Search results for: fault detection and classification

Authors: Nicholas S. Norbert Jr., John E. Bischoff, Christopher J. Willy

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Knowledge Management (KM) is globally recognized as a crucial element in securing competitive advantage through building and maintaining organizational memory, codifying and protecting intellectual capital and business intelligence, and providing mechanisms for collaboration and innovation. KM frameworks and approaches have been developed and defined identifying critical success factors for conducting KM within numerous industries ranging from scientific to business, and for ranges of organization scales from small groups to large enterprises. However, engineering and technical teams operating within restricted environments are subject to unique barriers and KM challenges which cannot be directly treated using the approaches and tools prescribed for other industries. This research identifies barriers in conducting KM within Hardware Development Engineering (HDE) teams and statistically compares significance to barriers upholding the four KM pillars of organization, technology, leadership, and learning for HDE teams. HDE teams suffer from restrictions in knowledge sharing (KS) due to classification of information (national security risks), customer proprietary restrictions (non-disclosure agreement execution for designs), types of knowledge, complexity of knowledge to be shared, and knowledge seeker expertise. As KM evolved leveraging information technology (IT) and web-based tools and approaches from Web 1.0 to Enterprise 2.0, KM may also seek to leverage emergent tools and analytics including expert locators and hybrid recommender systems to enable KS across barriers of the technical teams. The research will test hypothesis statistically evaluating if KM barriers for HDE teams affect the general set of expected benefits of a KM System identified through previous research. If correlations may be identified, then generalizations of success factors and approaches may also be garnered for HDE teams. Expert elicitation will be conducted using a questionnaire hosted on the internet and delivered to a panel of experts including engineering managers, principal and lead engineers, senior systems engineers, and knowledge management experts. The feedback to the questionnaire will be processed using analysis of variance (ANOVA) to identify and rank statistically significant barriers of HDE teams within the four KM pillars. Subsequently, KM approaches will be recommended for upholding the KM pillars within restricted environments of HDE teams.

Keywords: engineering management, knowledge barriers, knowledge management, knowledge sharing

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Authors: M. A. C. S. Sampath Fernando, James M. Curran, Renate Meyer

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Forensic DNA analysis has received much attention over the last three decades, due to its incredible usefulness in human identification. The statistical interpretation of DNA evidence is recognised as one of the most mature fields in forensic science. Peak heights in an Electropherogram (EPG) are approximately proportional to the amount of template DNA in the original sample being tested. A stutter is a minor peak in an EPG, which is not masking as an allele of a potential contributor, and considered as an artefact that is presumed to be arisen due to miscopying or slippage during the PCR. Stutter peaks are mostly analysed in terms of stutter ratio that is calculated relative to the corresponding parent allele height. Analysis of mixture profiles has always been problematic in evidence interpretation, especially with the presence of PCR artefacts like stutters. Unlike binary and semi-continuous models; continuous models assign a probability (as a continuous weight) for each possible genotype combination, and significantly enhances the use of continuous peak height information resulting in more efficient reliable interpretations. Therefore, the presence of a sound methodology to distinguish between stutters and real alleles is essential for the accuracy of the interpretation. Sensibly, any such method has to be able to focus on modelling stutter peaks. Bayesian nonparametric methods provide increased flexibility in applied statistical modelling. Mixture models are frequently employed as fundamental data analysis tools in clustering and classification of data and assume unidentified heterogeneous sources for data. In model-based clustering, each unknown source is reflected by a cluster, and the clusters are modelled using parametric models. Specifying the number of components in finite mixture models, however, is practically difficult even though the calculations are relatively simple. Infinite mixture models, in contrast, do not require the user to specify the number of components. Instead, a Dirichlet process, which is an infinite-dimensional generalization of the Dirichlet distribution, is used to deal with the problem of a number of components. Chinese restaurant process (CRP), Stick-breaking process and Pólya urn scheme are frequently used as Dirichlet priors in Bayesian mixture models. In this study, we illustrate an infinite mixture of simple linear regression models for modelling stutter ratio and introduce some modifications to overcome weaknesses associated with CRP.

