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Abstract Background Limited data are available regarding the prevalence of medication-related problems (MRPs) in kidney transplant recipients. This study aimed to determine the prevalence and types of medication-related problems. Methods A cross-sectional study was conducted including kidney transplant recipients aged ≥ 18 years who were receiving immunosuppressive agents for at least 3 months post-transplant. The primary outcome was to determine the prevalence of MRPs. The secondary outcomes were to identify the pharmacological classes, categories of medications contributing to MRPs, and predictors of developing > 3 MRPs. Results We enrolled 107 kidney transplant recipients. The mean ± standard deviation (SD) of age and body mass index (BMI) were 50 ± 15.8 years and 28.9 ± 5.3 (kg/m2), respectively, and 66.3% were male. The prevalence of MRPs was 28.97% [95% confidence intervals (CI) 19.68%, 41.125] in 1393 prescriptions. The frequent types of MRPs were drug‒drug interactions (46.1%), duplication (12%), and medication use without an indication (11.7%). Immunosuppressive agents and cardiac medications were the main classes causing MRPs. The number of medications and the years post-kidney-transplant were significant predictors of developing > 3 MRPs. Conclusion The results showed that drug‒drug interactions were the most frequent MRPs, with immunosuppressive agents being the most common class causing MRPs.

Abstract Nappi, fermented shrimp, is a traditional diet that is widely consumed by the ethnic communities of Southeast Asian countries, especially in Bangladesh. The present study was carried out to determine the proximate composition of Nappi collected from two different places, i.e., the Moheshkhali and Chaufaldandi areas of Cox’s Bazar district, Bangladesh. Traditional methods were used to prepare Nappi, and the standard procedure was followed to determine the proximate composition. The proximate composition of Nappi varied from place to place depending on the raw materials, preparation techniques, and surrounding conditions of the formulation places. Associated statistical analysis was conducted by r-programming (version 4.2.3). Proximate composition includes moisture, protein, ash, fat, and carbohydrate contents, amounting to 37.66 ± 1.51%, 34.34 ± 0.68%, 17.49 ± 0.46%, 8.53 ± 0.45%, and 1.39 ± 0.10%, respectively, for the Moheshkhali sample and 35.44 ± 1.37%, 37.23 ± 1.78%, 18.46 ± 0.59%, 6.00 ± 0.40%, and 2.66 ± 0.21%, respectively, for the Chaufaldandi sample. The study showed that moisture and fat content were significantly higher (ANOVA, p

Purpose: Burns are considered as serious health problem in low-income country. They lead to patients’ morbidity and mortality beside their burden on the health care system. This study was conducted to review the clinical presentation and management outcome in a secondary referral hospital in Sudan (sub-Saharan country). Methods: This is a retrospective hospital-based cohort study. It was conducted during 2019–2021. Data was collected to determine socio-demographic characteristics of patients, site of burns, cause of burn and management outcome. Any patient presented with burns during the study periods was enrolled in the study area. Results: Total number of surgical emergency admitted during the study periods were 8126. Total number of patients who are included in this study were 133 with male predominant (53 %). Seventy-two of them are below age of 10 years. Half of the patients (53 %) presented with scalding burn. Only, 36 % of them presented to the emergency department during the first 12 h. Seventy eight percentage of them were treated and discharged while 6 % of them died. Open wound dressing was done for 84 % of patients while skin graft was done in 12 % of patients. Conclusion: This study demonstrates the situation in a low-income setting with higher prevalence among children. There are challenges and difficulty in managing burn patients.

Background: Trigeminal neuralgia (TN), or Tic Douloureux, is one of the most common neuropathic pains. Gamma knife stereotactic radiosurgery (GNSRS) has been considered one of the procedures for treating primary TN. Objective: This study evaluates the effectiveness of GNSRS in treating primary TN in patients who are unresponsive to medical treatment in a single-center experience. Subjects and Methods: This study was conducted on 180 patients from January 2018 to October 2021. The study utilized the Barrow Neurological Institute (BNI) pain intensity score to assess pain before and after GNSRS treatment. Results: A total of 180 patients with primary TN were included in this study, 108 female with a 1.5:1 female: male ratio. Results showed significant pain score reduction post-GNSRS, indicating GNSRS as an effective second-line treatment for primary TN. The study suggests GNSRS's potential in decreasing medication dosage with minimal short-term complications, recommending further research to assess long-term benefits and side effects. A notable decrease in medication usage was reported, with minimal short-term complications. These results support the consideration of GNSRS as a viable alternative for TN patients who have exhausted traditional treatment options. Further research is recommended to explore long-term outcomes and potential side effects associated with GNSRS. Conclusion: The study's findings demonstrate that GNSRS is an effective and safe second-line treatment for patients with primary TN unresponsive to medical therapy. Significant reductions in pain intensity, as measured by the BNI pain scale, were observed following GNSRS treatment.

Abstract Extensive research is now being conducted on the design and construction of logic circuits utilizing quantum-dot cellular automata (QCA) technology. This area of study is of great interest due to the inherent advantages it offers, such as its compact size, high speed, low power dissipation, and enhanced switching frequency in the nanoscale domain. This work presents a design of a highly efficient RAM cell in QCA, utilizing a combination of a 3-input and 5-input Majority Voter (MV) gate, together with a 2 × 1 Multiplexer (MUX). The proposed design is also investigated for various faults such as single cell deletion, single cell addition and single cell displacement or misalignment defects. The circuit under consideration has a high degree of fault tolerance. The functionality of the suggested design is showcased and verified through the utilization of the QCADesigner tool. Based on the observed performance correlation, it is evident that the proposed design demonstrates effectiveness in terms of cell count, area, and latency. Furthermore, it achieves a notable improvement of up to 76.72% compared to the present configuration in terms of quantum cost. The analysis of energy dissipation, conducted using the QCAPro tool, is also shown for various scenarios. It is seen that this design exhibits the lowest energy dispersion, hence enabling the development of ultra-low power designs for diverse microprocessors and microcontrollers.

The most recent advancement in switched-capacitor boost inverters for high-frequency ac systems and solar PV utilization is their reduced component count. SC-based multilevel inverters (MLIs) are the ideal solution for PV applications since they have a larger voltage gain and a sensorless mechanism for self-voltage balancing. This article presents a boost inverter scheme for higher-level output that involves input voltage boosting. The proposed topology can be reconfigured to produce 9 and 13 levels of output voltage with alternative topologies and a voltage gain of four or three, respectively. Twelve semiconductor devices, three capacitors, and a single input source are used to achieve the voltage gain/higher levels. The proposed boost topology has been contrasted with a number of related topologies and empirically validated using a number of experimental findings.

Electrocardiography (ECG) is the most non-invasive diagnostic tool for cardiovascular diseases (CVDs). Automatic analysis of ECG signals assists in accurately and rapidly detecting life-threatening arrhythmias like atrioventricular blockage, atrial fibrillation, ventricular tachycardia, etc. The ECG recognition models need to utilize algorithms to detect various kinds of waveforms in the ECG and identify complicated relationships over time. However, the high variability of wave morphology among patients and noise are challenging issues. Physicians frequently utilize automated ECG abnormality recognition models to classify long-term ECG signals. Recently, deep learning (DL) models can be used to achieve enhanced ECG recognition accuracy in the healthcare decision making system. In this aspect, this study introduces an automated DL enabled ECG signal recognition (ADL-ECGSR) technique for CVD detection and classification. The ADL-ECGSR technique employs three most important subprocesses: pre-processed, feature extraction, parameter tuning, and classification. Besides, the ADL-ECGSR technique involves the design of a bidirectional long short-term memory (BiLSTM) based feature extractor, and the Adamax optimizer is utilized to optimize the trained method of the BiLSTM model. Finally, the dragonfly algorithm (DFA) with a stacked sparse autoencoder (SSAE) module is applied to recognize and classify EEG signals. An extensive range of simulations occur on benchmark PTB-XL datasets to validate the enhanced ECG recognition efficiency. The comparative analysis of the ADL-ECGSR methodology showed a remarkable performance of 91.24 % on the existing methods.

