Search results for: voice activity detection (VAD)

Authors: Seyed Sadegh Naseralavi

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This paper presents a Kernel parallelization equation for damage identification in structures under unknown periodic excitations. Herein, the dynamic differential equation of the motion of structure is viewed as a mapping from displacements to external forces. Utilizing this viewpoint, a new method for damage detection in structures under periodic loads is presented. The developed method requires only two periods of load. The method detects the damages without finding the input loads. The method is based on the fact that structural displacements under free and forced vibrations are associated with two parallel subspaces in the displacement space. Considering the concept, kernel parallelization equation (KPE) is derived for damage detection under unknown periodic loads. The method is verified for a case study under periodic loads.

Keywords: Kernel, unknown periodic load, damage detection, Kernel parallelization equation

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Authors: Akrpoum Souad, Lalaoui Korrichi

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Vicia faba L.,Vaccinium macrocarpon, Punica granatum, Lavandula officinalis, Artemisia absinthium, Linum capitatum and Camellia sinensis were frequently used in our alimentation. In this study, we have tested the antimicrobial activity of their ethanolic and methanolic extracts on some pathogen bacteria, then their ability to in vivo inhibit the growth of Strepcoccus pneumonia. The phytochemical screening has given the composition of the most active extracts. According to the obtained results, the ethanolic extract of Lavendula. officinalis and A absinthium has shown an inhibition of all the tested strains of becteria3. The ethanolic extract of L. officinalis has given the highest activity against S. pneumoniae, followed by the methanolic extract of C. sinensis 1, 2 and P. granatum. The phytochemical screening showed that the most active extracts contained mainly naturels compounds.

Keywords: plants, extracts, antimicrobial activity, streptococcus pneumoniae, phytochemical screening

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Authors: Siti Helmyati, Euis Nurdiyawati, Joko Susilo, Endri Yuliati, Siti Fadhilatun Nashriyah, Kurnia Widyastuti

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Background: Iron fortification is one of the most effective and sustainable strategies to overcome anemia. It contradictively, has negative effect on gut microbiota balance. Pathogenic bacteria required iron for their growth. The iron source have greatly affect iron absorption in the intestine. Probiotic can inhibit the growth of pathogen. Lactobacillus plantarum Dad 13, Indonesian local isolate provides many benefits for health while fructo-oligosaccharides (FOS) provides selective substrates for probiotics’ growth. Objective: To determine the effect of iron fortification (NaFeEDTA and FeSO4) on antibacterial activity of synbiotic fermented milk. Methods: The antibacterial activity test was performed using the disc diffusion method. Paper discs were soaked in three kinds of synbiotic fermented milk, which are: 1) fortified with NaFeEDTA, 2) FeSO4 and 3) control. Escherichia coli was inoculated on nutrient agar medium. The ability of inhibition was shown by the formation of clear zone around the paper disc and measured in diameter (mm). Results: Synbiotic fermented milk fortified with iron (either NaFeEDTA or FeSO4) had antibacterial activity against Escherichia coli with diameter of clear zone were 6.53 mm and 12.3 mm, respectively (p<0.05). Compared to control (10.73 mm), synbiotic fermented milk fortified with FeSO4 had similar antibacterial activity (p>0.05). Conclusions: In vitro, synbiotic fermented milk fortified with NaFeEDTA and FeSO4 had different antibacterial activity against Escherichia coli. Iron fortification compound affected the antibacterial activity of synbiotic fermented milk.

Keywords: lactobacillus plantarum Dad 13, FOS, NaFeEDTA, FeSO4, antibacterial activity

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Authors: Mariane G. Tadros, Shahira M. Ezzat, Maha M. Salama, Mohamed A. Farag

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In this study, the in vitro anticholinesterase activity of the volatile oils of both O. basilicum and O. africanum was investigated and both samples showed significant activity. As a result, the major constituents of the two oils were isolated using several column chromatography. Linalool, 1,8-cineol and eugenol were isolated from the volatile oil of O. basilicum and camphor was isolated from the volatile oil of O. africanum. The anticholinesterase activity of the isolated compounds were also evaluated where 1,8-cineol showed the highest inhibitory activity followed by camphor. To confirm these activities, learning and memory enhancing effects were tested in mice. Memory impairment was induced by scopolamine, a cholinergic muscarinic receptor antagonist. Anti-amnesic effects of both volatile oils and their terpenoids were investigated by the passive avoidance task in mice. We also examined their effects on brain acetylcholinesterase activity. Results showed that scopolamine-induced cognitive dysfunction was significantly attenuated by administration of the volatile oils and their terpenoids, eugenol and camphor, in the passive avoidance task and inhibited brain acetylcholinesterase activity. These results suggest that O. basilicum and O. africanum volatile oils can be good candidates for further studies on Alzheimer’s disease via their acetylcholinesterase inhibitory actions.

