Search results for: abnormal activity detection

Authors: Syrus Zimaze, George Nsimbe, Valley Mugwanya, Matiya Lule, Edgar Watson, Patrick Gwayambadde

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Physical inactivity is a growing global epidemic, also recognised as a major public health challenge. Globally, there are alarming rates of children reported with cardiovascular disease and obesity with limited interventions. In Sub Saharan Africa, there is limited information about involvement in physical activity especially among children aged 6 to 18 years. The aim of this study was to explore factors associated with involvement in physical activity among children in Uganda. Methods: We included all parents with children aged 6 to 18 years training with Excel Soccer Academy between January 2017 and June 2018. Physical activity definition was time spent participating in routine soccer training at the academy for more than 30 days. Each child's attendance was recorded, and parents provided demographic and social economic data. Data on predictors of physical activity involvement were collected using a standardized questionnaire. Descriptive statistics and frequency were used. Binary logistic regression was used at the multi variable level adjusting for education, residence, transport means and access to information technology. Results: Overall 356 parents were interviewed; Boys 318 (89.3%) engaged more in physical activity than girls. The median age for children was 13 years (IQR:6-18) and 42 years (IQR:37-49) among parents. The median time spent at the Excel soccer academy was 13.4 months (IQR: 4.6-35.7) Majority of the children attended formal education, p < 0.001). Factors associated with involvement in physical activity included: owning a permanent house compared to a rented house (odds ratio [OR] :2.84: 95% CI: 2.09-3.86, p < 0.0001), owning a car compared to using public transport (OR: 5.64 CI: 4.80-6.63, p < 0.0001), a parent having received formal education compared to non-formal education (OR: 2.93 CI: 2.47-3.46, p < 0.0001) and daily access to information technology (OR:0.40 CI:0.25-0.66, p < 0.001). Parent’s age and gender were not associated to involvement in physical activity. Conclusions: Socioeconomic factors were positively associated with involvement in physical activity with boys participating more than girls in soccer activities. More interventions are required geared towards increasing girl’s participation in physical activity and those targeting children from less privilege homes.

Keywords: physical activity, Sub-Saharan Africa, social economic factors, children

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Authors: Sonia Perez-Gamboa, Qingquan Sun, Yan Zhang

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Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption.

Keywords: deep learning, LSTM, CNN, human activity recognition, inertial sensor

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Authors: Sonia Lippke, Eric Rost, Volker Cihlar

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Objective: To examine the interrelation of physical activity with work-related variables in older individuals to determine pathways to promote the maintenance of an aging workforce’s ability and motivation to work. Design/methodology/approach: An interview-study was conducted with N=5,002 community-dwelling people aged 55 to 70 years (for T1). N=2,501 (50%) were interviewed 3 years later again (T2). Correlation-, Chi²-, MANOVA and moderated mediation analyses were performed. Findings: The less people worked, the more physically active they were. Working was only related to calendar age but not to subjective age. Men and women only differed in working hours and an interaction of gender and pension regarding working hours and subjective health revealed: Controlled for calendar age, the amount of worked hours while receiving pension was about the same in men and women, however, men worked significantly more hours if they did not receive pension. The relationship between physical activity and worked hours was mediated by life investment and subjective health in women, and by subjective health in men. Practical implications: Developing good health through performing physical activity should be done as part of work-place health promotion or by work organization and HR management to enable, and motivate older individuals to work even when receiving pension. Thus, such initiates should not only offered for younger and middle aged employees. Physical activity and company-facilitated sports activities can be an integral part in this. Originality/value: This is the first study testing these mechanisms in this age group, indicating the importance of not only understanding physical activity as a time challenge to work but also the potential to protect workability and to work aside from receiving pension.

Keywords: life investment, moderated mediation, physical activity, older workers, subjective health

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Authors: Azza T. Tahera, Eman M. Ahmeda, Nadia A. Khalila, Yassin M. Nissanb

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New 3-(pyridin-4-yl)-[1,2,4] triazolo [4,3-b] pyridazine derivatives 2a-i, 4a,b and 6a,b were designed, synthesized and evaluated as cytotoxic agents. All compounds were investigated for their in vitro cytotoxicity at a single dose 10-5M concentration towards 60 cancer cell lines according to USA NCI protocol. The preliminary screening results showed that the majority of tested compounds exhibited remarkable activity against SR (leukemia) cell panel. Molecular docking for all synthesized compounds was performed on the active site of c-Met kinase. The most active compounds, 2f and 4a were further evaluated at a seven dose level screening and their IC50 as a c-Met kinase inhibitors were determined in vitro.

