Search results for: abnormal activity detection

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: Richa Bhardwaj

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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

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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: Megha Agarwal

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The research paper is an effort to compare the earnings based and cash flow based methods of valuation of an enterprise. The theoretically equivalent methods based on either earnings such as Residual Earnings Model (REM), Abnormal Earnings Growth Model (AEGM), Residual Operating Income Method (ReOIM), Abnormal Operating Income Growth Model (AOIGM) and its extensions multipliers such as price/earnings ratio, price/book value ratio; or cash flow based models such as Dividend Valuation Method (DVM) and Free Cash Flow Method (FCFM) all provide different estimates of valuation of the Indian giant corporate Reliance India Limited (RIL). An ex-post analysis of published accounting and financial data for four financial years from 2008-09 to 2011-12 has been conducted. A comparison of these valuation estimates with the actual market capitalization of the company shows that the complex accounting based model AOIGM provides closest forecasts. These different estimates may be derived due to inconsistencies in discount rate, growth rates and the other forecasted variables. Although inputs for earnings based models may be available to the investor and analysts through published statements, precise estimation of free cash flows may be better undertaken by the internal management. The estimation of value from more stable parameters as residual operating income and RNOA could be considered superior to the valuations from more volatile return on equity.

Keywords: earnings, cash flows, valuation, Residual Earnings Model (REM)

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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

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Authors: Mohammad Arabi

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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

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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: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

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Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

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Authors: Martyn Queen, Diane Crone, Andrew Parker, Saul Bloxham

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Rationale: There is a growing body of evidence that supports the use of physical activity during and after cancer treatment. However, activity levels for patients remain low. As more cancer patients are treated successfully, and treatment costs continue to escalate, physical activity may be a promising adjunct to a person-centred healthcare approach to recovery. Aim: The aim was to further understand how physical activity may enhance the recovery process for a group of mixed-site cancer patients. Objectives: The research investigated longitudinal changes in physical activity and perceived the quality of life between two and six month’s post-exercise interventions. It also investigated support systems that enabled patients to sustain these perceived changes. Method: The respondent cohort comprised 14 mixed-site cancer patients aged 43-70 (11 women, 3 men), who participated in a two-phase physical activity intervention that took place at a university in the South West of England. Phase 1 consisted of an eight-week structured physical activity programme; Phase 2 consisted of four months of non-supervised physical activity. Semi-structured interviews took place three times over six months with each participant. Grounded theory informed the data collection and analysis which, in turn, facilitated theoretical development. Findings: Our findings propose three theories on the impact of physical activity for recovering cancer patients: 1) Knowledge gained through a structured exercise programme can enable recovering cancer patients to independently sustain physical activity to four-month follow-up. 2) Sustaining physical activity for six months promotes positive changes in the quality of life indicators of chronic fatigue, self-efficacy, the ability to self-manage and energy levels. 3) Peer support from patients facilitates adherence to a structured exercise programme and support from a spouse, or life partner facilitates independently sustained physical activity to four-month follow-up. Conclusions: This study demonstrates that qualitative research can provide an evidence base that could be used to support future care plans for cancer patients. Findings also demonstrate that a physical activity intervention can be effective at helping cancer patients recover from the side effects of their treatment, and recommends that physical activity should become an adjunct therapy alongside traditional cancer treatments.

Keywords: physical activity, health, cancer recovery, quality of life, support systems, qualitative, grounded theory, person-centred healthcare

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Authors: Rajesh A. Rane, Shital S. Naphade, Rajshekhar Karpoormath

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Thiozolidin-4-one, a mimic of thiazolobenzimidazole (TBZ) has drawn many attentions due to its potent and selective inhibition against the HIV-1 and low toxicity by binding to the allosteric site of the reverse transcriptase (RT) as a non-nucleoside RT inhibitor (NNRTI). Similarly, marine bromopyrrole alkaloids are well known for their diverse array of anti-infective properties. Hence, we have reported synthesis and in vitro HIV-1 RT inhibitory activity of a series of 4-thiazolidinone-bromopyrrole alkaloid hybrids tethered with amide linker. The results of in vitro HIV-1 RT kit assay showed that some of the compounds, such as 4c, 4d, and 4i could effectively inhibit RT activity. Among them, compounds 4c having 4-chlorophenyl substituted 4-thiazolidione ring was the best one with the IC50 value of 0.26 µM. The sturdy emerges with key structure-activity relationship that pyrrole-NH-free core benefited inhibition against HIV-1 RT inhibition. This study identified conjugate 4c with potent activity and selectivity as promising compound for further drug development to HIV.