Keywords: Ocimum baselicum, Ocimum africanum, GC/MS analysis, anticholinesterase

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Authors: Nadjib Melkemi, Salah Belaidi

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Quantitative Structure-Activity Relationship (QSAR) studies have been performed on nineteen molecules of 1,2-dithiole-3-thione analogues. The compounds used are the potent inducers of enzymes involved in the maintenance of reduced glutathione pools as well as phase-2 enzymes important to electrophile detoxication. A multiple linear regression (MLR) procedure was used to design the relationships between molecular descriptor and detoxication properties of the 1,2-dithiole-3-thione derivatives. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based of the following descriptors: qS2, qC3, qC5, qS6, DM, Pol, log P, MV, SAG, HE and EHOMO for the specific activity of quinone reductase; qS1, qS2, qC3, qC4, qC5, qS6, DM, Pol, logP, MV, SAG, HE and EHOMO for the production of growth hormone. To confirm the predictive power of the models, an external set of molecules was used. High correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR models.

Keywords: QSAR, quinone reductase activity, production of growth hormone, MLR

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Authors: N. Nalini, Lokesh B. Bhajantri

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In Distributed Sensor Networks, the sensor nodes are prone to failure due to energy depletion and some other reasons. In this regard, fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation Distributed Sensor Networks (DSNs). This paper proposes a node fault detection and recovery using Genetic Algorithm (GA) in DSN when some of the sensor nodes are faulty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using GA, which is used to detect the faulty nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). Finally, the performance parameters for the proposed scheme are evaluated.

Keywords: distributed sensor networks, genetic algorithm, fault detection and recovery, information technology

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Authors: M. Ouassaf, S. Belaidi

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Aromatase is an estrogen biosynthetic enzyme belonging to the cytochrome P450 family, which catalyzes the limiting step in the conversion of androgens to estrogens. As it is relevant for the promotion of tumor cell growth. A set of thirty 1,2,3-triazole derivatives was used in the quantitative structure activity relationship (QSAR) study using regression multiple linear (MLR), We divided the data into two training and testing groups. The results showed a good predictive ability of the MLR model, the models were statistically robust internally (R² = 0.982) and the predictability of the model was tested by several parameters. including external criteria (R²pred = 0.851, CCC = 0.946). The knowledge gained in this study should provide relevant information that contributes to the origins of aromatase inhibitory activity and, therefore, facilitates our ongoing quest for aromatase inhibitors with robust properties.

Keywords: aromatase inhibitors, QSAR, MLR, 1, 2, 3-triazole

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Authors: Cheeze Janito, Crisly Dawang

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It is important to determine the personality traits that exist in the workplace and the contribution of these personality traits in the staff’s daily work routines; a sedentary lifestyle is harmful to one’s health. This study reports the personality traits of the University of Southern Mindanao, Kabacan, Philippines, non-teaching staff, the physical activity involvement of the non-teaching staff, and the big five personality traits that shape the relationship of university non-teaching staff in engaging physical activities. A quantitative method approach, which comprised a three-part questionnaire, was used to collect the data. The fifty non-teaching staff complete the survey. The results revealed that among the big five personality traits, the university non-teaching staff scored higher in agreeableness as revealed, that there was a commonality among the respondents’ traits of consideration to the feelings of the co-workers in observance to not being rude and vividly display of respect to co-workers and workplace and scored least in the personality trait of neuroticism. The study also reported that the university non-teaching staff's main physical activity was house chores as a prime physical exercise in which respondents reported a physical activity frequency of once to twice a week; thus, this study reported that the respondents are less engaged in doing physical activities. Further, the relationship of personality traits and the physical activity of the non-teaching staff gained a p-value of .596 that indicates there is no significant relationship between the two variables, the personality trait and physical activities. This study recommends the tight promotion of staff in engaging in physical activity of at least one hundred fifty minutes of moderate-intensity activity each week. Added to this, the use of different platforms containing physical exercise literacy and the benefits of physical exercise for the holistic development of the university community.

Keywords: university staff, physical fitness, personality traits, physical activity

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Authors: C. P. Providakis, G. M. Angeli, M. J. Favvata, N. A. Papadopoulos, C. E. Chalioris, C. G. Karayannis

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An effort for the detection of damages in the reinforcement bars of reinforced concrete members using PZTs is presented. The damage can be the result of excessive elongation of the steel bar due to steel yielding or due to local steel corrosion. In both cases the damage is simulated by considering reduced diameter of the rebar along the damaged part of its length. An integration approach based on both electromechanical admittance methodology and guided wave propagation technique is used to evaluate the artificial damage on the examined longitudinal steel bar. Two actuator PZTs and a sensor PZT are considered to be bonded on the examined steel bar. The admittance of the Sensor PZT is calculated using COMSOL 3.4a. Fast Furrier Transformation for a better evaluation of the results is employed. An effort for the quantification of the damage detection using the root mean square deviation (RMSD) between the healthy condition and damage state of the sensor PZT is attempted. The numerical value of the RSMD yields a level for the difference between the healthy and the damaged admittance computation indicating this way the presence of damage in the structure. Experimental measurements are also presented.