BackgroundLimited research capacity has contributed to the lack of high-quality research from low-and middle-income countries. This is compounded by limited research training opportunities. Research capacity scale-up training was deployed as part of the implementation of the National Surgical, Obstetrics, Anaesthesia, and Nursing Plan for Nigeria. We report the impact of this locally contextualized efforts to scale up research capacity in sub-Saharan Africa.MethodsThis is an evaluation of the training of 65 participants in research, grant writing and manuscript writing and publication. Pre- and post-training surveys using a 5-point Likert scale and open-ended questions were administered to evaluate the impact of the programme.ResultsThere were 39 (60%) males and 26 (40%) females aged 26–62 years (median 42 years). Thirty-nine (60%) participants had previous training in research, but only 12 (18.5%) had previously received grant writing training, and 17 (26.2%) had previously received manuscript writing and publishing training. Following training, 45 (70.3%) participants agreed that the training was relevant. The research, grant writing and manuscript writing, and publication components of the training were rated high by the participants (45–59, 70.3–92.2%). However, 41.2% felt that there was not enough time, and 32.4% felt that the training was too comprehensive. Nearly all the participants agreed that the training had improved their skills in research, grant writing and manuscript writing and publication, and more than two-thirds subsequently engaged in informal mentoring of others. Overall, participants achieved success in designing their own research projects and publishing manuscripts and grants. Three (4.6%) of the participants had gone on to become faculty for the research training programme. The three top barriers encountered following training were time constraints (67.3%), lack of funding (36.5%) and not being able to find research collaborators (25%).ConclusionOutcome of this training programme is encouraging and highlights the feasibility and potential impact of deploying such programmes in low and middle income countries (LMICs). Despite the positive outcomes, barriers including time constraints, funding limitations, and difficulties in finding research collaborators remain to be addressed. Such training programmes need to be supported to strengthen the research capacity in this and similar settings.

تهدف الدراسة إلى تطوير طرق تدريس التربية البدنية وتحسين تجربة تعلم الطلاب في مجال التربية البدنية، وتهتم ان تساهم في تصميم مناهج تعليمية مبتكرة ومتكاملة لمادة التربية البدنية، وفي تحديد الأهداف التعليمية وتنظيم المحتوى وتحديد الطرق والوسائل المناسبة، توفير بيئة تعليمية تفاعلية ومبتكرة للطلاب، تتبع وقياس أداء الطلاب وتقديم ملاحظات فورية لتحسين أدائهم. منهج الدراسة: استعمل الباحثون المنهج التجريبي لمناسبته لطبيعة هذا البحث، وذلك بإتباع التصميم التجريبي لمجموعة تجريبية واحدة باستعمال القياسات القبلية والبعدية مجتمع وعينة الدراسة قام الباحثون باختيار عينة البحث بالطريقة العمدية من طلاب المرحلة الثالثة للعام الدراسي (2022/2023م). وعددهم (200) طالب واستنتج الباحثون -أن هندسة المناهج شهدت تحسنًا بنسبة 88.35٪ -أن استراتيجيات الذكاء الاصطناعي شهدت تحسنًا بنسبة67.5%- أن المنهجية الرقمية شهدت تحسنًا بنسبة 70.28 %، ويوصي الباحثون: السعي دائماً في استخدام تلك المعايير في بناء المنهجية الرقمية في الالعاب المختلفة في مادة التربية البدنية وعلوم الرياضة.

Recently, the technology of Underwater computer vision has played a vital role by improving the quality of underwater images owing to its significance in different applications in marines, such as military, resource development, biological research, and underwater environmental assessments. Moreover, light is absorbed and scattered while propagating through water, leading to color distortion. Additionally, floating micro-particles in the water contribute to low image contrast, resulting in blurry and poorly lit underwater images with a color cast. Therefore, many researchers have been attracted to developing diverse computer vision-based methods to improve the quality of underwater images, such as restoration, enhancement, and deep-learning techniques to restore and enhance degraded underwater images. Although numerous studies have attempted to address these issues, there is still much room for improvement in the quality of the produced images. To this end, this paper proposes a new enhancement method to improve underwater image quality. The presented approach utilizes the Coronavirus herd immunity optimizer algorithm for underwater image enhancement (CHIO-UIE) and is evaluated using standard measures on public datasets. The empirical results demonstrate that the CHIO-UIE method enhances the quality of images based on qualitative and quantitative evaluations, successfully improving underwater images with low contrast and light by significantly enhancing the visual impact of distorted underwater images across various underwater environments.

A teenage girl with pemphigus vulgaris (PV) and abetalipoproteinemia experienced an acute PV flare and wound myiasis on her right thigh. Dermatoscopy aided in larval identification as Phormia regina. Treatment included dexamethasone, oral prednisolone, turpentine oil flushes, and larval removal. At the 4-week follow-up, disease activity was controlled, and Magnetic Resonance Imaging (MRI) showed no larvae. This rare case highlighted the co-occurrence of wound myiasis and PV, likely due to poor living conditions. Dermatoscopy was crucial for larval identification, and turpentine oil flushes facilitated larval removal. Ethanol, chloroform spray, and ivermectin are also effective myiasis treatments.

AbstractThis paper describes deep neural network (DNN) models based on hyperparameter optimization for the prediction of the compressive strength of concrete. The novelty of this research lies in the implementation of optimized hyperparameters to train the DNN models with the aim to enhance their predictive accuracy. Utilizing the Keras Tuner library, the most effective hyperparameters for the DNN models were identified. These models are then trained and evaluated using an experimental dataset of 1030 instances, encompassing nine quantitative variables that include various concrete mix ingredients and age. The target variable for all models is the compressive strength of concrete. The assessment of model performance, based on metrics like mean squared error (MSE) and R2 values, was conducted on previously unseen test data. The optimal configuration for the hidden layers in the model was identified as five layers, containing 12, 16, 16, 40, and 26 neurons, respectively. The optimal learning rate for the model was 0.001. With this set of optimal hyperparameters, the best DNN model achieved an MSE of 28.76 and an R2 value of 0.89 on the testing data. The results demonstrate a significant improvement in the DNN models' performance when trained with the optimized hyperparameters. In comparison to the regression model, the performance of the DNN models was significantly better. Adopting the predictive models developed in this research offers the potential for substantial cost and time savings by circumventing the need for labour-intensive and time-consuming laboratory tests.

Halal business practices are developing in majority-Muslim countries and within companies in Muslim minority countries such as Finland. It consistently shows an interest in developing the halal industry, as evidenced by the development of companies that practice halal business in these countries. This study examines the factors influencing halal company practices while offering halal goods or services in Finland, a nation with a large Muslim population. The surveys were given online and offline to Finnish businesses that have achieved halal certification as a data collection strategy. PLS-SEM, or partial least square-structural equation modeling, was used to analyze data from 32 replies. This study found that halal values and the spirit of innovation encourage Finnish companies to enter the halal business. In addition, companies' halal business practices in Finland are strongly influenced by the interaction between internal and external driving factors. Businesses in nations where Muslims make up the minority can be encouraged to engage in halal commerce in several ways. Individuals who are aware of halal's advantages, are drawn to innovation, are under external pressure to conduct halal business and have internal circumstances that allow them to view the halal business phenomena as a chance to advance their nation's commercial interests.

