Search results for: feature detection

Authors: Apurv Kulkarni, Shreyas Badave, B. Rajiv

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Non-parametric reliability technique is useful for assessment of reliability of systems for which failure rates are not available. This is useful when detection of malfunctioning of any component is the key purpose during ongoing operation of the system. The main purpose of the Heat Exchanger Cycle discussed in this paper is to provide hot water at a constant temperature for longer periods of time. In such a cycle, certain components play a crucial role and this paper presents an effective way to predict the malfunctioning of the components by determination of system reliability. The method discussed in the paper is feasible and this is clarified with the help of various test cases.

Keywords: heat exchanger cycle, k-statistics, PID controller, system reliability

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Authors: Uraib Sharaha, Ahmad Salman, Eladio Rodriguez-Diaz, Elad Shufan, Klaris Riesenberg, Irving J. Bigio, Mahmoud Huleihel

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Antimicrobial drugs have an important role in controlling illness associated with infectious diseases in animals and humans. However, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global health-care problem. Rapid determination of antimicrobial susceptibility of a clinical isolate is often crucial for the optimal antimicrobial therapy of infected patients and in many cases can save lives. The conventional methods for susceptibility testing like disk diffusion are time-consuming and other method including E-test, genotyping are relatively expensive. Fourier transform infrared (FTIR) microscopy is rapid, safe, and low cost method that was widely and successfully used in different studies for the identification of various biological samples including bacteria. The new modern infrared (IR) spectrometers with high spectral resolution enable measuring unprecedented biochemical information from cells at the molecular level. Moreover, the development of new bioinformatics analyses combined with IR spectroscopy becomes a powerful technique, which enables the detection of structural changes associated with resistivity. The main goal of this study is to evaluate the potential of the FTIR microscopy in tandem with machine learning algorithms for rapid and reliable identification of bacterial susceptibility to antibiotics in time span of few minutes. The bacterial samples, which were identified at the species level by MALDI-TOF and examined for their susceptibility by the routine assay (micro-diffusion discs), are obtained from the bacteriology laboratories in Soroka University Medical Center (SUMC). These samples were examined by FTIR microscopy and analyzed by advanced statistical methods. Our results, based on 550 E.coli samples, were promising and showed that by using infrared spectroscopic technique together with multivariate analysis, it is possible to classify the tested bacteria into sensitive and resistant with success rate higher than 85% for eight different antibiotics. Based on these preliminary results, it is worthwhile to continue developing the FTIR microscopy technique as a rapid and reliable method for identification antibiotic susceptibility.

Keywords: antibiotics, E. coli, FTIR, multivariate analysis, susceptibility

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Authors: Anna Bykalyuk, Frédéric Kuznik, Kévyn Johannes

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In this paper, the influence of a vertical plate’s thermal capacity is numerically investigated in order to evaluate the evolution of the thermal boundary layer structure, as well as the convective heat transfer coefficient and the velocity and temperature profiles. Whereas the heat flux of the heated vertical plate is evaluated under time depending boundary conditions. The main important feature of this problem is the unsteadiness of the physical phenomena. A 2D CFD model is developed with the Ansys Fluent 14.0 environment and is validated using unsteady data obtained for plasterboard studied under a dynamic temperature evolution. All the phenomena produced in the vicinity of the thermal conductive vertical plate (plasterboard) are analyzed and discussed. This work is the first stage of a holistic research on transient free convection that aims, in the future, to study the natural convection in the vicinity of a vertical plate containing Phase Change Materials (PCM).

Keywords: CFD modeling, natural convection, thermal conductive plate, time-depending boundary conditions

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Authors: Betzabet García-Mendoza, Rocío Abascal-Mena

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Breast and cervical cancer are among the leading causes of death of women in Mexico. The mortality rate for these diseases is alarming, even though there have been many campaigns for making people self-aware of the importance of conducting gynecological studies for a timely prevention and detection, these have not been enough. This paper presents a mobile application for organizing and controlling gynecological studies in order to help and boost women to take care of their bodies and health. The process of analyzing and designing the mobile application is presented, along with all the steps carried out by following a user-centered design methodology.

