Search results for: activity learning

Authors: Goran Sporiš, Dinko Vuleta, Stefan Lovro

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The purpose of the present study was to determine the associations between health-related quality of life (HRQOL) and physical activity (PA) in different domains. In this cross-sectional study, participants were 169 Croatian police officers (mean age 35.14±8.95 yrs, mean height 180.93±7.53 cm, mean weight 88.39±14.05 kg, mean body-mass index 26.90±3.39 kg/m2). The dependent variables were two general domains extracted from the HRQOL questionnaire: (1) physical component scale (PCS) and (2) mental component scale (MCS). The independent variables were job-related, transport, domestic and leisure-time PA, along with other factors: age, body-mass index, smoking status, psychological distress, socioeconomic status and time spent in sedentary behaviour. The associations between dependent and independent variables were analyzed by using multiple regression analysis. Significance was set up at p < 0.05. PCS was positively associated with leisure-time PA (β 0.28, p < 0.001) and socioeconomic status (SES) (β 0.16, p=0.005), but inversely associated with job-related PA (β -0.15, p=0.012), domestic-time PA (β -0.14, p=0.014), age (β -0.12, p=0.050), psychological distress (β -0.43, p<0.001) and sedentary behaviour (β -0.15, p=0.009). MCS was positively associated with leisure-time PA (β 0.19, p=0.013) and SES (β 0.20, p=0.002), while inversely associated with age (β -0.23, p=0.001), psychological distress (β -0.27, p<0.001) and sedentary behaviour (β -0.22, p=0.001). Our results added new information about the associations between domain-specific PA and both physical and mental component scale in police officers. Future studies should deal with the same associations in other stressful occupations.

Keywords: health, fitness, police force, relations

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Authors: Pao-Huei Chen, Shu-Mei Lin, Yih-Ming Weng, Zer-Ran Yu, Be-Jen Wang

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Pancreatic α-amylase and intestinal α-glucosidase are the critical enzymes for the breakdown of complex carbohydrates into di- or mono-saccharide, which play an important role in modulating postprandial blood sugars. Angiotensin converting enzyme (ACE) converts inactive angiotensin-I into active angiotensin-II, which subsequently increase blood pressure through triggering vasoconstriction and aldosterone secretion. Thus, inhibition of carbohydrate-digestion enzymes and ACE will help the management of blood glucose and blood pressure, respectively. Studies showed Pleurotus citrinopileatus (PC), an edible mushroom and commonly cultured in oriental countries, exerted anticancer, immune improving, antioxidative, hypoglycemic and hypolipidemic effects. Previous studies also showed various molecular weights (MW) fractioned from extracts may affect biological activities due to varying contents of bioactive components. Thus, the objective of this study is to investigate the in vitro antioxidant, hypoglycemic and hypotenstive effects and distribution of active compounds of various MWs of cold water extract from P. citrinopileatus (CWEPC). CWEPC was fractioned into four various MW fractions, PC-I (＜1 kDa), PC-II (1-3.5 kDa), PC-III (3.5-10 kDa), and PC-IV (＞10 kDa), using an ultrafiltration system. The physiological activities, including antioxidant activities, the inhibition capabilities of pancreatic α-amylase, intestinal α-glucosidase, and hypertension-linked ACE, and the active components, including polysaccharides, protein, and phenolic contents, of CWEPC and four fractions were determined. The results showed that fractions with lower MW exerted a higher antioxidant activity (p<0.05), which was positively correlated to the levels of total phenols. In contrast, the inhibition effects on the activities of α-amylase, α-glucosidase, and ACE of PC-IV fraction were significantly higher than CWEPC and the other three low MW fractions (< 10 kDa), which was more related to protein contents. The inhibition capability of CWEPC and PC-IV on α-amylase activity was 1/13.4 to 1/2.7 relative to that of acarbose (positive control), respectively. However, the inhibitory ability of PC-IV on α-glucosidase (IC50 = 0.5 mg/mL) was significantly higher than acarbose (IC50 = 1.7 mg/mL). Kinetic data revealed that PC-IV fraction followed a non-competitive inhibition on α-glucosidase activity. In conclusion, the distribution of various bioactive components contribute to the functions of different MW fractions on oxidative stress prevention, and blood pressure and glucose modulation.

Keywords: α-Amylase, angiotensin converting enzyme, α-Glucosidase, Pleurotus citrinopileatus

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Authors: Linda Blümel, Bettina Bert, Jan Brosda, Heidrun Fink, Melanie Hamann

