Structural performance of precast foamed concrete sandwich panel subjected to axial load

In this paper, experimental and simple analytical studies on the structural behavior of Precast Foamed Concrete Sandwich Panel (PFCSP) were reported. Full-scale tests on six PFCSP panels varying in thickness were performed under axial load applications. Axial load-bearing capacity, load-deflection profiles, load-strain relationships, slenderness ratio, load-displacement, load-deformation, typical modes of failure and cracking patterns under constantly increasing axial loads were discussed. Nonlinear Finite Element Analysis (FEA) using LUSAS software to investigate the structural behavior of PFCSP was contacted. The computed ultimate strength values using American Concrete Institute equation (ACI318) and other empirical formulas developed by pervious researchers which applicable to predict the ultimate strength capacity of sandwich panels were compared with the experimental test results and FEA data obtained; therefore, very conservative values resulted, a significant agreement with the FEA data that presented a high degree of accuracy with experiments and an increase in slenderness function

Knowledge, beliefs, attitude, and practices of E-cigarette use among dental students: A multinational survey

E-cigarette use is a trend worldwide nowadays with mounting evidence on associated morbidities and mortality. Dentists can modify the smoking behaviors of their patients. This study aimed to explore the knowledge, beliefs, attitude, and practice of E-cigarette use among dental students. This multinational, cross-sectional, questionnaire-based study recruited undergraduate dental students from 20 dental schools in 11 countries. The outcome variable was current smoking status (non-smoker, E-cigarette user only, tobacco cigarette smoker only, dual user). The explanatory variables were country of residence, sex, age, marital status, and educational level. Multiple linear regression analysis was performed to explore the explanatory variables associated with E-cigarette smoking. Of the 5697 study participants, 5156 (90.8%) had heard about E-cigarette, and social media was the most reported source of information for 33.2% of the participants. For the 5676 current users of E-cigarette and/or tobacco smoking, 4.5% use E-cigarette, and 4.6% were dual users. There were significant associations between knowledge and country (P< 0.05), educational level (B = 0.12; 95% CI: 0.02, 0.21; P = 0.016) and smoking status (P< 0.05). The country of residence (P< 0.05) and smoking status (P< 0.05) were the only statistically significant factors associated with current smoking status. Similarly, there were statistically significant associations between attitude and country (P< 0.05 for one country only compared to the reference) and history of previous E-cigarette exposure (B = -0.52; 95% CI: -0.91, -0.13; P = 0.009). Also, the practice of E-cigarettes was significantly associated with country (P< 0.05 for two countries only compared to the reference) and gender (B = -0.33; 95% CI: -0.52, -0.13; P = 0.001). The knowledge of dental students about E-cigarette was unsatisfactory, yet their beliefs and attitudes were acceptable. Topics about E-cigarette should be implemented in the dental curriculum.Deanship of Scientific Research, King Saud University, for funding through the Vice Deanship of Scientific Research for Research Chairs. Qatar National Library for the open access funding

Oral health practices and self-reported adverse effects of E-cigarette use among dental students in 11 countries: an online survey

Objectives: E-cigarette use has become popular, particularly among the youth. Its use is associated with harmful general and oral health consequences. This survey aimed to assess self-reported oral hygiene practices, oral and general health events, and changes in physiological functions (including physical status, smell, taste, breathing, appetite, etc.) due to E-cigarette use among dental students. Methods: This online, multicounty survey involved undergraduate dental students from 20 dental schools across 11 different countries. The questionnaire included demographic characteristics, E-cigarette practices, self-reported complaints, and associated physiological changes due to E-cigarette smoking. Data were descriptively presented as frequencies and percentages. A Chi-square test was used to assess the potential associations between the study group and sub-groups with the different factors. Statistical analysis was performed using SPSS at P < 0.05. Results: Most respondents reported regular brushing of their teeth, whereas only 70% used additional oral hygiene aids. Reported frequencies of complaints ranged from as low as 3.3% for tongue inflammation to as high as 53.3% for headache, with significant differences between E-cigarette users and non-users. Compared to non-smokers, E-cigarette users reported significantly higher prevalence of dry mouth (33.1% vs. 23.4%; P < 0.001), black tongue (5.9% vs. 2.8%; P = 0.002), and heart palpitation (26.3%% vs. 22.8%; P = 0.001). Although two-thirds of the sample reported no change in their physiological functions, E-cigarette users reported significant improvement in their physiological functions compared to never smokers or tobacco users. Conclusion: Dental students showed good oral hygiene practices, but E-cigarette users showed a higher prevalence of health complications.Dental Biomaterials Research Chair, Deanship of Scientific Research, King Saud University. The funder has no role in the design of the study as well as in the methodology, analysis, and interpretation of the data