A novel approach for infrared (IR) energy harvesting through the integration of a resonating metasurface with a metal-insulator-metal (MIM) nano-rectenna is presented. The absorption of IR radiation at 28.3 THz is significantly enhanced through the localization of surface plasmons with the integration of a periodic metasurface on the top of the log spiral MIM rectenna. Additionally, a ground plane is introduced on the backside of the structure to further enhance absorption characteristics. Extensive characterization analysis of the antenna's absorbed E-fields for incident 28.3 THz radiation is conducted with and without the integrated metasurface using full-wave numerical simulation. Also, the rectification properties (I/V, resistivity, and responsivity) are studied with variations in metal (Au, Al, Ag, and Cu) and five different insulators (Al2O3, Cu2O, Ta2O5, TiO2, and ZnO) types. The results demonstrate that the presence of the metasurface enhances the absorbed IR E-field by the rectenna to levels of 80–90%. Furthermore, exceptional harvesting performance, including E-field, current density, resistivity, and rectification efficiency (responsivity), is achieved with the asymmetric Au-ZnO-Cu and Au-ZnO-Ag MIM and metasurface rectenna structures. The proposed design strategies can lead to the development of highly efficient IR energy harvesters in the future.

Generally, the agricultural field can enhance food needs and deliver healthy and nutritious food. Recognizing and classifying crop insects is a crucial threat for agriculturalists as it is essential in preventing crop damage and preserving quality. Traditional pest recognition models often needed more knowledgeable taxonomists who could precisely detect pests based on morphological aspects. Pest detection employing deep learning (DL) is a respected use of artificial intelligence (AI) in fields such as entomology, agriculture, and pest control. DL models, particularly convolutional neural networks (CNNs), have proven incredibly effective in precisely detecting and classifying pests in images. This study develops an Innovative Insect Detection and Classification for the Agricultural Sector Using a Gannet Optimization Algorithm with DL (IIDC-GOADL) approach. The main target of the IIDC-GOADL method is to recognize and classify the diverse insect types. The IIDC-GOADL method employs image preprocessing to remove the existing noises. In addition, the densely connected networks (DenseNet) approach is applied for feature extraction. Meanwhile, the GOA can select the optimum hyperparameter for the DenseNet architecture. Moreover, an attention-based bidirectional long short-term memory (ABiLSTM) approach was applied for automated insect discovery and categorization. The experimental outcomes of the IIDC-GOADL method are confirmed under insect datasets, and the results are checked using the dissimilar measures below. An extensive comparison research of the IIDC-GOADL method highlighted an enhanced accuracy outcome of 98.15% and 98.52% over other models under TRP/TSP.

Due to the sensitive and mission-critical nature of the data collected and transferred, security in IoT-assisted UAV networks is of great significance. Intrusion detection in IoT-assisted UAV networks includes the deployment of complex monitoring systems to identify and respond to cyberattacks, physical breaches, or unauthorized access. This system employs a combination of anomaly detection and signature-based methods to find malicious or unusual activities within the network. A robust intrusion detection mechanism is essential for protecting the security and integrity of the UAVs and the data collected, ensuring that any possible vulnerabilities are promptly addressed and identified. Consequently, this study introduces an adaptive mongoose optimizer algorithm with a deep learning-based intrusion detection (AMOA-DLID) method in IoT-assisted UAV networks. The AMOA-DLID technique intends to ensure security in the IoT-assisted UAV networks via an intrusion detection process. In the presented AMOA-DLID technique, AMOA is initially applied for the feature selection process. The following sparse autoencoder (SAE) model can be exploited for the recognition of the intrusions. Lastly, the recognition rate of the SAE model can be improved by employing the Harris Hawks optimizer (HHO) technique. The detailed experimental study of the AMOA-DLID model is performed on the benchmark dataset of IDS. The extensive results portrayed that the AMOA-DLID technique reaches improved security over other models on the IoT-assisted UAV networks.

Abstract The sugarcane industry often utilizes effluent for irrigation purposes; however, its intricate composition and elevated metal contaminants pose a potential risk of soil and crop contamination. Consequently, it is imperative to employ effective strategies to ensure the safe utilization of this resource for crop cultivation. One such strategy involves the dilution of sugarcane industry effluent. Dilution is a practical approach to mitigate its toxicity, minimizing its adverse impact on soil and crop health. That’s why the current study explored the best dilution of sugarcane industrial effluent (SW) for cultivating canola varieties. A total of 15 canola varieties were cultivated 0%, 20%, 40%, 60%, 80%, and 100% SW. Results showed that 60% SW Faisalabad Canola and Punjab Canola improved germination, shoot length, root length, shoot fresh and dry weight, root fresh and dry weight, and chlorophyll contents compared to other treatments and control. AARI Canola and CON-III showed poor growth and chlorophyll contents under 60%SW. Dunkled and Oscar cultivars showed moderate improvement in growth and chlorophyll contents under 60SW. The 60% SW can be recommended for maximum growth benefits in canola cultivars, specifically Faisalabad Canola and Punjab Canola. At 20SW, the root dry weight of Faisalabad Canola increased by 2.7%, while Punjab Canola increased by 3.4%. Canola showed the highest increase in POD activity compared to the control, with a 55.45% increase, followed by Sandal Canola, with a 43.26% increase. However, additional field-level experiments are needed to determine the best cultivars suitable for optimal growth under 80SW and 60SW irrigation conditions.

Content-based image retrieval (CBIR) in the healthcare field is an advanced technology that leverages the visual or content features of medical images to retrieve similar images from vast data. This technology has significant applications in healthcare, including medical diagnosis, research, and treatment planning. It is a diagnostic tool that enhances the explainability of computer-aided diagnoses (CAD) systems and provides decision-making support for healthcare professionals. A conventional way to CBIR is learning a distance metric by converting images into feature space where the distance between samples is a similarity measurement. CBIR systems can autonomously extract intricate features from medical images by leveraging convolutional neural networks (CNN) and other advanced deep learning techniques, enabling accurate and swift retrieval of relevant patient cases and diagnostic references. This manuscript designs an Archimedes Optimization Algorithm with Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector (AOADL-CBIRH) technique. The AOADL-CBIRH technique intends to retrieve similar images based on query images in the healthcare sector. In the presented AOADL-CBIRH technique, image pre-processing is initially performed using an adaptive bilateral filtering (ABF) approach to enhance the image quality. For deep feature extraction, the AOADL-CBIRH method applies the Efficient Channel Spatial Model (ECSM) with the ResNet50 model. To improve the retrieval performance, the AOADL-CBIRH technique employs AOA-based hyperparameter tuning for the ECSM-ResNet50 model. Lastly, the Manhattan distance metric determines the similarity between the images and retrieves them. The experimental evaluation of the AOADL-CBIRH algorithm is tested by using benchmark image dataset. The stimulation values signified the enhanced retrieval results of the AOADL-CBIRH technique over other models.