Keywords: triazolopyridazines, pyridazines, cytotoxic activity, cell panel

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Authors: Indranil Chakraborty, Kalyan Mandal

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Three new α-hydroxy carboxylate group functionalized MnFe₂O₄ nanoparticles (NPs) have been developed to explore the microscopic origin of ligand modified fluorescence and magnetic properties of nearly monodispersed MnFe₂O₄ NPs. The surface functionalization has been carried out with three small organic ligands (tartrate, malate, and citrate) having different number of α-hydroxy carboxylate functional group along with steric effect. Detailed study unveils that α-hydroxy carboxylate moiety of the ligands plays key role to generate intrinsic fluorescence in functionalized MnFe₂O₄ NPs through the activation of ligand to metal charge transfer transitions, associated with ligand-Mn²⁺/Fe³⁺ interactions along with d-d transition corresponding to d-orbital energy level splitting of Fe³⁺ ions on NP surface. Further, MnFe₂O₄ NPs show a maximum 140.88% increase in coercivity and 97.95% decrease in magnetization compared to its bare one upon functionalization. The ligands that induce smallest crystal field splitting of d-orbital energy level of transition metal ions are found to result in strongest ferromagnetic activation of the NPs. Finally, our developed tartrate functionalized MnFe₂O₄ (T-MnFe₂O₄) NPs have been utilized for studying DNA binding interaction and nuclease activity for stimulating their beneficial activities toward diverse biomedical applications. The spectroscopic measurements indicate that T-MnFe₂O₄ NPs bind calf thymus DNA by intercalative mode. The ability of T-MnFe₂O₄ NPs to induce DNA cleavage was studied by gel electrophoresis technique where the complex is found to promote the cleavage of pBR322 plasmid DNA from the super coiled form I to linear coiled form II and nicked coiled form III with good efficiency. This may be taken into account for designing new biomolecular detection agents and anti-cancer drug which can open up a new door toward diverse non-invasive biomedical applications.

Keywords: MnFe₂O₄ nanoparticle, α-hydroxy carboxylic acid, comparative fluorescence, magnetism study, DNA interaction, nuclease activity

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Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

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Authors: Othmane Bouferroum, Malika Boudraa

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In this study, a dual exponential model for CV formant transition is derived from locus theory of speech perception. Then, an algorithm for automatic vowel and consonant’s target formant frequency detection is developed and tested on real speech. The results show that vowels and consonants are detected through transitions rather than their small stable portions. Also, vowel reduction is clearly observed in our data. These results are confirmed by the observations made in perceptual experiments in the literature.

Keywords: acoustic invariance, coarticulation, formant transition, locus equation

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Authors: Ledi Neçaj

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Objectives: The target of this paper is (1) to explain objectively physical activity model throughout three chronic cardiopulmonary conditions, and (2) to study the connection among physical activity dimensions with disease severity, self-reported physical and emotional functioning, and exercise performance. Material and Methods: This is a cross-sectional study of patients in their domestic environment. Patients with cardiopulmonary diseases were: chronic obstructive pulmonary disease (COPD), (n-63), coronary heart failure (n=60), and patients with implantable cardioverter defibrillator (n=60). Main results measures: Seven ambulatory physical activity dimensions (total steps, percentage time active, percentage time ambulating at low, medium, and hard intensity, maximum cadence for 30 non-stop minutes, and peak performance) have been measured with an accelerometer. Results: Subjects with COPD had the lowest amount of ambulatory physical activity compared with topics with coronary heart failure and cardiac dysrhythmias (all 7 interest dimensions, P<.05); total step counts have been: 5319 as opposed to 7464 as opposed to 9570, respectively. Six-minute walk distance becomes correlated (r=.44-.65, P<.01) with all physical activity dimensions inside the COPD pattern, the most powerful correlations being with total steps and peak performance. In topics with cardiac impairment, maximal oxygen intake had the most effective small to slight correlations with five of the physical activity dimensions (r=.22-.40, P<.05). In contrast, correlations among 6-minute walk test distance and physical activity have been higher (r=.48-.61, P<.01) albeit in a smaller pattern of most effective patients with coronary heart failure. For all three samples, self-reported physical and mental health functioning, age, frame mass index, airflow obstruction, and ejection fraction had both exceptionally small and no significant correlations with physical activity. Conclusions: Findings from this study present a profitable benchmark of physical activity patterns in individuals with cardiopulmonary diseases for comparison with future studies. All seven dimensions of ambulatory physical activity have disfavor between subjects with COPD, heart failure, and cardiac dysrhythmias. Depending on the research or clinical goal, the use of one dimension, such as total steps, may be sufficient. Although physical activity had high correlations with performance on a six-minute walk test relative to other variables, accelerometers-based physical activity monitoring provides unique, important information about real-world behavior in patients with cardiopulmonary not already captured with existing measures.