Keywords: breast cancer, cervical cancer, gynecological mobile application, paper prototyping, storyboard, women health

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Authors: Vladimir Hovhannisyan, Chen Yuan Dong

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Recently nanoparticles (NPs) have been introduced in biomedicine as effective agents for cancer-targeted drug delivery and noninvasive tissue imaging. The most important requirements to these agents are their non-toxicity, biocompatibility and stability. In view of these criteria, the zeolite (ZL) nanoparticles (NPs) may be considered as perfect candidates for biomedical applications. ZLs are crystalline aluminosilicates consisting of oxygen-sharing SiO4 and AlO4 tetrahedral groups united by common vertices in three-dimensional framework and containing pores with diameters from 0.3 to 1.2 nm. Generally, the behavior and physical properties of ZLs are studied by SEM, X-ray spectroscopy, and AFM, whereas optical spectroscopic and microscopic approaches are not effective enough, because of strong scattering in common ZL bulk materials and powders. The light scattering can be reduced by using of ZL NPs. ZL NPs have large external surface area, high dispersibility in both aqueous and organic solutions, high photo- and thermal stability, and exceptional ability to adsorb various molecules and atoms in their nanopores. In this report, using multiphoton microscopy and nonlinear spectroscopy, we investigate nonlinear optical properties of clinoptilolite type of ZL micro- and nanoparticles with average diameters of 2200 nm and 240 nm, correspondingly. Multiphoton imaging is achieved using a laser scanning microscope system (LSM 510 META, Zeiss, Germany) coupled to a femtosecond titanium:sapphire laser (repetition rate- 80 MHz, pulse duration-120 fs, radiation wavelength- 720-820 nm) (Tsunami, Spectra-Physics, CA). Two Zeiss, Plan-Neofluar objectives (air immersion 20×∕NA 0.5 and water immersion 40×∕NA 1.2) are used for imaging. For the detection of the nonlinear response, we use two detection channels with 380-400 nm and 435-700 nm spectral bandwidths. We demonstrate that ZL micro- and nanoparticles can produce nonlinear optical response under the near-infrared femtosecond laser excitation. The interaction of hypericine, chlorin e6 and other dyes with ZL NPs and their photodynamic activity is investigated. Particularly, multiphoton imaging shows that individual ZL NPs particles adsorb Zn-tetraporphyrin molecules, but do not adsorb fluorescein molecules. In addition, nonlinear spectral properties of ZL NPs in native biotissues are studied. Nonlinear microscopy and spectroscopy may open new perspectives in the research and application of ZL NP in biomedicine, and the results may help to introduce novel approaches into the clinical environment.

Keywords: multiphoton microscopy, nanoparticles, nonlinear optics, zeolite

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Authors: Kars Beek, Remco Groeneveld, Sjaak Brinkkemper

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Although there is much literature available on requirement documentation in traditional software development, few studies have been conducted about this topic in open source software development. While open-source software development is becoming more important, the software development processes are often not as structured as corporate software development processes. Papers show that communities, creating open-source software, often lack structure and documentation. However, most recent studies about this topic are often ten or more years old. Therefore, this research has been conducted to determine if the lack of structure and documentation in requirement engineering is currently still the situation in these communities. Three open-source products have been chosen as subjects for conducting this research. The data for this research was gathered based on interviews, observations, and analyses of feature proposals and issue tracking tools. In this paper, we present a comparison and an analysis of the different methods used for requirements documentation to understand the current practices of requirements documentation in open source software development.

Keywords: case study, open source software, open source software development, requirement elicitation, requirement engineering

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Authors: Tibor Ajtai, Máté Pintér, Noémi Utry, Gergely Kiss-Albert, Andrea Palágyi, László Manczinger, Csaba Vágvölgyi, Gábor Szabó, Zoltán Bozóki

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According to the best knowledge of the authors, here we experimentally demonstrate first, a quantified correlation between the real-time measured optical feature of the ambient and the off-line measured toxicity data. Finally, using these correlations we are presenting a novel methodology for real time characterisation of ambient toxicity based on the multi wavelength aerosol phase photoacoustic measurement. Ambient carbonaceous particulate matter is one of the most intensively studied atmospheric constituent in climate science nowadays. Beyond their climatic impact, atmospheric soot also plays an important role as an air pollutant that harms human health. Moreover, according to the latest scientific assessments ambient soot is the second most important anthropogenic emission source, while in health aspect its being one of the most harmful atmospheric constituents as well. Despite of its importance, generally accepted standard methodology for the quantitative determination of ambient toxicology is not available yet. Dominantly, ambient toxicology measurement is based on the posterior analysis of filter accumulated aerosol with limited time resolution. Most of the toxicological studies are based on operational definitions using different measurement protocols therefore the comprehensive analysis of the existing data set is really limited in many cases. The situation is further complicated by the fact that even during its relatively short residence time the physicochemical features of the aerosol can be masked significantly by the actual ambient factors. Therefore, decreasing the time resolution of the existing methodology and developing real-time methodology for air quality monitoring are really actual issues in the air pollution research. During the last decades many experimental studies have verified that there is a relation between the chemical composition and the absorption feature quantified by Absorption Angström Exponent (AAE) of the carbonaceous particulate matter. Although the scientific community are in the common platform that the PhotoAcoustic Spectroscopy (PAS) is the only methodology that can measure the light absorption by aerosol with accurate and reliable way so far, the multi-wavelength PAS which are able to selectively characterise the wavelength dependency of absorption has become only available in the last decade. In this study, the first results of the intensive measurement campaign focusing the physicochemical and toxicological characterisation of ambient particulate matter are presented. Here we demonstrate the complete microphysical characterisation of winter time urban ambient including optical absorption and scattering as well as size distribution using our recently developed state of the art multi-wavelength photoacoustic instrument (4λ-PAS), integrating nephelometer (Aurora 3000) as well as single mobility particle sizer and optical particle counter (SMPS+C). Beyond this on-line characterisation of the ambient, we also demonstrate the results of the eco-, cyto- and genotoxicity measurements of ambient aerosol based on the posterior analysis of filter accumulated aerosol with 6h time resolution. We demonstrate a diurnal variation of toxicities and AAE data deduced directly from the multi-wavelength absorption measurement results.