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Age-related cognitive decline can eventually lead to dementia, the most common mental illness in elderly people and an immense challenge for patients, their families and caregivers. Cholinesterase inhibitors constitute the most commonly used antidementia prescription medication. The standardized Ginkgo biloba leaf extract EGb 761® is approved for treating age-associated cognitive impairment and has been shown to improve the quality of life in patients suffering from mild dementia. A clinical trial with 96 Alzheimer´s disease patients indicated that the combined treatment with donepezil and EGb 761® had fewer side effects than donepezil alone. In an animal model of cognitive aging, we compared the effect of combined treatment with EGb 761® or donepezil monotherapy and vehicle. We compared the effect of chronic treatment (15 days of pretreatment) with donepezil (1.5 mg/kg p. o.), EGb 761® (100 mg/kg p. o.), or the combination of the two drugs, or vehicle in 18 – 20 month old male OFA rats. Learning and memory performance were assessed by Morris water maze testing, motor behavior in an open field paradigm. In addition to chronic treatment, the substances were administered orally 30 minutes before testing. Compared to the first day and to the control group, only the combination group showed a significant reduction in latency to reach the hidden platform on the second day of testing. Moreover, from the second day of testing onwards, the donepezil, the EGb 761® and the combination group required less time to reach the hidden platform compared to the first day. The control group did not reach the same latency reduction until day three. There were no effects on motor behavior. These results suggest a superiority of the combined treatment of donepezil with EGb 761® compared to monotherapy.

Keywords: age-related cognitive decline, dementia, ginkgo biloba leaf extract EGb 761®, learning and memory, old rats

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Authors: Regis Pochon, Nicolas Stefaniak, Veronique Baltazart, Pamela Gobin

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Anxiety is known to have an impact on working memory. In reasoning or memory tasks, individuals with anxiety tend to show longer response times and poorer performance. Furthermore, there is a memory bias for negative information in anxiety. Given the crucial role of working memory in lexical learning, anxious students may encounter more difficulties in learning to read and spell. Anxiety could even affect an earlier learning, that is the activation of phonological representations, which are decisive for the learning of reading and writing. The aim of this study is to compare the access to phonological representations of beginning readers and writers according to their level of anxiety, using an auditory lexical decision task. Eighty students of 6- to 9-years-old completed the French version of the Revised Children's Manifest Anxiety Scale and were then divided into four anxiety groups according to their total score (Low, Median-Low, Median-High and High). Two set of eighty-one stimuli (words and non-words) have been auditory presented to these students by means of a laptop computer. Stimuli words were selected according to their emotional valence (positive, negative, neutral). Students had to decide as quickly and accurately as possible whether the presented stimulus was a real word or not (lexical decision). Response times and accuracy were recorded automatically on each trial. It was anticipated a) longer response times for the Median-High and High anxiety groups in comparison with the two others groups, b) faster response times for negative-valence words in comparison with positive and neutral-valence words only for the Median-High and High anxiety groups, c) lower response accuracy for Median-High and High anxiety groups in comparison with the two others groups, d) better response accuracy for negative-valence words in comparison with positive and neutral-valence words only for the Median-High and High anxiety groups. Concerning the response times, our results showed no difference between the four groups. Furthermore, inside each group, the average response times was very close regardless the emotional valence. Otherwise, group differences appear when considering the error rates. Median-High and High anxiety groups made significantly more errors in lexical decision than Median-Low and Low groups. Better response accuracy, however, is not found for negative-valence words in comparison with positive and neutral-valence words in the Median-High and High anxiety groups. Thus, these results showed a lower response accuracy for above-median anxiety groups than below-median groups but without specificity for the negative-valence words. This study suggests that anxiety can negatively impact the lexical processing in young students. Although the lexical processing speed seems preserved, the accuracy of this processing may be altered in students with moderate or high level of anxiety. This finding has important implication for the prevention of reading and spelling difficulties. Indeed, during these learnings, if anxiety affects the access to phonological representations, anxious students could be disturbed when they have to match phonological representations with new orthographic representations, because of less efficient lexical representations. This study should be continued in order to precise the impact of anxiety on basic school learning.

Keywords: anxiety, emotional valence, childhood, lexical access

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Authors: Morolake Josephine Adeagbo

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South Africa is one of those countries that openly rejected racism, and this is entrenched in its Bill of Rights. Despite the acceptance and incorporation of racial integration into the South Africa Constitution, the implementation within some sectors, most especially the educational sector, seems difficult. Recent occurrences of racism in some higher institutions of learning in South Africa are indications that racial integration / racial transformation is still farfetched in the country’s higher educational sector. It is against this background that this study was conducted to understand how gender and residential background influence racial integration in a South African university which was predominantly a white Afrikaner institution. Using a quantitative method to test the attitude of different categories of undergraduate students at the university, this study found that the factors- residential background and gender- used in measuring student’s attitude do not necessarily have a significant relationship towards racial integration. However, this study concludes with a call for more research with a range of other factors in order to better understand how racial integration can be promoted in South African institutions of higher learning.

