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Caffeine is a plant secondary metabolite, commonly known for its bioactivity properties. This molecule increases microbial activity during anaerobic digestion. The aim of this study was to determine the effects of caffeine doses on the rumen fermentation profile and nutrient digestibility when continuous culture fermenters were fed a lactating cow’s diet. We hypothesize that adding caffeine doses into continuous culture fermenters with a rumen fluid inoculum will not affect anaerobic fermentation or nutrient utilization. Fermenters were fed twice a day (at 0800 and 2000 h) with an experimental diet of high-producing dairy cows (53.55 g/DM day; Forage:Concentrate ratio, F:C of 40:60). Four levels of caffeine (0 ppm, 50 ppm, 100 ppm, and 150 ppm) were added to the diets as a treatment. The experiment was arranged in a randomized complete block design. Two blocks of four fermenters were run in two replicated periods of ten days. Statistical analyses were conducted in SAS version 9.4 for Windows (SAS Institute Inc., Cary, NC, USA) using the GLIMMIX procedure. The addition of caffeine at a 50 ppm dose on continuous culture fermentation decreased the protozoal counts (Diplodinium spp.) (p = 0.03) and ammonia concentrations (p < 0.05). A treatment of 50 ppm of caffeine increased the DM, OM, and starch digestibility (p < 0.05). When caffeine doses increased further than 50 ppm, the OM, DM, and starch digestibility decreased linearly (p = 0.01). The total volatile fatty acids and fatty acid proportions were unaffected. However, the acetate-to-propionate ratio (A:P) tended to decrease linearly among treatments (p = 0.07). The means of pH measurements and maximum pH had a significantly linear decrease effect (p < 0.01). Caffeine may represent a potential rumen fermentation modifier for use in lactating cow diets.

Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.

Background: Obese individuals have a higher risk of degenerative disc disease (DDD). Currently, body mass index is not sensitive enough to differentiate between muscle and fat distribution, and obesity-related health issues are linked to the way body fat is distributed. Therefore, this study aims to investigate the association between the dorsal subcutaneous fat thickness (DSFT) of the lumbar spine, an alternative measurement tool of body fat distribution, and DDD. Methods: A total of 301 patients with DDD and 123 participants without the disease were recruited. Using length functions of magnetic resonance imaging (MRI) console, the DSFT of L1 to S1 intervertebral disc levels was measured in mid-sagittal spin-echo T2 weighted image. The Mann–Whitney U test and Chi-squared test (X2) were utilized to examine any variations between the case and control groups. Logistic regression models were built to explore the association of the DSFT with DDD. Results: The logistical regression model showed a positive association between DDD and DSFT [OR: 1.30, 95% CI: 1.02–1.64, p = 0.03]. In the stratified logistic regression analysis, a positive association was found between DDD and DSFT among younger participants and females [OR young: 1.48; 95% CI (1.02–2.20); p = 0.04—OR female: 1.37; 95% CI (1–1.88); p = 0.05]. Conclusions: Younger females with thicker DSFT at the L1–L2 level are more likely to develop DDD. This suggests that increased DSFT may be a contributing factor to DDD.

Objective: We investigated the potential relationship between endometriosis and risk of ovarian, endometrial, cervical, and breast cancers using the National Inpatient Sample (NIS) database. Methods: We utilized the International Classification of Diseases (ICD-10) system to identify relevant codes from the NIS database (2016–2019). Univariate and multivariate regression analyses (adjusted for age, race, hospital region, hospital teaching status, income Zip score, smoking, alcohol use, and hormonal replacement therapy) were conducted to evaluate the association between endometriosis and gynecologic cancers and summarized as odds ratios (ORs) with 95% confidence intervals (CIs). Results: In the examined dataset, there were 1164 and 225,323 gynecologic cancer patients with and without endometriosis, respectively. Univariate analysis showed endometriosis was significantly associated with a higher risk of ovarian (OR = 3.42, 95% CI: 3.05–3.84, p < 0.001) and endometrial (OR = 3.35, 95% CI: 2.97–3.79, p < 0.001) cancers. There was no significant association between endometriosis and cervical cancer (OR = 1.05, 95% CI: 0.85–1.28, p = 0.663). Interestingly, endometriosis was significantly associated with a low risk of breast cancer (OR = 0.12, 95% CI: 0.10–0.17, p < 0.001). Multivariate analysis after Bonferroni correction (p < 0.006) showed that endometriosis was significantly associated with a high risk of ovarian (adjusted OR = 3.34, 95% CI: 2.97–3.75, p < 0.001) and endometrial (adjusted OR = 3.61, 95% CI: 3.12–4.08, p < 0.001) cancers. Conversely, there was no significant association between endometriosis and cervical cancer (OR = 0.80, 95% CI: 0.65–0.99, p = 0.036). Conclusions: Patients with endometriosis exhibited unique gynecologic cancer risk profiles, with higher risks for ovarian and endometrial cancers, and no significant risk for cervical cancer. The observed connection between endometriosis and a reduced risk of breast cancer remains a perplexing phenomenon, which cannot be put into context to date.

Including dietary fat can increase the energy density of diets fed to ruminants, reducing dry matter intake (DMI). Effects of different fat sources on nutrient digestion and fermentation can vary depending on dietary fat concentration and the forage-to-concentrate ratio (F:C). Therefore, this study’s objective was to screen the effects of fat sources supplemented at different concentrations to high- and low-forage diets on in vitro digestibility and fermentation. Treatments included either low forage (LF; 35%) or high forage (HF; 70%) with two fat levels (6 or 9% DM) using six different fat sources, plus control. The control diet (CON) had a basal level of fat in the diet (3% fat; 0% fat inclusion), and fat sources were added to attain 6% or 9% dietary fat and consisted of the following: Coconut oil, CO; Poultry fat, PF; Palm oil, PO; Palm kernel oil, PKO; Soybean oil, SOY; and Ca Salts, MEG. In vitro Gas Production (GP) modules were randomly assigned to treatments in a 2 × 2 × 7 factorial design and were incubated for four 24 h runs. The CO-fed module had the highest dry matter (DM) apparent digestibility (AD) (p < 0.01), followed by SOY and PF. The true DM digestibility (TDMD) and organic matter (OM) AD were the highest in CO (p < 0.01) than the other fat types. The AD for DM, OM, neutral detergent fiber (NDF), and acid detergent fiber (ADF) was higher in LF (p < 0.01). The 6% fat inclusion had a higher GP (109 vs. 103 mL ± 2.09; p < 0.03). Total volatile fatty acid (VFA) concentration was lower in different fat types than the CON and the acetate molar proportion (p > 0.01). The propionate was the lowest for the CON, which increased the acetate to propionate (A:P) ratio (p < 0.01). These results suggest that LF diets with high fat concentrations can be utilized, and different fat sources may improve DM and fiber digestibility.

Phosphine is the most widely used fumigant for stored grains due to a lack of better alternatives, all of which have serious shortcomings that restrict their use. The extensive use of phosphine has led to the development of resistance among insect pests of grain, which threatens its status as a reliable fumigant. Understanding the mode of action of phosphine as well as its resistance mechanisms provides insight that may lead to improved phosphine efficacy and pest control strategies. The mechanisms of action in phosphine vary from disrupting metabolism and oxidative stress to neurotoxicity. Phosphine resistance is genetically inherited and is mediated by the mitochondrial dihydrolipoamide dehydrogenase complex. In this regard, laboratory studies have revealed treatments that synergistically enhance phosphine toxicity that may be used to suppress resistance development and enhance efficacy. Here, we discuss the reported phosphine modes of action, mechanisms of resistance and interactions with other treatments.