The Internet of Things (IoT) has revolutionized various domains, enabling interconnected devices to communicate and exchange data. The integration of Artificial Intelligence (AI) in IoT systems further enhances their capabilities and potential benefits. Unfortunately, in the era of AI, ensuring the privacy and security of the IoT faces novel and specific challenges. IoT security is imperative, necessitating comprehensive strategies, including comprehension of IoT security challenges, implementation of AI methodologies, adoption of resilient security frameworks, and handling of privacy and ethical concerns to construct dependable and secure IoT systems. It is vital to note that the term ‘security’ encompasses a more comprehensive view than cyberattacks alone. Therefore, with an emphasis on securing against cyberattacks, this comprehensive survey also includes physical security threats on the IoT. It investigates the complexities and solutions for IoT systems, placing particular emphasis on AI-based security techniques. The paper undertakes a categorization of the challenges associated with ensuring IoT security, investigates the utilization of AI in IoT security, presents security frameworks and strategies, underscores privacy and ethical considerations, and provides insights derived from practical case studies. Furthermore, the survey sheds light on emerging trends concerning IoT security in the AI era. This survey provides significant contributions to the understanding of establishing dependable and secure IoT systems through an exhaustive examination of the present condition of IoT security and the ramifications of AI on it.

Biomedical image segmentation is a vital task in the analysis of medical imaging, including the detection and delineation of pathological regions or anatomical structures within medical images. It has played a pivotal role in a variety of medical applications, involving diagnoses, monitoring of diseases, and treatment planning. Conventionally, clinicians or expert radiologists have manually conducted biomedical image segmentation, which is prone to human error, subjective, and time-consuming. With the advancement in computer vision and deep learning (DL) algorithms, automated and semi-automated segmentation techniques have attracted much research interest. DL approaches, particularly convolutional neural networks (CNN), have revolutionized biomedical image segmentation. With this motivation, we developed a novel equilibrium optimization algorithm with a deep learning-based biomedical image segmentation (EOADL-BIS) technique. The purpose of the EOADL-BIS technique is to integrate EOA with the Faster RCNN model for an accurate and efficient biomedical image segmentation process. To accomplish this, the EOADL-BIS technique involves Faster R-CNN architecture with ResNeXt as a backbone network for image segmentation. The region proposal network (RPN) proficiently creates a collection of a set of region proposals, which are then fed into the ResNeXt for classification and precise localization. During the training process of the Faster RCNN algorithm, the EOA was utilized to optimize the hyperparameter of the ResNeXt model which increased the segmentation results and reduced the loss function. The experimental outcome of the EOADL-BIS algorithm was tested on distinct benchmark medical image databases. The experimental results stated the greater efficiency of the EOADL-BIS algorithm compared to other DL-based segmentation approaches.

Load forecasting in Smart Grids (SG) is a major module of current energy management systems, that play a vital role in optimizing resource allocation, improving grid stability, and assisting the combination of renewable energy sources (RES). It contains the predictive of electricity consumption forms over certain time intervals. Load Forecasting remains a stimulating task as load data has exhibited changing patterns because of factors such as weather change and shifts in energy usage behaviour. The beginning of advanced data analytics and machine learning (ML) approaches; particularly deep learning (DL) has mostly enhanced load forecasting accuracy. Deep neural networks (DNNs) namely Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) have achieved popularity for their capability to capture difficult temporal dependencies in load data. This study designs a Short-Load Forecasting scheme using a Hybrid Deep Learning and Beluga Whale Optimization (LFS-HDLBWO) approach. The major intention of the LFS-HDLBWO technique is to predict the load in the SG environment. To accomplish this, the LFS-HDLBWO technique initially uses a Z-score normalization approach for scaling the input dataset. Besides, the LFS-HDLBWO technique makes use of convolutional bidirectional long short-term memory with an autoencoder (CBLSTM-AE) model for load prediction purposes. Finally, the BWO algorithm could be used for optimal hyperparameter selection of the CBLSTM-AE algorithm, which helps to enhance the overall prediction results. A wide-ranging experimental analysis was made to illustrate the better predictive results of the LFS-HDLBWO method. The obtained value demonstrates the outstanding performance of the LFS-HDLBWO system over other existing DL algorithms with a minimum average error rate of 3.43 and 2.26 under FE and Dayton grid datasets, respectively.

Intelligent Transportation Systems (ITS) make use of advanced technologies to optimize interurban and urban traffic, reduce congestion and enhance overall traffic flow. Deep learning (DL) approaches can be widely used for traffic flow monitoring in the ITS. This manuscript introduces the Artificial Hummingbird Optimization Algorithm with Hierarchical Deep Learning for Traffic Management (AHOA-HDLTM) technique in the ITS environment. The purpose of the AHOA-HDLTM technique is to predict traffic flow levels in smart cities, enabling effective traffic management. Primarily, the AHOA-HDLTM model involves data preprocessing and an Improved Salp Swarm Algorithm (ISSA) for feature selection. For the prediction of traffic flow, the Hierarchical Extreme Learning Machine (HELM) model can be used. The HELM extracts complex features and patterns, with an additional Artificial Hummingbird Optimization Algorithm (AHOA)-based hyperparameter selection process to enhance predictive outcomes. The simulation result analysis under different traffic data demonstrates the better performance of the AHOA-HDLTM technique over existing models. The hierarchical structure of the HELM model along with AHOA-based hyperparameter tuning helps to accomplish enhanced prediction performance. The AHOA-HDLTM technique presents a robust solution for traffic management in ITS, showcasing enhanced performance in forecasting traffic patterns and congestion. The AHOA-HDLTM technique can be used in various smart cities and urban regions. Its abilities in real-time traffic flow prediction can be helpful in the design of efficient, sustainable, and resilient transportation networks.

Cervical cancer (CC), the most common cancer among women, is most commonly diagnosed through Pap smears, a crucial screening process that includes collecting cervical cells for examination. Artificial intelligence (AI)-powered computer-aided diagnoses (CAD) system becomes a promising tool for improving CC diagnosis. Deep learning (DL), a branch of AI, holds particular potential in CAD systems for early detection and accurate diagnosis. DL algorithm is trained to identify abnormalities and patterns in Pap smear images, such as dysplasia, cellular changes, and other markers of CC. So, this study presents a Computer Aided Cervical Cancer Diagnosis utilizing the Gazelle Optimizer Algorithm with Deep Learning (CACCD-GOADL) model on Pap smear images. The foremost objective of the CACCD-GOADL approach is to examine the image detection of CC. To accomplish this, the CACCD-GOADL methodology uses an improved MobileNetv3 model for extracting complex patterns in Pap smear images. In addition, the CACCD-GOADL technique designs a new GOA for the hyperparameter tuning of the improved MobileNetv3 system. For the classification and identification of cancer, the CACCD-GOADL technique uses a stacked extreme learning machine (SELM) methodology. The simulation validation of the CACCD-GOADL approach is verified on a benchmark dataset of Herlev. Experimental results highlighted that the CACCD-GOADL algorithm reaches superior outcomes over other methods.

The fast development of smart home devices and the Internet of Things (IoTs) presents unprecedented accessibility into our day-to-day lives; however, it has also increased major problems regarding security and privacy. A smart home network is a vital element of modern home automation systems, enabling the interconnectivity and control of different smart devices. These networks allow homeowners to remotely control lighting, security, temperature, and entertainment systems via voice commands or smartphones. These offer energy efficiency, convenience, and improved security by permitting residents to monitor and modify their living surroundings. Safeguarding the flexibility of smart home networks against cyberattacks and unauthorized access is important to comprehending the maximum ability of smart living while retaining data integrity and privacy of connected devices. This research develops the Blockchain with Red-Tailed Hawk Algorithm-Enabled Deep Learning (BC-RTHADL) model, aimed to strengthen the safety of smart home systems. BC-RTHADL integrates the safety features of blockchain with a strong malicious action recognition procedure. The blockchain module certifies immutability, transparency, and decentralization, donating to a safe smart home atmosphere. The malicious action detection influences the Red-Tailed Hawk Algorithm for feature selection and an ensemble of Extreme Learning Machine (ELM), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) techniques for precise recognition. The Equilibrium Optimizer algorithm enhances parameters for improved effectiveness. Complete tests show the greater performance of BC-RTHADL across numerous metrics, reaffirming its promising potential in safeguarding smart home networks.