Keywords: photoacoustic spectroscopy, absorption Angström exponent, toxicity, Ames-test

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Authors: Lynda K. Eneh, Okal N. Mike, Anna-Karin Borg-Karlson, Ulrike Fillinger, Jenny M. Lindh

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The responses of individual gravid Anopheles gambiae sensu stricto (s.s.) to hay infusion volatiles were evaluated under laboratory conditions. Such infusions have long been known to be effective baits for monitoring mosquitoes that vector arboviral and filarial diseases but have previously not been tested for malaria vectors. Hay infusions were prepared by adding sun-dried Bermuda grass to lake water and leaving the mixture in a covered bucket for three days. The proportions of eggs laid by gravid An. gambiae s.s. in diluted (10%) and concentrated infusions ( ≥ 25%) was compared to that laid in lake water in two-choice egg-count bioassays. Furthermore, with the aim to develop a method that can be used to collect volatiles that influence the egg-laying behavior of malaria mosquitoes, different volatile trapping methods were investigated. Two different polymer-traps eluted using two different desorption methods and three parameters were investigated. Porapak®-Q traps and solvent desorption was compared to Tenax®-TA traps and thermal desorption. The parameters investigated were: collection time (1h vs. 20h), addition of salt (0.15 g/ml sodium chloride (NaCl) vs. no NaCl), and stirring the infusion (0 vs. 300 rpm). Sample analysis was with gas chromatography-mass spectrometry (GC-MS). An. gambiae s.s was ten times less likely to lay eggs in concentrated hay infusion than in lake water. The volatiles were best characterized by thermally desorbed Tenax traps, collected for 20 hours from infusion aliquots with sodium chloride added. Ten volatiles identified from headspace and previously indicated as putative oviposition semiochemicals for An. gambiae s.s. or confirmed semiochemicals for other mosquito species were tested in egg-count bioassays. Six of these (3-methylbutanol, phenol, 4-methylphenol, nonanal, indole and 3-methylindole), when added to lake water, were avoided for egg-laying when lake water was offered as the alternative in dual-choice egg count bioassays. These compounds likely contribute to the unfavorable oviposition responses towards hay infusions. This difference in oviposition response of different mosquito species should be considered when designing control measures.

Keywords: Anopheles gambiae, oviposition behaviour, egg-count cage bioassays, hay infusions, volatile detection, semiochemicals

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Authors: Reuben Mugisha, Stella Bakibinga

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The cervical cancer burden is a global threat, but more so in low income settings where more than 85% of mortality cases occur due to lack of sufficient screening programs. There is consequently a lack of early detection of cancer and precancerous cells among women. Studies show that 3% to 35% of deaths could have been avoided through early screening depending on prognosis, disease progression, environmental and lifestyle factors. In this study, a systematic literature review is undertaken to understand potential barriers and facilitators as documented in previous studies that focus on the application of telehealth in cervical cancer screening programs for early detection of cancer and precancerous cells. The study informs future studies especially those from low income settings about lessons learned from previous studies and how to be best prepared while planning to implement telehealth for cervical cancer screening. It further identifies the knowledge gaps in the research area and makes recommendations. Using a specified selection criterion, 15 different articles are analyzed based on the study’s aim, theory or conceptual framework used, method applied, study findings and conclusion. Results are then tabulated and presented thematically to better inform readers about emerging facts on barriers and facilitators to telehealth implementation as documented in the reviewed articles, and how they consequently lead to evidence informed conclusions that are relevant to telehealth implementation for cervical cancer screening. Preliminary findings of this study underscore that use of low cost mobile colposcope is an appealing option in cervical cancer screening, particularly when coupled with onsite treatment of suspicious lesions. These tools relay cervical images to the online databases for storage and retrieval, they permit integration of connected devices at the point of care to rapidly collect clinical data for further analysis of the prevalence of cervical dysplasia and cervical cancer. Results however reveal the need for population sensitization prior to use of mobile colposcopies among patients, standardization of mobile colposcopy programs across screening partners, sufficient logistics and good connectivity, experienced experts to review image cases at the point-of-care as important facilitators to the implementation of mobile colposcope as a telehealth cervical cancer screening mechanism.

