the care and treatment of skin wounds, hydrogels produced from natural polymers with controlled drug release play a crucial role. Arabinoxylan is a well-known and widely available biological macromolecule. We produced various formulations of blended composite hydrogels (BCHs) from arabinoxylan (ARX), carrageenan (CG), and reduced graphene oxide (rGO) using and cross-linked them with an optimal amount of tetraethyl orthosilicate (TEOS). The structural, morphological, and mechanical behavior of the BCHs samples were determined using Fourier-transform infrared spectroscopy (FT-IR), Scanning electron microscope (SEM), mechanical testing, and wetting, respectively. The swelling and degradation assays were performed in phosphate-buffered saline (PBS) solution and aqueous media. Maximum swelling was observed at pH 7 and the least swelling in basic pH regions. All composite hydrogels were found to be hemocompatible. In vitro, silver sulfadiazine release profile in PBS solution was analyzed via the Franz diffusion method, and maximum drug release (87.9%) was observed in 48 h. The drug release kinetics was studied against different mathematical models (zero-order, first-order, Higuchi, Hixson-Crowell, Korsmeyer-Peppas, and Baker-Lonsdale models) and compared their regression coefficient (R2) values. It was observed that drug release follows the Baker-Lonsdale model, as it has the highest value (0.989) of R2. Hence, the obtained results indicated that, due to optimized swelling, wetting, and degradation, the blended composite hydrogel BCH-3 could be an essential wound dressing biomaterial for sustained drug release for skin wound care and treatment.

A Robust Image Encrypted Watermarking Technique for Neurodegenerative Disorder Diagnosis and Its Applications.

The use of Internet technology has led to the availability of different multimedia data in various formats. The unapproved customers misuse multimedia information by conveying them on various web objections to acquire cash deceptively without the first copyright holder's intervention. Due to the rise in cases of COVID-19, lots of patient information are leaked without their knowledge, so an intelligent technique is required to protect the integrity of patient data by placing an invisible signal known as a watermark on the medical images. In this paper, a new method of watermarking is proposed on both standard and medical images. The paper addresses the use of digital rights management in medical field applications such as embedding the watermark in medical images related to neurodegenerative disorders, lung disorders, and heart issues. The various quality parameters are used to figure out the evaluation of the developed method. In addition, the testing of the watermarking scheme is done by applying various signal processing attacks.

Adjunctive ketamine for sedation in critically ill mechanically ventilated patients: an active-controlled, pilot, feasibility clinical trial.

OBJECTIVE: Ketamine has been shown to decrease sedative requirements in intensive care unit (ICU). Randomized trials are limited on patient-centered outcomes. We designed this pilot trial to evaluate the feasibility of a large randomized controlled trial (RCT) testing the effect of ketamine as an adjunct analgosedative compared with standard of care alone as a control group (CG) in critically ill patients with mechanical ventilation (MV). We also provided preliminary evidence on clinically relevant outcomes to plan a larger trial. MATERIAL AND METHODS: Pilot, active-controlled, open-label RCT was conducted at medical, surgical, and transplant ICUs at a large tertiary and quaternary care medical institution (King Faisal Specialist Hospital and Research Center, Saudi Arabia). The study included adult patients who were intubated within 24 h, expected to require MV for the next calendar day, and had institutional pain and sedation protocol initiated. Patients were randomized in a 1:1 ratio to adjunct ketamine infusion 1-2 µg/kg/min for 48 h or CG alone. RESULTS: Of 437 patients screened from September 2019 through November 2020, 83 (18.9%) patients were included (43 in CG and 40 in ketamine) and 352 (80.5%) were excluded. Average enrollment rate was 3-4 patients/month. Consent and protocol adherence rates were adequate (89.24% and 76%, respectively). Demographics were balanced between groups. Median MV duration was 7 (interquartile range [IQR] 3-9.25 days) in ketamine and 5 (IQR 2-8 days) in CG. Median VFDs was 19 (IQR 0-24.75 days) in ketamine and 19 (IQR 0-24 days) in the CG (p = 0.70). More patients attained goal Richmond Agitation-Sedation Scale at 24 and 48 h in ketamine (67.5% and 73.5%, respectively) compared with CG (52.4% and 66.7%, respectively). Sedatives and vasopressors cumulative use, and hemodynamic changes were similar. ICU length-of-stay was 12.5 (IQR 6-21.2 days) in ketamine, compared with 12 (IQR 5.5-23 days) in CG. No serious adverse events were observed in either group. CONCLUSIONS: Ketamine as an adjunct analgosedative agent appeared to be feasible and safe with no negative impact on outcomes, including hemodynamics. This pilot RCT identified areas of improvement in study protocol before conducting a large, adequately powered, multicenter RCT which is likely justified to investigate ketamine association with patient-centered outcomes further. Trial registration ClinicalTrials.gov: NCT04075006. Registered on 30 August 2019. Current controlled trials: ISRCTN14730035. Registered on 3 February 2020.