The flutter of a composite wing reinforced with functionally graded carbon nanotubes (CNTs) has been investigated. A rectangular plate models a supersonic wing with cantilever boundary conditions. To determine displacement fields of a moderately thick plate, shear deformation theory is used. Using the Hamilton principle, a first-order piston theory was used to simulate supersonic airflow. This study examines four types of CNT thickness. Also, four different CNT distribution patterns are investigated. In a two-layer asymmetric composite, the effects of patch mass, mass distribution, fiber orientation angle, and distribution of CNTs were examined. Moreover, the results are compared and verified with other studies. A greater mass ratio led to a smaller flutter boundary, while a longer added mass increased the flutter boundary. A variation in the distribution pattern in CNT fiber orientation results in a distinct behavior of the flutter boundary for asymmetric composites with increasing orientation angles. The artificial neural network is utilized to predict the damping ratio, and the results showed great accuracy compared to the study results. Hyperparameter tuning is employed for better optimizing the predictive models.

In recent times, mostly in the Middle East region, Red Palm Weevils (RPW) are one of the most dangerous pests of palm trees worldwide. The RPW causes significant harm to several palm species. The existing detection method includes the symptoms detection of RPW through sound or visual assessment and chemical recognition of volatile signatures created by diseased palm trees. However, an effective recognition of RPW disease at earlier stages is assumed that a very complex problem for cultivating date palms. This is another reason why the use of state-of-the-art technologies is supported in the avoidance of the spread of the RPW on palm trees. Several researchers are working on determining the correct process for the localization, classification, and detection of RPW pests. Therefore, this paper presents an intelligent Red Palm Weevil Detection using the Bird Swarm Algorithm with Deep Learning (IRPWD-BSADL) model. The major aim of the IRPWD-BSADL technique focuses on the identification and classification of RPW using CV and DL models. Primarily, the bilateral filtering (BF) approach can be utilized to remove the noise that exists in the images. In the presented IRPWD-BSADL technique, an improved ShuffleNet model can be applied for feature extraction purposes. To enhance the recognition results, the IRPWD-BSADL technique makes use of BSA for the hyperparameter tuning process. For RPW detection and classification, an extreme gradient boosting (XGBoost) classifier can be used. The simulation analysis of the IRPWD-BSADL method can be tested on the RPW dataset. An extensive comparison study stated the improved performance of the IRPWD-BSADL algorithm on the RPW detection method.

Network security is essential to our daily communications and networks. Cybersecurity researchers initiate the significance of emerging proficient network intrusion detection systems (IDS) for providing secure networks. While attackers endure to progress novel kinds of attacks and the network sizes endure to develop, the necessity for effectual IDS becomes significant. Additionally, IDS aims to offer confidentiality, integrity, and availability for the data communicated in networked computers by avoiding illegal access to the network. Several studies executed machine learning (ML) systems for emerging effectual IDS; then, with the advent of deep learning (DL) systems and artificial neural networks (ANNs) that create features automatically without human interference, researchers started to depend on DL approaches. This study introduces a new Golden Jackal Optimization Algorithm with Deep Learning Assisted Intrusion Detection System for Network Security (GJOADL-IDSNS) technique. The major intention of the GJOADL-IDSNS system lies in the effectual recognition and classification of the intrusions, to achieve network security. Primarily, data normalization is performed to scale the input data into a useful format. In the presented GJOADL-IDSNS technique, the GJOA-based feature selection (GJOA-FS) approach can be employed to elect an optimum subset of features. Next, the GJOADL-IDSNS methodology applies the attention-based bi-directional long short-term memory (A-BiLSTM) model. For hyperparameter tuning of the A-BiLSTM model, the GJOADL-IDSNS technique uses the salp swarm algorithm (SSA). The simulation value of the GJOADL-IDSNS technique has been tested utilizing benchmark datasets. The comparative results stated that the GJOADL-IDSNS technique achieves better performance than other models.

Marine algae have been visualized as fostering a prevalent actuation of bioactive substances that have noteworthy therapeutic effects. The current investigations deployed a trio of separate drying methods, i.e., oven drying, freeze drying, and sun drying, to quantify the total phenolic content (TPC), total flavonoid content (TFC), and dietary vitality of Ulva intestinalis and Padina tetrastromatica. By mining and scrutinizing it to the quercetin and gallic acid standards, TPC and TFC were derived. Among three drying techniques, freeze drying asserted higher TPC (12.59 ± 1.07 in U. intestinalis and 68.74 ± 1.95 mg of GA/g in P. tetrastromatica) and TFC (7.92 ± 0.97 in U. intestinalis and 58.15 ± 2.05 mg of quercetin/g in P. tetrastromatica) values. Ulva intestinalis and P. tetrastromatica also contain a high amount of protein (18.34% and 8.7%, respectively). The findings demonstrated that green seaweed possessed higher levels of potassium (2.20%) and molybdenum (2.41%) and brown seaweed showed higher amounts of potassium (2.41%) and calcium (1.11%). Where rapid drying methods are recommended to enhance constituent levels, freeze-drying could be an attractive alternative to oven and sun drying, as it addresses the drawbacks associated with prolonged drying periods.

AbstractWatershed-scale management practices (WMPs) aimed at mitigating land degradation from soil erosion have been implemented across Ethiopia, including in the study area, yet their effect remains under-evaluated. A useful method to assess the effect of WMPs involves comparing soil characteristics between micro-watersheds subjected to different treatments. For this analysis, the Urago and Mende micro-watersheds in Becho district, central Ethiopia, were chosen for their similar physical attributes, though only Urago received government-initiated WMP interventions. This study aims to assess the effect of WMPs on soil properties under varying land use types (LUTs) and topographic positions (TPs) in conserved versus non-conserved areas. To achieve this objective, soil samples were systematically collected from three distinct LUTs at upper, middle, and lower TPs in both conserved (Urago) and non-conserved (Mende) micro-watersheds. The analysis of soil properties across different micro-watersheds revealed no significant differences. However, there were indications of minor improvements in soil organic carbon (SOC), total nitrogen (TN), and percent base saturation (PBS) in the conserved micro-watershed. These improvements are likely attributable to interventions such as stone and soil bunds, land rehabilitation, and improved drainage. Soil texture and SOC levels were strongly influenced by LUTs, showing better results in conserved areas. Soil pH, cation-exchange capacity (CEC), exchangeable Ca2+, and PBS varied with TPs, with higher values found at lower elevations. Higher elevations had somewhat higher SOC, TN, and exchangeable Na+, potentially due to specific replantation efforts, though these increases were not statistically significant. However, WMPs were less effective on lower-elevation farmlands, with minimal impact on combating land degradation. This underscores the necessity for comprehensive and tailored sustainable land management approaches in these areas to enhance soil quality.

