Evaluating The Robustness of Self-Supervised Representations to Background/Foreground Removal

Despite impressive empirical advances of SSL in solving various tasks, the problem of understanding and characterizing SSL representations learned from input data remains relatively under-explored. We provide a comparative analysis of how the representations produced by SSL models differ when masking parts of the input. Specifically, we considered state-of-the-art SSL pretrained models, such as DINOv2, MAE, and SwaV, and analyzed changes at the representation levels across 4 Image Classification datasets. First, we generate variations of the datasets by applying foreground and background segmentation. Then, we conduct statistical analysis using Canonical Correlation Analysis (CCA) and Centered Kernel Alignment (CKA) to evaluate the robustness of the representations learned in SSL models. Empirically, we show that not all models lead to representations that separate foreground, background, and complete images. Furthermore, we test different masking strategies by occluding the center regions of the images to address cases where foreground and background are difficult. For example, the DTD dataset that focuses on texture rather specific objects

Galaxolide-contaminated soil and tolerance strategies in soybean plants using biofertilization and selenium nanoparticle supplementation

The current study aimed to address the response of soybean (Glycine max) plants to biofertilization and selenium supplementation treatments under galaxolide contamination of soil. In this regard, a pot experiment was carried out where the soybean plants were treated with the plant growth-promoting Actinobacteria (Actinobacterium sp.) as a biofertilizer (PGPB treatment) and/or selenium nanoparticles (Se treatment; 25 mg L-1) under two non-polluted and galaxolide-polluted soils (250 mg galaxolide per kg of soil) to assess the modifications in some plant physiological and biochemical traits. Although higher accumulation of oxidative biomarkers, including hydrogen peroxide (+180%), malondialdehyde (+163%), and protein oxidation (+125%), indicating oxidative stress in galaxolide-contaminated plants, an apparent decline in their contents was observed in response to biofertilization/supplementation treatments in contaminated soil, especially. It was mainly related to the higher detoxification of ROS in PGPB- and Se-treated plants under galaxolide-induced oxidative stress, in which the direct ROS-scavenging enzymes (+44 -179%), enzymatic (+34 - 293%) and non-enzymatic (+35 - 98%) components of the ascorbate-glutathione pathway, and antioxidant molecules (+38 - 370%) were more activated than in control plants. In addition, a higher accumulation of detoxification activity markers, including phytochelatins (+32%) and metallothioneins (+79%), were found in the combined treatments (PGPB+Se) under galaxolide contamination. Moreover, combined treatment with PGPB and Se ameliorated the levels of chlorophyll a content (+58%), stomatal conductance (+57%), the maximum efficiency of photosystem II (PSII) (+36%), and photorespiratory metabolism (including +99% in glycolate oxidase and +54% in hydroxypyruvate reductase activity) in leaves under galaxolide contamination, which resulted in higher photosynthesis capacity (+36%) and biomass production (+74%) in galaxolide-stressed plants as compared to control group. In conclusion, the application of beneficial Actinobacteria and selenium nanoparticles as biofertilization/supplementation is expected to be useful for improving plant toleration and adaptation against galaxolide contamination

A survey on predicting workloads and optimizing QoS in the cloud computing

computing delivers quality of service (QoS) to the end-user. Next, it discusses how to schedule one’s workload in the infrastructure using technologies that have recently emerged such as Machine Learning (ML). That is followed by an overview of how ML can be used for resource management. Then, this paper aims to outline the benefits of using ML to schedule upcoming demands to achieve QoS and conserve energy. In addition, we reviewed the research related to ML methods for predicting workloads in cloud computing. It also provides information on the approaches to elasticity, while another section discusses the methods of prediction used in previous studies

Free sugar intake is associated with reduced proportion of circulating invariant natural killer T cells among women experiencing overweight and obesity