Brain Tumor Classification and Detection Using Hybrid Deep Tumor Network

Brain tumor (BTs) is considered one of the deadly, destructive, and belligerent disease, that shortens the average life span of patients. Patients with misdiagnosed and insufficient medical treatment of BTs have less chance of survival. For tumor analysis, magnetic resonance imaging (MRI) is often utilized. However, due to the vast data produced by MRI, manual segmentation in a reasonable period of time is difficult, which limits the application of standard criteria in clinical practice. So, efficient and automated segmentation techniques are required. The accurate early detection and segmentation of BTs is a difficult and challenging task in biomedical imaging. Automated segmentation is an issue because of the considerable temporal and anatomical variability of brain tumors. Early detection and treatment are therefore essential. To detect brain cancers or tumors, different classical machine learning (ML) algorithms have been utilized. However, the main difficulty with these models is the manually extracted features. This research provides a deep hybrid learning (DeepTumorNetwork) model of binary BTs classification and overcomes the above-mentioned problems. The proposed method hybrid GoogLeNet architecture with a CNN model by eliminating the 5 layers of GoogLeNet and adding 14 layers of the CNN model that extracts features automatically. On the same Kaggle (Br35H) dataset, the proposed model key performance indicator was compared to transfer learning (TL) model (ResNet, VGG-16, SqeezNet, AlexNet, MobileNet V2) and different ML/DL. Furthermore, the proposed approach outperformed based on a key performance indicator (Acc, Recall, Precision, and F1-Score) of BTs classification. Additionally, the proposed methods exhibited high classification performance measures, Accuracy (99.51%), Precision (99%), Recall (98.90%), and F1-Score (98.50%). The proposed approaches show its superiority on recent sibling methods for BTs classification. The proposed method outperformed current methods for BTs classification using MRI images

Brain Tumor Classification and Detection Using Hybrid Deep Tumor Network

Brain tumor (BTs) is considered one of the deadly, destructive, and belligerent disease, that shortens the average life span of patients. Patients with misdiagnosed and insufficient medical treatment of BTs have less chance of survival. For tumor analysis, magnetic resonance imaging (MRI) is often utilized. However, due to the vast data produced by MRI, manual segmentation in a reasonable period of time is difficult, which limits the application of standard criteria in clinical practice. So, efficient and automated segmentation techniques are required. The accurate early detection and segmentation of BTs is a difficult and challenging task in biomedical imaging. Automated segmentation is an issue because of the considerable temporal and anatomical variability of brain tumors. Early detection and treatment are therefore essential. To detect brain cancers or tumors, different classical machine learning (ML) algorithms have been utilized. However, the main difficulty with these models is the manually extracted features. This research provides a deep hybrid learning (DeepTumorNetwork) model of binary BTs classification and overcomes the above-mentioned problems. The proposed method hybrid GoogLeNet architecture with a CNN model by eliminating the 5 layers of GoogLeNet and adding 14 layers of the CNN model that extracts features automatically. On the same Kaggle (Br35H) dataset, the proposed model key performance indicator was compared to transfer learning (TL) model (ResNet, VGG-16, SqeezNet, AlexNet, MobileNet V2) and different ML/DL. Furthermore, the proposed approach outperformed based on a key performance indicator (Acc, Recall, Precision, and F1-Score) of BTs classification. Additionally, the proposed methods exhibited high classification performance measures, Accuracy (99.51%), Precision (99%), Recall (98.90%), and F1-Score (98.50%). The proposed approaches show its superiority on recent sibling methods for BTs classification. The proposed method outperformed current methods for BTs classification using MRI images

Acid and Sulphate Attacks on a Rubberized Engineered Cementitious Composite Containing Graphene Oxide

The objective of this research was to determine the durability of an engineered cementitious composite (ECC) incorporating crumb rubber (CR) and graphene oxide (GO) with respect to resistance to acid and sulphate attacks. To obtain the mix designs used for this study, response surface methodology (RSM) was utilized, which yielded the composition of 13 mixes containing two variables (crumb rubber and graphene oxide). The crumb rubber had a percentage range of 0–10%, whereas the graphene oxide was tested in the range of 0.01–0.05% by volume. Three types of laboratory tests were used in this study, namely a compressive test, an acid attack test to study its durability against an acidic environment, and a sulphate attack test to examine the length change while exposed to a sulphate solution. Response surface methodology helped develop predictive responsive models and multiple objectives that aided in the optimization of results obtained from the experiments. Furthermore, a rubberized engineered cementitious composite incorporating graphene oxide yielded better chemical attack results compared to those of a normal rubberized engineered cementitious composite. In conclusion, nano-graphene in the form of graphene oxide has the ability to enhance the properties and overcome the limitations of crumb rubber incorporated into an engineered cementitious composite. The optimal mix was attained with 10% crumb rubber and 0.01 graphene oxide that achieved 43.6 MPa compressive strength, 29.4% weight loss, and 2.19% expansion. The addition of GO enhances the performance of rubberized ECC, contributing to less weight loss due to the deterioration of acidic media on the ECC. It also contributes to better resistance to changes in the length of the rubberized ECC samples