Keywords: concrete reinforcement, damage detection, electromechanical admittance, experimental measurements, finite element method, guided waves, PZT

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Authors: Farhad Azadi, Mohammad Mahdi Mohammadi, Mohsen Vahedi, Zahra Mahdiin

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Context: The aging population is growing all over the world and maintaining physical activity is essential for healthy aging. However, fear of falling is a major obstacle to physical activity among the elderly. The aim of this study is to investigate and compare the relationship between fear of falling and physical activity in Iranian urban and rural elderly. Research Aim: The main aim of this cross-sectional analytical study is to investigate and compare the relationship between fear of falling and physical activity in Iranian rural and urban elderly. Methodology: The study used simple non-probability sampling to select 350 participants aged 60 years and older from rural and urban areas of Konarak, Sistan and Baluchistan provinces in Iran. The Persian versions of the Falls Efficacy Scale - International, Rapid Physical Activity Assessment, Activities of Daily Living, and Instrumental Activities of Daily Living questionnaires were used to assess fear of falling and physical activity. The data were analyzed using Pearson correlation tests. Findings: The study found a statistically significant negative correlation between fear of falling and physical activity, as measured by ADL, IADL, and RAPA1(aerobic ), in all elderly and rural and urban elderly (p<0.001). Fear of falling was higher in rural areas, while physical activity levels measured by ADL and RAPA1 were higher in urban areas. No significant difference was found between the two groups in IADL and RAPA2 (strength and flexibility) scores. Theoretical Importance: This study highlights the importance of considering the fear of falling as a significant obstacle to proper physical activity, especially among the elderly living in rural areas. Furthermore, the study provides insight into the difference between rural and urban elderly people in terms of fear of falling and physical activity. Data Collection and Analysis Procedures: Data was collected through questionnaires and analyzed using Pearson correlation tests. Questions Addressed: The study attempted to answer the following questions: Is there a relationship between fear of falling and physical activity in Iranian urban and rural elderly people? Is there a difference in fear of falling and physical activity between rural and urban elderly? Conclusion: Fear of falling is a major obstacle to physical activity among the elderly, especially in rural areas. The study found a significant negative correlation between fear of falling and physical activity in all elderly and rural and urban elderly. In addition, urban and rural elderly have differences in aerobic activity levels, but they do not differ in terms of flexibility and strength. Therefore, proper interventions are required to ensure that the elderly can maintain physical activity, especially in rural and deprived areas.

Keywords: aged, fear of falling, physical activity, urban population, rural population

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Authors: Mubina Rauf

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Critical thinking is a highly valued outcome of university education. There is an agreement in literature that it is demonstrated through the abilities to highlight issues and assumptions, find links between ideas and concepts, make correct inferences, evaluate evidence or authority and deduce conclusions (Tsui, 2002). Although Critical thinking plays a significant role in developing all academic skills, its role in developing writing skills is significant (Kurfiss, 1988). SAW (student academic writing) is an observable output of critical thinking (Wilson K. , 2016). When students apply critical thinking to their writing, they present clear, accurate, significant and logical arguments constructing their own voice in the form of an essay or dissertation (Matsuda, 2001). This presentation will show how a rubric can be used to find evidence of critical thinking in SAW. Participants will experience how evidence-based written arguments supported by background knowledge and authorial voice can develop students into efficient critical thinkers. Participants will have an opportunity to use the rubric to find the evidence of critical thinking in SAW samples. This presentation is intended for classroom teachers with or without the basic knowledge of implementing critical thinking in academic settings. Participants will also learn tips how various features of critical thinking can be developed among students. After the session, the participants will be able to use or adapt the rubric according to their needs to find evidence of critical thinking in SAW within their context.

Keywords: critical thinking, Rubric, student academic writing, argumentation, text analysis

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Authors: Nassim Belkacem, Amina Azzam, Dalila Haouchine, Kahina Bennacer, Samira Soufit

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Objective: To evaluate the antioxidant activity of the methanolic extract along with the antimicrobial activity of the essential oil of the aerial parts of Rosmarinus officinalis L. collected in the region of Bejaia (northern center of Algeria). Materials and methods: The polyphenols and flavonoids contents of the methanolic extract were measured. The antioxidant activity was evaluated using two methods: the ABTS method and DPPH assay. The antimicrobial activity was studied by the agar diffusion method against five bacterial strains (Three Gram positive strains and two Gram negative strains) and one fungus. Results: The total polyphenol and flavonoid content was about 43.8 mg gallic acid equivalent per gram (GA Eq/g) and 7.04 mg quercetin equivalent per gram (Q Eq/g), respectively. In the ABTS assay, the rosemary extract has shown an inhibition of 98.02% at the concentration of 500ug/ml with a half maximal inhibitory concentration value (IC50) of 194.92ug/ml. The results of DPPH assay have shown that the rosemary extract has an inhibition of 94.67 % with an IC50 value of 17.87ug/ml, which is lower than that of Butylhydroxyanisol (BHA) about 6.03ug/ml and ascorbic acid about 1.24μg/ml. The yield in essential oil of rosemary obtained by hydrodistillation was 1.42%. Based on the determination of the diameter of inhibition, different antimicrobial activity of the essential oil was revealed against the six tested microbes. Escherichia coli from the University Hospital (UH), Streptococcus aureus (UH) and Pseudomonas aeruginosa ATCC have a minimum inhibitory concentration value (MIC) of 62.5µl/ml. However, Bacillus sp (UH) and Staphylococcus aureus ATCC have an MIC value of 125μl/ml. The inhibition zone against Candida sp was about 24 mm. The aromatograms showed that the essential oil of rosemary exercises an antifungal activity more important than the antibacterial one.