BackgroundHigher prevalence of obesity has been observed among women compared to men, which can be explained partly by the higher consumption of sweets and physical inactivity. Obesity can alter immune cell infiltration, and therefore increase the susceptibility to develop chronic inflammation and metabolic disorders. In this study, we aimed to explore the association between free sugar intake and other unhealthy lifestyle habits in relation to the proportion of circulating iNKT cells among women with healthy weight and women experiencing overweight and obesity.MethodsA cross-sectional study was conducted on 51 Saudi women > 18 years, wherein their daily free sugar intake was assessed using the validated Food Frequency Questionnaire. Data on smoking status, physical activity, and supplement use were also collected. Anthropometric data including height, weight, waist circumference were objectively measured from each participants. The proportion of circulating iNKT cells was determined using flow cytometry.ResultsSmoking, physical activity, supplement use, and weight status were not associated with proportion of circulating iNKT cells. Significant association was found between proportion of circulating iNKT cells and total free sugar intake and free sugar intake coming from solid food sources only among women experiencing overweight and obesity (Beta: -0.10: Standard Error: 0.04 [95% Confidence Interval: -0.18 to -0.01], p= 0.034) and (Beta: -0.15: Standard Error: 0.05 [95% Confidence Interval: -0.25 to -0.05], p= 0.005), respectively.ConclusionExcessive free sugar consumption may alter iNKT cells and consequently increase the risk for chronic inflammation and metabolic disorders

The role of rapid maxillary expansion in the promotion of oral and general health

Rapid maxillary expansion (RME) is an effective orthopedic procedure that can be used to address problems concerned with the growth of the midface. This procedure also may produce positive side effects on the general health of the patient. The aim of the present consensus paper was to identify and evaluate studies on the changes in airway dimensions and muscular function produced by RME in growing patients. A total of 331 references were retrieved from a database search (PubMed). The widening of the nasal cavity base after midpalatal suture opening in growing patients allows the reduction in nasal airway resistance with an improvement of the respiratory pattern. The effects of RME on the upper airway, however, have been described as limited and local, and these effects become diminished farther down the airway, possibly as a result of soft-tissue adaptation. Moreover, limited information is available about the long-term stability of the airway changes produced by RME. Several studies have shown that maxillary constriction may play a role in the etiology of more severe breathing disorders such as obstructive sleep apnea (OSA) in growing subjects. Early orthodontic treatment with RME is able to reduce the symptoms of OSA and improve polysomnographic variables. Finally, early orthopedic treatment with RME also is beneficial to avoid the development of facial skeletal asymmetry resulting from functional crossbites that otherwise may lead to functional and structural disorders of the stomatognathic system later in life

Factors determining the need for general anesthesia to deliver dental treatment for adults with intellectual and developmental disabilities

AIM: To investigate factors determining the need for general anesthesia (GA) to deliver dental treatment for adult people with intellectual and developmental disabilities (IDD). METHODS: This study involved a retrospective review of medical records of adult patients with IDD who received dental treatment under GA at Tabuk Specialist Dental Center, Saudi Arabia, between 2018 and 2020. Demographic characteristics and dental-related details, level of cooperation, and methods of delivering dental treatment were collected. RESULTS: A total of 86 adult patients with IDD were included. The mean age of the study participants was 34.8 years (standard deviation [SD] 6.5), and the majority were males (n = 47, 54.7%). Eighteen patients had aphasia (20.9%), 16 had epilepsy (18.6%), and 10 had cerebral palsy (11.6%). Most dental treatments delivered were complex dental treatments (n = 39, 45.3%) followed by dental extraction (n = 25, 29.1%), and non-surgical periodontal therapy (n = 22, 25.5%). Females had higher odds of undergoing GA compared to males (Odds ratio (OR) =6.79, 95% Confidence intervals (CI): 1.62–28.41). Furthermore, patients who had aphasia had higher odds of undergoing GA compared to patients who had no medical conditions (OR = 14.03, 95% CI: 1.05–186.7). CONCLUSION: Being female or having aphasia are independent factors related to the need for GA to deliver dental treatment for Saudi adults with IDD

Detecting Credit Card Fraud using Machine Learning

Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. Customary techniques use rule-based master frameworks to identify fraud practices, ignoring assorted circumstances, the outrageous lopsidedness of positive and negative examples. In this paper, we propose a CNN-based fraud detection system, to catch the natural examples of fraud practices gained from named information. Bountiful exchange information is spoken to by an element lattice, on which a convolutional neural organization is applied to recognize a bunch of idle examples for each example. Trials on true monstrous exchanges of a significant business bank show its boss presentation contrasted and some best-in-class strategies. The aim of this paper is to merge between Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Auto-encoder (AE) to increase credit card fraud detection and enhance the performance of the previous models. By using these four models; CNN, AE, LSTM, and AE&LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy =0.99, the CNN model has accuracy =0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&LSTM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models