In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM’s uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

This paper investigates the effect of a gene expression time delay on the Brusselator model with reaction and diffusion terms in one dimension. We obtain ODE systems analytically by using the Galerkin method. We determine a condition that assists in showing the existence of theoretical results. Full maps of the Hopf bifurcation regions of the stability analysis are studied numerically and theoretically. The influences of two different sources of diffusion coefficients and gene expression time delay parameters on the bifurcation diagram are examined and plotted. In addition, the effect of delay and diffusion values on all other free parameters in this system is shown. They can significantly affect the stability regions for both control parameter concentrations through the reaction process. As a result, as the gene expression time delay increases, both control concentration values increase, while the Hopf points for both diffusion coefficient parameters decrease. These values can impact solutions in the bifurcation regions, causing the region of instability to grow. In addition, the Hopf bifurcation points for the diffusive and non-diffusive cases as well as delay and non-delay cases are studied for both control parameter concentrations. Finally, various examples and bifurcation diagrams, periodic oscillations, and 2D phase planes are provided. There is close agreement between the theoretical and numerical solutions in all cases.

Background: Simulation-based education (SBE) provides a safe, effective, and stimulating environment for training medical and healthcare students. This is especially valuable for skills that cannot be practiced on real patients due to ethical and practical reasons. We aimed to assess medical students’ attitude, perception, and experience of simulation-based medical education in Saudi Arabia. Method: A validated cross-sectional survey, using the KidSIM scale, was conducted to measure the level of perception and experience of students from different health sciences specialties toward integrating simulation as an educational tool. Participants responded to questions investigated the importance of simulation, opportunities for Inter-Professional Education (IPE), communication, roles and responsibilities, and situation awareness. Only students with previous experience of SBE were considered for participation. Result: This survey was completed by 246 participants, of whom 165 (67%) were male students and 228 (93%) were aged between the range of 18–30 years old. Of the respondents, 104 (67%) were respiratory care students, 90 (37%) were anesthesia technology students, and 45 (18%) were nursing students. Most of the participants had previous experience in IPE simulation activities (84%), and more than half of the students (54%) had a grade point average (GPA) ranging between 5.00 and 4.50. Overall, students had positive attitudes toward and beliefs about SBE, with a mean score of 129.76 ± 14.27, on the KidSIM scale, out of 150. Students’ GPA was significantly associated with a better perception to the relevance of simulation (p = 0.005), communication (p = 0.003), roles and responsibilities (p = 0.04), and situation awareness (p = 0.009). GPA is merely the sole predictor for positive attitude toward simulation with coefficient Beta value of 4.285 (p = 0.001). There were no significant correlations between other students’ characteristic variables (gender, specialty, study year, experience in IPE, and prior critical care experience). Conclusion: We found that health sciences students’ perception of SBE in Saudi Arabia is generally positive, and students’ performance is a significant determinant of the positive perception.

Due to the Internet’s explosive growth, network security is now a major concern; as a result, tracking network traffic is essential for a variety of uses, including improving system efficiency, fixing bugs in the network, and keeping sensitive data secure. Firewalls are a crucial component of enterprise-wide security architectures because they protect individual networks from intrusion. The efficiency of a firewall can be negatively impacted by issues with its design, configuration, monitoring, and administration. Recent firewall security methods do not have the rigor to manage the vagueness that comes with filtering packets from the exterior. Knowledge representation and reasoning are two areas where fuzzy Petri nets (FPNs) receive extensive usage as a modeling tool. Despite their widespread success, FPNs’ limitations in the security engineering field stem from the fact that it is difficult to represent different kinds of uncertainty. This article details the construction of a novel packet-filtering firewall model that addresses the limitations of current FPN-based filtering methods. The primary contribution is to employ Simplified Neutrosophic Petri nets (SNPNs) as a tool for modeling discrete event systems in the area of firewall packet filtering that are characterized by imprecise knowledge. Because of SNPNs’ symbolic ability, the packet filtration model can be quickly and easily established, examined, enhanced, and maintained. Based on the idea that the ambiguity of a packet’s movement can be described by if–then fuzzy production rules realized by the truth-membership function, the indeterminacy-membership function, and the falsity-membership functional, we adopt the neutrosophic logic for modelling PN transition objects. In addition, we simulate the dynamic behavior of the tracking system in light of the ambiguity inherent in packet filtering by presenting a two-level filtering method to improve the ranking of the filtering rules list. Results from experiments on a local area network back up the efficacy of the proposed method and illustrate how it can increase the firewall’s susceptibility to threats posed by network traffic.

Background and Objectives: The effect of obesity on the development/progression of thyroid nodules with uncertain cytology is unknown. Therefore, our objective was to assess the role of body mass index (BMI) in predicting malignancy in patients with atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) nodules. Materials and Methods: We retrospectively analyzed 113 patients with available BMI data and final histopathology of benign or differentiated thyroid cancer. Patients were classified into four groups based on BMI: 2. The association between risk of malignancy and BMI was examined for all data and subgroups based on nodule size, sex, and age. Results: Overall, 44.2% were obese, 36.3% were ≥45 years, and 75.4% were women. Final pathological results showed malignant nodules in 52 patients (46%) and benign nodules in 61 patients (54%) (mean age: 41 ± 11.6 vs. 39.9 ± 11.7 years; p = 0.62). Men had more malignant nodules than benign nodules (32.7% vs. 16.4%, p < 0.05). Overall, no significant correlation was identified between the risk of thyroid cancer and BMI, and the risk of malignancy was not significantly different between obese men and women (p = 0.4). However, in individuals with BMI < 30 kg/m2 (non-obese group), malignant nodules were more frequent in men than in women (71% vs. 41%, p = 0.04). No significant difference was observed in mean nodule size between the benign and malignant groups. Furthermore, BMI was not related to increased risk of malignancy in multiple logistic regression models using all data, even after controlling for confounding variables (odds ratio, 0.99, 95% confidence interval: 0.93–1.06, p = 0.87) or when stratifying by sex. Conclusions: Our study showed no correlation between obesity and thyroid cancer in patients with AUS/FLUS. Moreover, men had more malignant nodules than benign nodules. Further well-designed prospective studies are required to confirm our findings.

The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document’s idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document’s main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model’s ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10–15% for each increase in the number of samples in the dataset.

Background and Objectives: Cardiovascular disease (CVD) is a major contributor to the high mortality rate among individuals with ovarian cancer. Nevertheless, there is limited understanding regarding the specific patient attributes that might impact the risk of CVD in this group. Materials and Methods: A retrospective cohort study was performed using the SEER database to analyze primary ovarian cancer cases from 2000 to 2019. Multivariable logistic regression analysis was employed to identify patient characteristics linked to cardiovascular mortality. Results: The cohort included 41,930 cases of patients who were alive, 54,829 cases of cancer-related deaths, 3003 cases of cardiovascular-related deaths, and 10,238 cases with other causes of death. Poorly differentiated cancer cells and distant metastasis were associated with a higher risk of cardiovascular mortality. Logistic regression analysis identified age, year of diagnosis, race, laterality, and staging as significant risk factors for cardiovascular cause of death. The risk of cardiovascular cause of death was lower in patients aged 31–60 and higher in those aged over 60 years old, and the risk also increased with a later year of diagnosis. Patients who were not white were at a higher risk of cardiovascular cause of death. Additionally, bilateral ovarian cancer and distant staging disease were linked to elevated risks of cardiovascular cause of death. Conclusion: Cardiovascular mortality is a significant concern in ovarian cancer patients, and several patient characteristics are associated with an increased risk. Our study suggests that targeted interventions to improve cardiovascular health in high-risk patients, such as those with comorbidities or an advanced stage at diagnosis, may improve survival in this population.