Keywords: racial integration, gender, residential background, transformation

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Authors: Parul Grover, K. A. Suri, Raj Kumar, Gulshan Bansal

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Background: Nyctanthes arbortristis Linn is traditionally used as anticancer herb in Indian system of medicine, but its introduction into modern system of medicine is still awaited due to lack of systematic scientific studies. Objective: The objective of the present study was to isolate and characterize anticancer phytoconstituents from N. arbortristis L. leaves based on bioactivity guided fractionation. Method: Different extracts of the leaves of the plant were prepared by Soxhlet extractor. Each extract was evaluated for anticancer activity against HL-60 cell lines. Chloroform and HA extract showed potent anticancer activity and hence were selected for fractionation. Fraction C1 from chloroform extract was found to be most potent amongst all when tested against three cell lines (HL-60, A-549, and HCT-116) and thus was selected for further fractionation and a pure compound CP-01 was isolated. RP-HPLC method has been developed for quantification of isolated compound by using Kinetex C-18 column with gradient elution at 0.7 mL/min using mobile phase containing potassium dihydrogen phosphate (0.01 M, pH 3.0) with acetonitrile. The wavelength of maximum absorption (λₘₐₓ) selected was 210 nm. Results: The structure of potent anticancer CP-01 was determined on the basis spectroscopic methods like IR, 1H-NMR, ¹³C-NMR and Mass Spectrometry and it was characterized as 1,1,2-tris(2’,4’-di-tert-butylbenzene)-4,4-dimethyl-pent-1-ene. The content of CP-01 was found to be 0.88 %w/w of chloroform extract and 0.08 %w/w of N.arbortristis leaves. Conclusion: The study supports the traditional use of N. arbortristis as anticancer herb & the identified compound CP-01 can serve as an excellent lead to develop potent and safe anticancer drugs.

Keywords: anticancer, HL-60 cell lines, Nyctanthes arbor-tristis, RP-HPLC

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Authors: Kelsey Fortin, Susan Harvey

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The intersection between hunger and health outcomes is beginning to gain traction among the research community. With new interventions focusing on collaborations between the medical and social service sectors, this study aimed to understand the acceptability and approach of a health coaching intervention within a county-wide Midwest food pantry. Through formative research, the study used mixed methods to review secondary data and conduct surveys and semi-structured interviews with food pantry clients (n=30), staff (n=7), and volunteers (n=10). Supplemental secondary data collected and provided by pantry staff were reviewed to understand the broader pantry context of clientele health and health behaviors, annual food donations, and current pantry programming. Results from secondary data showed that the broader pantry client population reported high rates of chronic disease, low consumption of fruits and vegetables, and poor self-reported health, while annual donation data showed increases in produce availability on pantry shelves. This disconnect between produce availability, client health status, and behaviors was supported in the current study, with pantry staff and volunteers reporting lack of knowledge in produce selection and preparation being amongst the most common client inquiries and barriers to healthy food selection. Additional supports to secondary data came from pantry clients in the current study through self-reported high rates of both individual (60%, n=18) and household (43%, n=13 ) disease diagnosis, low consumption of fruits and vegetables averaging zero to one servings of vegetables (67%, n=20) and fruits (47%, n=14) per day, and low levels of physical activity averaging zero to 120 minutes per week (67%, n=20). Further, pantry clients provided health coaching programmatic recommendations through interviews with feedback such as non-judgmental coaching, accountability measures, and providing participant incentives as considerations for future program design and approach. Volunteers and staff reported the need for client education in food preparation, basic nutrition and physical activity, and the need for additional health expertise to educate and respond to diet related nutrition recommendations. All three stakeholder groups supported hosting a health coach within the pantry to focused on nutrition, physical activity, and health programming, with one client stating, 'I am hoping it really works out [the health coaching program]. I think it would be great for something like this to be offered for someone that isn’t knowledgeable like me.' In conclusion, high rates of chronic disease, partnered with low food, nutrition, and physical activity literacy among pantry clients, demonstrates the need to address health behaviors. With all three stakeholder groups showing acceptability of a health coaching program, partnered with existing literature showing health coaching success as a behavior change intervention, further research should be conducted to pilot the design and implementation of such a program in the pantry setting.

Keywords: food insecurity, formative research, food pantries, health coaching, hunger and health

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Authors: Mohini Mohamed, Nurul Huda Mas’od

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This study was aimed at designing and developing an assistive mathematical teaching aid (courseware) in helping dyslexic students in learning multiplication. Computers and multimedia interactive courseware has benefits students in terms of increase learner’s motivation and engage them to stay on task in classroom. Most disability student has short attention span thus with the advantage offered by multimedia interactive courseware allows them to retain the learning process for longer period as compared to traditional chalk and talk method. This study was conducted in a public school at a primary level with the help of three special education teachers and six dyslexic students as participants. Qualitative methodology using interview with special education teachers and observations in classes were conducted. The development of the multimedia interactive courseware in this study was divided to three processes which were analysis and design, development and evaluation. The courseware was evaluated by using User Acceptance Survey Form and interview. Feedbacks from teachers were used to alter, correct and develop the application for a better multimedia interactive courseware.