Utilization of Agro-Industrial Wastes for the Production of Quality Oyster Mushrooms

The objective of this study was to utilize agro-lignocellulosic wastes for growing oyster mushroom which become problematic for disposal. Pleurotus ostreatus was cultivated on five agro-industrial wastes: rice straw (RS), wheat straw (WS), corncobs (CC), saw dust and rice husk @ 3:1 (SR) and sugarcane bagasse (SB). Approximately 500 g sized polypropylene bags (20.32 × 30.48 cm) were used for each substrate. The SR significantly improved the number of fruiting body (27.80), size of the fruiting body (5.39 g), yield (115.13 g/packet), ash and shortened the days for stimulation to primordial initiation and harvest (9.2 days). The maximum percentage of visual mycelium growth with the least time (15.0 days) to complete the mycelium running was found in SB, whereas the highest biological efficiency value (56.5) was calculated in SR. The topmost value of total sugar (33.20%) and ash (10.87 g/100 g) were recorded in WS, whereas the utmost amount of protein (6.87 mg/100 g) and total polyphenolics (196.88 mg GAE/100 g) were detected from SB and SR, respectively. Overall SR gave the highest amount of the fruiting body with the topmost polyphenols and ash, moderate protein and total sugar, and secured maximum biological efficiency too. The results demonstrate that saw dust with rice husk could be used as an easy alternative substrate for oyster mushroom cultivation

Emergency Response to the COVID-19 Pandemic of the King Abdulaziz University in Jeddah: A Report on Stakeholder’s Opinions

The devastating effect of COVID-19 has impacted global citizens for the past three years. More than six hundred and forty-six million people have been infected and there have been almost seven million casualties. Consequently, new variants have been discovered in quick succession around the world. Global communities have witnessed cruel fatalities and lost properties and businesses, and experienced the usual activities of service sectors being hampered, including those of post-secondary educational institutions, and the consequences of the COVID-19 pandemic ultimately damaged family life and society in general. Emergency management strategies were adopted by educational institutions around the world, including in the Middle East, in order to manage the ongoing pandemic. This study aimed to evaluate the emergency response mechanisms to COVID-19 at the King Abdulaziz University (KAU) by interviewing major stakeholders to ascertain their opinions through a cross-sectional survey. A total of 350 responses were recorded from students (64.28%), faculty members (21.42), and staff (14.28). The collected data were analyzed using statistical methods and illustrated using different schemes, graphs, and diagrams. Interestingly, the KAU emergency response plan for COVID-19 was appreciated by the respondents and it has emerged as a success story at a post-secondary educational institution in the KSA

Design of a New Phthalocyanine-Based Ion-Imprinted Polymer for Selective Lithium Recovery from Desalination Plant Reverse Osmosis Waste

In this study, a novel technique is introduced that involves the combination of an ion-imprinted polymer and solid-phase extraction to selectively adsorb lithium ions from reverse osmosis brine. In the process of synthesizing ion-imprinted polymers, phthalocyanine acrylate acted as the functional monomer responsible for lithium chelation. The structural and morphological characteristics of the molecularly imprinted polymers and non-imprinted polymers were assessed using Fourier transform infrared spectroscopy and scanning electron microscopy. The adsorption data for Li on an ion-imprinted polymer showed an excellent fit to the Langmuir isotherm, with a maximum adsorption capacity (Qm) of 3.2 mg·g−1. Comprehensive chemical analyses revealed a significant Li concentration with a higher value of 45.36 mg/L. Through the implementation of a central composite design approach, the adsorption and desorption procedures were systematically optimized by varying the pH, temperature, sorbent mass, and elution volume. This systematic approach allowed the identification of the most efficient operating conditions for extracting lithium from seawater reverse osmosis brine using ion-imprinted polymer–solid-phase extraction. The optimum operating conditions for the highest efficiency of adsorbing Li+ were determined to be a pH of 8.49 and a temperature of 45.5 °C. The efficiency of ion-imprinted polymer regeneration was evaluated through a cycle of the adsorption–desorption process, which resulted in Li recoveries of up to 80%. The recovery of Li from the spiked brine sample obtained from the desalination plant reverse osmosis waste through the ion-imprinted polymer ranged from 62.8% to 71.53%