Purpose: Smartphone addiction is prevalent among medical students, and there is a concern that the coronavirus disease 2019 (COVID-19) pandemic fueled a rise in smartphone addiction. Earlier studies suggest a link between excessive smartphone usage and negative outcomes such as depression, stress, and reduced academic achievement. However, there is a dearth of both local studies in Saudi Arabia and studies conducted during the COVID-19 pandemic exploring the prevalence of smartphone addiction and its association with academic performance, depression, and perceived stress, which is the purpose of the current study. Methods: In 2021, a cross-sectional research project took place among medical students at King Saud University and the Vision Colleges located in Riyadh, Saudi Arabia. An online self-administered questionnaire consisting of demographic variables, grade point average (GPA), the Patient Health Questionnaire-9 (PHQ-9), the Perceived Stress Scale-4 (PSS-4), and the Smartphone Addiction Scale—Short Version (SAS-SV) was deployed. Results: Three hundred and fifteen students participated. Around 47.9% of students reported smartphone addiction, and the mean SAS-SV score was 32.31 ± 12.01 points. Both PHQ-9 and PSS-4 scores showed a significant positive correlation with the SAS-SV score (r = 0.216, p < 0.001 and r = 247, p < 0.001, respectively), while GPA did not (r = −0.027, p = 0.639). An adjusted analysis showed that the PSS-4 score was positively associated with the SAS-SV score (odds ratio (OR) = 1.206, p < 0.001), while the PHQ-9 score was not (OR = 102, p = 0.285). Conclusions: Smartphone addiction is prevalent among medical students and associated with perceived stress. Additional research is required to gain a deeper comprehension of this issue and to assess the success of intervention initiatives aimed at encouraging healthy smartphone usage, particularly in times of crisis like the COVID-19 pandemic.

Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback.

Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm’s mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS’ 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results.

Background and Objectives: The effect of non-pharmacological aerobic exercise training on blood pressure in sedentary older individuals receiving social home care in Saudi Arabia has not been investigated. This study aimed to examine the effects of aerobic exercise on blood pressure in sedentary older Saudis with hypertension residing in these settings. Materials and Methods: A pilot randomized control trial was conducted with 27 sedentary individuals, aged 60–85, diagnosed with hypertension, and living in social home care in Makkah, Saudi Arabia. Recruitment took place between November 2020 and January 2021, and participants were randomly assigned to either the experimental or control group. The experimental group engaged in three 45 min sessions of low-to-moderate intensity aerobic activity per week for eight weeks. This trail was registered with the ISRCTN registry (ISRCTN50726324). Results: Following eight weeks of mild to moderate aerobic exercise training, the primary outcome of resting blood pressure showed a significant reduction in the experimental group (systolic blood pressure: mean difference [MD] = 2.91 mmHg, 95% confidence interval [CI] = 1.61, 4.21, p = 0.001; and diastolic blood pressure: MD = 1.33 mmHg, 95% CI = 1.16, 1.50, p = 0.001) compared to the control group. Within the experimental group, there was also a significant decrease in systolic blood pressure (MD = −2.75 mmHg, 95% CI = −7.73, 2.22, p = 0.005) and diastolic blood pressure (MD = −0.83 mmHg, 95% CI = −5.81, 4.14, p = 0.02). Conclusions: This trial demonstrates the feasibility and potential benefits of low-to-moderate intensity aerobic exercise training in reducing resting blood pressure among sedentary older Saudis with hypertension residing in this aged care setting.

To combat malicious domains, which serve as a key platform for a wide range of attacks, domain name service (DNS) data provide rich traces of Internet activities and are a powerful resource. This paper presents new research that proposes a model for finding malicious domains by passively analyzing DNS data. The proposed model builds a real-time, accurate, middleweight, and fast classifier by combining a genetic algorithm for selecting DNS data features with a two-step quantum ant colony optimization (QABC) algorithm for classification. The modified two-step QABC classifier uses K-means instead of random initialization to place food sources. In order to overcome ABCs poor exploitation abilities and its convergence speed, this paper utilizes the metaheuristic QABC algorithm for global optimization problems inspired by quantum physics concepts. The use of the Hadoop framework and a hybrid machine learning approach (K-mean and QABC) to deal with the large size of uniform resource locator (URL) data is one of the main contributions of this paper. The major point is that blacklists, heavyweight classifiers (those that use more features), and lightweight classifiers (those that use fewer features and consume the features from the browser) may all be improved with the use of the suggested machine learning method. The results showed that the suggested model could work with more than 96.6% accuracy for more than 10 million query–answer pairs.

The rapid growth of the Internet of Things (IoT) has led to an increased automation and interconnectivity of devices without requiring user intervention, thereby enhancing the quality of our lives. However, the security of IoT devices is a significant concern as they are vulnerable to cyber-attacks, which can cause severe damage if not detected and resolved in time. To address this challenge, this study proposes a novel approach using a combination of deep learning and three-level algorithms to detect attacks in IoT networks quickly and accurately. The Bot-IoT dataset is used to evaluate the proposed approach, and the results show significant improvements in detection performance compared to existing methods. The proposed approach can also be extended to enhance the security of other IoT applications, making it a promising contribution to the field of IoT security.

Recently, signature verification systems have been widely adopted for verifying individuals based on their handwritten signatures, especially in forensic and commercial transactions. Generally, feature extraction and classification tremendously impact the accuracy of system authentication. Feature extraction is challenging for signature verification systems due to the diverse forms of signatures and sample circumstances. Current signature verification techniques demonstrate promising results in identifying genuine and forged signatures. However, the overall performance of skilled forgery detection remains rigid to deliver high contentment. Furthermore, most of the current signature verification techniques demand a large number of learning samples to increase verification accuracy. This is the primary disadvantage of using deep learning, as the figure of signature samples is mainly restricted to the functional application of the signature verification system. In addition, the system inputs are scanned signatures that comprise noisy pixels, a complicated background, blurriness, and contrast decay. The main challenge has been attaining a balance between noise and data loss, since some essential information is lost during preprocessing, probably influencing the subsequent stages of the system. This paper tackles the aforementioned issues by presenting four main steps: preprocessing, multifeature fusion, discriminant feature selection using a genetic algorithm based on one class support vector machine (OCSVM-GA), and a one-class learning strategy to address imbalanced signature data in the practical application of a signature verification system. The suggested method employs three databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Experimental results depict that the proposed approach outperforms current systems in terms of false acceptance rate (FAR), false rejection rate (FRR), and equal error rate (EER).