Keywords: Rosmarinus officinalis L., maceration, essential oil, antioxidant, antimicrobial activity

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Authors: C. P. Providakis, G. M. Angeli, M. J. Favvata, N. A. Papadopoulos, C. E. Chalioris, C. G. Karayannis

Abstract:

An effort for the detection of damages in the reinforcement bars of reinforced concrete members using PZTs is presented. The damage can be the result of excessive elongation of the steel bar due to steel yielding or due to local steel corrosion. In both cases the damage is simulated by considering reduced diameter of the rebar along the damaged part of its length. An integration approach based on both electro-mechanical admittance methodology and guided wave propagation technique is used to evaluate the artificial damage on the examined longitudinal steel bar. Two actuator PZTs and a sensor PZT are considered to be bonded on the examined steel bar. The admittance of the Sensor PZT is calculated using COMSOL 3.4a. Fast Furrier Transformation for a better evaluation of the results is employed. An effort for the quantification of the damage detection using the root mean square deviation (RMSD) between the healthy condition and damage state of the sensor PZT is attempted. The numerical value of the RSMD yields a level for the difference between the healthy and the damaged admittance computation indicating this way the presence of damage in the structure. Experimental measurements are also presented.

Keywords: concrete reinforcement, damage detection, electromechanical admittance, experimental measurements, finite element method, guided waves, PZT

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Authors: Linda Chularojmontri, Seewaboon Sireeratawong, Suvara Wattanapitayakul

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Terminalia arjuna Roxb. (family Combretaceae) is commonly known as ‘Sa maw thet’ in Thai. The fruit is used in traditional medicine as natural mild laxatives, carminative and expectorant. Aim of the study: This research aims to study the anti-inflammatory, analgesic and antipyretic activities of Terminalia arjuna extract by using animal models in comparison to the reference drugs. Materials and Methods: The anti-inflammatory study was conducted by two experimental animal models namely ethyl phenylpropionate (EPP)-induced ear edema and carrageenan-induced paw edema. The study of analgesic activity used two methods of pain induction including acetic acid and heat-induced pain. In addition, the antipyretic activity study was performed by induced hyperthermia with yeast. Results: The results showed that the oral administration of Terminalia arjuna extract possessed acute anti-inflammatory effect in carrageenan-induced paw edema. Terminalia arjuna extract showed the analgesic activity in acetic acid-induced writhing response and heat-induced pain. This indicates its peripheral effect by inhibiting the biosynthesis and/or release of some pain mediators and some mechanism through Central nervous system. Moreover, Terminalia arjuna extract at the dose of 1000 and 1500 mg/kg body weight showed the antipyretic activity, which might be because of the inhibition of prostaglandins. Conclusion: The findings of this study indicated that the Terminalia arjuna extract possesses the anti-inflammatory, analgesic and antipyretic activities in animals.

Keywords: analgesic activity, anti-inflammatory activity, antipyretic activity, Terminalia arjuna extract

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Authors: Chia-Yen Lee, Hao-Jen Wang, Jhih-Hao Lai

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In recent years, infrared (IR) imaging has been considered as a potential tool to assess the efficacy of chemotherapy and early detection of breast cancer. Regions of tumor growth with high metabolic rate and angiogenesis phenomenon lead to the high temperatures. Observation of differences between the heat maps in long term is useful to help assess the growth of breast cancer cells and detect breast cancer earlier, wherein the multi-time infrared image alignment technology is a necessary step. Representative feature points detection and matching are essential steps toward the good performance of image registration and quantitative analysis. However, there is no clear boundary on the infrared images and the subject's posture are different for each shot. It cannot adhesive markers on a body surface for a very long period, and it is hard to find anatomic fiducial markers on a body surface. In other words, it’s difficult to detect and match features in an IR sequence images. In this study, automated feature detection and matching algorithms with two type of automatic feature points (i.e., vascular branch points and modified Harris corner) are developed respectively. The preliminary results show that the proposed method could identify the representative feature points on the IR breast images successfully of 98% accuracy and the matching results of 93% accuracy.

Keywords: Harris corner, infrared image, feature detection, registration, matching

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Authors: Mehrshad Khosraviani, Faramarz Abbaspour Leyl Abadi

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The fundamental component of the computer network of modern information society will be considered. These networks are connected to the network of the internet generally. Due to the fact that the primary purpose of the Internet is not designed for, in recent decades, none of these networks in many of the attacks has been very important. Today, for the provision of security, different security tools and systems, including intrusion detection systems are used in the network. A common diagnosis system based on artificial immunity, the designer, the Adhasaz Foundation has been evaluated. The idea of using artificial safety methods in the diagnosis of abnormalities in computer networks it has been stimulated in the direction of their specificity, there are safety systems are similar to the common needs of m, that is non-diagnostic. For example, such methods can be used to detect any abnormalities, a variety of attacks, being memory, learning ability, and Khodtnzimi method of artificial immune algorithm pointed out. Diagnosis of the common system of education offered in this paper using only the normal samples is required for network and any additional data about the type of attacks is not. In the proposed system of positive selection and negative selection processes, selection of samples to create a distinction between the colony of normal attack is used. Copa real data collection on the evaluation of ij indicates the proposed system in the false alarm rate is often low compared to other ir methods and the detection rate is in the variations.