Keywords: Chinese restaurant process, Dirichlet prior, infinite mixture model, PCR stutter

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Authors: A. Khan, I. Khan, M. Younus, S. E. Haque, U. Waheed, H. Neubauer, A. A. Anjum, S. A. Muhammad, A. Idrees T. Abbas, S. Raza, M. A. Ali, M. Farooq, M. Mahmood, A. Hussain, H. Danish, U. Tayyab, M. Zafar, M. Aslam.

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Brucellosis is one of the major problems of milk producing animals in our country which deteriorate the health of livestock. It is a disease of zoonotic significance which is capable of producing disease in humans leading to infertility, orchitis, abortions, and synovitis. In this particular study, milk and serum samples of cattle and buffalo (n=402) were collected from different districts of Punjab including Narowal, Gujranwala and Gujrat. Milk samples were analyzed by Milk Ring Test (MRT), while serum samples were tested through Rose Bengal Plate agglutination Test (RBPT) and Indirect Enzyme Linked Immunosorbant Assay (i-ELISA). The sample tested with MRT were 9.5% positive, including cattle 9.6% and buffalo 9.3%. While using the RBPT test for the detection of serum samples and for screening purpose it was observed that 16.4% animals were seropositive, cattle were 18.8% and buffalo were 13.9% seropositive. The higher prevalence of brucellosis indicates the danger of the disease to human population. The serum samples positive by RBPT were further confirmed by the use of most specific and sensitive serological test known as i-ELISA. 11.4% animals were confirmed as seropositive by i-ELISA including cattle 13.5% seropositive and buffalo 9.3%. The results indicated high seroprevalence of brucellosis in cattle as compared to buffalos. Different risk factors were also studied to know the association between disease and their spread. Advanced age, larger herds, history of abortion and pregnancy of the animals is considered to be the important factors for the prevalence and spread of the hazardous zoonotic disease. It is a core issue of developing countries like Pakistan and has major public health impact.

Keywords: humans, bovines, infertility, orchitis, abortions, seroprevalence, brucellosis

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Authors: Natalia Sypion-Dutkowska

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Abstract — The main research objective of the paper is to discover the relationship between the age groups of residents and crime in particular districts of a large city. The basic analytical tool is specific crime rates, calculated not in relation to the total population, but for age groups in a different social situation - property, housing, work, and representing different generations with different behavior patterns. They are the communities from which criminals and victims of crimes come. The analysis of literature and national police reports gives rise to hypotheses about the ability of a given age group to generate crime as a source of offenders and as a group of victims. These specific indicators are spatially differentiated, which makes it possible to detect socio-demographic determinants of spatial patterns of urban crime. A multi-feature classification of districts was also carried out, in which specific crime rates are the diagnostic features. In this way, areas with a similar structure of socio-demographic determinants of spatial patterns on urban crime were designated. The case study is the city of Szczecin in Poland. It has about 400,000 inhabitants and its area is about 300 sq km. Szczecin is located in the immediate vicinity of Germany and is the economic, academic and cultural capital of the region. It also has a seaport and an airport. Moreover, according to ESPON 2007, Szczecin is the Transnational and National Functional Urban Area. Szczecin is divided into 37 districts - auxiliary administrative units of the municipal government. The population of each of them in 2015-17 was divided into 8 age groups: babes (0-2 yrs.), children (3-11 yrs.), teens (12-17 yrs.), younger adults (18-30 yrs.), middle-age adults (31-45 yrs.), older adults (46-65 yrs.), early older (66-80) and late older (from 81 yrs.). The crimes reported in 2015-17 in each of the districts were divided into 10 groups: fights and beatings, other theft, car theft, robbery offenses, burglary into an apartment, break-in into a commercial facility, car break-in, break-in into other facilities, drug offenses, property damage. In total, 80 specific crime rates have been calculated for each of the districts. The analysis was carried out on an intra-city scale, this is a novel approach as this type of analysis is usually carried out at the national or regional level. Another innovative research approach is the use of specific crime rates in relation to age groups instead of standard crime rates. Acknowledgments: This research was funded by the National Science Centre, Poland, registration number 2019/35/D/HS4/02942.