Keywords: antiviral drugs, bromopyrrole alkaloids, HIV-1 RT inhibition, 4-thiazolidinone

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Authors: Adnan A. Y. Mustafa

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Image matching is a fundamental problem that arises frequently in many aspects of robot and computer vision. It can become a time-consuming process when matching images to a database consisting of hundreds of images, especially if the images are big. One approach to reducing the time complexity of the matching process is to reduce the search space in a pre-matching stage, by simply removing dissimilar images quickly. The Probabilistic Matching Model for Binary Images (PMMBI) showed that dissimilarity detection between binary images can be accomplished quickly by random pixel mapping and is size invariant. The model is based on the gamma binary similarity distance that recognizes an image and its inverse as containing the same scene and hence considers them to be the same image. However, in many applications, an image and its inverse are not treated as being the same but rather dissimilar. In this paper, we present a comparative analysis of dissimilarity detection between PMMBI based on the gamma binary similarity distance and a modified PMMBI model based on a similarity distance that does distinguish between an image and its inverse as being dissimilar.

Keywords: binary image, dissimilarity detection, probabilistic matching model for binary images, image mapping

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Authors: Cebrail Çiflikli, Emre Öner Tartan

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Parallel to the fast worldwide increase of elderly population and spreading unhealthy life habits, there is a significant rise in the number of patients and health problems. The supervision of people who have health problems and oversight in detection of people who have potential risks, bring a considerable cost to health system and increase workload of physician. To provide an efficient solution to this problem, in the recent years mobile applications have shown their potential for wide usage in health monitoring. In this paper we present an Android mobile application that records and evaluates ECG signal using Pan-Tompkins algorithm for QRS detection. The application model includes an alarm mechanism that is proposed to be used for sending message including abnormality information and location information to health supervisor.

Keywords: Android mobile application, ECG monitoring, QRS detection, Pan-Tompkins Algorithm

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Authors: Ilia Zharikov, Philipp Nikitin, Ilia Vasiliev, Vladimir Dokholyan

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With the increasing popularity of document analysis and recognition systems, text detection (TD) and optical character recognition (OCR) in document images become challenging tasks. However, according to our best knowledge, no publicly available datasets for these particular problems exist. In this paper, we introduce a Distorted Document Images dataset (DDI-100) and provide a detailed analysis of the DDI-100 in its current state. To create the dataset we collected 7000 unique document pages, and extend it by applying different types of distortions and geometric transformations. In total, DDI-100 contains more than 100,000 document images together with binary text masks, text and character locations in terms of bounding boxes. We also present an analysis of several state-of-the-art TD and OCR approaches on the presented dataset. Lastly, we demonstrate the usefulness of DDI-100 to improve accuracy and stability of the considered TD and OCR models.

Keywords: document analysis, open dataset, optical character recognition, text detection

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Authors: Recep Kesli, Huseyin Bilgin, Yasar Unlu, Gokhan Gungor

