Measuring the Engagement of the Learner in a Controlled Environment Using Three Different Biosensors

Irrespective of the educational model, the major challenge is how to achieve maximum efficiency of the education process and keep learners engaged during learning. This paper investigates the relationship between emotions and engagement in the E-learning environment, and how recognizing the learners emotions and changing the content delivery accordingly can affect the efficiency of the E-learning process. The proposed experiment aims to identify ways to increase the engagement of the learners, hence, enhance the efficiency of the learning process and the quality of learning. A controlled experiment was conducted to investigate participants emotions using bio sensors such as eye tracker, EEG, and camera to capture facial images in different emotional states. One-way analysis of variance (ANOVA) test and t-Test was carried out to compare the performance of the three groups and show if there was an effect of using the Affective E-learning system to improve the learners performance. Our findings support the conclusion that using bio sensors as a quantitative research tool to investigate human behaviours and measure emotions in real time can significantly enhance the efficiency of E-learning

Affective Computing to Enhance E-Learning in Segregated Societies

According to UN Women, to build stronger economies, it is essential to empower women to participate fully in economic life across all sectors. Increasing women and girls’ education enhances their chances to participate in the labor market. In certain cultures, like in Saudi Arabia, women contribution to the public economy growth is very limited. According to the World Bank, less than 20 percent of the female population participate in the labor force. This low participation rate has many reasons. One of them, is the educational level and educational quality for females. Although Saudi Arabia has about thirty three universities, opportunities are still limited for women because of the restrictions of access put upon them. A mixture of local norms, traditions, social beliefs, and principles preventing women from receiving full benefits from the educational system. Gender segregation is one of the challenges that limits the women access for education. It causes a problem due to the shortage of female faculty throughout the country. To overcome this problem, male faculty are allowed to teach female students under certain regulations and following a certain method of education delivery and interaction. However, most of these methods lack face-to-face communication between the teacher and students, which lowers the interactivity level and, accordingly, the students’ engagement, and increases the need for other alternatives. The e-learning model is one of high benefit for female students in such societies. Recognizing the students’ engagement is not straightforward in the e-learning model. To measure the level of engagement, the learner’s mood or emotions should be taken into consideration to help understanding and judging the level of engagement. This paper is to investigate the relationship between emotions and engagement in the e-learning environment, and how recognizing the learner’s emotions and change the content delivery accordingly can affect the efficiency of the e-learning process. The proposed experiment alluded to herein should help to find ways to increase the engagement of the learners, hence, enhance the efficiency of the learning process and the quality of learning, which will increase the chances and opportunities for women in such societies to participate more effectively in the labor market

Brain-Inspired Intelligent Systems for Daily Assistance

The fields of machine learning and cognitive computing have been in the last decade revolutionised with neural-inspired algorithms (e.g., deep ANNs and deep RL) and brain-intelligent systems that assist in many real-world learning tasks from robot monitoring and interaction at home to complex decision-making about emotions and behaviours in humans and animals. While there are remarkable advances in these brain-inspired algorithms and systems, they need to be trained with huge data sets, and their results lack flexibility to adapt to diverse learning tasks and sustainable performance over long periods of time. To address these challenges, it is essential to gain an analytical understanding of the principles that allow biological inspired intelligent systems to leverage knowledge and how they can be translated to hardware for daily assistance and practical applications. This special issue brings researchers from interdesciplinary domains to report their latest research work on algorithms and neural-inspired systems that flexibly adapt to new learning tasks, learn from the environment using multimodal signals (e.g., neural, physiological, and kinematic), and produce autonomous adaptive agencies, which utilise cognitive and affective data, within a social neuroscientific framework. In this special issue, we have selected five papers out of fourteen high-quality papers after a careful reviewing process, which brings the acceptance rate to 35.7 percent. The five papers are representative of the current state-of-the-art in this area

3D reconstruction of medical images from slices automatically landmarked with growing neural models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid

Bovine mastitis is a polymicrobial disease requiring a polydiagnostic approach

peer-reviewedBovine mastitis, an inflammation of the udder, is associated with increases in milk somatic cell count usually resulting from bacterial infection. We analysed 50 mastitic milk samples via cultivation, 16S rRNA sequencing and a combination of the two (culturomics) to define the complete microbial content of the milk. Most samples contained over 10,000 cfu mL-1 total bacterial counts including isolates that were haemolysin positive (n = 36). Among colonies isolated from blood agar plates, Streptococcus uberis was dominant (11/50) followed by Streptococcus dysgalactiae (6/50), Pseudomonas (6/50), Enterococcus faecalis (6/50), Escherichia coli (6/50), Staphylococcus argenteus (4/50), Bacillus (4/50) and Staphylococcus aureus (2/50). 16S rRNA profiling revealed that amplicons were dominated by Rhodococcus, Staphylococcus, Streptococcus and Pseudomonas. A higher inter-sample diversity was noted in the 16S rRNA readouts, which was not always reflected in the plating results. The combination of the two methods highlights the polymicrobial complexity of bovine mastitis

Prevalence of Molar-Incisor Hypomineralization In Milwaukee, Wisconsin, USA: A Pilot Study