Keywords: ambulatory physical activity, walking, monitoring, COPD, heart failure, implantable defibrillator, exercise performance

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Authors: Alexandra Von Guionneau, William Sloper, Charlotte Burford

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The benefits of exercise for the prevention and management of chronic disease are well established and the importance of primary care practitioners in promoting exercise is becoming increasingly recognized. However, those with severe manifestations of the chronic disease are managed in a secondary care setting. Secondary care practitioners, therefore, have a role to play in promoting physical activity. Methods: In order to assess secondary care doctors’ knowledge of the Chief Medical Officer’s guidelines for physical activity, a 12-question survey was administered to staff working in a district general hospital in South England during team and unit meetings. Questions related to knowledge of the current guidelines for both 19 - 64 year olds and older adults (65 years and above), barriers to exercise discussion or prescription and doctors’ own exercise habits. Responses were collected anonymously and analyzed using SPSS Version 24.0. Results: 96 responses were collected. Doctors taking part in the survey ranged from foundation years (26%) to consultants (40%). 17.7% of participants knew the guidelines for moderate intensity activity for 19 - 64 year olds. Only one participant knew all of the guidance for both 19 - 64 year olds and older adults. While 71.6% of doctors felt they were adequately informed about how to exercise, only 45.6% met the minimum recommended guidance for moderate intensity activity. Conclusion: More work is needed to promote the physical activity guidelines and exercise prescription to doctors working within a secondary care setting. In addition, doctors require more support to personally meet the recommended minimum level of physical activity.

Keywords: exercise is medicine, exercise prescription, physical activity guidelines, exercise habits

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Authors: Yushuai Wang, Feng Xu, Junbo Tan, Xueqian Wang, Bin Liang

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In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (RMPC) and set theoretic fault detection and isolation (FDI) is extended to linear parameter varying (LPV) systems. First, a group of set-valued observers are designed for passive fault detection (FD) and the observer gains are obtained through minimizing the size of invariant set of state estimation-error dynamics. Second, an input set for fault isolation (FI) is designed offline through set theory for actively isolating faults after FD. Third, an RMPC controller based on state estimation for LPV systems is designed to control the system in the presence of disturbance and measurement noise and tolerate faults. Besides, an FTC algorithm is proposed to maintain the plant operate in the corresponding mode when the fault occurs. Finally, a numerical example is used to show the effectiveness of the proposed results.

Keywords: fault detection, linear parameter varying, model predictive control, set theory

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Authors: Fatma Acheuk, Idir Bitam, Leila Bendifallah, Malika Ramdani, Fethia Barika

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Phytochemical study of the plant Artemisia judaica showed the presence of various groups of natural products: saponins, tannins, coumarins, flavonoids, carbohydrates, and reducer compounds. However, alkaloids are present as traces. The crude ethanol extract of the test plant presented significant insecticidal activity on mosquito larvae in stage I, II and III. The LD50 highlighted the excellent insecticidal effect of the tested extract. Similarly, the LT50 are achieved early with high doses. The results obtained are encouraging and suggest the possibility of using the secondary metabolites of this plant such as bio-insecticide.

Keywords: Atamisia judaica, crud extract, mosquito, insecticidal activity

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Authors: Farheen Tabassum, Shoab Ahmed Khan

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The brutality of attacks on networks and decisive infrastructures are on the climb over recent years and appears to continue to do so. Distributed Denial of service attack is the most prevalent and easy attack on the availability of a service due to the easy availability of large botnet computers at cheap price and the general lack of protection against these attacks. Application layer DDoS attack is DDoS attack that is targeted on wed server, application server or database server. These types of attacks are much more sophisticated and challenging as they get around most conventional network security devices because attack traffic often impersonate normal traffic and cannot be recognized by network layer anomalies. Conventional techniques of single-hosted security systems are becoming gradually less effective in the face of such complicated and synchronized multi-front attacks. In order to protect from such attacks and intrusion, corporation among all network devices is essential. To overcome this issue, a collaborative intrusion detection system (CIDS) is proposed in which multiple network devices share valuable information to identify attacks, as a single device might not be capable to sense any malevolent action on its own. So it helps us to take decision after analyzing the information collected from different sources. This novel attack detection technique helps to detect seemingly benign packets that target the availability of the critical infrastructure, and the proposed solution methodology shall enable the incident response teams to detect and react to DDoS attacks at the earliest stage to ensure that the uptime of the service remain unaffected. Experimental evaluation shows that the proposed collaborative detection approach is much more effective and efficient than the previous approaches.