Keywords: disability students, dyslexia, mathematics teaching aid, multimedia interactive courseware

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Authors: Katalin Juhos, Enikő Papdi, Flórián Kovács, Vasileios P. Vasileiadis, Andrea Veres

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Mulching techniques can be a solution for better utilization of precipitation and irrigation water and for mitigating soil degradation and drought damages. Waste fibres as alternative biodegradable mulch materials are increasingly coming to the fore. The effect of wool mulch (WM) on water use efficiency of pepper seedlings were investigated in different soil types (sand, clay loam, peat) in a pot experiment. Two semi-field experiments were also set up to investigate the effect of WM-plant interaction on sweet pepper yield in comparison with agro-textile and straw mulches. Soil parameters (moisture, temperature, DHA, β-glucosidase enzymes, permanganate-oxidizable carbon) were measured during the growing season. The effect of WM on yield and biomass was more significant with less frequent irrigation and the greater the water capacity of soils. The microbiological activity was significantly higher in the presence of plants, because of the water retention of WM, the metabolic products of roots and the more balanced soil temperature caused by plants. On the sandy soil, the straw mulch had a significantly better effect on microbiological parameters and yields than the agro-textile and WM. WM is a sustainable practice for improving soil biological parameters and water use efficiency on soils with a higher water capacity.

Keywords: β-glucosidase, DHA enzyme activity; labile carbon, straw mulch; plastic mulch, evapotranspira-tion coefficient, soil temperature

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Sehrish Iftikhar, Kiran Nawaz, Ahmad A. Shahid, Waheed Anwar, Muhammad S. Haider

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Early blight caused by Alternaria solani Sorauer is one of the most serious foliage diseases of the potato (Solanum tuberosum L.). This disease causes huge crop losses and has major economic importance worldwide. The antifungal activity for three medicinal plants (Foeniculum vulgare, Syzygium aromaticum, and Eucalyptus citriodora) against Alternaria solani has been evaluated. The inhibitory potential of selected essential oils on the radial mycelial growth and germination of spore was measured in vitro at various concentrations (5%, 2.5%. 1.25%, 0.625%, and 0.312%) using agar well diffusion assay. Essential oil of E. citriodora was most effective causing 85% inhibition of mycelial growth and 88% inhibition of spore germination at 0.625% and 1.25% concentrations. Essential oil of Foeniculum vulgare also caused 80% and 82% inhibition of the above mentioned parameters but at double the concentrations 1.25% and 2.5%. While essential oil of Syzygium aromaticum was least effective in controlling the mycelial growth and spore germination with 76% and 77% inhibition at 1.25% and 2.5%. All the selected essential oils, especially E. citriodora, showed marked antimicrobial activity significant at higher concentration. These results suggest that the use of essential oils for the control of A. solani can reduce environmental risks related with commercial fungicides, lower cost for control, and the chances for resistance development. Additional studies are essential to evaluate the potential of essential oils as natural treatments for this disease.

Keywords: clove, essential oils, fennel, potato

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Authors: Randa Ismail Sharafeddine

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The Islamic economic system is distinctive in that it implicitly recognizes money as a separate, independent component of production capable of assuming risk and so entitled to the same reward as other Entrepreneurial Factors of Production (EFP). Conventional theory does not identify money capital explicitly as a component of production; rather, interest is recognized as a reward for capital, the interest rate is the cost of money capital, and it is also seen as a cost of physical capital. The conventional theory of production examines how diverse non-entrepreneurial resources (Land, Labor, and Capital) are selected; however, the economic theory community is largely unaware of the reasons why these resources choose to remain as non-entrepreneurial resources as opposed to becoming entrepreneurial resources. Should land, labor, and financial asset owners choose to work for others in return for rent, income, or interest, or should they engage in entrepreneurial risk-taking in order to profit. This is a decision made often in the actual world, but it has never been effectively treated in economic theory. This article will conduct a critical analysis of the conventional classification of factors of production and propose a classification for resource allocation and income distribution (Rent, Wages, Interest, and Profits) that is more rational, even within the conventional theoretical framework for evaluating and developing production and distribution theories. Money is an essential component of production in an Islamic economy, and it must be used to sustain economic activity.

Keywords: financial capital, production theory, distribution theory, economic activity, riba avoidance, institution of participation

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Authors: Iwona Misztalewska-Turkowicz, Agnieszka Z. Wilczewska, Karolina H. Markiewicz