Purpose – The purpose of this study is to examine both accruals and real earnings management in a large sample of private companies in the UK using data from 2002 to 2009 following the implementation of the UK Act of 2006. Design/methodology/approach – A panel data analysis using GMM has been adopted to examine the objectives of the study and answer the research questions. Findings – The results of this study showed that the imposition of the Companies Act of 2006, on its own, did lead to changes in earnings management behaviour, in both accruals-based earnings and real earnings management. Moreover, this study also found that firms that chose to provide IFRS financial statements tended to show less discretionary earnings management, however, it tended to have no impact on real earnings management. Practical implications – In accordance with the research findings, standard setters with some insight tend to determine how capital markets see the information provided under the legislation such as the UK Act of 2006 in developed countries and thereby ensure long-term sustainability in a modern and sophisticated financial world. This study provides an insight into the successful implementation of the UK act of 2006, and its influence on the aspect of financial reporting. Originality/value – The novel conclusion reached in the study is that there exists a strong and direct link between the smooth implementation of UK Act of 2006 and the practices of both accruals and real earnings management in real-world business and financial scenarios, particularly, in private companies.

Resource scheduling (RS) for makespan optimization in a cloud computing (CC) environment is an important aspect of handling effective resources in the cloud. Makespan optimization defines the minimization of time required to complete a collection of tasks in a computational environment. In the context of CC, makespan optimization aims to reduce the overall time required to execute tasks while effectively allocating and managing resources. RS in CC is a difficult task because of the number and variation of resources accessible and the volatility of usage-patterns of the resource assuming that the resource setting is on the service providers. Therefore, this article presents a Farmland Fertility Algorithm based Resource Scheduling for Makespan Optimization (FFARS-MSO) in Cloud Computing Environment. The presented FFARS-MSO technique is mainly based on FFA, which is stimulated by the farmland fertility in nature where the farmers split the various regions of the farm based on soil quality, and thereby every region's soil quality is distinct from others. In addition, the presented FFARS-MSO technique is utilized for load balancing and uniform distribution of resources depending upon the demand. The simulation outcomes ensure that the FFARS-MSO approach has reached effectual resource allocation over other optimization algorithms.

It is an emerging concept now a day that epicardial adipose tissue (EAT) thickness has an essential character in the progression for cardiac risk profile. It is now consider as a new and consistent cardiovascular risk factor. EAT thickness is the indicator for cardiac adiposity. It modifies the cardiac morphology and function. It has close anatomical relation to myocardium. It causes local paracrine interactions between myocardial tissues. It causes enlargement of left ventricle with its diastolic dysfunction. Measurement and assessment of epicardial adipose tissue thickness is now done by new method echocardiography. It appraises visceral adiposity and inflammation. Association of EAT location and its physiological or pathological impor – tance requires advance research. EAT in future may replace waist circumference, as a marker of abdominal obesity. This article briefly discussion the structure of epicardial adipose tissue and its function. It also describes the method for its assessment. KEY WORDS: Epicardial adipose tissue thickness, Cardiac adipose tissue, Brown adipose tissue, Visceral adipose tissue

Background Many national studies in Saudi Arabia have revealed a lack of knowledge about eye donation. The current study assessed awareness and attitudes towards eye donation among health faculty students in Jeddah, Saudi Arabia. It aims to increase their awareness as future healthcare providers are expected to raise general awareness to attain more local corneal donations. Methods A cross-sectional study including 1,060 health faculty students was conducted at King Abdulaziz University. Data were collected through an online questionnaire that covered participants’ demographics, academic year, knowledge, and attitudes regarding eye donation. Results Thirty-five percent of students had heard about eye donation, with the most common sources of information being social media (29%) and health workers (24%). Most respondents, 61% (n = 643), indicated willingness to donate their eyes after death. Of these respondents, 93% (n = 986), 6.8% (n = 72), and 0.2% (n = 2) had poor, fair, and good knowledge levels, respectively. A total of 66% (n = 696) and 34% (n = 364) had negative and positive attitudes, respectively. Conclusion Students in this study showed low levels of knowledge and negative attitudes about eye donation. Students should be adequately educated about the significance of eye donation.

Seaweeds have become the focus of experts in recent years due to their biological characteristics and the variety of uses they have for both humans and animals. Biochemical composition, amino acids, fatty acids, and phenolic components were analyzed to explore the nutritive value of Gracilariopsis longissima, Padina tetrastromatica, and Ulva intestinalis from the Bay of Bengal, Bangladesh coast. Proximate composition and mineral content were determined according to the AOAC method. The high-performance liquid chromatography amino acid analysis system was used for the amino acid analysis and the fatty acid profile of the extracted oils was assessed as their methyl esters. The Folin–Ciocalteu technique was used to estimate the phenolic content and the aluminum chloride colorimetric technique was used to calculate the total flavonoid content. The three different species of seaweed had significantly different proximate compositions (P

The ability of phase change materials (PCM) to store thermal energy has gained wide application area, like battery thermal management, solar water desalination and many other. The melting process of beeswax phase change material within the cube geometry with constant wall temperature (65 °C) boundary condition has been investigated using solidification and melting model. The fluid flow and heat transfer governing equations are solved using second order finite volume scheme. A PRESTO algorithm is applied for pressure-velocity coupling. The convergence criteria of 10−10 have been selected for energy equation, while 10−8 is selected for both momentum and continuity equations. The results like percentage variation along length-height and height-width plane for transient liquid fraction and temperature has been plotted, along with velocity streamlines within the cube geometry. From the obtained results it is concluded that the melting fraction and temperature of beeswax PCM is different in different planes and the major factors which affect the complete melting process is wall temperature, and the geometry. A difference of more than 0.1 °C in temperature has been recorded between mid-length-height and height-width plane while a difference of more than 2% in liquid fraction of PCM is observed. Even the uniformity of temperature and liquid fraction is notably influenced and vary along length, height, and width of cube geometry. Thus, it is concluded that melting process of PCM may affect the ability to store and release the heat energy which further affect the performance parameters of applied physical system.

A sudden emergence of Stenotrophomonas maltophilia as a primary pathogen both in immunocompromised and immunocompetent individuals has raised a serious concern, as it is associated with significant case fatality ratio. We intended to study the clinico-microbiological profile of S. maltophilia isolates from various samples and outcome of the infections in a tertiary healthcare center, Pune, India. This is an observational cross-sectional study was conducted from January 2021 to June 2022 at Department of Microbiology of a tertiary care Centre in Pune, India. Of the 12049 samples received for culture, S. maltophilia was isolated in 57 samples. Only 42 samples with pure growth of S. maltophilia were included in the study with 15 excluded due to mixed growth. All isolates were confirmed by VITEK-MS (bioMerieux, SA, France) which uses Matrix Assisted Laser Desorption Ionization Time-of-Flight (MALDI-TOF) technology. Of the 42 isolates, majority were isolated from pus(28.6%) and most of patients (61.9%) were from acute health care settings. The isolates had high susceptibility to Cotrimoxazole (85.7%) and Minocycline (85.7%) and low susceptibility to Ceftazidime (45.2%). A case fatality rate of 7.1% (3/42 cases) was noted and 39 cases were discharged after complete treatment. All the three fatal cases were susceptible to levofloxacin, ciprofloxacin, cotrimoxazole and minocycline and all three fatal cases were resistant to ceftazidime. S. maltophilia has recently shown an increase in nosocomial infections especially in acute healthcare settings like ICU and other critical care wards. The isolates of the present study had high susceptibility to trimethoprim-sulfamethoxazole (TMP-SXT) and Minocycline and low susceptible to Ceftazidime.