Keywords: cervical cancer screening, digital technology, hand-held colposcopy, knowledge-sharing

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Authors: Ava Dodd, Jonathan Adams

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The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools, and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represents another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

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Authors: Sofia Matoug, Amr Abdel-Dayem

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This paper presents a comprehensive survey of recent research studies to segment and classify brain MR (magnetic resonance) images in order to detect significant changes to brain ventricles. The paper also presents a general framework for detecting regions that atrophy, which can help neurologists in detecting and staging Alzheimer. Furthermore, a prototype was implemented to segment brain MR images in order to extract the region of interest (ROI) and then, a classifier was employed to differentiate between normal and abnormal brain tissues. Experimental results show that the proposed scheme can provide a reliable second opinion that neurologists can benefit from.

Keywords: Alzheimer, brain images, classification techniques, Magnetic Resonance Images MRI

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Authors: Xiuying Wang, Changliang Li, Bo Xu

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A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.

Keywords: deep learning, hierarchical tree long short-term memory, sentence representation, sentiment analysis

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Authors: Barkha Ramteke, Sonali Ridhorkar

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An election is an important event in all countries. Traditional voting has several drawbacks, including the expense of time and effort required for tallying and counting results, the cost of papers, arrangements, and everything else required to complete a voting process. Many countries are now considering online e-voting systems, but the traditional e-voting systems suffer a lack of trust. It is not known if a vote is counted correctly, tampered or not. A lack of transparency means that the voter has no assurance that his or her vote will be counted as they voted in elections. Electronic voting systems are increasingly using blockchain technology as an underlying storage mechanism to make the voting process more transparent and assure data immutability as blockchain technology grows in popularity. The transparent feature, on the other hand, may reveal critical information about applicants because all system users have the same entitlement to their data. Furthermore, because of blockchain's pseudo-anonymity, voters' privacy will be revealed, and third parties involved in the voting process, such as registration institutions, will be able to tamper with data. To overcome these difficulties, we apply Ethereum smart contracts into blockchain-based voting systems.

Keywords: blockchain, AMV chain, electronic voting, decentralized

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Authors: Ardalan Sabamehr, Ashutosh Bagchi

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Recently, Structural Health Monitoring (SHM) based on the vibration of structures has attracted the attention of researchers in different fields such as: civil, aeronautical and mechanical engineering. Operational Modal Analysis (OMA) have been developed to identify modal properties of infrastructure such as bridge, building and so on. Frequency Domain Decomposition (FDD), Stochastic Subspace Identification (SSI) and Continuous Wavelet Transform (CWT) are the three most common methods in output only modal identification. FDD, SSI, and CWT operate based on the frequency domain, time domain, and time-frequency plane respectively. So, FDD and SSI are not able to display time and frequency at the same time. By the way, FDD and SSI have some difficulties in a noisy environment and finding the closed modes. CWT technique which is currently developed works on time-frequency plane and a reasonable performance in such condition. The other advantage of wavelet transform rather than other current techniques is that it can be applied for the non-stationary signal as well. The aim of this paper is to compare three most common modal identification techniques to find modal properties (such as natural frequency, mode shape, and damping ratio) of three story steel frame which was built in Concordia University Lab by use of ambient vibration. The frame has made of Galvanized steel with 60 cm length, 27 cm width and 133 cm height with no brace along the long span and short space. Three uniaxial wired accelerations (MicroStarin with 100mv/g accuracy) have been attached to the middle of each floor and gateway receives the data and send to the PC by use of Node Commander Software. The real-time monitoring has been performed for 20 seconds with 512 Hz sampling rate. The test is repeated for 5 times in each direction by hand shaking and impact hammer. CWT is able to detect instantaneous frequency by used of ridge detection method. In this paper, partial derivative ridge detection technique has been applied to the local maxima of time-frequency plane to detect the instantaneous frequency. The extracted result from all three methods have been compared, and it demonstrated that CWT has the better performance in term of its accuracy in noisy environment. The modal parameters such as natural frequency, damping ratio and mode shapes are identified from all three methods.