Cinnamon is one of the most commonly used spices worldwide. In some Arab countries, cinnamon is used with other ingredients to relieve bronchospasm and treatment of airways-related disorders. In the current study, GC, GC-MS and tracheal relaxant effect comparison were performed using the three available types in Saudi Arabia, Cinnamomum verum (Ceylon cinnamon), C. cassia (Chinese cinnamon) and C. loureiroi (Vietnamese cinnamon). The essential oil of C. verum was the most potent in the relaxation of guinea pig isolated tracheal muscles against carbachol (CCh, 1 uM)-evoked bronchospasm at the concentration range from 0.03 to 3 mg/mL followed by C. bureiroi at 0.03 to 5 mg/mL; whereas, C. cassia was the least potent oil. Cinnamaldehyde (1), isolated as the main component of the three oils induced complete relaxation of low K+ (25 mM)-evoked contractions, with mild effect on the contractions evoked by high K+ (80 mM). Pre-incubation of the tracheal tissues with glibenclamide (10 μM) significantly opposed the relaxation of low K+ by cinnamaldehyde. The standard drug, cromakalim also inserted glibenclamide-sensitive inhibition of low K+ without relaxing high K+. These results indicate that cinnamaldehyde acts predominantly by ATP-specific K+ channel opening followed by weak Ca++ antagonistic effects. The obtained results justify the medicinal value of cinnamon oil in respiratory disorders.

The Internet of Things (IoT) encompasses a wide range of applications and service domains, from smart cities, autonomous vehicles, surveillance, medical devices, to crop control. Virtualization in wireless sensor networks (WSNs) is widely regarded as the most revolutionary technological technique used in these areas. Due to node failure or communication latency and the regular identification of nodes in WSNs, virtualization in WSNs presents additional hurdles. Previous research on virtual WSNs has focused on issues such as resource maximization, node failure, and link-failure-based survivability, but has neglected to account for the impact of communication latency. Communication connection latency in WSNs has an effect on various virtual networks providing IoT services. There is a lack of research in this field at the present time. In this study, we utilize the Evolutionary Multi-Objective Crowding Algorithm (EMOCA) to maximize fault tolerance and minimize communication delay for virtual network embedding in WSN environments for service-oriented applications focusing on heterogeneous virtual networks in the IoT. Unlike the current wireless virtualization approach, which uses the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), EMOCA uses both domination and diversity criteria in the evolving population for optimization problems. The analysis of the results demonstrates that the proposed framework successfully optimizes fault tolerance and communication delay for virtualization in WSNs.

Indoor object detection is a fundamental activity for the development of applications of mobility-assistive technology for visually impaired people (VIP). The challenge of seeing interior objects in a real indoor environment is a challenging one since there are numerous complicated issues that need to be taken into consideration, such as the complexity of the background, occlusions, and viewpoint shifts. Electronic travel aids that are composed of the necessary sensors may assist VIPs with their navigation. The sensors have the ability to detect any obstacles, regardless of whether they are static or dynamic, and offer information on the context of an interior scene. The characteristics of an interior scene are not very clear and are subject to a great deal of variation. Recent years have seen the emergence of methods for dealing with issues of this kind, some of which include the use of neural networks, probabilistic methods, and fuzzy logic. This study describes a method for detecting indoor objects using a rotational ultrasonic array and neutrosophic logic. A neutrosophic set has been seen as the next evolution of the fuzzy set because of its indeterminate membership value, which is absent from conventional fuzzy sets. The suggested method is constructed to reflect the position of the walls (obstacle distance) and to direct the VIP to move freely (ahead, to the right, or to the left) depending on the degree of truthiness, the degree of indeterminacy, and the degree of falsity for the reflected distance. The results of the experiments show that the suggested indoor object detecting system has good performance, as its accuracy rate (a mean average precision) is 97.2 ± 1%.

In a chaotic system, deterministic, nonlinear, irregular, and initial-condition-sensitive features are desired. Due to its chaotic nature, it is difficult to quantify a chaotic system’s parameters. Parameter estimation is a major issue because it depends on the stability analysis of a chaotic system, and communication systems that are based on chaos make it difficult to give accurate estimates or a fast rate of convergence. Several nature-inspired metaheuristic algorithms have been used to estimate chaotic system parameters; however, many are unable to balance exploration and exploitation. The fruit fly optimization algorithm (FOA) is not only efficient in solving difficult optimization problems, but also simpler and easier to construct than other currently available population-based algorithms. In this study, the quantum fruit fly optimization algorithm (QFOA) was suggested to find the optimum values for chaotic parameters that would help algorithms converge faster and avoid the local optimum. The recommended technique used quantum theory probability and uncertainty to overcome the classic FA’s premature convergence and local optimum trapping. QFOA modifies the basic Newtonian-based search technique of FA by including a quantum behavior-based searching mechanism used to pinpoint the position of the fruit fly swarm. The suggested model has been assessed using a well-known Lorenz system with a specified set of parameter values and benchmarked signals. The results showed a considerable improvement in the accuracy of parameter estimates and better estimation power than state-of-the art parameter estimation approaches.

Background: Periodontal diseases lead to bone loss, crestal defects and even loss of the tooth, which also further makes it difficult to replace the tooth. Autogenous bone grafts are considered the gold standard in bone regenerative procedures. This study aimed to compare and evaluate the bone regenerative effects of i-PRF (Injectable- Platelet-rich fibrin) with a bone graft and a bone graft alone in mandibular grade II furcation defects over a period of 9 months. Method: This was a comparative study of 12 participants, who were randomly selected and grouped into two groups: test and control. Following phase I therapy, both groups were subjected to open flap debridement. In the test group, after debridement, a nano-hydroxyapatite bone graft mixed with i-PRF was inserted, whereas in the control group only a nano-hydroxyapatite bone graft was inserted. The clinical parameters such as plaque index (PI), gingival index (GI), pocket probing depth (PPD), clinical attachment level (CAL), horizontal probing depth (HPD) and vertical probing depth (VPD) were recorded at baseline, 3 months, 6 months and 9 months following the surgery. The bone area fill (BAF) was assessed using intraoral periapical radiographs (IOPARs) taken at baseline and 9 months after surgery. Results: At the baseline, there was no statistically significant difference between the tested parameters. After 9 months all the clinical parameters, PI, GI, PPD, CAL, HPD and VPD as well as radiographic bone fill showed a significant increase in both the groups (p < 0.05) (PI-TGr; CGr–VPD—3.5 ± 0.54 to 0.66 ± 0.51; 3.3 ± 0.81 to 2 ± 0.63/BAF—2.9 ± 0.88 to 5.6 ± 1.10; 3.4 ± 1.39 to 3.9 ± 1.4). On comparison the test group showed better results for each clinical parameter. Conclusion: The results showed increased improvement in clinical conditions in both groups, although better results were seen in the group where i-PRF with a nano-HA bone graft was used in the furcation defect.

Background and Objectives: Empathy is an important attribute of a healthy doctor–patient relationship. Although multiple studies have assessed empathy in different countries, little is known about its levels among Saudi residents and its association with perceived stress. Objectives: To assess the levels of empathy and to identify if there is an association with stress in general and across the demographic and training characteristics of residents. Materials and Methods: A cross-sectional questionnaire-based study was carried out from December 2020 to March 2021 among residents training at a tertiary academic center in Riyadh, Saudi Arabia. Empathy and perceived stress were measured using the Jefferson Scale of Empathy (JSE) and the Perceived Stress Scale (PSS). Results: A total of 229 residents participated. The mean JSE score was 105.25 ± 15.35. The mean JSE scores were significantly higher among residents training in pediatrics (mean difference (MD) = 17.35, p < 0.001), family medicine (MD = 12.24, p = 0.007), and medical specialties (MD = 11.11, p = 0.012) when compared with surgical specialties and anesthesia. In addition, residents who worked 1–4 on-calls per month had a higher mean JSE score (MD = 11.23, p = 0.028) compared with those who worked 7 or more on-calls. Lastly, no correlation between empathy and perceived stress was detected in the whole sample (r = −0.007, p = 0.913); however, there was a correlation among residents training in medical specialties (r = −0.245, p = 0.025). Conclusion: Residents in our study had empathy levels comparable with Asian but lower than Western residents. We recommend qualitative studies that explore potential factors that might affect empathy among residents and studying the association between empathy and perceived stress among medical residents. Postgraduate curricula should incorporate interventions that foster a more empathetic doctor–patient relationship.