Keywords: age groups, determinants of crime, spatial crime pattern, urban crime

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Authors: Priscilla Akua Agyapong

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Background: In a country of nearly 34 million people, Ghana suffers from rapidly growing pediatric CHD cases and not enough pediatric specialists to attend to the burgeoning needs of these children. Most of the cases are either missed or diagnosed late, resulting in increased mortality. According to the National Cardiothoracic Centre, 1 in every 100,000 births in Ghana has CHD; however, there is limited data on the clinical presentation and its management, one of the many reasons I decided to do this case study coupled with the loss my 2 month old niece to multiple Ventricular Septal Defect 3 years ago due late diagnoses. Method: A retrospective cohort study was performed at the child health clinic of one of Ghana’s public tertiary Institutions using data from their electronic health record (EHR) from February 2021 to April 2022. All suspected or provisionally diagnosed cases were included in the analysis. Results: Records of over 3000 children were reviewed with an approximate male to female ratio of 1:1.53 cases diagnosed during the period of study, most of whom were less than 5 years of age. 25 cases had complete clinical records, with acyanotic septal defects being the most diagnosed. 62.5% of the cases were ventricular septal defects, followed by Patent Ductus Arteriosus (23%) and Atrial Septal Defects (4.5%). Tetralogy of Fallot was the most predominant and complex cyanotic CHD with 10%. Conclusion: The indeterminate coronary anatomy of infants makes it difficult to use only echocardiography and other conventional clinical methods in screening for CHDs. There are rising modernizations and new innovative ways that can be employed in Ghana for early detection, hence preventing the delay of a potential surgical repair. It is, therefore, imperative to create the needed awareness about these “SILENT CRISES” and help save the Ghanaian child’s life.

Keywords: congenital heart defect(CHD), ventricular septal defect(VSD), atrial septal defect(ASD), patent ductus arteriosus(PDA)

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Authors: Nadia H. Mohammed, Anmar Al-Taie, Fadia H. Al-Sultany

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Although breast cancer prevalence continues to increase, mortality has been decreasing as a result of early detection and improvement in adjuvant systemic therapy. Nevertheless, this disease required further efforts to understand and identify the associated potential risk factors that could play a role in the prevalence of this malignancy among Iraqi women. The objective of this study was to assess the perception of certain predictive risk factors on the prevalence of breast cancer types among a sample of Iraqi women diagnosed with breast cancer. This was a retrospective observational study carried out at National Cancer Research Center in College of Medicine, Baghdad University from November 2017 to January 2018. Data of 100 patients with breast cancer whose biopsies examined in the National Cancer Research Center were included in this study. Data were collected to structure a detailed assessment regarding the patients’ demographic, medical and cancer records. The majority of study participants (94%) suffered from ductal breast cancer with mean age 49.57 years. Among those women, 48.9% were obese with body mass index (BMI) 35 kg/m². 68.1% of them had positive family history of breast cancer and 66% had low parity. 40.4% had stage II ductal breast cancer followed by 25.5% with stage III. It was found that 59.6% and 68.1% had positive oestrogen receptor sensitivity and positive human epidermal growth factor (HER2/neu) receptor sensitivity respectively. In regard to the impact of prediction of certain variables on the incidence of ductal breast cancer, positive family history of breast cancer (P < 0.0001), low parity (P< 0.0001), stage I and II breast cancer (P = 0.02) and positive HER2/neu status (P < 0.0001) were significant predictive factors among the study participants. The results from this study provide relevant evidence for a significant positive and potential association between certain risk factors and the prevalence of breast cancer among Iraqi women.

Keywords: Ductal Breast Cancer, Hormone Sensitivity, Iraq, Risk Factors

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Authors: Hamed Karimian, Noraziah Nordin, Mohamad Ibrahim Noordin, Syam Mohan, Mahboubeh Razavi, Najihah Mohd Hashim, Happipah Mohd Ali