Pathogenicity of <i>Aspergillus</i> Airborne Fungal Species Collected from Indoor and Outdoor Public Areas in Tianjin, China

Airborne fungi play an important role in air pollution and may have various negative effects on human health. In particular, Aspergillus fungi are pathogenic to humans and several domestic animals. In this work, Aspergillus strains isolated from airborne fungal communities sampled from different indoor and outdoor environments in Tianjin University were tested for pathogenicity on Drosophila melanogaster. Airborne fungi were sampled using an HAS-100B air sampler, over a one-year sampling period. Isolated fungal strains were identified based on morphological and molecular analysis. The Aspergillus-centered study was conducted as part of a larger work focusing on the total airborne fungal community in the analyzed environments, which yielded 173 fungal species. In this context, the genus Aspergillus showed the second-highest species richness, with 14 isolated species. Pathogenicity tests performed on male adults of Drosophila melanogaster through a bodily contact bioassay showed that all analyzed airborne Aspergillus species were pathogenic to fruit flies, with high insect mortality rates and shortened lifespan. All the studied fungi induced 100% mortality of fruit flies within 30 culture days, with one exception constituted by A. creber (39 days), while the shortest lifespan (17 days) was observed in fruit flies treated with A. tubingensis. Our results allow us to hypothesize that the studied airborne fungal species may have a pathogenic effect on humans, given the affinity between fruit flies and the human immune system, and may help to explain the health risk linked with Aspergillus fungi exposure in densely populated environments

IoT-Based Motorbike Ambulance: Secure and Efficient Transportation

The predilection for 5G telemedicine networks has piqued the interest of industry researchers and academics. The most significant barrier to global telemedicine adoption is to achieve a secure and efficient transport of patients, which has two critical responsibilities. The first is to get the patient to the nearest hospital as quickly as possible, and the second is to keep the connection secure while traveling to the hospital. As a result, a new network scheme has been suggested to expand the medical delivery system, which is an agile network scheme to securely redirect ambulance motorbikes to the nearest hospital in emergency cases. This research provides a secured and efficient telemedicine transport strategy compatible with the vehicle social network (VSN). The proposed telemedicine method should find the best ambulance motorbike route for getting patients to the hospital as quickly as possible. This approach also enables the secure exchange of information between ambulance motorbikes and hospitals. Ant colony optimization (ACO) is utilized as a SWARM technique to expand the capabilities of 5G-wireless mesh networks to determine the best path. To secure communication, the secure socket layer (SSL), which is boosted once by the advanced encryption standard (AES), has achieved a new suggested scheme as a cybersecurity approach. According to the performance evaluation, this approach will determine the optimal route for motorbike ambulances. Additionally, this technique establishes a secure connection between ambulance motorbikes and the hospital. The study enhances telemedicine transportation

ISUC: IoT-Based Services for the User’s Comfort

Emotions are alluded to as characteristic intuitive perspectives from certain conditions or temperaments. IoT applications can help in routine tasks and businesses. Most advances have not been taken advantage of regarding emotions. Emotions could be detected via the data gathered through IoT. Our investigation of related works revealed an absence of strategic methodologies in planning IoT frameworks according to feelings and shrewd alteration rules; thus, we present a philosophy that can rapidly assist in planning an IoT framework in this situation, where the identification of users is significant. We applied the proposed phases to test an IoT recommender framework named ISUC. The framework involves anticipating a user’s future emotions by utilizing boundaries gathered from IoT gadgets. It suggests new exercises for the user to obtain the ‘last’ state. Experimental results confirm our recommended framework has achieved over 85% exactness in anticipating users’ emotions in the future. The examination results presumed that an IoT-based framework could be created to detect positive emotions (e.g., peace, concretism, patience, enjoyment, and comfort) and negative emotions (e.g., irritation, abstraction, impatience, displeasure, and discomfort) to incite good emotions

Divergent effects of entomopathogenic fungi and medicinal mushroom species on Drosophila melanogaster lifespan

We tested the effect of entomogenous fungi isolated from the Coleoptera species Ophrida xanthospilota and of the medicinal mushroom Lentinula edodes on the lifespan of Drosophila melanogaster. All the four analyzed strains of entomogenous fungi showed pathogenicity to D. melanogaster by causing 100% mortality of fruit flies within 20 culture days. The shortest lifespan was recorded in flies treated with Fusarium verticillioides (15 days). On the contrary, L. edodes showed a remarkable lifespan-extending effect on the model organism, with fruit fly individuals surviving for 55 culture days. Fungal growth was microscopically observed on the bodies of dead fruit flies treated with the four entomogenous fungal species, confirming that the tested fungi were responsible for the mortality of the model insects. Our findings showed that the four fungal species previously found on the body of Coleoptera individuals are entomopathogenic and could be exploited in agriculture applications for the biocontrol of noxious pests. The significant lifespan extending effect on Drosophila produced by the analyzed L. edodes strain could represent very important experimental evidence of the anti-aging and lifespan prolonging effects of this medicinal mushroom species