Keywords: Distributed Denial-of-Service (DDoS), Collaborative Intrusion Detection System (CIDS), Slowloris, OSSIM (Open Source Security Information Management tool), OSSEC HIDS

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Authors: M. Ahmadi, M. Kafil, H. Ebrahimi

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Nowadays, induction motors have a significant role in industries. Condition monitoring (CM) of this equipment has gained a remarkable importance during recent years due to huge production losses, substantial imposed costs and increases in vulnerability, risk, and uncertainty levels. Motor current signature analysis (MCSA) is one of the most important techniques in CM. This method can be used for rotor broken bars detection. Signal processing methods such as Fast Fourier transformation (FFT), Wavelet transformation and Empirical Mode Decomposition (EMD) are used for analyzing MCSA output data. In this study, these signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors. Based on wavelet transformation method, an index for fault detection, CF, is introduced which is the variation of maximum to the mean of wavelet transformation coefficients. We find that, in the broken bar condition, the amount of CF factor is greater than the healthy condition. Based on EMD method, the energy of intrinsic mode functions (IMF) is calculated and finds that when motor bars become broken the energy of IMFs increases.

Keywords: broken bar, condition monitoring, diagnostics, empirical mode decomposition, fourier transform, wavelet transform

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Authors: Lineda Rouissat-Dahane, Abdelkrim Cheriti, Abbderazak Marouf, Reddy Kandappa H., Govender Patrick

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Natural phytochemicals extracted from folk herbal have drawn much attention in complementary and alternative medicine, and the plant kingdom is considered for developing new molluscicide. The aqueous and acetone extract of the aerial parts of some Asteraceae (Anvillea radiata, Bubonium graveolens, Launaea arborescens, Launaea nudicaulis and Warionia saharae) were investigated for its molluscicidal activity against Lymnaea acuminata showed significant molluscicidal activity with a median lethal concentration (LC50) of aqueous extract (8,178mg/ml) and organic extract 0.002μg/mL, which was indicated higher potency than the positive control, (LC50=100mg /mL for aqueous extract ; LC50=11.6 μg/mL for organic extract). Among the extract and their fractions, those of aerial parts of Launaea nudicaulis and Warionia saharae were found to exhibit significant molluscicidal activities. Among different solvent fractions of the acetone extract of Warionia saharae, the dichloromethane (DCM) soluble fraction showed the most potent molluscicidal activity against Lymnaea acuminata. Plants in species Anvillea radiata, Bubonium graveolens, Launaea arborescens, Launaea nudicaulis, and Warionia saharae produce a great variety of Flavonoids, Glucoside flavonoids, and Saponins that confer natural resistance against several pests. Most extracts were found to exhibit significant molluscicidal activity.

Keywords: acetone extract, aqueous extract, Asteraceae, molluscicidal activity, Lymnaea acuminata

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Authors: Assia Amri, Naima Layachi, Ali Ladjama

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The characterization of some Algerian honey was carried out on the basis of their physico-chemical properties: moisture,hydroxy methyl furfural, diastase activity, pH,free, total and lactonic acidity, electrical conductivity, minerals and proline content. Studied samples are found to be low in moisture and therefore safe from fermentation, low in HMF level and high in diastase activity. Additionally the diastase activity and the HMF content are widely recognized parameters indicating the freshness of honey. Phenolic compounds present in honey are classified into two groups - simple phenols and polyphenols. The simple phenols in honey are various phenol acids, but polyphenols are various flavonoids and flavonides. The aim of our work was to determine antioxidant properties of various Algerian honey samples–the total phenol content, total flavonoids content, as well as honey anti radical activity.The quality of honey samples differs on account of various factors such as season, packaging and processing conditions, floral source, geographical origin and storage period. It is important that precautions should be taken to ensure standardization and rationalization of beekeeping techniques, manufacturing procedures and storing processes to improve honey quality.