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Satureja bachtiarica Bunge is a perennial medicinal plant belonging to the Lamiaceae family and endemic species in Iran. Satureja bachtiarica Bunge with the local name of Marzeh koohi is edible vegetable use as flavoring agent, anti-bacterial and to relieve cough and indigestion. In this study, the anti-cancer effect of Satureja bachtiarica Bunge on the MDA-MB-231 cell line as an Breast cancer cell model has been analyzed for the first time. Satureja bachtiarica Bunge was extracted using different solvents in the order of increasing polarity. Cytotoxicity activity of hexane extract of Satureja bachtiarica Bunge (SBHE) was observed using MTT assay. Acridine orange/Propidium iodide staining was used to detect early apoptosis; Annexin-V-FITC assay was carried out to observe the detection of cell-surface Phosphatidylserine (PS), with Annexin-Vserving as a marker for apoptotic cells. Caspase 3/7, 8 and-9 assays showed significantly activation of caspase-9 where lead intrinsic mitochondrial pathway. Bcl-2/Bax expressions and cell cycle arrest were also investigated. SBHE had exhibited significantly higher cytotoxicity against MDA-MB-231 Cell line compare to other cell lines. A significant increase in chromatin condensation in the cell nucleus was observed by fluorescence analysis. Treatment of MDA-MB-231 cells with SBHE encouraged apoptosis, by down-regulating Bcl-2 and up-regulating Bax, which lead the activation of caspase 9. Moreover, SBHE treatment significantly arrested MDA-MB-231 cells in the G1 phase. Together, the results presented in this study demonstrated that SBHE inhibited the proliferation of MDA-MB-231 cells, leading cell cycle arrest and programmed cell death, which was confirmed to be through the mitochondrial pathway.

Keywords: Satureja bachtiarica Bunge, MDA-MB-231, apoptosis, annexin-V, cell cycle

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Authors: Yarra Sulina, Nunna Tagore Sai Priya, Ankhi Banerjee

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With the rapid growth in tourist arrivals, there has been a huge demand for the growth of infrastructure in the destinations. With the increasing preference of tourists to stay near attractions, there has been a considerable change in the land use around tourist sites. However, with the inclusion of certain regulations and guidelines provided by the authorities based on the nature of tourism activity and geographical constraints, the pattern of growth of built form is different for various tourist sites. Therefore, this study explores the patterns of growth of built-up for a decade from 2009 to 2019 through two-dimensional and three-dimensional analysis. Land use maps are created through supervised classification of satellite images obtained from LANDSAT 4-5 and LANDSAT 8 for 2009 and 2019, respectively. The overall expansion of the built-up area in the region is analyzed in relation to the distance from the city's geographical center and the tourism-related growth regions are identified which are influenced by the proximity of tourist attractions. The primary tourist sites of various destinations with different geographical characteristics and tourism activities, that have undergone a significant increase in built-up area and are occupied with tourism-related infrastructure are selected for further study. Proximity analysis of the tourism-related growth sites is carried out to delineate the influence zone of the tourist site in a destination. Further, a temporal analysis of volumetric growth of built form is carried out to understand the morphology of the tourist precincts over time. The Digital Surface Model (DSM) and Digital Terrain Model (DTM) are used to extract the building footprints along with building height. Factors such as building height, and building density are evaluated to understand the patterns of three-dimensional growth of the built area in the region. The study also explores the underlying reasons for such changes in built form around various tourist sites and predicts the impact of such growth patterns in the region. The building height and building density around tourist site creates a huge impact on the appeal of the destination. The surroundings that are incompatible with the theme of the tourist site have a negative impact on the attractiveness of the destination that leads to negative feedback by the tourists, which is not a sustainable form of development. Therefore, proper spatial measures are necessary in terms of area and volume of the built environment for a healthy and sustainable environment around the tourist sites in the destination.

Keywords: sustainable tourism, growth patterns, land-use changes, 3-dimensional analysis of built-up area

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Authors: Orkide Donma, Mustafa M. Donma