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Carbenes - species which possess neutral carbon atom with two shared and two unshared valence electrons, are known for their high reactivity and instability. Nevertheless, it is also known, that some carbenes i.e. N-heterocyclic carbenes (NHCs), can form stable crystals. The usability of NHCs in organic synthesis was studied. Due to their exceptional properties (high nucleophilicity) NHCs are commonly used as organocatalysts and also as ligands in transition metal complexes. NHC ligands possess better electron-donating properties than phosphines. Moreover, they exhibit lower toxicity. Due to these features, phosphines are frequently replaced by NHC ligands. In this research is discussed the synthesis of five-membered NHCs which are mainly obtained by deprotonation of azolium salts, e.g., imidazolium or imidazolinium salts. Some of them are immobilized on a solid support what leads to formation of heterogeneous, recyclable catalysts. Magnetic nanoparticles (MNPs) are often used as a solid support for catalysts. MNPs can be easily separated from the reaction mixture using an external magnetic field. Due to their low size and high surface to volume ratio, they are a good choice for immobilization of catalysts. Herein is presented synthesis of N-heterocyclic carbene copper complexes directly on the surface of magnetic nanoparticles. Formation of four different catalysts is discussed. They vary in copper oxidation state (Cu(I) and Cu(II)) and structure of NHC ligand. Catalysts were tested in Huisgen reaction, a type of copper catalyzed azide-alkyne cycloaddition (CuAAC) reaction. Huisgen reaction represents one of the few universal and highly efficient reactions in which 1,2,3-triazoles can be obtained. The catalytic activity of all synthesized catalysts was compared with activity of commercially available ones. Different reaction conditions (solvent, temperature, the addition of reductant) and reusability of the obtained catalysts were investigated and are discussed. The project was financially supported by National Science Centre, Poland, grant no. 2016/21/N/ST5/01316. Analyses were performed in Centre of Synthesis and Analyses BioNanoTechno of University of Bialystok. The equipment in the Centre of Synthesis and Analysis BioNanoTechno of University of Bialystok was funded by EU, as a part of the Operational Program Development of Eastern Poland 2007-2013, project: POPW.01.03.00-20-034/09-00 and POPW.01.03.00-20-004/11.

Keywords: N-heterocyclic carbenes, click reaction, magnetic nanoparticles, copper catalysts

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Authors: Elnaz Lashgari, Emel Demircan

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Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: electromyography, manifold learning, ISOMAP, Laplacian Eigenmaps, locally linear embedding

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Authors: Kunto Nurcahyoko

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The fundamental perception used in this study is that teaching and learning activities in Indonesian classroom have potentially generated individual’s sensitivity on cross-cultural understanding. This study aims at investigating Indonesian university students’ perception on cross-cultural understanding after doing Cultural Snapshot Project. The data was critically analyzed through multicultural ideology and diversity theories. The subjects were 30 EFL college students in one of colleges in Indonesia. Each student was assigned to capture a photo which depicted the existence of any cultural manifestation in their surrounding such as discrimination, prejudice and stereotype. Students were then requested asked to reflect on the picture by writing a short description on the picture and make an exhibition using their pictures. In the end of the project, students were instructed to fill in questionnaires to show their perception before and after the project. The result reveals that Cultural Snapshot Project has given the opportunity for the students to better realize cross-cultural understanding in their environment. In conclusion, the study shows that Cultural Snapshot Project has specifically enhanced students’ perception of multiculturalism in three major areas: cultural sensitivity and empathy, social tolerance, and understanding of diversity.

Keywords: cultural snapshot, cross-cultural understanding, students’ perception, multiculturalism

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Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: Chungkham Sarojnalini, Wahengbam Sarjubala Devi

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Antioxidant properties and nutritive values of raw and cooked Pool barb, Puntius sophore (Hamilton-Buchanan) of Eastern Himalayas, India were determined. Antioxidant activity of the methanol extract of the raw, steamed, fried and curried Pool barb was evaluated by using 1,1-diphenyl-2-picrylhydrazyl (DPPH) scavenging assay. In DPPH scavenging assay the IC50 value of the raw, steamed, fried and curried Pool barb was 1.66 microgram/ml, 16.09 microgram/ml, 8.99 microgram/ml, 0.59 microgram/ml whereas the IC50 of the reference ascorbic acid was 46.66 microgram/ml. This results shows that the fish have high antioxidant activity. Protein content was found highest in raw (20.50±0.08%) and lowest in curried (18.66±0.13%). Moisture content in raw, fried and curried was 76.35±0.09, 46.27±0.14 and 57.46±0.24 respectively. Lipid content was recorded 2.46±0.14% in raw and 21.76±0.10% in curried. Ash content varies from 12.57±0.11 to 22.53±0.07%. The total aminoacids were varied from 36.79±0.02 and 288.43±0.12 mg/100 g. Eleven essential mineral elements were found abundant in all the samples. The samples had a considerable amount of Fe ranging from 152.17 to 320.39 milligram/100 gram, Ca 902.06 to 1356.02 milligram/100 gram, Zn 91.07 to 138.14 milligram/100 gram, K 193.25 to 261.56 milligram/100 gram, Mg 225.06 to 229.10 milligram/100 gram. Ni was not detected in the curried fish. The Mg and K contents were significantly decreased in frying method; however the Fe, Cu, Ca, Co and Mn content were increased significantly in all the cooked samples. The Mg and Na contents were significantly increased in curried sample and the Cr content was decreased significantly (p<0.05) in all the cooked samples.

Keywords: antioxidant property, pool barb, minerals, aminoacids, proximate composition, cooking methods

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Authors: Dawood Al Chanti, Alice Caplier

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Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates.