Abstract Honokiol is a neolignan biphenol found in aerial parts of the Magnolia plant species. The Magnolia plant species traditionally belong to China and have been used for centuries to treat many pathological conditions. Honokiol mitigates the severity of several pathological conditions and has the potential to work as an anti-inflammatory, anti-angiogenic, anticancer, antioxidant, and neurotherapeutic agent. It has a long history of being employed in the healthcare practices of Southeast Asia, but in recent years, a greater scope of research has been conducted on it. Plenty of experimental evidence suggests it could be beneficial as a neuroprotective bioactive molecule. Honokiol has several pharmacological effects, leading to its exploration as a potential therapy for neurological diseases (NDs), including Alzheimer's disease (AD), Parkinson’s disease (PD), cerebral ischemia, anxiety, depression, spinal cord injury, and so on. So, based on the previous experimentation reports, our goal is to discuss the neuroprotective properties of honokiol. Besides, honokiol derivatives have been highlighted recently as possible therapeutic options for NDs. So, this review focuses on honokiol's neurotherapeutic actions and toxicological profile to determine their safety and potential use in neurotherapeutics.

Depression detection is the procedure of recognizing the individuals exhibiting depression symptoms, which is a mental illness that is characterized by hopelessness, feelings of sadness, persistence and loss of interest in day-to-day activities. Depression detection in Social Networking Sites (SNS) is a challenging task due to the huge volume of data and its complicated variations. However, it is feasible to detect the depression of the individuals by examining the user-generated content utilizing Deep Learning (DL), Machine Learning (ML) and Natural Language Processing (NLP) approaches. These techniques demonstrate optimum outcomes in early and accurate detection of depression, which in turn can support in enhancing the treatment outcomes and avoid more complications related to depression. In order to provide more insights, both ML and DL approaches possibly offer unique features. These features support the evaluation of unique patterns that are hidden in online interactions and address them to expose the mental state amongst the SNS users. In the current study, we develop the Modified Arithmetic Optimization Algorithm with Deep Learning for Depression Detection in Twitter Data (MAOADL-DDTD) technique. The presented MAOADL-DDTD technique focuses on identification and classification of the depression sentiments in Twitter data. In the presented MAOADL-DDTD technique, the noise in the tweets is pre-processed in different ways. In addition to this, the Glove word embedding technique is used to extract the features from the preprocessed data. For depression detection, the Sparse Autoencoder (SAE) model is applied. The MAOA is used for optimum hyperparameter tuning of the SAE approach so as to optimize the performance of the SAE model, which helps in accomplishing better detection performance. The MAOADL-DDTD algorithm is simulated using the benchmark database and experimentally validated. The experimental values of the MAOADL-DDTD methodology establish its promising performance over another recent state-of-the-art approaches.

Ammas Siraj Mohammed,1 Kirubel Minsamo Mishore,1 Tadesse Bekele Tafesse,2 Abera Jambo,1 Ahmed Mohammed Husen,3 Addisu Alemu4 1Clinical Pharmacy Department, School of Pharmacy, College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia; 2Department of Pharmaceutical Chemistry and Pharmacognosy, School of Pharmacy, College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia; 3Department of Pediatrics, School of Medicine, College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia; 4Department of Reproductive Health and Nutrition, School of Public Health, College of Health and Medical Sciences, Haramaya University, Harar, EthiopiaCorrespondence: Kirubel Minsamo Mishore, Email Kira2031@gmail.comBackground: Epilepsy is one of the common chronic neurological disorders with varying therapeutic responses. Despite the high prevalence of epilepsy and the significant treatment gaps in developing nations, such as Ethiopia, there is a dearth of data on seizure remission and its predictors in Eastern Ethiopia in particular.Objective: This study aimed to determine seizure remission and its predictors among epileptic patients on follow-up in Hiwot Fana Specialized University Hospital (HFSUH) and Dilchora Referral Hospital (DCRH), from July 2 to 31, 2021.Methods: A retrospective cohort study was conducted among 418 newly diagnosed epilepsy patients receiving anti-seizure drugs (ASDs) between July 1, 2014, and July 31, 2019, in two public hospitals in Eastern Ethiopia. Relevant data were collected for all patients with a minimum follow-up period of two years. Data were analyzed using SPSS Version 21. Cox proportional hazards model was performed to identify predictors of seizure remission.Results: Overall, 252 (60.3%) of the study participants have achieved seizure remission for at least one year. The mean time to achieve seizure remission was 1.9 ± 0.87 years. Regarding the seizure remission pattern, 171 (40.9%) patients achieved early remission, 81 (19.4%) achieved late remission, and 166 (39.7%) achieved no remission. Shorter pre-treatment duration (AHR = 2.36, 95% CI: 1.28– 4.37); good adherence to ASDs (AHR = 2.40, 95% CI: 1.33– 4.34); and monotherapy (AHR = 0.56, 95% CI: 0.32– 0.98) were predictors of seizure remission.Conclusion: We observed that less than two-thirds of epileptic patients had achieved seizure remission. A shorter pre-treatment duration, good adherence to ASDs, and monotherapy were predictors of seizure remission. Therefore, we recommend the requirement of an integrated effort from different health disciplines that increases patients’ adherence to ASDs, promotes early visits to medical facilities, and improves the health-seeking behavior of epileptic patients.Keywords: seizure, epilepsy, remission, predictors, Eastern Ethiopia

Performance measures are crucial in evaluating the effectiveness of teaching methods and skills in karate. Studies have shown that utilizing a program based on multiple intelligences can improve basic skills and cognitive load. Furthermore, special training can affect physical skill requirements in offensive karate kumite players. Factors such as general motor efficiency, specific speed, specific agility, and technical efficiency can also be identified. The study sample is chosen from karate athletes in preparatory schools and practitioners, with a sample size representative of the population of interest. Data collection tools include surveys, assessments, and observations. The study sample was chosen from Karate athletes in preparatory schools and 50 students of karate practitioners.A research design utilizes quantitative and qualitative data collection techniques to evaluate the effectiveness of teaching methods and skills in karate. The study was conducted in karate training centers or schools, and data analysis using statistical methods to examine the relationship between performance measures and teaching methods. These results suggest that all teaching methods significantly impact the participants' performance compared to the control group.Training methods used in the experiment group successfully enhanced karate performance.Ethical considerations must be taken into account when analyzing performance measures.

Abstract While aggression is an adaptive behavior mostly triggered by competition for resources, it can also in and of itself be rewarding. Based on the common notion that female rats are not aggressive, much of aggression research has been centered around males, leading to a gap in the understanding of the female aggression neurobiology. Therefore, we asked whether intact virgin female rats experience reward from an aggressive interaction and assessed aggression seeking behavior in rats of both sexes. To validate the involvement of reward signaling, we measured mesolimbic dopamine turnover and determined the necessity of dopamine signaling for expression of aggression-seeking. Together our data indicate that female rats exhibit aggressive behavior outside of maternal context, experience winning aggressive behaviors as rewarding, and do so to a similar extent as male rats and in a dopamine-dependent manner.