Keywords: honey, physico-chemical characterization, phenolic coumpound, HMF, diastase activity

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Authors: Hyojin Lim, Cuong Nguyen Khac, Yeongyu Choi, Ho-Youl Jung

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In this paper, we propose a simple moving object detection, which is based on motion vectors obtained from pyramidal Lucas-Kanade optical flow. The proposed method detects moving objects such as pedestrians, the other vehicles and some obstacles at the front-side of the host vehicle, and it can provide the warning to the driver. Motion vectors are obtained by using pyramidal Lucas-Kanade optical flow, and some outliers are eliminated by comparing the amplitude of each vector with the pre-defined threshold value. The background model is obtained by calculating the mean and the variance of the amplitude of recent motion vectors in the rectangular shaped local region called the cell. The model is applied as the reference to classify motion vectors of moving objects and those of background. Motion vectors are clustered to rectangular regions by using the unsupervised clustering K-means algorithm. Labeling method is applied to label groups which is close to each other, using by distance between each center points of rectangular. Through the simulations tested on four kinds of scenarios such as approaching motorbike, vehicle, and pedestrians to host vehicle, we prove that the proposed is simple but efficient for moving object detection in parking lots.

Keywords: moving object detection, dynamic scene, optical flow, pyramidal optical flow

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Authors: Debdut Roy, Vidyasagar Guggilla

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In this research, we highlight our exploratory work on modified zeolite based catalysts for catalytic cracking of hydrocarbons for production of light olefin i.e. ethylene and propylene. The work is focused on understanding the catalyst structure and activity correlation. Catalysts are characterized by surface area and pore size distribution analysis, inductively coupled plasma optical emission spectrometry (ICP-OES), Temperature Programmed Desorption (TPD) of ammonia, pyridine Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Thermo-gravimetric Analysis (TGA) and correlated with the catalytic activity. It is observed that the yield of lighter olefins increases with increase of Bronsted acid strength.

Keywords: catalytic cracking, zeolite, propylene, structure-activity correlation

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The problem of Dyslexia and Dysgraphia, two learning disabilities that affect reading and writing abilities, respectively, is a major concern for the educational system. Due to the complexity and uniqueness of the Sinhala language, these conditions are especially difficult for children who speak it. The traditional risk detection methods for Dyslexia and Dysgraphia frequently rely on subjective assessments, making it difficult to cover a wide range of risk detection and time-consuming. As a result, diagnoses may be delayed and opportunities for early intervention may be lost. The project was approached by developing a hybrid model that utilized various deep learning techniques for detecting risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16 and YOLOv8 were integrated to detect the handwriting issues, and their outputs were fed into an MLP model along with several other input data. The hyperparameters of the MLP model were fine-tuned using Grid Search CV, which allowed for the optimal values to be identified for the model. This approach proved to be effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention of these conditions. The Resnet50 model achieved an accuracy of 0.9804 on the training data and 0.9653 on the validation data. The VGG16 model achieved an accuracy of 0.9991 on the training data and 0.9891 on the validation data. The MLP model achieved an impressive training accuracy of 0.99918 and a testing accuracy of 0.99223, with a loss of 0.01371. These results demonstrate that the proposed hybrid model achieved a high level of accuracy in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, Dyslexia, Dysgraphia, deep learning, learning disabilities, data science

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Authors: Nimisha Jadon, Rajeev Jain, Swati Sharma

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A chemically modified carbon paste sensor has been developed for the simultaneous determination of amlodipine (AML) and nebivolol (NBV). Carbon paste electrode (CPE) was fabricated by the addition of Gr/PANI-CeO2. Gr/PANI-CeO2/CPE has achieved excellent electrocatalytic activity and sensitivity. AML and NBV exhibited oxidation peaks at 0.70 and 0.90 V respectively on Gr/ PANI-CeO2/CPE. The linearity range of AML and NBV was 0.1 to 1.6 μgmL-1 in BR buffer (pH 8.0). The Limit of detection (LOD) was 20.0 ngmL-1 for AML and 30.0 ngmL-1 for NBV and limit of quantification (LOQ) was 80.0 ngmL-1 for AML and 100 ngmL-1 for NBV respectively. These analyses were also determined in pharmaceutical formulation and human serum and good recovery was obtained for the developed method.