Keywords: artificial immune system, abnormality detection, intrusion detection, computer networks

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Authors: Dipanshu Sur, Ratnabali Chakravorty, Rimi Pal, Siddhartha Chatterjee, Joyshree Chaterjee, Amal Mallik

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Background: Women with polycystic ovary disease (PCOD) frequently suffer from metabolic disturbances. PCOD is a common ovulatory disorder in young women, which affects 5-10% of the population and results in infertility due to anovulation. Importantly, aromatase in ovarian granulosa and luteinized granulosa cells plays an important role for women of reproductive age. Generation and metabolism of androgen is directly related to aromatase activity. The E2/T ratio provides important information about aromatase activity because conversion of androgens to estrogens is mediated by CYP19, suggesting that the E2/T ratio may be a direct marker of aromatase activity. The nature of the interaction between ovarian aromatase activity and PCOD in women has been controversial, and the impact of weight gain on aromatase activity as well as E2 levels is unknown. Aim: The objective of this study was to investigate the association and relation between aromatase activity and levels of body mass index (BMI) from a reproductive hormone perspective in a group of women with or without PCOD. Methods: We designed a cohort study which included 200 individuals. It enrolled 100 cases of PCOD based on 2006 Rotterdam criteria and 100 ovulatory normal- non PCOD, healthy, age-matched controls. Plasma sex hormones viz. estradiol (E2), testosterone (T), follicle stimulating hormone (FSH), and luteinizing hormone (LH) were measured by ELISA on the second day of the menstrual cycle, together with BMI and E2/T were calculated. Aromatase activity in PCOD patients with different BMI, T and E2 levels were compared. Results: PCOD patients showed significantly increased levels of BMI, E2 (P=0.004), T and LH, while their E2/T (P= <0.001), FSH and FSH/LH values were decreased compared with the control group. Higher E2 levels correlated with a relatively enhanced E2/T as well as T and LH levels but reduced BMI, FSH and FSH/LH levels in women with PCOD. Hyperandrogenic PCOD patients had increased E2 levels but their aromatase activity was markedly inhibited independent of their BMI values. Conclusions: We found a significant decrease of ovarian aromatase activity in women with PCOD as compared to controls. Our study showed that ovarian aromatase activity in PCOD was decreased which was independent of BMI. Enhancing aromatase activity may become an optimized strategy for developing therapies for PCOD women, especially those with obesity.

Keywords: aromatase activity, polycystic ovary disease, obesity, body mass index

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Authors: Atefeh Heydari, Mohammadali Tavakoli, Naomie Salim

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In recent years, we have witnessed that online reviews are the most important source of customers’ opinion. They are progressively more used by individuals and organisations to make purchase and business decisions. Unfortunately, for the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead not only potential customers to make appropriate purchasing decisions and organisations to reshape their business, but also opinion mining techniques by preventing them from reaching accurate results. Spam reviews could be divided into two main groups, i.e. multiple and singleton spam reviews. Detecting a singleton spam review that is the only review written by a user ID is extremely challenging due to lack of clue for detection purposes. Singleton spam reviews are very harmful and various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a novel supervised technique to detect singleton spam reviews. To achieve this, various features are proposed in this study and are to be combined with the most appropriate features extracted from literature and employed in a classifier. In order to compare the performance of different classifiers, SVM and naive Bayes classification algorithms were used for model building. The results revealed that SVM was more accurate than naive Bayes and our proposed technique is capable to detect singleton spam reviews effectively.

Keywords: classification algorithms, Naïve Bayes, opinion review spam detection, singleton review spam detection, support vector machine

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Authors: Hulya Tuba Kiyan, Nilgun Ozturk

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Gentiana, a member of Gentianaceae, is represented by approximately 400 species in the world and 12 species in Turkey. Flavonoids, iridoids, triterpenoids and also xanthones are the major compounds of this genus, have been previously reported to have antiinflammatory, antimicrobial, antioxidant, hepatoprotective, hypotensive, hypoglycaemic, DNA repair and immunomodulatory properties. The methanolic extract of the aerial parts of Gentiana brachyphylla Vill. from Turkey was evaluated for its biological activities and its total phenolic content in the present study. According to the antioxidant activity results, G. brachyphylla methanolic extract showed very strong anti-DNA damage antioxidant activity with an inhibition of 81.82%. It showed weak ferric-reducing power with a EC50 value of 0.65 when compared to BHT (EC50 = 0.2). Also, at 0.5 mg/ml concentration, the methanolic extract inhibited ABTS radical cation activity with an inhibition of 20.13% when compared to Trolox (79.01%). Chelating ability of G. brachyphylla was 44.71% whereas EDTA showed 78.87% chelating activity at 0.2 mg/ml. Also G. brachyphylla showed weak 27.21% AChE, 20.23% BChE, strong 67.86% MAO-A and moderate 50.06% MAO-B, weak 19.14% COX-1, 29.11% COX-2 inhibitory activities at 0.25 mg/ml. The total phenolic content of G. brachyphylla was 156.23 ± 2.73 mg gallic acid equivalent/100 g extract.