Keywords: dictionary learning, random projection, pose and spontaneous facial expression, sparse representation

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Authors: Yu-Chen Wei

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The relationship between organizational human capital and organizational effectiveness have been a common topic of interest to organization researchers. Much of this research has concluded that higher human capital can predict greater organizational outcomes. Whereas human capital research has traditionally focused on organizations, the current study turns to the team level human capital. In addition, there are no known empirical studies assessing the effect of human capital divergence on team performance. Team human capital refers to the sum of knowledge, ability, and experience embedded in team members. Team human capital divergence is defined as the variation of human capital within a team. This study is among the first to assess the role of human capital divergence as a moderator of the effect of team human capital on team performance. From the traditional perspective, team human capital represents the collective ability to solve problems and reducing operational risk of all team members. Hence, the higher team human capital, the higher the team performance. This study further employs social learning theory to explain the relationship between team human capital and team performance. According to this theory, the individuals will look for progress by way of learning from teammates in their teams. They expect to have upper human capital, in turn, to achieve high productivity, obtain great rewards and career success eventually. Therefore, the individual can have more chances to improve his or her capability by learning from peers of the team if the team members have higher average human capital. As a consequence, all team members can develop a quick and effective learning path in their work environment, and in turn enhance their knowledge, skill, and experience, leads to higher team performance. This is the first argument of this study. Furthermore, the current study argues that human capital divergence is negative to a team development. For the individuals with lower human capital in the team, they always feel the pressure from their outstanding colleagues. Under the pressure, they cannot give full play to their own jobs and lose more and more confidence. For the smart guys in the team, they are reluctant to be colleagues with the teammates who are not as intelligent as them. Besides, they may have lower motivation to move forward because they are prominent enough compared with their teammates. Therefore, human capital divergence will moderate the relationship between team human capital and team performance. These two arguments were tested in 510 team-seasons drawn from major league baseball (1998–2014). Results demonstrate that there is a positive relationship between team human capital and team performance which is consistent with previous research. In addition, the variation of human capital within a team weakens the above relationships. That is to say, an individual working with teammates who are comparable to them can produce better performance than working with people who are either too smart or too stupid to them.

Keywords: human capital divergence, team human capital, team performance, team level research

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: David Martinez-Pernia, Alvaro Rivera-Rei, Alejandro Troncoso, Gonzalo Forno, Andrea Slachevsky, David Huepe, Victoria Silva-Mack, Jorge Calderon, Mayte Vergara, Valentina Carrera

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Background: Recent theories in the field of emotions have taken the relevance of motor control beyond a system related to personal autonomy (walking, running, grooming), and integrate it into the emotional dimension. However, to our best knowledge, there are no studies that specifically investigate how emotional stimuli related to motor control modify emotional states in terms of postural control and phenomenological experience. Objective: The main aim of this work is to investigate the emotions produced by stimuli of bodily imbalance (neutral, pleasant and unpleasant) in the postural control and the phenomenological experience of young, healthy adults. Methodology: 46 healthy young people are shown emotional videos (neutral, pleasant, motor unpleasant, and non-motor unpleasant) related to the body imbalance. During the period of stimulation of each of the videos (60 seconds) the participant is standing on a force platform to collect temporal and spatial data of postural control. In addition, the electrophysiological activity of the heart and electrodermal activity is recorded. In relation to the two unpleasant conditions (motor versus non-motor), a phenomenological interview is carried out to collect the subjective experience of emotion and body perception. Results: Pleasant and unpleasant emotional videos have significant changes with respect to the neutral condition in terms of greater area, higher mean velocity, and greater mean frequency power on the anterior-posterior axis. The results obtained with respect to the electrodermal response was that the pleasurable and unpleasant conditions produced a significant increase in the phasic component with respect to the neutral condition. Regarding the electrophysiology of the heart, no significant change was found in any condition. Phenomenological experiences in the two unpleasant conditions differ in body perception and the emotional meaning of the experience. Conclusion: Emotional stimuli related to bodily imbalance produce changes in postural control, electrodermal activity, and phenomenological experience. This experimental setting could be relevant to be implemented in people with motor disorders (Parkinson, Stroke, TBI) to know how emotions affect motor control.

Keywords: body imbalance stimuli, emotion, phenomenological experience, postural control

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Jarel Elgin Tolentino, Arby Denise Nera, Mary Rose Roco, Angela Vianca Aspa, Nikko Beltran, Else Dapat

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Drug resistance by cells has been the problem in the medical field for decades now. The use of medicinal plants as a source of creating powerful drugs has been nowadays recognized worldwide to treat such resistant diseases. In the present study, the potential for Diospyros philippinensis A. DC. to inhibit growth of both bacteria and cancer cell line was conducted. The leaf crude extracts were screened for the presence of phytochemicals and examined for potential bioactivities by employing several assays like Kirby-Bauer disc diffusion method, DPPH (2,2-diphenyl-1-picryl-hydrazyl-hydrate) assay and MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) tetrazolium assay for the antibacterial, antioxidant and cytotoxic activities of the extract, respectively. Phytochemical test results of the extracts revealed the presence of alkaloids, flavonoids, saponins, phenols, quinones, cardiac glycosides, phlobatannins, carbohydrate, cardenolides and proteins. The leaf extracts were found to exhibit antibacterial activity against gram-positive bacteria, high antioxidant activity (99.22% ± 0.005) but did not show any sign of cytotoxicity towards HCT116 (ATCC CCL-247). The study therefore concludes that D. philippinensis A. DC. leaf extract can be a source of antibacterial and chemopreventive agents. This claim may be used as basis for future investigation.