Keywords: amlodipine, nebivolol, square wave voltammetry, carbon paste electrode, simultaneous quantification

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Authors: Fatimah Abd Elghani, Raymonde Szargel, Vered Shani, Hazem Safory, Haya Hamza, Mor Savyon, Ruth Rott, Rina Bandopadhyay, Simone Engelender

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Background: PINK1 is a mitochondrial kinase mutated in some familial cases of Parkinson’s disease. Down regulation of PINK1 results in abnormal mitochondrial morphology and altered membrane potential. Although PINK1 has a predicted mitochondrial import sequence, it’s cellular, and submitochondrial localization remains unclear, in part because it is rapidly degraded. In this work, we investigated the mechanisms involved in PINK1 degradation and how this may affect PINK1 stability and function, with implications for mitochondrial function in PD. In addition, pharmacological inhibition of proteasome activity was shown to lead to PINK1 accumulation, indicating that PINK1 degradation depends on the ubiquitin-proteasome system (UPS). Methods: Using co-immunoprecipitation assays, we identified E3 ubiquitin ligase SIAH1 as a PINK1-interacting protein in HEK293 cells as well as on rat brain tissues. In addition, we determined the effect of SIAH 1, SIAH2 and Parkin on PINK1 steady-state levels by Western blot analysis, and checked their possibility to ubiquitinate and mediate PINK1 degradation through the proteasome carried out in vivo ubiquitination experiments. Results: We have obtained results showing that SIAH-1 interacts with and ubiquitinates PINK1. The ubiquitination promoted by SIAH-1 leads to the proteasomal degradation of PINK1. We confirmed these findings by knocking down SIAH-1 and observing important accumulation of PINK1 in cells. Besides, we found that SIAH-1 decreases PINK1 steady-state levels but not the E3 ligase Parkin. We also investigated the interaction of SIAH-1 with PINK1 disease mutants and its ability to promote their ubiquitination and degradation. Although, no clear difference in the ability of SIAH-1 to promote the degradation of PINK1 disease mutants was observed. It is possible that dysfunction of proteasomal activity in the disease may lead to the accumulation and aggregation of ubiquitinated PINK1 in patients with PINK1 mutations, with possible implications to the pathogenesis of PD. Conclusions: Here, we demonstrated that SIAH-1 ubiquitinates and promotes the degradation of PINK1. In addition, SIAH-1 represents now a target that may help the improvement of mitophagy in PD. Further investigations needed to understand how mitophagy is regulated by PINK1-SIAH-1 axis to provide targets for future therapeutics.

Keywords: PD, Parkinson's disease, PINK1, PTEN-induced kinase1, SIAH, seven in absentia homolog, SN, substantia nigra

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Authors: Zouhair Haddi, Stephane Delliaux, Jean-Francois Pons, Ismail Kechaf, Jean-Claude De Haro, Mustapha Ouladsine

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Atrial fibrillation (AF) has been considered as the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Nowadays, telemedical approaches targeting cardiac outpatients situate AF among the most challenged medical issues. The automatic, early, and fast AF detection is still a major concern for the healthcare professional. Several algorithms based on univariate analysis have been developed to detect atrial fibrillation. However, the published results do not show satisfactory classification accuracy. This work was aimed at resolving this shortcoming by proposing multivariate data analysis methods for automatic AF detection. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR intervals window and then four specific features were calculated. Two pattern recognition methods, i.e., Principal Component Analysis (PCA) and Learning Vector Quantization (LVQ) neural network were used to develop classification models. PCA, as a feature reduction method, was employed to find important features to discriminate between AF and Normal Sinus Rhythm. Despite its very simple structure, the results show that the LVQ model performs better on the analyzed databases than do existing algorithms, with high sensitivity and specificity (99.19% and 99.39%, respectively). The proposed AF detection holds several interesting properties, and can be implemented with just a few arithmetical operations which make it a suitable choice for telecare applications.

Keywords: atrial fibrillation, multivariate data analysis, automatic detection, telemedicine

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Authors: James Kaweesa

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The Internet of Things (IoT) has become a critical component of modern technology, enabling the connection of numerous devices to the internet. The interconnected nature of IoT devices, along with their heterogeneous and resource-constrained nature, makes them vulnerable to various types of attacks, such as malware, denial-of-service attacks, and network scanning. Intrusion Detection Systems (IDSs) are a key mechanism for protecting IoT networks and from attacks by identifying and alerting administrators to suspicious activities. In this review, the paper will discuss the different types of IDSs available for IoT systems and evaluate their effectiveness in detecting and preventing attacks. Also, examine the various evaluation methods used to assess the performance of IDSs and the challenges associated with evaluating them in IoT environments. The review will highlight the need for effective and efficient IDSs that can cope with the unique characteristics of IoT networks, including their heterogeneity, dynamic topology, and resource constraints. The paper will conclude by indicating where further research is needed to develop IDSs that can address these challenges and effectively protect IoT systems from cyber threats.