Over the last years, extensive motivation has emerged towards the application of supercritical carbon dioxide (SCCO2) for particle engineering. SCCO2 has great potential for application as a green and eco-friendly technique to reach small crystalline particles with narrow particle size distribution. In this paper, an artificial intelligence (AI) method has been used as an efficient and versatile tool to predict and consequently optimize the solubility of oxaprozin in SCCO2 systems. Three learning methods, including multi-layer perceptron (MLP), Kriging or Gaussian process regression (GPR), and k-nearest neighbors (KNN) are selected to make models on the tiny dataset. The dataset includes 32 data points with two input parameters (temperature and pressure) and one output (solubility). The optimized models were tested with standard metrics. MLP, GPR, and KNN have error rates of 2.079 × 10−8, 2.173 × 10−9, and 1.372 × 10−8, respectively, using MSE metrics. Additionally, in terms of R-squared, they have scores of 0.868, 0.997, and 0.999, respectively. The optimal inputs are the same as the maximum possible values and are paired with a solubility of 1.26 × 10−3 as an output.

Amphotericin B (AMB) is commonly used to treat life-threatening systemic fungal infections. AMB formulations that are more efficient and less nephrotoxic are currently unmet needs. In the current study, new ZnO-PEGylated AMB (ZnO-AMB-PEG) nanoparticles (NPs) were synthesized and their antifungal effects on the Candida spp. were investigated. The size and zeta potential values of AMB-PEG and ZnO-AMB-PEG NPs were 216.2 ± 26.9 to 662.3 ± 24.7 nm and −11.8 ± 2.02 to −14.2 ± 0.94 mV, respectively. The FTIR, XRD, and EDX spectra indicated that the PEG-enclosed AMB was capped by ZnO, and SEM images revealed the ZnO distribution on the surface NPs. In comparison to ZnO-AMB NPs and free AMB against C.albicans and C.neoformans, ZnO-AMB-PEG NPs significantly reduced the MIC and MFC. After a week of single and multiple dosage, the toxicity was investigated utilizing in vitro blood hemolysis, in vivo nephrotoxicity, and hepatic functions. ZnO-AMB-PEG significantly lowered WBC count and hematocrit concentrations when compared to AMB and ZnO-AMB. RBC count and hemoglobulin content, on the other hand, were unaltered. ZnO-AMB-PEG considerably lowered creatinine and blood urea nitrogen (BUN) levels when compared to AMB and ZnO-AMB. The difference in liver function indicators was determined to be minor by all formulae. These findings imply that ZnO-AMB-PEG could be utilized in the clinic with little nephrotoxicity, although more research is needed to determine the formulation’s in vivo efficacy.

The contemporary speed at which opinions move on social media makes them an undeniable force in the field of opinion mining (OM). This may cause the OM challenge to become more social than technical. This is when the process can determinately represent everyone to the degree they are worth. Nevertheless, considering perspectivism can result in opinion dynamicity. Pondering the existence of opinion dynamicity and uncertainty can provide smart OM on social media. This study proposes a neutrosophic-based OM approach for Twitter that handles perspectivism, its consequences, and indeterminacy. For perspectivism, a social network analysis (SNA) was conducted using popular SNA tools (e.g., Graphistry). An influence weighting of users was performed using an artificial neural network (ANN) based on the SNA provided output and people’s reactions to the OM analyzed texts. The initiative adoption of neutrosophic logic (NL) to integrate users’ influence with their OM scores is to deal with both the opinion dynamicity and indeterminacy. Thus, it provides new uncertainty OM scores that can reflect everyone. The OM scores needed for integration were generated using TextBlob. The results show the ability of NL to improve the OM process and accurately consider the innumerable degrees. This will eventually aid in a better understanding of people’s opinions, helping OM in social media to become a real pillar of many applications, especially business marketing.

Background and Objectives: In both pandemic and non-pandemic situations, nonpharmaceutical public health measures may offer easy, low-cost, and effective means of reducing the spread and impact of acute respiratory infections. It is unknown whether such measures would be acceptable to the Saudi community beyond the current pandemic. Materials and Methods: A validated survey was used to test community acceptance of the measures. Respondents were asked which infection control practices they planned to maintain and which they believed should be policies for the community as a whole after the COVID-19 pandemic has subsided. Results: The survey was completed by 2057 people (95% completion rate), 1486 (72%) of whom were female, 259 (12.5%) of whom were current smokers, and 72 (3.5%) of whom had chronic lung disease. The most prevalent age groups were 18–30 years (933; 45.4%) and 31–40 years (483; 23.5%), with 641 individuals over 40 years old. Of the responses, 93% indicated that they would continue washing their hands more often; 92% wanted both clinicians and patients to wear masks in hospitals; 86% would continue avoiding smoking in indoor and outdoor areas; 73% would continue wearing a face covering on public transportation; 70% indicated that they would continue wearing a face covering in indoor public places. Regarding the respiratory virus infection control measures, 85% (11/13) received significant support (≥70% acceptability level) for continuation as policies in the future. Wearing face coverings outdoors and social distancing outdoors received little support (45% and 66%, respectively). Of the respiratory virus infection control measures, 54% received less support from current smokers than non-smokers (acceptability level < 70%). People with chronic respiratory disease supported 77% of the measures being regarded as policies in the future. Conclusion: The Saudi community supports nonpharmacological respiratory infection control measures that reduce the likelihood of infection. Public health campaigns should target smokers to increase awareness of the importance of these measures in lowering infections. Based on the findings of this study, nonpharmacological treatments should be presented and included in future recommendations for both the public and patients diagnosed with chronic respiratory diseases.

Carvedilol (CRV) is a non-selective third generation beta-blocker used to treat hypertension, congestive heart failure and angina pectoris. Oral administration of CRV showed poor bioavailability (25%), which might be ascribed to its extensive first-pass metabolism. Buccal delivery is known to boost drugs bioavailability. The aim of this study is to investigate the efficacy of bilosomes-based mucoadhesive carvedilol nanosponge for enhancing the oral bioavailability of CRV. The bilosomes were prepared, optimized and characterized for particle size, surface morphology, encapsulation efficiency and ex-vivo permeation studies. Then, the optimized formula was incorporated into a carboxymethyl cellulose/hydroxypropyl cellulose (CMC/HPC) composite mixture to obtain buccal nanosponge enriched with CRV bilosomes. The optimized bilosome formula (BLS9), showing minimum vesicle size, maximum entrapment, and highest cumulative in vitro release, exhibited a spherical shape with 217.2 nm in diameter, 87.13% entrapment efficiency, and sustained drug release for up to 24 h. In addition, ex-vivo drug permeation across sheep buccal mucosa revealed enhanced drug permeation with bilosomal formulations, compared to aqueous drug suspension. Consecutively, BLS9 was incorporated in a CMC/HPC gel and lyophilized for 24 h to obtain bilosomal nanosponge to enhance CRV buccal delivery. Morphological analysis of the prepared nanosponge revealed improved swelling with a porosity of 67.58%. The in vivo assessment of rats indicated that CRV-loaded nanosponge efficiently enhanced systolic/diastolic blood pressure, decreased elevated oxidative stress, improved lipid profile and exhibited a potent cardio-protective effect. Collectively, bilosomal nanosponge might represent a plausible nanovehicle for buccal delivery of CRV for effective management of hypertension.