Keywords: bioassay, medicinal plants, plant crude extracts, phytochemical screening

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Authors: Yuri V. Bykov, V. A. Baturin

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Aim of the study: The aim of the study was to perform a comparative analysis of the levels of autoantibodies (AAB) to the S-100 protein as well as to the dopamine and NMDA receptors in children with type 1 diabetes mellitus (DM) in therapeutic remission. Materials and methods: Blood serum obtained from 42 children ages 4 to 17 years (20 boys and 22 girls) was analyzed. Twenty-one of these children had a diagnosis of type 1 DM and were in therapeutic remission (study group). The mean duration of disease in children with type 1 DM was 9.6±0.36 years. Children without DM were included in a group of "apparently healthy children" (21 children, comparison group). AAB to the S-100 protein, the dopamine, and NMDA receptors were measured by ELISA. The normal range of IgG AAB was specified as up to 10 µg/mL. In order to compare the central parameters of the groups, the following parametric and non-parametric methods were used: Student's t-test or Mann-Whitney U test. The level of significance for inter-group comparisons was set at p<0.05. Results: The mean levels of AAB to the S-100B protein were significantly higher (p=0.0045) in children with DM (16.84±1.54 µg/mL) when compared with "apparently healthy children" (2.09±0.05 µg/mL). The detected elevated levels of AAB to NMDA receptors may indicate that in children with type 1 DM, there is a change in the activity of the glutamatergic system, which in its turn suggests the presence of excitotoxicity. The mean levels of AAB to dopamine receptors were higher (p=0.0082) in patients comprising the study group than in the children of the comparison group (40.47±2.31 µg/mL and 3.91±0.09 µg/mL). The detected elevated levels of AAB to dopamine receptors suggest an altered activity of the dopaminergic system in children with DM. This can also be viewed as indirect evidence of altered activity of the brain's glutamatergic system. The mean levels of AAB to NMDA receptors were higher in patients with type 1 DM compared with the "apparently healthy children," at 13.16±2.07 µg/mL and 1.304±0.05 µg/mL, respectively (p=0.0021). The elevated mean levels of AAB to the S-100B protein may indicate damage to brain tissue in children with type 1 DM. A difference was also detected between the mean values of the measured AABs, and this difference depended on the duration of the disease: mean AAB values were significantly higher in patients whose disease had lasted more than five years. Conclusions: The elevated mean levels of AAB to the S-100B protein may indicate damage to brain tissue in the setting of excitotoxicity in children with type 1 DM. The discovered elevation of the levels of AAB to NMDA and dopamine receptors may indicate the activation of the glutamatergic and dopaminergic systems. The observed abnormalities indicate the presence of central nervous system damage in children with type 1 DM, with a tendency towards the elevation of the levels of the studied AABs with disease progression.

Keywords: autoantibodies, brain damage, children, diabetes mellitus

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Authors: Paramvir Singh

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The locomotion of aquatic organisms has long fascinated biologists and engineers alike, with fish fins serving as a prime example of nature's remarkable adaptations for efficient underwater propulsion. This paper presents a comprehensive study focused on the hydrodynamic analysis of fish fin kinematics, employing an innovative approach that combines machine learning and image processing techniques. Through high-speed videography and advanced computational tools, we gain insights into the complex and dynamic motion of the fins of a Tilapia (Oreochromis Niloticus) fish. This study was initially done by experimentally capturing videos of the various motions of a Tilapia in a custom-made setup. Using deep learning and image processing on the videos, the motion of the Caudal and Pectoral fin was extracted. This motion included the fin configuration (i.e., the angle of deviation from the mean position) with respect to time. Numerical investigations for the flapping fins are then performed using a Computational Fluid Dynamics (CFD) solver. 3D models of the fins were created, mimicking the real-life geometry of the fins. Thrust Characteristics of separate fins (i.e., Caudal and Pectoral separately) and when the fins are together were studied. The relationship and the phase between caudal and pectoral fin motion were also discussed. The key objectives include mathematical modeling of the motion of a flapping fin at different naturally occurring frequencies and amplitudes. The interactions between both fins (caudal and pectoral) were also an area of keen interest. This work aims to improve on research that has been done in the past on similar topics. Also, these results can help in the better and more efficient design of the propulsion systems for biomimetic underwater vehicles that are used to study aquatic ecosystems, explore uncharted or challenging underwater regions, do ocean bed modeling, etc.