Keywords: cyber-threats, iot, intrusion detection system, networks

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Authors: Keyvan Molanorouzi, Selina Khoo, Tony Morris

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In recent years, there has been a decline in physical activity among adults. Motivation has been shown to be a crucial factor in maintaining physical activity. The purpose of this study was to whether PA motives measured by the Physical Activity and Leisure Motivation Scale PALMS predicted actual amount of PA at a later time to provide evidence for the construct validity of the PALMS. A quantitative, cross-sectional descriptive research design was employed. The Demographic Form, PALMS, and International Physical Activity Questionnaire Short form (IPAQ-S) questionnaires were used to assess motives and amount for physical activity in adults on two occasions. A sample of 640 (489 male, 151 female) undergraduate students aged 18 to 25 years (mean ±SD; 22.30±8.13 years) took part in the study. Male participants were divided into three types of activities, namely exercise, racquet sport, and team sports and female participants only took part in one type of activity, namely team sports. After 14 weeks, all 640 undergraduate students who had filled in the initial questionnaire (Occasion 1) received the questionnaire via email (Occasion 2). Of the 640 students, 493 (77%; 378 males, 115 females) emailed back the completed questionnaire. The results showed that not only were pertinent sub-scales of PALMS positively related to amount of physical activity, but separate regression analyses showed the positive predictive effect of PALMS motives for amount of physical activity for each type of physical activity among participants. This study supported the construct validity of the PALMS by showing that the motives measured by PALMS did predict amount of PA. This information can be obtained to match people with specific sport or activity which in turn could potentially promote longer adherence to the specific activity.Methods: A quantitative, cross-sectional descriptive research design was employed. The Demographic Form, PALMS, and International Physical Activity Questionnaire Short form (IPAQ-S) questionnaires were used to assess motives and amount for physical activity in adults on two occasions. A sample of 640 (489 male, 151 female) undergraduate students aged 18 to 25 years (mean ±SD; 22.30±8.13 years) took part in the study. Male participants were divided into three types of activities, namely exercise, racquet sport, and team sports and female participants only took part in one type of activity, namely team sports. After 14 weeks, all 640 undergraduate students who had filled in the initial questionnaire (Occasion 1) received the questionnaire via email (Occasion 2). Of the 640 students, 493 (77%; 378 males, 115 females) emailed back the completed questionnaire. Results: The results showed that not only were pertinent sub-scales of PALMS positively related to amount of physical activity, but separate regression analyses showed the positive predictive effect of PALMS motives for amount of physical activity for each type of physical activity among participants. This study supported the construct validity of the PALMS by showing that the motives measured by PALMS did predict amount of PA. Conclusion: This information can be obtained to match people with specific sport or activity which in turn could potentially promote longer adherence to the specific activity.

Keywords: physical activity, motivation, level of physical activity, type of physical activity

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Authors: Desissa Yadeta Muleta

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Purely organic long-persistent luminescence materials (OLPLMs) have been developed as emerging organic materials due to their simple production process, low preparation cost and better biocompatibilities. Notably, OLPLMs with afterglow-time-tunable long-persistent luminescence (LPL) characteristics enable higher-level protection applications and have great prospects in biological applications. The realization of these advanced performances depends on our ability to gradually tune LPL duration under ambient conditions, however, the strategies to achieve this are few due to the lack of unambiguous mechanisms. Here, we propose a two-step strategy to gradually tune LPL duration of OLPLMs over a wide range of seconds in water and air, by using derivatives as the guest and introducing a third-party material into the host-immobilized host–guest doping system. Based on this strategy, we develop an analysis method for deoxyribonucleic acid (DNA) content detection without DNA separation in aqueous samples, which circumvents the influence of the chromophore, fluorophore and other interferents in vivo, enabling a certain degree of in situ detection that is difficult to achieve using today’s methods. This work will expedite the development of afterglow-time-tunable OLPLMs and expand new horizons for their applications in data protection, bio-detection, and bio-sensing

Keywords: deoxyribonucliec acid, long persistent luminescent materials, water, air

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Authors: Maryam Nejat Dehkordi, Per Lincoln, Hassan Momtaz

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Interaction of Schiff base complexes of iron and manganese (iron [N, N’ Bis (5-(triphenyl phosphonium methyl) salicylidene) -1, 2 ethanediamine) chloride, [Fe Salen]Cl, manganese [N, N’ Bis (5-(triphenyl phosphonium methyl) salicylidene) -1, 2 ethanediamine) acetate) with DNA were investigated by spectroscopic and isothermal titration calorimetry techniques (ITC). The absorbance spectra of complexes have shown hyper and hypochromism in the presence of DNA that is indication of interaction of complexes with DNA. The linear dichroism (LD) measurements confirmed the bending of DNA in the presence of complexes. Furthermore, isothermal titration calorimetry experiments approved that complexes bound to DNA on the base of both electrostatic and hydrophobic interactions. Furthermore, ITC profile exhibits the existence of two binding phases for the complex. Antibacterial activity of ligand and complexes were tested in vitro to evaluate their activity against the gram positive and negative bacteria.