Ahmed Saad AL-Zomia, Ibrahim Ali AL-Zehefa, Abdulrhman Mohammed Alqarni, Abdulaziz Mohammed Al Muidh, Abdulmajeed Mesfer Almousa, Abdullah Faez Al-Qaed, Abdullrahman Saeed Alshahrani, Bandar Mohammed Asiri, Ghufran Badr Asiri, Lama Ali Lahiq, Mohammed Abdulaziz Al-Amri, Mohammed Saeed Al-Nujimi, Saud Mamdoh Alfaisal, Ibrahim Tawhari Faculty of Medicine, King Khalid University, Abha, Saudi ArabiaCorrespondence: Ahmed Saad AL-Zomia, Medical Intern Faculty of Medicine, King Khalid University, Abha, Aseer, 61421, Saudi Arabia, Email Ahmedszomia@gmail.comBackground: Ocular malignancies are uncommon among eye diseases; however, they jeopardize both vision and life. The main objective of this study was to use to describe the epidemiology of eye and ocular adnexa malignancies across different ages and sex.Methods: The King Khaled University institutional review board approved this study. Data on ocular cancer were retrieved from the Saudi Cancer Registry between 1994 and 2018. The registry collected important patient information such as demographic information (age, gender, and nationality), clinical details, and tumor classification.Results: The total number of cases with ocular cancer diagnosed was 1051 cases. The highest number was recorded in Riyadh (35.39%, n=372), followed by Makkah (16.93%, n=178). The incidence was higher in the 0– 4 years’ age group (55.21%), and it got down as people got older. The data also revealed differences in the number of reported cases over time, as well as in the representation of eye cancer cases by gender and nationality. While many ocular cancer pathologies were seen, with “Retinoblastoma, not otherwise specified” being the most common (53.32%), the incidence rates for males and females remained largely stable over time.Conclusion: The study emphasizes the need for continued monitoring, research, and analysis of potential of epidemiology of ocular cancer occurrence in Saudi Arabia. Identifying the geographical distribution and age pattern of Ocular malignancies have the potential to assist healthcare authorities and policymakers in developing precise strategies to reduce, recognize at an early stage, and successfully manage this condition.Keywords: ocular neoplasms, clinicopathologic characteristics, incidence rates, targeted strategies, retinoblastoma, Saudi Arabia

The investigation of novel phytochemicals for the prevention and treatment of infections caused by multidrug-resistant pathogens is gaining attention. The current study evaluated the in-vitro antimicrobial activity of kaempferol against methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa. Its in-vivo efficacy in inhibiting these pathogens was determined using an excision wound model in nicotinamide-streptozocin- -induced diabetic rats. Kaempferol displayed an inhibitory effect against the tested bacteria both in vitro and in vivo. It also healed excision wounds at a 1% (w/w) concentration. An increase in antioxidant enzymes in wounded tissue was observed on kaempferol treatment. A reduction in the MRSA and P. aeruginosa counts in wounded tissue together with a reduced epithelization period was observed when compared to the infected control. A thicker epithelium, new capillaries, and a decrease in inflammatory cells were detected by hematoxylin and eosin staining. Furthermore, an increase in collagen fibers and their deposition was observed by Masson’s trichrome staining. Kaempferol at 40 µM did not display any toxicity for human keratinocytes grown in media containing high glucose and it did not affect the expression of the pro-healing cytokines genes vascular endothelial growth factor (VEGF) and transforming growth factor-b-1 (TGFβ1). Kaempferol displayed antibacterial and antioxidant actions but did not increase the expression of proliferative genes.

Ephedra foeminea is traditionally used to treat breast cancer in several Arab countries. Scientific studies have reported different effects of this plant on some cancer cell lines. The current study determined the anti-cancer potential of the methanolic extract of Ephedra foeminea against four different types of breast cancer cell lines in-vitro. The extract was prepared by maceration and phytoconstituents were identified by LC-MS analysis. The IC50 value was determined against MDA-MB-231, MCF-7, 4 T1, and MCF-10 cell lines using the MTT assay. Further investigations were carried out using IC50 concentration of the extract (40.09 µg/ml) to determine live/dead cells by acridine orange/ethidium bromide staining. The effect on the expression of reactive oxygen species (ROS) was evaluated by flow cytometry. The results were analyzed using one-way ANOVA followed by Tukey’s test. The LC-MS analysis revealed the presence of 34 and 30 phytoconstituents in positive and negative modes respectively. The Ephedra foeminea extract was most effective against 4 T1 cells in a dose-dependent manner (P

The aim of this study was to evaluate the performance of CHIRPS and TAMSAT satellite rainfall data over the Upper Gelana watershed, where gauged meteorological data to understand the nature of the climate are scarce. In addition, variability and trends in rainfall and temperature were examined from 1983 to 2021. To evaluate satellite rainfall, categorical and continuous validation statistics were used. Trends were analyzed using Mann-Kendall, Sen's Slope estimator, and innovative trend analysis (ITA) methods. The study also utilized time-series geostatistical analysis techniques. The validation statistics show that TAMSAT performs better on the daily timescale, while the two products have comparable performance on the monthly timescale. TAMSAT was chosen for rainfall analysis because of its higher resolution and performance. The results reveal high inter-annual spatiotemporal variability and strong irregularities in monthly rainfall. The Mann-Kendall test indicates statistically significant positive trends in kiremt and annual rainfall, but belg rainfall exhibits an insignificant negative trend. In the kiremt season, we found a 96.1, 101.6, and 104.8 mm decadal rate of rainfall increment in the lower weina dega (LWD), upper weina dega (UWD), and dega agroecological zones, respectively. In contrast, belg season rainfall declined by 16.4, 16.2, and 14.0 mm per decade in the LWD, UWD, and dega agroecology zones, respectively. The pixel-wise trend analysis also revealed trends and magnitudes of monthly, seasonal, and annual rainfall that vary across the study area. In both LWD and UWD annual minimum and maximum temperatures, respectively, showed significant decreasing and increasing trends, but in dega agroecology the trends were insignificant. The findings of rainfall and temperature trends using the ITA method demonstrated its ability to discover some hidden trends that were not detected by the MK test.

Green environmental security refers to the state of human-environment interactions that include reducing resource shortages, pollution, and biological dangers that can cause societal disorder. In IoT-enabled smart cities, due to the advancement of technologies, sensors and actuators collect vast quantities of data that are analyzed to extract potentially useful information. However, due to the noise and diversity of the data generated, only a small portion of the massive data collected from smart cities is used. In sustainable Land Use and Land Cover (LULC) management, environmental deterioration resulting from improper land usage in the digital ecosystem is a global issue that has garnered attention. The deep learning techniques of AI are recognized for their capacity to manage vast amounts of erroneous and unstructured data. In this paper, we propose a morphologically augmented fine-tuned DenseNet-121(MAFDN) LULC classification model to automate the categorization of high spatial resolution scene images for environmental conservation. This work includes an augmentation process (i.e. erosion, dilation, blurring, and contrast enhancement operations) to extract spatial patterns and enlarge the training size of the dataset. A few state-of-the-art techniques are incorporated for contrasting the efficacy of the proposed approach. This facilitates green resource management and personalized provision of services.

Aim and Objective: This study was intended to compare and determine the potency of strontium-doped nano-hydroxyapatite paste against a topical cream containing casein phosphopeptide-amorphous calcium phosphate and a regular dentifrice for remineralization of white spot lesions of enamel following orthodontic debonding. Methods: Ninety individuals with white spot lesions on their enamel who visited the orthodontic department for de-bonding were selected for the research. Patients were randomly assigned to three distinct groups with each group consisting of 30 patients. Group 1 served as the control and received regular dentifrice, Group 2 received strontium-doped nano-hydroxyapatite paste, and Group 3 received casein phosphopeptide-amorphous calcium phosphate topical cream. Patients were instructed to locally apply a specified amount of the prescribed preparation twice daily for six weeks. The data were analysed using the statistical programme SPSS 22.0 (SPSS Inc., Chicago, IL, USA), and the level of significance had been set at p