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Aim: The aim of this study was to compare diagnostic values of two different molecular-based tests (GenoType® HelicoDR ve Seeplex® H. pylori-ClaR- ACE Detection) in detection presence of the H. pylori from gastric biopsy specimens. In addition to this also was aimed to determine resistance ratios of H. pylori strains against to clarytromycine and quinolone isolated from gastric biopsy material cultures by using both the genotypic (GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection) and phenotypic (gradient strip, E-test) methods. Material and methods: A total of 266 patients who admitted to Konya Education and Research Hospital Department of Gastroenterology with dyspeptic complaints, between January 2011-June 2013, were included in the study. Microbiological and histopathological examinations of biopsy specimens taken from antrum and corpus regions were performed. The presence of H. pylori in all the biopsy samples was investigated by five differnt dignostic methods together: culture (C) (Portagerm pylori-PORT PYL, Pylori agar-PYL, GENbox microaer, bioMerieux, France), histology (H) (Giemsa, Hematoxylin and Eosin staining), rapid urease test (RUT) (CLOtest, Cimberly-Clark, USA), and two different molecular tests; GenoType® HelicoDR, Hain, Germany, based on DNA strip assay, and Seeplex ® H. pylori -ClaR- ACE Detection, Seegene, South Korea, based on multiplex PCR. Antimicrobial resistance of H. pylori isolates against clarithromycin and levofloxacin was determined by GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection, and gradient strip (E-test, bioMerieux, France) methods. Culture positivity alone or positivities of both histology and RUT together was accepted as the gold standard for H. pylori positivity. Sensitivity and specificity rates of two molecular methods used in the study were calculated by taking the two gold standards previously mentioned. Results: A total of 266 patients between 16-83 years old who 144 (54.1 %) were female, 122 (45.9 %) were male were included in the study. 144 patients were found as culture positive, and 157 were H and RUT were positive together. 179 patients were found as positive with GenoType® HelicoDR and Seeplex ® H. pylori -ClaR- ACE Detection together. Sensitivity and specificity rates of studied five different methods were found as follows: C were 80.9 % and 84.4 %, H + RUT were 88.2 % and 75.4 %, GenoType® HelicoDR were 100 % and 71.3 %, and Seeplex ® H. pylori -ClaR- ACE Detection were, 100 % and 71.3 %. A strong correlation was found between C and H+RUT, C and GenoType® HelicoDR, and C and Seeplex ® H. pylori -ClaR- ACE Detection (r:0.644 and p:0.000, r:0.757 and p:0.000, r:0.757 and p:0.000, respectively). Of all the isolated 144 H. pylori strains 24 (16.6 %) were detected as resistant to claritromycine, and 18 (12.5 %) were levofloxacin. Genotypic claritromycine resistance was detected only in 15 cases with GenoType® HelicoDR, and 6 cases with Seeplex ® H. pylori -ClaR- ACE Detection. Conclusion: In our study, it was concluded that; GenoType® HelicoDR and Seeplex ® H. pylori -ClaR- ACE Detection was found as the most sensitive diagnostic methods when comparing all the investigated other ones (C, H, and RUT).

Keywords: Helicobacter pylori, GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection, antimicrobial resistance

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Authors: Aline Giardin

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Introduction: This review addresses the relationship between physical education and mental health and its main objective is to discuss the meanings that circulate in Psychiatric Hospitalization Units and Psychosocial Care Centers (CAPS) about the presence of physical education teachers and the practices developed by Them within these services. Material and methods: It is based on the theoretical contribution of the Psychiatric Reform and is methodologically inspired by the Bibliographic Review. Objectives: The objective of this review was to identify the main scientific evidence on the effects of physical activity on the main psychological aspects associated with mental health during the hospitalization process. Results: It was observed that physical activity has beneficial effects in the psychological, social and cognitive aspects, being thus a fundamental aspect of the lifestyle in promoting a healthy and successful treatment. In studies evaluating the effects of physical activity on mental health, the most frequently evaluated outcomes include anxiety, depression, and health-related quality of life (eg, self-esteem and self-efficacy). Evidence from epistemological studies indicates that the level of physical activity is positively associated with good mental health, when mental health is defined as good mood, general well-being and decreased symptoms. Conclusion: It is necessary to intervene and a greater interest of the professionals of physical education in the treatment with the people with mental disorders so that the negative symptoms are modified, through the aid of the physical activity, by better quality of life, physical condition, nutritional state and A healthy emotional appearance.

Keywords: health mental, physical activity, benefits, treatment

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Authors: Robert Dutkiewicz, Monika Szpringer, Mariola Wojciechowska

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Physical activity is one of the factors greatly influencing healthy lifestyle. The recent research into physical activity of the Polish society reveals that contribution of physical culture to healthy lifestyle is insufficient. Students, regardless of age, spend most of free-time in front of a TV or computer. The research attempted to identify the level of physical activity and healthy lifestyle among students of medical sciences and other students doing their teaching degrees. The findings of physical activity research conducted in 2014, which covered 364 students of medical sciences and future teachers from the University of Jan Kochanowski in Kielce were analysed. The research involved the method of diagnostic survey based on a questionnaire. It attempted to establish to what extent such factors as the field of studies, the place of residence and BMI affect students’ physical activity. Empirical material was analysed by means of SPSS/PC, the leading statistical software. The field of study significantly influences physical activity of the respondents. The students of physiotherapy and public health tend to be more physically active than students of biology and geography: 46.8% students of geography and 51.8 % biology students seldom take up physical activity. Obesity and overweight are currently serious problems of university students: 6.6% of them are obese and 19% overweight. It is alarming that these students are not willing to find ways to be more physically active. Most of the obese and overweight respondents study biology or geography and live in a rural area. Unequal chances in terms of youth physical culture are determined by the differences between rural and urban environments. Young people living in rural areas are less physically active, particularly in terms of the frequency and the amount of time devoted to physical activity. This is caused by poor infrastructure to perform physical activity, the lack of or limited number of sports clubs and centres. It is thought-provoking that most of the students claim that they do not have enough time to do sports or other activities, but at the same time they spend a lot of time at a computer or watching TV.