Keywords: ambient vibration, frequency domain decomposition, stochastic subspace identification, continuous wavelet transform

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Authors: Zhen Cao, Yu Zhu, Junxue Fu

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Immunoassay, i.e., antigen-antibody reaction, is crucial for disease diagnostics. To achieve the adequate signal of the antigen protein detection, a large amount of sample and long incubation time is needed. However, the amount of protein is usually small at the early stage, which makes it difficult to detect. Unlike cells and DNAs, no valid chemical method exists for protein amplification. Thus, an alternative way to improve the signal is through particle manipulation techniques to concentrate proteins, among which dielectrophoresis (DEP) is an effective one. DEP is a technique that concentrates particles to the designated region through a force created by the gradient in a non-uniform electric field. Since DEP force is proportional to the cube of particle size and square of electric field gradient, it is relatively easy to capture larger particles such as cells. For smaller ones like proteins, a super high gradient is then required. In this work, three-dimensional Ag/SiO2 nanorods arrays, fabricated by an easy physical vapor deposition technique called as oblique angle deposition, have been integrated with a DEP device and created the field gradient as high as of 2.6×10²⁴ V²/m³. The nanorods based DEP device is able to enrich bovine serum albumin (BSA) protein by 1800-fold and the rate has reached 180-fold/s when only applying 5 V electric potential. Based on the above nanorods integrated DEP platform, an immunoassay of mouse immunoglobulin G (IgG) proteins has been performed. Briefly, specific antibodies are immobilized onto nanorods, then IgG proteins are concentrated and captured, and finally, the signal from fluorescence-labelled antibodies are detected. The limit of detection (LoD) is measured as 275.3 fg/mL (~1.8 fM), which is a 20,000-fold enhancement compared with identical assays performed on blank glass plates. Further, prostate-specific antigen (PSA), which is a cancer biomarker for diagnosis of prostate cancer after radical prostatectomy, is also quantified with a LoD as low as 2.6 pg/mL. The time to signal saturation has been significantly reduced to one minute. In summary, together with an easy nanorod fabrication and integration method, this nanorods based DEP platform has demonstrated highly sensitive immunoassay performance and thus poses great potentials in applications for early point-of-care diagnostics.

Keywords: dielectrophoresis, immunoassay, oblique angle deposition, protein concentration

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Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

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Authors: Matteo Calabrese, Martin Cimmino, Dimos Kapetis, Martina Manfrin, Donato Concilio, Giuseppe Toscano, Giovanni Ciandrini, Giancarlo Paccapeli, Gianluca Giarratana, Marco Siciliano, Andrea Forlani, Alberto Carrotta

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In this paper we describe a machine learning methodology for Predictive Maintenance (PdM) applied on woodworking industrial machines. PdM is a prominent strategy consisting of all the operational techniques and actions required to ensure machine availability and to prevent a machine-down failure. One of the challenges with PdM approach is to design and develop of an embedded smart system to enable the health status of the machine. The proposed approach allows screening simultaneously multiple connected machines, thus providing real-time monitoring that can be adopted with maintenance management. This is achieved by applying temporal feature engineering techniques and training an ensemble of classification algorithms to predict Remaining Useful Lifetime of woodworking machines. The effectiveness of the methodology is demonstrated by testing an independent sample of additional woodworking machines without presenting machine down event.

Keywords: predictive maintenance, machine learning, connected machines, artificial intelligence

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Authors: Sontana Mimapan, Phattarawadee Wattanasuntorn, Phanom Saijit

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Aflatoxin contamination in maize, one of several agriculture crops grown for livestock feeding, is still a problem throughout the world mainly under hot and humid weather conditions like Thailand. In this study Aspergillus flavus (A. Flavus), the key fungus for aflatoxin production especially aflatoxin B1 (AFB1), isolated from naturally infected maize were identified and characterized according to colony morphology and PCR using ITS, Beta-tubulin and calmodulin genes. The strains were analysed for the presence of four aflatoxigenic biosynthesis genes in relation to their capability to produce AFB1, Ver1, Omt1, Nor1, and aflR. Aflatoxin production was then confirmed using immunoaffinity column technique. A loop-mediated isothermal amplification (LAMP) was applied as an innovative technique for rapid detection of target nucleic acid. The reaction condition was optimized at 65C for 60 min. and calcein flurescent reagent was added before amplification. The LAMP results showed clear differences between positive and negative reactions in end point analysis under daylight and UV light by the naked eye. In daylight, the samples with AFB1 producing A. Flavus genes developed a yellow to green color, but those without the genes retained the orange color. When excited with UV light, the positive samples become visible by bright green fluorescence. LAMP reactions were positive after addition of purified target DNA until dilutions of 10⁻⁶. The reaction products were then confirmed and visualized with 1% agarose gel electrophoresis. In this regards, 50 maize samples were collected from dairy farms and tested for the presence of four aflatoxigenic biosynthesis genes using LAMP technique. The results were positive in 18 samples (36%) but negative in 32 samples (64%). All of the samples were rechecked by PCR and the results were the same as LAMP, indicating 100% specificity. Additionally, when compared with the immunoaffinity column-based aflatoxin analysis, there was a significant correlation between LAMP results and aflatoxin analysis (r= 0.83, P < 0.05) which suggested that positive maize samples were likely to be a high- risk feed. In conclusion, the LAMP developed in this study can provide a simple and rapid approach for detecting AFB1 producing A. Flavus genes from maize and appeared to be a promising tool for the prediction of potential aflatoxigenic risk in livestock feedings.