Keywords: biomimetics, fish fin kinematics, image processing, fish tracking, underwater vehicles

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Authors: Meenalochana Inguva

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The context: The Center for Preparatory study is one of the strongest and most vibrant academic teaching units of the Sultan Qaboos University (SQU). The Foundation Programme English Language (FPEL) is part of a larger foundation programme which was implemented at SQU in fall 2010. The programme has been designed to prepare the students who have been accepted to study in the university in order to achieve the required educational goals (the learning outcomes) that have been designed according to Oman Academic Standards and published by the Omani Authority for Academic Accreditation (OAAA) for the English language component. The curriculum: At the CPS, the English language curriculum is based on the learning outcomes drafted for each level. These learning outcomes guide the students in meeting what is expected of them by the end of each level. These six levels are progressive in nature and are seen as a continuum. The study: A periodic evaluation of language programmes is necessary to improve the quality of the programmes and to meet the set goals of the programmes. An evaluation may be carried out internally or externally depending on the purpose and context. A self-study programme was initiated at the beginning of spring semester 2015 with a team comprising a total of 11 members who worked with-in the assigned course areas (level and programme specific). Only areas specific to FPEL have been included in the study. The study was divided into smaller tasks and members focused on their assigned courses. The self-study primarily focused on analyzing the programme LOs, curriculum planning, materials used and their relevance against the GFP exit standards. The review team also reflected on the assessment methods and procedures followed to reflect on student learning. The team has paid attention to having standard criteria for assessment and transparency in procedures. A special attention was paid to the staging of LOs across levels to determine students’ language and study skills ability to cope with higher level courses. Findings: The findings showed that most of the LOs are met through the materials used for teaching. Students score low on objective tests and high on subjective tests. Motivated students take advantage of academic support activities others do not utilize the student support activities to their advantage. Reading should get more hours. In listening, the format of the listening materials in CT 2 does not match the test format. Some of the course materials need revision. For e.g. APA citation, referencing etc. No specific time is allotted for teaching grammar Conclusion: The findings resulted in taking actions in bridging gaps. It will also help the center to be better prepared for the external review of its FPEL curriculum. It will also provide a useful base to prepare for the self-study portfolio for GFP standards assessment and future audit.

Keywords: curriculum planning, learning outcomes, reflections, self-evaluation

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Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

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Authors: Matthew Armitt

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The revolutionary school of architectural teaching – VKhUTEAMAS (1923-1926) was a new approach for a new society bringing architectural education to the masses and masses to the growing industrial production. The school's pedagogical contribution of the 1920s made it an important school of the modernist movement, engaging pedagogy as a mode of experimentation. The teachers and students saw design education not just as a process of knowledge transfer but as a vehicle for design innovation developing an approach without precedent. This process of teaching and learning served as a vehicle for venturing into the unknown through a discipline of architectural teaching called “Space” developed by the Soviet architect Nikolai Ladovskii (1881-1941). The creation of “Space” was paramount not only for its innovative pedagogy but also as an experimental laboratory for developing new architectural language. This paper discusses whether the historical teaching of “Space” can function in the construction of the modern teaching identity today to promote value, richness, quality, and diversity inherent in architectural design education. The history of “Space” teaching remains unknown within academic circles and separate from the current architectural teaching debate. Using VKhUTEMAS and the teaching of “Space” as a pedagogical lens and drawing upon research carried out in the Russian Federation, America, Canada, Germany, and the UK, this paper discusses how historically different models of teaching and learning can intersect through examining historical based educational research by exploring different design studio initiatives; pedagogical methodologies; teaching and learning theories and problem-based projects. There are strong arguments and desire for pedagogical change and this paper will promote new historical and educational research to widen the current academic debate by exposing new approaches to architectural teaching today.

Keywords: VKhUTEMAS, discipline space, modernist pedagogy, teaching identity

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Authors: Surendra Kumar Gautam, Mahesh Dhungana

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Magnesium oxide nanoparticles (MgO NPs) are less toxic to humans and the environment as compared to other metal oxide nanoparticles. Various conventional chemical and physical methods are used for synthesis whose toxicity level is high and highly expensive. As the best alternative, phyto-assisted synthesis has emerged, which uses extracts from plant parts for the synthesis of nanoparticles. Here, we report the synthesis of MgO nanoparticles with the assistance of beetroot extract and leaf extract of P. guajava and A. adenophora. The synthesized MgO NPs were characterized by X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR), and UV-visible spectroscopy. X-ray analysis for the broadening of peaks was used to evaluate the crystallite size and lattice strain using Debye-Scherer and Williamson–Hall method. The results of crystallite size obtained by both methods are in close proximity. The crystallite size obtained by the Williamson-Hall method seems more accurate, with values being 8.1 nm and 13.2 nm for beetroot MgO NPs and P. guajava MgO NPs, respectively. The FT-IR spectroscopy revealed the dominance of chemical bonds as well as functional groups on MgO NPs surfaces. The UV-visible absorption spectra of MgO NPs were found to be 310 nm, 315 nm, and 315 nm for beetroot, P. guajava, and A. adenophora leaf extract, respectively. Among the three samples, beetroot-mediated MgO NPs were effective antibacterial against both gram-positive and Gram-negative bacteria. In addition, synthesized MgO NPs also show significant antioxidant efficacy against 1,1-diphenyl-2-picrylhydrazyl radical. Further, beetroot MgO NPs showed the highest photocatalytic activity of about 91% in comparison with other samples.

Keywords: MgO NPs, XRD, FTIR, antibacterial, antioxidant and photocatalytic activity

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