Keywords: BMI, healthy lifestyle, sports activity, students

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Authors: Issa Qabaja, Fadi Thabtah

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Email phishing classification is one of the vital problems in the online security research domain that have attracted several scholars due to its impact on the users payments performed daily online. One aspect to reach a good performance by the detection algorithms in the email phishing problem is to identify the minimal set of features that significantly have an impact on raising the phishing detection rate. This paper investigate three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones in phishing detection. We measure the degree of influentially by applying four data mining algorithms on a large set of features. We compare the accuracy of these algorithms on the complete features set before feature selection has been applied and after feature selection has been applied. After conducting experiments, the results show 12 common significant features have been chosen among the considered features by the feature selection methods. Further, the average detection accuracy derived by the data mining algorithms on the reduced 12-features set was very slight affected when compared with the one derived from the 47-features set.

Keywords: data mining, email classification, phishing, online security

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Authors: Kashif Jabbar

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Milk adulteration is a global concern, and the current study was plan to synthesize Selenium nanoparticles by green method using plant extract of garlic, Allium Sativum, and to characterize Selenium nanoparticles through different analytical techniques and to apply Selenium nanoparticles as fast and easy technique for the detection of salicylic acid in milk. The highly selective, sensitive, and quick interference green synthesis-based sensing of possible milk adulterants i.e., salicylic acid, has been reported here. Salicylic acid interacts with nanoparticles through strong bonding interactions, hence resulting in an interruption within the formation of selenium nanoparticles which is confirmed by UV-VIS spectroscopy, scanning electron microscopy, and x-ray diffraction. This interaction in the synthesis of nanoparticles resulted in transmittance wavelength that decrease with the increasing amount of salicylic acid, showing strong binding of selenium nanoparticles with adulterant, thereby permitting in-situ fast detection of salicylic acid from milk having a limit of detection at 10-3 mol and linear coefficient correlation of 0.9907. Conclusively, it can be draw that colloidal selenium could be synthesize successfully by garlic extract in order to serve as a probe for fast and cheap testing of milk adulteration.

Keywords: adulteration, green synthesis, selenium nanoparticles, salicylic acid, aggregation

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Authors: Kheira A. Bekhechi, Belkacem Khiat

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The goal of our study is to know if there is an influence of the regular sporting physical-activity on the school performances of Algerian children. An experimental group composed of 55 sporting pupils and a reference group of 55 non-sporting pupils between 6 to10 years old (boys and girls) of the primary schools in Oran (Algeria) were followed during 15 months (Five terms). The socio-demographic data was collected from a survey given to pupils of the two groups and the school results from the administration at the end of each term. The sporting pupils have a general school average significantly higher than those of the non- sporting pupils (p < 0.05). The practice of physical activity and regular sporting by the children would deserve to be largely encouraged based on the beneficial effects not only on health but also on the academic performance. The parents, teachers and health professionals should be strongly aware.

Keywords: cognitive capacities, physical activity and sport, school children, school performances

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Authors: Ansah Iris Baffour, Dong-Ho Kim, Sung-Gyu Park

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The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus and is gradually shifting to an endemic phase which implies the outbreak is far from over and will be difficult to eradicate. Global cooperation has led to unified precautions that aim to suppress epidemiological spread (e.g., through travel restrictions) and reach herd immunity (through vaccinations); however, the primary strategy to restrain the spread of the virus in mass populations relies on screening protocols that enable rapid on-site diagnosis of infections. Herein, we employed surface enhanced Raman spectroscopy (SERS) for the rapid detection of SARS-CoV-2 lysate on an Au-modified Au nanodimple(AuND)electrode. Through in situone-pot Au electrodeposition on the AuND electrode, Au-lysate nanocomposites were synthesized, generating3D internal hotspots for large SERS signal enhancements within 30 s of the deposition. The capture of lysate into newly generated plasmonic nanogaps within the nanocomposite structures enhanced metal-spike protein contact in 3D spaces and served as hotspots for sensitive detection. The limit of detection of SARS-CoV-2 lysate was 5 x 10-2 PFU/mL. Interestingly, ultrasensitive detection of the lysates of influenza A/H1N1 and respiratory syncytial virus (RSV) was possible, but the method showed ultimate selectivity for SARS-CoV-2 in lysate solution mixtures. We investigated the practical application of the approach for rapid on-site diagnosis by detecting SARS-CoV-2 lysate spiked in normal human saliva at ultralow concentrations. The results presented demonstrate the reliability and sensitivity of the assay for rapid diagnosis of COVID-19.