Keywords: Aflatoxin B1, Aspergillus flavus genes, maize, loop-mediated isothermal amplification

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Authors: Deepak Jugran, Rajesh Gill

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Purpose: To study the demographic, sociological, gender and clinical profile of the children presented for childhood cataract at a public sector tertiary eye care centre in India. Methodology: The design of the study is retrospective, and hospital-based data is available with the Central Registration Department of the PGIMER, Chandigarh. The majority of the childhood cataract cases are being reported in this hospital, yet not each and every case of childhood cataract approaches PGI, Chandigarh. Nevertheless, this study is going to be pioneering research in India, covering five-year data of the childhood cataract patients who visited the Advanced Eye Centre, PGIMER, Chandigarh, from 1.1.2015 to 31.12.2019. The SPSS version 23 was used for all statistical calculations. Results: A Total of 354 children were presented for childhood cataract from 1.1.2015 to 31.12.2019. Out of 354 children, 248 (70%) were male, and 106 (30%) were female. In-spite of 2 flagship programmes, namely the National Programme for Control of Blindness (NPCB) and Aayushman Bharat (PM JAY) for eradication of cataract, no children received any financial assistance from these two programmes. A whopping 99% of these children belong to the poor families. In most of these families, the mothers were house-wives and did not employ anywhere. These interim results will soon be conveyed to the Govt. of India so that a suitable mechanism can be evolved to address this pertinent issue. Further, the disproportionate ratio of male and female children in this study is an area of concern as we don’t know whether the prevalence of childhood cataract is lower in female children or they are not being presented on time in the hospital by the families. Conclusion: The World Health Organization (WHO) has categorized Childhood blindness resulting from cataract as a priority area and urged all member countries to develop institutionalized mechanisms for its early detection, diagnosis and management. The childhood cataract is an emerging and major cause of preventable and avoidable childhood blindness, especially in low and middle-income countries. In the formative years, the children require a sound physical, mental and emotional state, and in the absence of either one of them, it can severely dent their future growth. The recent estimate suggests that India could suffer an economic loss of US$12 billion (Rs. 88,000 Crores) due to blindness, and almost 35% of cases of blindness are preventable and avoidable if detected at an early age. Besides reporting these results to the policy makers, synchronized efforts are needed for early detection and management of avoidable causes of childhood blindness such as childhood cataract.

Keywords: childhood blindness, cataract, Who, Npcb

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Authors: Amine Ganibardi, Cherif Arab Ali

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This paper addresses the data cleaning issue as a part of web usage data preprocessing within the scope of Web Usage Mining. Weblog data recorded by web servers within log files reflect usage activity, i.e., End-users’ clicks and underlying user-agents’ hits. As Web Usage Mining is interested in End-users’ behavior, user-agents’ hits are referred to as noise to be cleaned-off before mining. Filtering hits from clicks is not trivial for two reasons, i.e., a server records requests interlaced in sequential order regardless of their source or type, website resources may be set up as requestable interchangeably by end-users and user-agents. The current methods are content-centric based on filtering heuristics of relevant/irrelevant items in terms of some cleaning attributes, i.e., website’s resources filetype extensions, website’s resources pointed by hyperlinks/URIs, http methods, user-agents, etc. These methods need exhaustive extra-weblog data and prior knowledge on the relevant and/or irrelevant items to be assumed as clicks or hits within the filtering heuristics. Such methods are not appropriate for dynamic/responsive Web for three reasons, i.e., resources may be set up to as clickable by end-users regardless of their type, website’s resources are indexed by frame names without filetype extensions, web contents are generated and cancelled differently from an end-user to another. In order to overcome these constraints, a clustering-based cleaning method centered on the logging structure is proposed. This method focuses on the statistical properties of the logging structure at the requested and referring resources attributes levels. It is insensitive to logging content and does not need extra-weblog data. The used statistical property takes on the structure of the generated logging feature by webpage requests in terms of clicks and hits. Since a webpage consists of its single URI and several components, these feature results in a single click to multiple hits ratio in terms of the requested and referring resources. Thus, the clustering-based method is meant to identify two clusters based on the application of the appropriate distance to the frequency matrix of the requested and referring resources levels. As the ratio clicks to hits is single to multiple, the clicks’ cluster is the smallest one in requests number. Hierarchical Agglomerative Clustering based on a pairwise distance (Gower) and average linkage has been applied to four logfiles of dynamic/responsive websites whose click to hits ratio range from 1/2 to 1/15. The optimal clustering set on the basis of average linkage and maximum inter-cluster inertia results always in two clusters. The evaluation of the smallest cluster referred to as clicks cluster under the terms of confusion matrix indicators results in 97% of true positive rate. The content-centric cleaning methods, i.e., conventional and advanced cleaning, resulted in a lower rate 91%. Thus, the proposed clustering-based cleaning outperforms the content-centric methods within dynamic and responsive web design without the need of any extra-weblog. Such an improvement in cleaning quality is likely to refine dependent analysis.