In recent years, conjugated microporous polymers (CMPs) have become important precursors for environmental and energy applications, compared with inorganic electrode materials, due to their ease of preparation, facile charge storage process, π-conjugated structures, relatively high thermal and chemical stability, abundance in nature, and high surface areas. Therefore, in this study, we designed and prepared new benzobisthiadiazole (BBT)-linked CMPs (BBT–CMPs) using a simple Sonogashira couplings reaction by reaction of 4,8-dibromobenzo(1,2-c;4,5-c′)bis(1,2,5)thiadiazole (BBT–Br2) with ethynyl derivatives of triphenylamine (TPA-T), pyrene (Py-T), and tetraphenylethene (TPE-T), respectively, to afford TPA–BBT–CMP, Py–BBT–CMP, and TPE–BBT–CMP. The chemical structure and properties of BBT–CMPs such as surface areas, pore size, surface morphologies, and thermal stability using different measurements were discussed in detail. Among the studied BBT–CMPs, we revealed that TPE–BBT–CMP displayed high degradation temperature, up to 340 °C, with high char yield and regular, aggregated sphere based on thermogravimetric analysis (TGA) and scanning electron microscopy (SEM), respectively. Furthermore, the Py–BBT–CMP as organic electrode showed an outstanding specific capacitance of 228 F g−1 and superior capacitance stability of 93.2% (over 2000 cycles). Based on theoretical results, an important role of BBT–CMPs, due to their electronic structure, was revealed to be enhancing the charge storage. Furthermore, all three CMP polymers featured a high conjugation system, leading to improved electron conduction and small bandgaps.

The purpose of the current study was to develop Brigatinib (BGT)-loaded nanospanlastics (BGT-loaded NSPs) (S1-S13) containing Span 60 with different edge activators (Tween 80 and Pluronic F127) and optimized based on the vesicle size, zeta potential (ZP), and percent entrapment efficiency (%EE) using Design-Expert® software. The optimum formula was recommended with desirability of 0.819 and composed of Span-60:Tween 80 at a ratio of 4:1 and 10 min as a sonication time (S13). It showed predicted EE% (81.58%), vesicle size (386.55 nm), and ZP (−29.51 mv). The optimized nanospanlastics (S13) was further coated with chitosan and further evaluated for Differential Scanning Calorimetry (DSC), X-ray Diffraction (XRD), in vitro release, Transmission Electron Microscopy (TEM), stability and in-vitro cytotoxicity studies against H-1975 lung cancer cell lines. The DSC and XRD revealed complete encapsulation of the drug. TEM imagery revealed spherical nanovesicles with a smooth surface. Also, the coated formula showed high stability for three months in two different conditions. Moreover, it resulted in improved and sustained drug release than free BGT suspension and exhibited Higuchi kinetic release mechanism. The cytotoxic activity of BGT-loaded SPs (S13) was enhanced three times in comparison to free the BGT drug against the H-1975 cell lines. Overall, these results confirmed that BGT-loaded SPs could be a promising nanocarrier to improve the anticancer efficacy of BGT.

Simulating the phase behavior of a reservoir fluid requires the determination of many parameters, such as gas–oil ratio and formation volume factor. The determination of such parameters requires knowledge of the critical properties and compressibility factor (Z factor). There are many techniques to determine the compressibility factor, such as experimental pressure, volume, and temperature (PVT) tests, empirical correlations, and artificial intelligence approaches. In this work, two different models based on statistical regression and multi-layer-feedforward neural network (MLFN) were developed to predict the Z factor of natural gas by utilizing the experimental data of 1079 samples with a wide range of pseudo-reduced pressure (0.12–25.8) and pseudo reduced temperature (1.3–2.4). The statistical regression model was proposed and trained in R using the “rjags” package and Markov chain Monte Carlo simulation, while the multi-layer-feedforward neural network model was postulated and trained using the “neural net” package. The neural network consists of one input layer with two anodes, three hidden layers, and one output layer. The input parameters are the ratio of pseudo-reduced pressure and the pseudo-reduced temperature of the natural hydrocarbon gas, while the output is the Z factor. The proposed statistical and MLFN models showed a positive correlation between the actual and predicted values of the Z factor, with a correlation coefficient of 0.967 and 0.979, respectively. The results from the present study show that the MLFN can lead to accurate and reliable prediction of the natural gas compressibility factor.

The present study was carried out to evaluate the effects of partial replacement of soybean with shrimp by-products (SBp) in African catfish (Clarias lazera) diets on productive (growth, digestibility, body composition, dressing yield, blood parameters, immunity) and economic performances. Therefore, 750 fingerlings (~20.0 g) were distributed into five groups of 150 fingerlings/group with three replicates (n = 50) per group. Group 1 was fed a basal diet, while G2 and G3 were fed diets containing 25% SBp (autoclaved and acid-treated, respectively) as soybean replacer for 12 weeks, while, in G4 and G5, SBp level was 50%. All growth and nutrient utilization parameters (body weight, Wg, ADG, PER, FCR, and digestibility), plasma proteins, and immunity significantly increased in G4 and G5. Anemia screening markers recorded insignificant differences between the tested groups and control one, whereas lipid markers of plasma and fish body decreased by autoclaved SBp and increased by acid-treated SBp. The African catfish body dry matter and crude protein percentages were enriched by the addition of SBp. Replacing the soybean meal decreases diet costs and diet costs/1 kg of weight gain. In conclusion, replacing soybean with SBp (especially 50% acid-treated) positively influenced productive and economic performances with friendly effects to avoid the environmental pollution by these wastes.

Sprouts are regarded as an untapped source of bioactive components that display various biological properties. Endophytic bacterium inoculation can enhance plant chemical composition and improve its nutritional quality. Herein, six endophytes (Endo 1 to Endo 6) were isolated from Chenopodium plants and morphologically and biochemically identified. Then, the most active isolate Endo 2 (strain JSA11) was employed to enhance the growth and nutritive value of the sprouts of three Chenopodium species, i.e., C. ambrosoides, C. ficifolium, and C. botrys. Endo 2 (strain JSA11) induced photosynthesis and the mineral uptake, which can explain the high biomass accumulation. Endo 2 (strain JSA11) improved the nutritive values of the treated sprouts through bioactive metabolite (antioxidants, vitamins, unsaturated fatty acid, and essential amino acids) accumulation. These increases were correlated with increased amino acid levels and phenolic metabolism. Consequently, the antioxidant activity of the Endo 2 (strain JSA11)-treated Chenopodium sprouts was enhanced. Moreover, Endo 2 (strain JSA11) increased the antibacterial activity against several pathogenic bacteria and the anti-inflammatory activities as evidenced by the reduced activity of cyclooxygenase and lipoxygenase. Overall, the Endo 2 (strain JSA11) treatment is a successful technique to enhance the bioactive contents and biological properties of Chenopodium sprouts.