Keywords: label-free detection, nanocomposites, SARS-CoV-2, surface-enhanced raman spectroscopy

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Authors: Mannan Malik Muhammad Naeem, Jeong Myung Yung

Abstract:

Functional near infrared spectroscopy (fNIRS) has a favorable position in non-invasive brain imaging techniques. The concentration change of oxygenated hemoglobin and de-oxygenated hemoglobin during particular cognitive activity is the basis for this neuro-imaging modality. Two wavelengths of near-infrared light can be used with modified Beer-Lambert law to explain the indirect status of neuronal activity inside brain. The temporal resolution of fNIRS is very good for real-time brain computer-interface applications. The portability, low cost and an acceptable temporal resolution of fNIRS put it on a better position in neuro-imaging modalities. In this study, an optimization model for impulse response function has been used to estimate/predict initial dip using fNIRS data. In addition, the activity strength parameter related to motor based cognitive task has been analyzed. We found an initial dip that remains around 200-300 millisecond and better localize neural activity.

Keywords: fNIRS, brain-computer interface, optimization algorithm, adaptive signal processing

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Authors: Jorge Maestre Vidal, Ana Lucila Sandoval Orozco, Luis Javier García Villalba

Abstract:

With the increase in popularity of mobile devices, new and varied forms of malware have emerged. Consequently, the organizations for cyberdefense have echoed the need to deploy more effective defensive schemes adapted to the challenges posed by these recent monitoring environments. In order to contribute to their development, this paper presents a malware detection strategy for mobile devices based on sequence alignment algorithms. Unlike the previous proposals, only the system calls performed during the startup of applications are studied. In this way, it is possible to efficiently study in depth, the sequences of system calls executed by the applications just downloaded from app stores, and initialize them in a secure and isolated environment. As demonstrated in the performed experimentation, most of the analyzed malicious activities were successfully identified in their boot processes.

Keywords: android, information security, intrusion detection systems, malware, mobile devices

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Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

Abstract:

Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: moving object detection, histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine

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Authors: Aika Umemuro, Mitsuru Sato, Mizuki Narita, Saya Hori, Saya Sakurai, Tomomi Nakayama, Ayano Nakazawa, Toshihiro Ogura

Abstract:

Mammography requires the detection of very small calcifications, and physicians search for microcalcifications by magnifying the images as they read them. The mouse is necessary to zoom in on the images, but this can be tiring and distracting when many images are read in a single day. Therefore, an image magnification system combining an eye-detector and a simple electroencephalograph (EEG) scanner was devised, and its operability was evaluated. Two experiments were conducted in this study: the measurement of eye-detection error using an eye-detector and the measurement of the time required for image magnification using a simple EEG scanner. Eye-detector validation showed that the mean distance of eye-detection error ranged from 0.64 cm to 2.17 cm, with an overall mean of 1.24 ± 0.81 cm for the observers. The results showed that the eye detection error was small enough for the magnified area of the mammographic image. The average time required for point magnification in the verification of the simple EEG scanner ranged from 5.85 to 16.73 seconds, and individual differences were observed. The reason for this may be that the size of the simple EEG scanner used was not adjustable, so it did not fit well for some subjects. The use of a simple EEG scanner with size adjustment would solve this problem. Therefore, the image magnification system using the eye-detector and the simple EEG scanner is useful.