Keywords: antioxidant activity, cholinesterase inhibitory activity, Gentiana brachyphylla Vill., total phenolic content

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Authors: Hadj Abd El Kader Benghenia, Fethi Bereksi Reguig

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In clinical medicine, blood pressure, raised blood hemodynamic monitoring is rich pathophysiological information of cardiovascular system, of course described through factors such as: blood volume, arterial compliance and peripheral resistance. In this work, we are interested in analyzing these signals to propose a detection algorithm to delineate the different sequences and especially systolic blood pressure (SBP), diastolic blood pressure (DBP), and the wave and dicrotic to do their analysis in order to extract the cardiovascular parameters.

Keywords: blood pressure, SBP, DBP, detection algorithm

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Authors: Casey Keenan Joiner, Shayna Tayloe

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Classical voice training for the novice singer has long relied on the guidance and instruction of vocalise collections, such as those written and compiled by Marchesi, Lütgen, Vaccai, and Lamperti. These vocalise collections purport to encourage healthy vocal habits and instill technical longevity in both aspiring and established singers, though their scope has long been somewhat confined to the classical idiom. For pedagogues and students specializing in other vocal genres, such as musical theatre and CCM (contemporary commercial music,) low-impact and pertinent vocal training aids are in short supply, and much of the suggested literature derives from classical methodology. While the tenants of healthy vocal production remain ubiquitous, specific stylistic needs and technical emphases differ from genre to genre and may require a specified extension of vocal acuity. As musical theatre continues to grow in popularity at both the professional and collegiate levels, the need for specialized training grows as well. Pedagogical literature geared specifically towards musical theatre (MT) singing and vocal production, while relatively uncommon, is readily accessible to the contemporary educator. Practitioners such as Norman Spivey, Mary Saunders Barton, Claudia Friedlander, Wendy Leborgne, and Marci Rosenberg continue to publish relevant research in the field of musical theatre voice pedagogy and have successfully identified many common MT vocal faults, their subsequent diagnoses, and their eventual corrections. Where classical methodology would suggest specific vocalises or training exercises to maintain corrected vocal posture following successful fault diagnosis, musical theatre finds itself without a relevant body of work towards which to transition. By analyzing the existing vocalise literature by means of a specialized set of parameters, including but not limited to melodic variation, rhythmic complexity, vowel utilization, and technical targeting, we have composed a set of vocalises meant specifically to address the training and conditioning of adult musical theatre voices. These vocalises target many pedagogical tenants in the musical theatre genre, including but not limited to thyroarytenoid-dominant production, twang resonance, lateral vowel formation, and “belt-mix.” By implementing these vocalises in the musical theatre voice studio, pedagogues can efficiently communicate proper musical theatre vocal posture and kinesthetic connection to their students, regardless of age or level of experience. The composition of these vocalises serves MT pedagogues on both a technical level as well as a sociological one. MT is a relative newcomer on the collegiate stage and the academization of musical theatre methodologies has been a slow and arduous process. The conflation of classical and MT techniques and training methods has long plagued the world of voice pedagogy and teachers often find themselves in positions of “cross-training,” that is, teaching students of both genres in one combined voice studio. As MT continues to establish itself on academic platforms worldwide, genre-specific literature and focused studies are both rare and invaluable. To ensure that modern students receive exacting and definitive training in their chosen fields, it becomes increasingly necessary for genres such as musical theatre to boast specified literature and a collection of musical theatre-specific vocalises only aids in this effort. This collection of musical theatre vocalises is the first of its kind and provides genre-specific studios with a basis upon which to grow healthy, balanced voices built for the harsh conditions of the modern theatre stage.

Keywords: voice pedagogy, targeted methodology, musical theatre, singing

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Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

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Authors: Shreyas Gambhirrao, Aditya Vichare, Aniket Tembhurne, Shahuraj Bhosale

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In today's rapidly evolving environment, stress has emerged as a significant health concern across different age groups. Stress that isn't controlled, whether it comes from job responsibilities, health issues, or the never-ending news cycle, can have a negative effect on our well-being. The problem is further aggravated by the ongoing connection to technology. In this high-tech age, identifying and controlling stress is vital. In order to solve this health issue, the study focuses on three key metrics for stress detection: body temperature, heart rate, and galvanic skin response (GSR). These parameters along with the Support Vector Machine classifier assist the system to categorize stress into three groups: 1) Stressed, 2) Not stressed, and 3) Moderate stress. Proposed training model, a NodeMCU combined with particular sensors collects data in real-time and rapidly categorizes individuals based on their stress levels. Real-time stress detection is made possible by this creative combination of hardware and software.