The interconnection between diverse Distribution Generations (DGs) that utilize various technologies and complex structure of networks are the most characteristic of modern Distribution Networks (DN). The wide adoption of DGs considerably affects the power flow dynamics in the DN and consequently the fault characteristics. The excessive level of fault currents can pose risks of heat (high temperature) and pressure in accordance to Arc Flash (AF) incident energy in microgrids. This research studies the relationship between AF severity and the solving of coordination problem of Overcurrent Relays (OCRs) in DN, and introduces a novel equation that considers the AF qualities in solving the coordination problem for OCRs. In this study, a novel optimization problem, the AF severity with the optimal coordination of OCRs in DN is presented and the Water Cycle Optimization Method (WCOM) is employed to find the best combination of the OCR’s settings in the DN while considering the AF induced energy. The proposed optimization approach and the novel equation are evaluated with an IEC microgrid and compared with the conventional protection method and Particle Swarm Optimization (PSO) used in optimizing the coordination of OCR in the DN. The optimal settings of the OCR scheme are achieved and examined on the modified IEC microgrid benchmark system. In order to verify the result, an industrial simulation package (ETAP) and OCR (GE Multiin, model-750/760) was used in this work.

The development of multirotor vehicles can often be a dangerous and costly undertaking due to the possibility of crashes resulting from faulty controllers. The matter of safety in such activities has primarily been addressed through the use of testbeds. However, testbeds for testing multirotor vehicles with suspended loads have previously not been reported. In this study, a simple yet novel testing platform was designed and built to aid in testing and evaluating the performances of multirotor flying vehicles, including vehicles with suspended loads. The platform allows the flying vehicle to move with all six degrees of freedom (DOF). Single or three-DOF motions can also be performed. Moreover, the platform was designed to enable the determination of the mass properties (center of mass and moments of inertia) of small multirotor vehicles (which are usually required in the development of new control systems). The applicability of the test platform for the in-flight performance testing of a multirotor vehicle was successfully demonstrated using a Holybro X500 quadcopter with a suspended load. The test platform was also successfully used to determine the mass properties of the vehicle.

Mobile Computing (MC) is a relatively new concept in the world of distributed computing that is rapidly gaining traction. Due to the dynamic nature of mobility and the limited bandwidth available on wireless networks, this new computing environment for mobile devices presents significant challenges in terms of fault-tolerant system development. As a consequence, traditional fault-tolerance techniques are inherently inapplicable to these systems. External circumstances often expose mobile systems to failures in communication or data storage. In this article, a quantum game theory-based recovery model is proposed in the case of a mobile host’s failure. Several of the state-of-the-art recovery protocols are selected and analyzed in order to identify the most important variables influencing the recovery mechanism, such as the number of processes, the time needed to send messages, and the number of messages logged-in time. Quantum game theory is then adapted to select the optimal recovery method for the given environment variables using the proposed utility matrix of three players. Game theory is the study of mathematical models of situations in which intelligent rational decision-makers face conflicting interests (alternative recovery procedures). The purpose of this study is to present an adaptive algorithm based on quantum game theory for selecting the most efficient context-aware computing recovery procedure. The transition from a classical to a quantum domain is accomplished in the proposed model by treating strategies as a Hilbert space rather than a discrete set and then allowing for the existence of linear superpositions between classical strategies; this naturally increases the number of possible strategic choices available to each player from a numerable to a continuous set. Numerical data are provided to demonstrate feasibility.

This paper presents a rapid diagnostic device for the detection of the pandemic coronavirus (COVID-19) using a micro-immunosensor cavity resonator. Coronavirus has been declared an international public health crisis, so it is important to design quick diagnostic methods for the detection of infected cases, especially in rural areas, to limit the spread of the virus. Herein, a proof-of-concept is presented for a portable laboratory device for the detection of the SARS-CoV-2 virus using electromagnetic biosensors. This device is a microwave cavity resonator (MCR) composed of a sensor operating at industrial, scientific and medical (ISM) 2.45 GHz inserted in 3D housing. The changes of electrical properties of measured serum samples after passing the sensor surface are presented. The three change parameters of the sensor are resonating frequency value, amplitude and phase of the reflection coefficient |S11|. This immune-sensor offers a portable, rapid and accurate diagnostic method for the SARS-CoV-2 virus, which can enable on-site diagnosis of infection. Medical validation for the device is performed through biostatistical analysis using the ROC (Receiver Operating Characteristic) method. The predictive accuracy of the device is 63.3% and 60.6% for reflection and phase, respectively. The device has advantages of low cost, low size and weight and rapid response. It does need a trained technician to operate it since a software program operates automatically. The device can be used at ports’ quarantine units, hospitals, etc.

In the development of human–machine interfaces, facial expression analysis has attracted considerable attention, as it provides a natural and efficient way of communication. Congruence between facial and behavioral inference in face processing is considered a serious challenge that needs to be solved in the near future. Automatic facial expression is a difficult classification issue because of the high interclass variability caused by the significant interdependence of the environmental conditions on the face appearance caused by head pose, scale, and illumination occlusions from their variances. In this paper, an adaptive model for smile classification is suggested that integrates a row-transform-based feature extraction algorithm and a cascade classifier to increase the precision of facial recognition. We suggest a histogram-based cascade smile classification method utilizing different facial features. The candidate feature set was designed based on the first-order histogram probability, and a cascade classifier with a variety of parameters was used at the classification stage. Row transformation is used to exclude any unnecessary coefficients in a vector, thereby enhancing the discriminatory capacity of the extracted features and reducing the sophistication of the calculations. Cascading gives the opportunity to train an extremely precise classification by taking a weighted average of poor learners’ decisions. Through accumulating positive and negative images of a single object, this algorithm can build a complete classifier capable of classifying different smiles in a limited amount of time (near real time) and with a high level of precision (92.2–98.8%) as opposed to other algorithms by large margins (5% compared with traditional neural network and 2% compared with Deep Neural Network based methods).

Poly (vinyl alcohol) (PVA)-based solid polymer electrolytes doped with ammonium thiocyanate (NH4SCN) and glycerol were fabricated using a solution casting method. Lithium-based energy storage devices are not environmentally friendly materials, and they are toxic. Thus, proton-conducting materials were used in this work as they are harmless and are smaller than lithium. The interaction between PVA and the electrolyte elements was shown by FTIR analysis. The highest conductivity of 1.82 × 10−5 S cm−1 was obtained by the highest-conducting plasticized system (PSP_2) at room temperature. The mobility, diffusion coefficient, and number density of anions and cations were found to increase with increasing glycerol. FESEM was used to investigate the influence of glycerol on film morphology. TNM showed that the cations and anions were the main charge carriers. LSV showed that the electrochemical stability window of the PSP_2 system was 1.99 V. The PSP_2 system was applied in the preparation of an electrical double layer capacitor device. The shape of the cyclic voltammetry (CV) curve was nearly rectangular with no Faradaic peaks. From the galvanostatic charge-discharge analysis, the power density, energy density, and specific capacitance values were nearly constant beyond the first cycle at 318.73 W/Kg, 2.06 Wh/Kg, and 18.30 F g−1, respectively, for 450 cycles.