Keywords: EEG scanner, eye-detector, mammography, observers

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Authors: Hao Mi, Ming Yang, Tian-yue Yang

Abstract:

Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: remote monitoring, non-destructive testing, embedded Linux system, image processing

Procedia PDF Downloads 225

Authors: Dina Helmy El-Ghonemy, Thanaa Hamed Ali

Abstract:

The aim of this study was to purify and characterize a keratinolytic enzyme produced by Aspergillus sp. DHE7 cultured in basal medium containing chicken feather as substrate. The enzyme was purified through ammonium sulfate saturation of 60%, followed by gel filtration chromatography in Sephadex G-100, with a 16.4-purification fold and recovery yield of 52.2%. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis revealed that the purified enzyme is a monomeric enzyme with an apparent molecular mass of 30 kDa — the purified keratinase of Aspergillus sp. DHE7 exhibited activity in a broad range of pH (7- 9) and temperature (40℃-60℃) profiles with an optimal activity at pH eight and 50℃. The keratinolytic activity was inhibited by protease inhibitors such as phenylmethylsulfonyl fluoride and ethylenediaminetetraacetate, while no reduction of activity was detected by the addition of dimethyl sulfoxide (DMSO). Bivalent cations, Ca²⁺ and Mn²⁺, were able to greatly enhance the activity of keratinase by 125.7% and 194.8%, respectively, when used at one mM final concentration. On the other hand, Cu²⁺ and Hg²⁺ inhibited the enzyme activity, which might be indicative of essential vicinal sulfhydryl groups of the enzyme for productive catalysis. Furthermore, the purified keratinase showed significant stability and compatibility against the tested commercial detergents at 37ºC. Therefore, these results suggested that the purified keratinase from Aspergillus sp. DHE7 may have potential use in the detergent industry and should be of interest in the processing of poultry feather waste.

Keywords: Aspergillus sp. DHE7, biochemical characterization, keratinase, purification, waste management

Procedia PDF Downloads 126

Authors: K. Biswas, U. H. Armin, S. M. J. Prodhan, J. A. Prithul, S. Sarker, F. Afrin

Abstract:

Alzheimer’s disease (AD) (a progressive neurodegenerative disorder) is mostly predominant cause of dementia in the elderly. Prolonging the function of acetylcholine by inhibiting both acetylcholinesterase and butyrylcholinesterase is most effective treatment therapy of AD. Traditionally Pterocarpus santalinus L. is widely known for its medicinal use. In this study, in vitro acetylcholinesterase inhibitory activity was investigated and methanolic extract of the plant showed significant activity. To confirm this activity (in vivo), learning and memory enhancing effects were tested in mice. For the test, memory impairment was induced by scopolamine (cholinergic muscarinic receptor antagonist). Anti-amnesic effect of the extract was investigated by the passive avoidance task in mice. The study also includes brain acetylcholinesterase activity. Results proved that scopolamine induced cognitive dysfunction was significantly decreased by administration of the extract solution, in the passive avoidance task and inhibited brain acetylcholinesterase activity. These results suggest that bark extract of Pterocarpus santalinus can be better option for further studies on AD via their acetylcholinesterase inhibitory actions.

Keywords: Pterocarpus santalinus, cholinesterase inhibitor, passive avoidance, Alzheimer’s disease

Procedia PDF Downloads 249

Authors: Chin-Yuan Hsu, Yu-Lung Chuang

Abstract:

The expression, concentration, and activity of mitochondrial energy-utilized molecules and cellular energy-regulated molecules decreased with age in the trophocytes and oenocytes of honeybees (Apis mellifera), but those of cellular energy-metabolized molecules is unknown. In this study, the expression, concentration, and activity of cellular energy-metabolized molecules were assayed in the trophocytes and fat cells of young and old worker bees by using the techniques of cell and biochemistry. The results showed that (i) the •-hydroxylacyl-coenzyme A dehydrogenase (HOAD) activity/citrate synthase (CS) activity ratio, non-esterified fatty acids concentrations, the expression of eukaryotic initiation factor 4E, and the expression of phosphorylated eIF4E binding protein 1 decreased with age; (ii) fat and glycogen accumulation increased with age; and (iii) the pyruvate dehydrogenase (PDH) activity/citrate synthase (CS) activity ratio was not correlated with age. These finding indicated that •-oxidation (HOAD/CS) and protein synthsis decreased with age. Glycolysis (PDH/CS) was unchanged with age. The most likely reason is that sugars are the vital food of worker bees. Taken together these data reveal that young workers have higher cellular energy metabolism than old workers and that aging results in a decline in the cellular energy metabolism in worker honeybees.

Keywords: aging, energy, honeybee, metabolism

Procedia PDF Downloads 470