Keywords: real time stress detection, NodeMCU, sensors, heart-rate, body temperature, galvanic skin response (GSR), support vector machine

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Authors: Richa Bhardwaj

Abstract:

Tecomella undulata (Sm.) Seem is a monotypic genus belonging to family Bignoniaceae. The plant holds tremendous potential of medicinal value and has been traditionally used in various ailments like syphilis, leukoderma, blood disorders to name a few. The plant has gained prominence due to the presence of some prominent secondary metabolites. The present study focuses on the GC-MS analysis of leaf extracts of T. undulata which revealed the presence of certain bioactive compounds like stigmasterol, sitosterol, thiazoline, phytol, pthalic acid, methyl alpha ketopalmitate and so forth. A total of about 20 bioactive compounds were identified from the leaf extract spectra. Antimicrobial activity of the leaf extract was assayed against pathogenic bacteria and fungi. The alkaloids from leaf extracts showed antimicrobial activity against E.coli and B.subtilis. The flavonoids from leaves showed positive activity against Penicillium species and Candida albicans. The study thus infers that the presence of bioactive components may be the principle behind the antimicrobial property of different plant parts and therefore Tecomella forms a potential plant for herbal drug formulation.

Keywords: Tecomella undulata, bioactive compounds, GC-MS, antimicrobial activity

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Authors: L. L. Tundisi, A. Pessoa Jr., E. B. Tambourgi, E. Silveira, P. G. Mazzola

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L-asparaginase (L-ASNase) is the gold standard treatment for acute lymphoblastic leukemia that mainly affects pediatric patients; treatment increases survival from 20% to 90%. The characterization of other L-Asparaginases, apart from the most used from Escherichia coli and Erwinia chrysanthemi, has been reported, but the choice of the most appropriate is still under debate. This choice should be based on its pharmacokinetics, immune hypersensitivity, doses, prices, pharmacodynamics. The main factors influencing the antileukemic activity of ASNase are enzymatic activity, Km, glutaminase activity, clearance of the enzyme and development of resistance. However, most of the commercialized enzyme present an intrinsic glutaminase activity, which is responsible for some side effects. In this study, glutaminase free asparaginase produced from Aspergillus oryzae was precipitated in different percentages of ethanol (0–80%), until optimum ethanol concentration of 60% (w/w) was found. Following, precipitation of crude L-ASNase was performed in a single step, using 60% (w/w) ethanol, under constant agitation and temperature. It presented activity of 135.45 U/mg and after gel filtration chromatography with Sephadex G-the enzymatic activity was 322.02 U/mg. The apparent molecular mass of the purified L-ASNase fraction was estimated by 10% SDS-PAGE. Proteins were stained with Coomassie Brilliant Blue R-250. The molar mass range was from 10 kDa to 250 kDa. L-ASNase from Aspergillus oryzae was characterized aiming possible therapeutic use. Four different buffers (phosphate-citrate buffer pH 2.6 to 5.8; phosphate buffer pH 5.8 to 7.4; Tris - HCl pH 7.4 to 9.0; and carbonate buffer pH 9.8 to 10.6) were used to measure the optimum pH for L-ASNase activity. The optimum temperature for enzyme activity was measured at optimal pH conditions (Tris-HCl and phosphate buffer, pH 7.4) at different temperatures ranging from 5 to 55°C. All activities were calculated by quantifying the free ammonia, using the Nessler reagent. The kinetic parameters calculation, e.g. Michaelis-Menten constant (Km), maximum velocity (Vmax) and Hills coefficient (n), were performed by incubating the enzyme in different concentrations of the substrate at optimum conditions of pH and fitted on Hill’s equation. This glutaminase free asparaginase showed a low Km (3.39 mM and 3.81 mM) and enzymatic activity of 135.45 U/mg after precipitation with ethanol. After gel filtration chromatography it rose to 322.02 U/mg. Optimum activity was found between pH 5.8 - 9.0, best activity results with phosphate buffer pH 7.4 and Tris-HCl pH 7.4 and showed activity from 5°C to 55°C. These results indicate that L-ASNase from A. oryzae has the potential for human use.

Keywords: biopharmaceuticals, bioprocessing, bioproducts, biotechnology, enzyme activity, ethanol precipitation

Procedia PDF Downloads 292

Authors: Muhammad Mehran Arshad Khan, Chengliang Wang, Zou Minhui, Danyal Badar Soomro

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Security of the smart home in terms of behavior activity pattern recognition is a totally dissimilar and unique issue as compared to the security issues of other scenarios. Sensor devices (low capacity and high capacity) interact and negotiate each other by detecting the daily behavior activity of individuals to execute common tasks. Once a device (e.g., surveillance camera, smart phone and light detection sensor etc.) is compromised, an adversary can then get access to a specific device and can damage daily behavior activity by altering the data and commands. In this scenario, a group of common instruction processes may get involved to generate deadlock. Therefore, an effective suitable security solution is required for smart home architecture. This paper proposes seamless distributed Scheme which fortifies low computational wireless devices for secure communication. Proposed scheme is based on lightweight key-session process to upheld cryptic-link for trajectory by recognizing of individual’s behavior activities pattern. Every device and service provider unit (low capacity sensors (LCS) and high capacity sensors (HCS)) uses an authentication token and originates a secure trajectory connection in network. Analysis of experiments is revealed that proposed scheme strengthens the devices against device seizure attack by recognizing daily behavior activities, minimum utilization memory space of LCS and avoids network from deadlock. Additionally, the results of a comparison with other schemes indicate that scheme manages efficiency in term of computation and communication.