Keywords: clustering approach, data cleaning, data preprocessing, weblog data, web usage data

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Authors: Priya Arora, Ashutosh Mishra

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Gene identification, towards the pursuit of mutated genes, leading to Parkinson’s disease, puts forward a challenge towards proactive cure of the disorder itself. Computational analysis is an effective technique for exploring genes in the form of protein sequences, as the theoretical and manual analysis is infeasible. The limitations and effectiveness of a particular computational method are entirely dependent on the previous data that is available for disease identification. The article presents a sequence-based classification method for the identification of genes responsible for Parkinson’s disease. During the initiation phase, the physicochemical properties of amino acids transform protein sequences into a feature vector. The second phase of the method employs Jaccard distances to select negative genes from the candidate population. The third phase involves artificial neural networks for making final predictions. The proposed approach is compared with the state of art methods on the basis of F-measure. The results confirm and estimate the efficiency of the method.

Keywords: disease gene identification, Parkinson’s disease, physicochemical properties of amino acid, protein sequences

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Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

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Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: bioassay, machine learning, preprocessing, virtual screen

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Authors: Adeolu O. Dairo

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Getting the consumer marketing strategy right is a crucial and complex task for firms with a large customer base such as mobile operators in a competitive mobile market. While empirical studies have made efforts to identify key constructs, no geospatial model has been developed to comprehensively assess the viability and interdependency of ground realities regarding the customer, competition, channel and the network quality of mobile operators. With this research, a geo-analytic framework is proposed for strategy formulation and allocation for mobile operators. Firstly, a fuzzy analytic network using a self-organizing feature map clustering technique based on inputs from managers and literature, which depicts the interrelationships amongst ground realities is developed. The model is tested with a mobile operator in the Nigeria mobile market. As a result, a customer-centric geospatial and visualization solution is developed. This provides a consolidated and integrated insight that serves as a transparent, logical and practical guide for strategic, tactical and operational decision making.

Keywords: geospatial, geo-analytics, self-organizing map, customer-centric

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Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

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Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

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Authors: Yan Lyu, Yiqun Pan, Zhizhong Huang

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In the design stage of a new building, the energy model of this building is often required for the analysis of the performance on energy efficiency. In practice, a certain degree of geometric simplification should be done in the establishment of building energy models, since the detailed geometric features of a real building are hard to be described perfectly in most energy simulation engine, such as ESP-r, eQuest or EnergyPlus. Actually, the detailed description is not necessary when the result with extremely high accuracy is not demanded. Therefore, this paper analyzed the relationship between the error of the simulation result from building energy models and the geometric simplification of the models. Finally, the following two parameters are selected as the indices to characterize the geometric feature of in building energy simulation: the southward projected area and total side surface area of the building, Based on the parameterization method, the simplification from an arbitrary column building to a typical shape (a cuboid) building can be made for energy modeling. The result in this study indicates that this simplification would only lead to the error that is less than 7% for those buildings with the ratio of southward projection length to total perimeter of the bottom of 0.25~0.35, which can cover most situations.

Keywords: building energy model, simulation, geometric simplification, design, regression

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Authors: Attila Kiss, Erzsébet Némedi, Zoltán Naár