Purpose: This pilot study investigated the prevalence of Molar-Incisor Hypomineralization (MIH) in third-grade school children in Milwaukee Wisconsin, USA. Methods: A convenience sample of third-grade school children in the Milwaukee Public School System (MPS) participated in the study. Calibrated examiners trained on the European Academy of Paediatric Dentistry (EAPD) MIH recommendations examined the children between December 1, 2014 and June 30, 2015. Children were examined at their schools using a flashlight and mirror after receiving consent from parents/caregivers and assent from each child. Findings were recorded onto a standardized form by one of five trained examiners. Summary statistics were calculated, and bivariate analysis were done to identify factors associated with MIH. Results: A total of 375 children (average age =8.66 years, range 7–12) were examined, 60% females and 41% Hispanics. Overall, 36 (9.6%) of the children demonstrated findings consistent with the diagnosis of MIH. Among the teeth with MIH defects, severe defects were higher in lower molars. There were no statistically significant differences between those with and without MIH by sex, race/ethnicity, and socioeconomic status in this study. Conclusion: The study revealed that 9.6% of the children examined were affected by MIH. Future studies should focus on statewide and/or nationwide surveys in the United States to ascertain the extent and severity of the condition

Quantitative RT-PCR luminometric hybridization assay with an RNA-internal standard for cytokeratin-19 mRNA in peripheral blood of patients with breast cancer. Clin Biochem

6 normal PBMC and is highly specific as none of the 26 healthy controls tested had detectable CK-19 mRNA levels, while 10 out of 14 (71.4%) and 9 out of 37 (24.3%) patients with stage IV and stage I/II breast cancer, respectively, were tested positive. Conclusion: The developed quantitative RT-PCR hybridization assay for CK-19 is reproducible, highly sensitive and specific, and can be used for a large-scale prospective evaluation of clinical samples

Vancomycin and nisin A are effective against biofilms of multi-drug resistant Staphylococcus aureus isolates from human milk

Human milk provides complete nutrition for infants and at the same time promotes the growth of specific bacteria in the infant gastrointestinal tract. Breastfeeding can often be discontinued due to mastitis which is an inflammation of the breast tissue. We isolated 18 Staphylococcus aureus strains from milk donated by healthy (n = 6), subclinical (n = 6), and mastitic (n = 6) mothers, two strains of which were VISA (Vancomycin Intermediate S. aureus). All tested strains (n = 12) were able to form biofilms. We then examined the impact of nisin A and vancomycin alone and in combination on biofilm formation and eradication of selected strains (n = 8). We observed strain-specific responses, with the combinatorial treatment at 1/4X MIC (for both singularly) significantly inhibiting biofilm formation for seven out of eight strains when compared with nisin A or vancomycin alone. None of the selected treatments were able to eradicate pre-formed biofilms. Finally, we selected two strains, namely a VISA (APC3814H) and a strong biofilm former (APC3912CM) and used confocal microscopy to evaluate the effects of the antimicrobial agents at 1X MIC on biofilm inhibition and eradication. All treatments inhibited biofilm formation of APC3814H but were ineffective in eradicating a pre-formed biofilm. Single treatments at 1X MIC against APC3912CM cells did not prevent biofilm formation whereas combination treatment caused increased death of APC3912CM cells. Finally, the combination treatment reduced the thickness of the pre-formed APC3912CM biofilm as compared with the single treatments

A Hybrid Spam Detection Method Based on Unstructured Datasets

This document is the accepted manuscript version of the following article: Shao, Y., Trovati, M., Shi, Q. et al. Soft Comput (2017) 21: 233. The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-015-1959-z. © Springer-Verlag Berlin Heidelberg 2015.The identification of non-genuine or malicious messages poses a variety of challenges due to the continuous changes in the techniques utilised by cyber-criminals. In this article, we propose a hybrid detection method based on a combination of image and text spam recognition techniques. In particular, the former is based on sparse representation-based classification, which focuses on the global and local image features, and a dictionary learning technique to achieve a spam and a ham sub-dictionary. On the other hand, the textual analysis is based on semantic properties of documents to assess the level of maliciousness. More specifically, we are able to distinguish between meta-spam and real spam. Experimental results show the accuracy and potential of our approach.Peer reviewedFinal Accepted Versio

Chromoendoscopy in magnetically guided capsule endoscopy

BACKGROUND: Diagnosis of intestinal metaplasia and dysplasia via conventional endoscopy is characterized by low interobserver agreement and poor correlation with histopathologic findings. Chromoendoscopy significantly enhances the visibility of mucosa irregularities, like metaplasia and dysplasia mucosa. Magnetically guided capsule endoscopy (MGCE) offers an alternative technology for upper GI examination. We expect the difficulties of diagnosis of neoplasm in conventional endoscopy to transfer to MGCE. Thus, we aim to chart a path for the application of chromoendoscopy on MGCE via an ex-vivo animal study. METHODS: We propose a modified preparation protocol which adds a staining step to the existing MGCE preparation protocol. An optimal staining concentration is quantitatively determined for different stain types and pathologies. To that end 190 pig stomach tissue samples with and without lesion imitations were stained with different dye concentrations. Quantitative visual criteria are introduced to measure the quality of the staining with respect to mucosa and lesion visibility. Thusly determined optimal concentrations are tested in an ex-vivo pig stomach experiment under magnetic guidance of an endoscopic capsule with the modified protocol. RESULTS: We found that the proposed protocol modification does not impact the visibility in the stomach or steerability of the endoscopy capsule. An average optimal staining concentration for the proposed protocol was found at 0.4% for Methylene blue and Indigo carmine. The lesion visibility is improved using the previously obtained optimal dye concentration. CONCLUSIONS: We conclude that chromoendoscopy may be applied in MGCE and improves mucosa and lesion visibility. Systematic evaluation provides important information on appropriate staining concentration. However, further animal and human in-vivo studies are necessary