Keywords: authentication, key-session, security, wireless sensors

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Authors: Ibrahim M. Labouta, Marwa H. El-Wakil, Hayam M. Ashour, Ahmed M. Hassan, Manal N. Saudi

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The receptor tyrosine kinase c-Met is an attractive target for therapeutic treatment of cancers nowadays. Among the wide variety of heterocycles that have been explored for developing c-Met kinase inhibitors, the 1,2,4-triazines have been rarely investigated, although they are well known in the literature to possess antitumor activities. Herein we describe the design and synthesis of a novel series of 1,2,4-triazine derivatives possessing N-acylarylhydrazone moiety and another series combining the 1,2,4-triazine scaffold to the well-known anticancer drug 6-MP in order to explore their “double-drug” effect. The synthesized compounds were evaluated for their in vitro antitumor activity against three c-Met addicted cancer cell lines (A549, HT-29 and MKN-45). Most compounds showed moderate to excellent antiproliferative activity and four compounds showed potent inhibitory activity more than the reference drug Foretinib against one or more cancer cell lines. The obtained results revealed that the potent compounds are highly selective to A549 (lung adenocarcinoma) cancer cell line. The c-Met kinase inhibitory activity of the potent derivatives is still under investigation. The present study clearly demonstrates that the 1,2,4-triazine core ring exhibits promising antitumor activity with potential c-Met kinase inhibitory activity.

Keywords: 1, 2, 4-triazine, antitumor, c-Met inhibitor, double-drug

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

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In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

Procedia PDF Downloads 197

Authors: Mohammad Arabi

Abstract:

The development of advanced technologies in the field of signal processing and vibration analysis has enabled more accurate analysis and fault detection in electrical systems. This research investigates the application of temporal and frequency features in detecting faults in electric motor bearings, aiming to enhance fault detection accuracy and prevent unexpected failures. The use of methods such as deep learning algorithms and neural networks in this process can yield better results. The main objective of this research is to evaluate the efficiency and accuracy of methods based on temporal and frequency features in identifying faults in electric motor bearings to prevent sudden breakdowns and operational issues. Additionally, the feasibility of using techniques such as machine learning and optimization algorithms to improve the fault detection process is also considered. This research employed an experimental method and random sampling. Vibration signals were collected from electric motors under normal and faulty conditions. After standardizing the data, temporal and frequency features were extracted. These features were then analyzed using statistical methods such as analysis of variance (ANOVA) and t-tests, as well as machine learning algorithms like artificial neural networks and support vector machines (SVM). The results showed that using temporal and frequency features significantly improves the accuracy of fault detection in electric motor bearings. ANOVA indicated significant differences between normal and faulty signals. Additionally, t-tests confirmed statistically significant differences between the features extracted from normal and faulty signals. Machine learning algorithms such as neural networks and SVM also significantly increased detection accuracy, demonstrating high effectiveness in timely and accurate fault detection. This study demonstrates that using temporal and frequency features combined with machine learning algorithms can serve as an effective tool for detecting faults in electric motor bearings. This approach not only enhances fault detection accuracy but also simplifies and streamlines the detection process. However, challenges such as data standardization and the cost of implementing advanced monitoring systems must also be considered. Utilizing temporal and frequency features in fault detection of electric motor bearings, along with advanced machine learning methods, offers an effective solution for preventing failures and ensuring the operational health of electric motors. Given the promising results of this research, it is recommended that this technology be more widely adopted in industrial maintenance processes.

Keywords: electric motor, fault detection, frequency features, temporal features

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Authors: Aysun Sezer

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Magnetic Resonance Imaging (MRI) is one of the advanced diagnostic tools for evaluating shoulder pathologies. Proton Density (PD)-weighted MRI sequences prove highly effective in detecting edema. However, they are deficient in the anatomical identification of bones due to a trauma-induced decrease in signal-to-noise ratio and blur in the traumatized cortices. Computer-based diagnostic systems require precise segmentation, identification, and localization of anatomical regions in medical imagery. Deep learning-based object detection algorithms exhibit remarkable proficiency in real-time object identification and localization. In this study, the YOLOv8 model was employed to detect humeral head and scapular regions in 665 axial PD-weighted MR images. The YOLOv8 configuration achieved an overall success rate of 99.60% and 89.90% for detecting the humeral head and scapula, respectively, with an intersection over union (IoU) of 0.5. Our findings indicate a significant promise of employing YOLOv8-based detection for the humerus and scapula regions, particularly in the context of PD-weighted images affected by both noise and intensity inhomogeneity.

Keywords: YOLOv8, object detection, humerus, scapula, IRM

Procedia PDF Downloads 66