Selenium (Se) has been recently reported to play a crucial role in ameliorating the negative impact of abiotic stress, including salinity, on several plant species. Two field experiments (2016/17 and 2017/18) were carried out to investigate the possible effect of exogenous Se application at two levels (25, 50 mg L−1) on growth, bulb yield, physio-biochemical attributes, and antioxidant activities of onion grown under saline (5.25 dS m−1) soil condition. Se (25 or 50 mg L−1) foliar application enhanced growth characteristics, as well as membrane stability index (MSI) and relative water content (RWC) as a result of the osmotic adjustment by accumulating more osmoprotectants and enhancing the activity of antioxidants defense system, thus improving photosynthetic efficiency and bulb yield. Proline, glycine betaine, choline, and total soluble sugars content were higher in leaves and bulbs of Se-treated plants under salt stress. Se (25 or 50 mg L−1) significantly increased the activities of enzymatic (ascorbate peroxidase, superoxide dismutase, and catalase) and non-enzymatic (ascorbic acid and glutathione) antioxidants in both leaves and bulbs of salt-stressed onion. Se application at 25–50 mg L−1 may find, in the future, a potential application as anti-abiotic stresses for improving plant growth and productivity under saline soil condition.

Green synthesis of gold nanoparticles (GNPs) with plant extracts has gained considerable interest in the field of biomedicine. Recently, the bioreduction nature of herbal extracts has helped to synthesize spherical GNPs of different potential from gold salt. In this study, a fast ecofriendly method was adopted for the synthesis of GNPs using fresh peel (aqueous) extracts of Benincasa hispida, which acted as reducing and stabilizing agents. The biosynthesized GNPs were characterized by UV–VIS and Fourier transform infrared spectroscopy, transmission electron microscopy (TEM), and dynamic light scattering. In addition, the in vitro antibacterial and anticancer activities of synthesized GNPs were investigated. The formation of gold nanoparticles was confirmed by the existence of a sharp absorption peak at 520 nm, corresponding to the surface plasmon resonance (SPR) band of the GNPs. TEM analysis revealed that the prepared GNPs were spherical in shape and had an average particle size of 22.18 ± 2 nm. Most importantly, the synthesized GNPs exhibited considerable antibacterial activity against different Gram-positive and Gram-negative bacteria. Furthermore, the biosynthesized GNPs exerted remarkable in vitro cytotoxicity against human cervical cancer cell line, while sparing normal human primary osteoblast cells. Such cytotoxic effect was attributed to the increased production of reactive oxygen species (ROS) that contributed to the damage of HeLa cells. Collectively, peel extracts of B. hispida can be efficiently used for the synthesis of GNPs, which can be adopted as a natural source of antimicrobial and anticancer agent.

A distributed database model can be effectively optimized through using query optimization. In such a model, the optimizer attempts to identify the most efficient join order, which minimizes the overall cost of the query plan. Successful query processing largely relies on the methodology implemented by the query optimizer. Many researches are concerned with the fact that query processing is considered an NP-hard problem especially when the query becomes bigger. Regarding large queries, it has been found that heuristic methods cannot cover all search spaces and may lead to falling in a local minimum. This paper examines how quantum-inspired ant colony algorithm, a hybrid strategy of probabilistic algorithms, can be devised to improve the cost of query joins in distributed databases. Quantum computing has the ability to diversify and expand, and thus covering large query search spaces. This enables the selection of the best trails, which speeds up convergence and helps avoid falling into a local optimum. With such a strategy, the algorithm aims to identify an optimal join order to reduce the total execution time. Experimental results show that the proposed quantum-inspired ant colony offers a faster convergence with better outcome when compared with the classic model.

Maximization of drug-loading can significantly reduce the size of dosage form and consequently decrease the cost of manufacture. In this research, two challenges were addressed: poor flow and tableting problems of high-drug loading (>70%) formulation of canagliflozin (CNG), by adopting the moisture-activated dry granulation (MADG) process. In this method, heating and drying steps were omitted so, called green granulation process. A 32 full-factorial design was performed for optimization of key process variables, namely the granulation fluid level (X1) and the wet massing time (X2). Granulation of CNG was carried out in the presence of polyvinylpyrrolidone, and the prepared granules were compressed into tablets. Regression analysis demonstrated the significant (p ≤ 0.05) effect of X1 and X2 on properties of granules and corresponding tablets, with pronounced impact of X1. Additionally, marked improvement of granules’ properties and tableting of CNG were observed. Furthermore, the optimized process conditions that produced good flow properties of granules and acceptable tablets were high level of granulation fluid (3.41% w/w) and short wet massing time (1.0 min). Finally, the MADG process gives the opportunity to ameliorate the poor flow and tableting problems of CNG with lower amounts of excipients, which are important for successful development of uniform dosage unit.

This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. A fractal dimension of a cloud of points gives an estimate of the intrinsic dimensionality of the data in that space. The main drawback of this technique is the long computing time. The main contribution of the suggested work is to investigate the effect of fractal transform by adding R-tree index structure-based entropy to existing grid computing models to obtain a balanced infrastructure with minimal fault. In this regard, the presented work is going to extend the commonly scheduling algorithms that are built based on the physical grid structure to a reduced logical network. The objective of this logical network is to reduce the searching in the grid paths according to arrival time rate and path’s bandwidth with respect to load balance and fault tolerance, respectively. Furthermore, an optimization searching technique is utilized to enhance the grid performance by investigating the optimum number of nodes extracted from the logical grid. The experimental results indicated that the proposed model has better execution time, throughput, makespan, latency, load balancing, and success rate.

Blockchain technology has been commonly used in the last years in numerous fields, such as transactions documenting and monitoring real assets (house, cash) or intangible assets (copyright, intellectual property). The internet of things (IoT) technology, on the other hand, has become the main driver of the fourth industrial revolution, and is currently utilized in diverse fields of industry. New approaches have been established through improving the authentication methods in the blockchain to address the constraints of scalability and protection in IoT operating environments of distributed blockchain technology by control of a private key. However, these authentication mechanisms do not consider security when applying IoT to the network, as the nature of IoT communication with numerous entities all the time in various locations increases security risks resulting in extreme asset damage. This posed many difficulties in finding harmony between security and scalability. To address this gap, the work suggested in this paper adapts multimodal biometrics to strengthen network security by extracting a private key with high entropy. Additionally, via a whitelist, the suggested scheme evaluates the security score for the IoT system with a blockchain smart contract to guarantee that highly secured applications authenticate easily and restrict compromised devices. Experimental results indicate that our system is existentially unforgeable to an efficient message attack, and therefore, decreases the expansion of infected devices to the network by up to 49 percent relative to traditional schemes.

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin–Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model.

The solubility and thermodynamic analysis of baricitinib (BNB) in various dimethyl sulfoxide (DMSO) + water mixtures were performed. The “mole fraction solubilities (xe)” of BNB in DMSO and water mixtures were determined at “T = 298.2–323.2 K” and “p = 0.1 MPa” using an isothermal saturation technique. “Hansen solubility parameters (HSPs)” of BNB, pure DMSO, pure water and “DMSO + water” mixtures free of BNB were also estimated. The xe data of BNB was regressed well by five different thermodynamics-based co-solvency models, which included “Apelblat, Van’t Hoff, Yalkowsky-Roseman, Jouyban-Acree and Jouyban-Acree-Van’t Hoff models” with overall deviations of xe value of BNB was computed in pure DMSO (1.69 × 10−1 at T = 323.2 K) and pure water (2.23 × 10−5 at T = 298.2 K), respectively. The HSP of BNB was found to be closer to that of pure DMSO. Based on activity coefficient data, maximum solute–solvent molecular interactions were observed in BNB-DMSO compared to BNB-water. The results of “apparent thermodynamic analysis” indicated endothermic and entropy-drive dissolution of BNB in all “DMSO + water” combinations including mono-solvents (water and DMSO). “Enthalpy-entropy compensation analysis” showed enthalpy-driven to be the main mechanism of solvation of BNB.