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As natural prebiotic (non-digestible) carbohydrates stimulate the growth of colon microflora and contribute to maintain the health of the host, analytical studies aiming at revealing the chemical behavior of these beneficial food components came to the forefront of interest. Food processing (especially baking) may lead to a significant conversion of the parent compounds, hence it is of utmost importance to characterize the transformation patterns and the plausible decomposition products formed by thermal degradation. The relevance of this work is confirmed by the wide-spread use of these carbohydrates (fructo-oligosaccharides, cyclodextrins, raffinose and resistant starch) in the food industry. More and more functional foodstuffs are being developed based on prebiotics as bioactive components. 12 different types of oligosaccharides have been investigated in order to reveal their thermal degradation characteristics. Different carbohydrate derivatives (D-fructose and D-glucose oligomers and polymers) have been exposed to elevated temperatures (150 °C 170 °C, 190 °C, 210 °C, and 220 °C) for 10 min. An advanced HPLC method was developed and used to identify the decomposition products of carbohydrates formed as a consequence of thermal treatment. Gradient elution was applied with binary solvent elution (acetonitrile, water) through amine based carbohydrate column. Evaporative light scattering (ELS) proved to be suitable for the reliable detection of the UV/VIS inactive carbohydrate degradation products. These experimental conditions and applied advanced techniques made it possible to survey all the formed intermediers. Change in oligomer distribution was established in cases of all studied prebiotics throughout the thermal treatments. The obtained results indicate increased extent of chain degradation of the carbohydrate moiety at elevated temperatures. Prevalence of oligomers with shorter chain length and even the formation of monomer sugars (D-glucose and D-fructose) might be observed at higher temperatures. Unique oligomer distributions, which have not been described previously are revealed in the case of each studied, specific carbohydrate, which might result in various prebiotic activities. Resistant starches exhibited high stability when being thermal treated. The degradation process has been modeled by a plausible reaction mechanism, in which proton catalyzed degradation and chain cleavage take place.

Keywords: prebiotics, thermal degradation, fructo-oligosaccharide, HPLC, ELS detection

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

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Authors: Alicja Starczewska, Aleksander Nawrat, Krzysztof Daniec, Jarosław Homa, Kacper Hołda

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Border Gateway Protocol is the main routing protocol that enables routing establishment between all autonomous systems, which are the basic administrative units of the internet. Due to the poor protection of BGP, it is important to use additional BGP security systems. Many solutions to this problem have been proposed over the years, but none of them have been implemented on a global scale. This article describes a system capable of building images of real-time BGP network topology in order to detect BGP anomalies. Our proposal performs a detailed analysis of BGP messages that come into local network cards supplemented by information collected by remote collectors in different localizations.

Keywords: BGP, BGP hijacking, cybersecurity, detection

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Authors: Erick C. Valverde, Wansu Lim

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Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER system.

Keywords: deep learning, face detection, facial emotion recognition, network optimization methods

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Authors: Amal Saafan, Kareem Al Sofy, Sameh AbdelGhani, Magdy Amin

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Background: Acinetobacter spp. resistance towards β-lactam antibiotics is mediated mainly by different classes of β-lactamases production; detection of some genes responsible for production of β-lactamases is the objective of the study. Methods: One hundred fifty bacterial isolates were recovered from blood, sputum, and urine specimens from different hospitals in Egypt. Sixty-nine isolate were identified as Acinetobacter baumannii using traditional biochemical tests, CHROM agar, MicroScan and PCR amplification of blaoxa-51like gene. Acinetobacterbaumannii isolates were grouped into carbapenem resistant group (GP1), cefotaxime, ceftazidime and cefoxitin resistant group (GP2) and carbapenem and cephalosporin non-resistant group (GP3). Carbapenemase activity was screened using modified Hodge test (MHT) for GP1.Metallo-β-lactamases screening was performed for MHT positive isolates using double disk synergy test (DDST) and combined disk test (CDT). Amp C activity was screened using Amp C disk test with Tris-EDTA, DDST, and CDT for GP2. Finally, PCR amplification of blaoxa-51like, blaoxa-23like, blaIMP-like, blaVIM-like, and blaADC-like genes was performed for isolates that showed, at least, two positive results of three for both AmpC and carbapenemases phenotypic screening tests (obvious activity), in addition to GP3 (for comparison). Detection of blaoxa-51like and blaADC-like genes preceded by ISAba1 was also performed. Results: Antibiogram of 69 pure Acinetobacter baumannii isolates resulted in 57, 64, and 2 isolates enrolled into GP1, GP2, and GP3, respectively. Carbapenemase activity was shown by 49(85.9%) isolate using MHT. Metallo-β-lactamases screening revealed 32(65.3%) and 35(71.4%) using DDST and CDT, respectively.AmpC activity was shown by 43(67.2%) and 50 (78.1%) isolates using AmpC disk test with Tris-EDTA, and both DDST and CDT, respectively. Twenty-seven isolates showed obvious activity, all of them (100%) were harboring blaoxa-51like and blaADC-like genes, while blaoxa-23like, blaIMP-like andblaVIM-like genes were harbored by 23(85.2%), 9 (33.%) and no isolate respectively. Only 12 (44.4%) isolates harbored blaoxa-51like and blaADC-like genes preceded by ISAba1. GP3 isolates showed only positive blaoxa-51like and blaADC-like genes. Conclusion: It is not possible to correlate resistance with presence of blaoxa-51like and blaADC-like genes and presence of ISAba1 was immediate as transcriptional promoter. A blaoxa-23like gene played an important role in carbapenem resistance when compared with blaIMP-like and blaVIM-like gene.

Keywords: acinetobacter, beta-lactams, resistance, antimicrobial agents

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