Keywords: Schiff base complexes, ct-DNA, linear dichroism (LD), isothermal titration calorimetry (ITC), antibacterial activity

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

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Authors: H. Abu-Ali, A. Nabok, T. Smith

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Aptamers are single-strand of DNA or RNA nucleotides sequence which is designed in vitro using selection process known as SELEX (systematic evolution of ligands by exponential enrichment) were developed for the selective detection of many toxic materials. In this work, we have developed an electrochemical biosensor for highly selective and sensitive detection of Hg2+ and Pb2+ using a specific aptamer probe (SAP) labelled with ferrocene (or methylene blue) in (5′) end and the thiol group at its (3′) termini, respectively. The SAP has a specific coil structure that matching with G-G for Pb2+ and T-T for Hg2+ interaction binding nucleotides ions, respectively. Aptamers were immobilized onto surface of screen-printed gold electrodes via SH groups; then the cyclic voltammograms were recorded in binding buffer with the addition of the above metal salts in different concentrations. The resulted values of anode current increase upon binding heavy metal ions to aptamers and analyte due to the presence of electrochemically active probe, i.e. ferrocene or methylene blue group. The correlation between the anodic current values and the concentrations of Hg2+ and Pb2+ ions has been established in this work. To the best of our knowledge, this is the first example of using a specific DNA aptamers for electrochemical detection of heavy metals. Each increase in concentration of 0.1 μM results in an increase in the anode current value by simple DC electrochemical test i.e (Cyclic Voltammetry), thus providing an easy way of determining Hg2+ and Pb2+concentration.

Keywords: aptamer, based, biosensor, DNA, electrochemical, highly, specific

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Authors: Sujay Kotwale, Ramasubba Reddy M.

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Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

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Authors: McClain Thiel

Abstract:

Object detection and localization has historically required two or more sensors due to the loss of information from 3D to 2D space, however, most surveillance systems currently in use in the real world only have one sensor per location. Generally, this consists of a single low-resolution camera positioned above the area under observation (mall, jewelry store, traffic camera). This is not sufficient for robust 3D tracking for applications such as security or more recent relevance, contract tracing. This paper proposes a lightweight system for 3D person tracking that requires no additional hardware, based on compressed object detection convolutional-nets, facial landmark detection, and projective geometry. This approach involves classifying the target into a demographic category and then making assumptions about the relative locations of facial landmarks from the demographic information, and from there using simple projective geometry and known constants to find the target's location in 3D space. Preliminary testing, although severely lacking, suggests reasonable success in 3D tracking under ideal conditions.

Keywords: monocular distancing, computer vision, facial analysis, 3D localization

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Authors: T. Hojageldiyev, Y. Bolmammedov, S. Gurbanaliyev

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It is known that drugs used in the treatment of tuberculosis show toxic effect to organism especially to liver besides its therapeutic effect. Because of ineffectiveness of drugs used in the treatment regimen of tuberculosis against multidrug resistance (MDR) and extensively drug-resistance (XDR) tuberculosis requires the development of new treatment methods and new, novel drugs. Considering the usage of Artemisia absinthium in traditional medicine in treatment of wounds which suggests its antibacterial activity it seems that, also it may have significant antimycobacterial activity. The objective of present study was to evaluate antibacterial activity of ethanolic extract of A. absinthium against M. tuberculosis. In this study, the effect of ethanolic extract of A. absinthium was tested against tuberculosis and pharmaco-toxicological properties evaluated on laboratory animals. The 20%, 40%, 70% and 96% ethanolic extracts of A. absinthium prepared then its bacteriostatic and bactericidal activities were evaluated by validated methods. Data were analyzed by GraphPad Prism 7.0 at the level P < 0.05. Results showed that ethanolic extracts of A. absinthium show no toxicological properties with having high LD50. All concentrations of extract show high bacteriostatic activity on M. tuberculosis. 96% ethanolic extract has highest bactericidal effect among other concentrations. By conducting further studies, as a result of our study, antimycobacterial drug can be prepared from A. absinthium.

Keywords: Artemisia absinthium, antimycobacterial, ethanolic extract, Mycobacteria tuberculosis

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