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Obesity is associated with increased fat mass as well as fat percentage. Minerals are the elements, which are of vital importance. In this study, the relationships between body as well as bone mineral profile and the percentage as well as mass values of fat, fat-free portion, protein, skeletal muscle were evaluated in adult men with normal body mass index (N-BMI), and those classified according to different stages of obesity. A total of 103 adult men classified into five groups participated in this study. Ages were within 19-79 years range. Groups were N-BMI (Group 1), overweight (OW) (Group 2), first level of obesity (FLO) (Group 3), second level of obesity (SLO) (Group 4) and third level of obesity (TLO) (Group 5). Anthropometric measurements were performed. BMI values were calculated. Obesity degree, total body fat mass, fat percentage, basal metabolic rate (BMR), visceral adiposity, body mineral mass, body mineral percentage, bone mineral mass, bone mineral percentage, fat-free mass, fat-free percentage, protein mass, protein percentage, skeletal muscle mass and skeletal muscle percentage were determined by TANITA body composition monitor using bioelectrical impedance analysis technology. Statistical package (SPSS) for Windows Version 16.0 was used for statistical evaluations. The values below 0.05 were accepted as statistically significant. All the groups were matched based upon age (p > 0.05). BMI values were calculated as 22.6 ± 1.7 kg/m², 27.1 ± 1.4 kg/m², 32.0 ± 1.2 kg/m², 37.2 ± 1.8 kg/m², and 47.1 ± 6.1 kg/m² for groups 1, 2, 3, 4, and 5, respectively. Visceral adiposity and BMR values were also within an increasing trend. Percentage values of mineral, protein, fat-free portion and skeletal muscle masses were decreasing going from normal to TLO. Upon evaluation of the percentages of protein, fat-free portion and skeletal muscle, statistically significant differences were noted between NW and OW as well as OW and FLO (p < 0.05). However, such differences were not observed for body and bone mineral percentages. Correlation existed between visceral adiposity and BMI was stronger than that detected between visceral adiposity and obesity degree. Correlation between visceral adiposity and BMR was significant at the 0.05 level. Visceral adiposity was not correlated with body mineral mass but correlated with bone mineral mass whereas significant negative correlations were observed with percentages of these parameters (p < 0.001). BMR was not correlated with body mineral percentage whereas a negative correlation was found between BMR and bone mineral percentage (p < 0.01). It is interesting to note that mineral percentages of both body as well as bone are highly affected by the visceral adiposity. Bone mineral percentage was also associated with BMR. From these findings, it is plausible to state that minerals are highly associated with the critical stages of obesity as prominent parameters.

Keywords: bone, men, minerals, obesity

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Authors: Novee Lor C. Leyso, Maylin C. Palatino

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Under-five malnutrition continues to persist in the Philippines, particularly in the island Province of Marinduque, with prevalence of some forms of malnutrition even worsening in recent years. Local spatial cluster detection provides a spatial perspective in understanding this phenomenon as key in analyzing patterns of geographic variation, identification of community-appropriate programs and interventions, and focused targeting on high-risk areas. Using data from a province-wide household-based census conducted in 2014–2016, this study aimed to determine and evaluate spatial clusters of under-five malnutrition, across the province and within each municipality at the individual level using household location. Malnutrition was defined as weight-for-age z-score that fall outside the 2 standard deviations from the median of the WHO reference population. The Kulldorff’s elliptical spatial scan statistic in binomial model was used to locate clusters with high-risk of malnutrition, while adjusting for age and membership to government conditional cash transfer program as proxy for socio-economic status. One large significant cluster of under-five malnutrition was found southwest of the province, in which living in these areas at least doubles the risk of malnutrition. Additionally, at least one significant cluster were identified within each municipality—mostly located along the coastal areas. All these indicate apparent geographical variations across and within municipalities in the province. There were also similarities and disparities in the patterns of risk of malnutrition in each cluster across municipalities, and even within municipality, suggesting underlying causes at work that warrants further investigation. Therefore, community-appropriate programs and interventions should be identified and should be focused on high-risk areas to maximize limited government resources. Further studies are also recommended to determine factors affecting variations in childhood malnutrition considering the evidence of spatial clustering found in this study.

Keywords: Binomial model, Kulldorff’s elliptical spatial scan statistic, Philippines, under-five malnutrition

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Authors: Asma Naseer Cheema, Attya Bhatti, Jabar Ali, John Peter

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Background: High cholesterol levels, endothelial dysfunction, inefficient coagulation cascade and hyper inflammatory response all are the basis of coronary artery disease (CAD). Several studies are carried out to see the genetic influence of these factors on disease outcome. Objective: The objective of our study was to see the association of 10 putative loci with coronary artery disease in our population. Materials & Methods: We screened our population for 10 putative loci of CAD showing significant association (p < 5x10-8) with candidate genes (regulating the cholesterol metabolism, endothelial function, coagulation cascade and inflammatory response of body). Hardy-Weinberg equilibrium and linkage disequilibrium in cases and controls s were estimated separately. Approximately 5-10 ng of dried DNA in 384 well plate format was used to genotype each sample on the Sequenom iPLEX assay at University of Pittsburgh Genomics and Proteomics Core Laboratories. It was built on single-base primer extension with the MALDI-TOF MS detection possessing high sensitivity and specificity. The SNPs were genotyped through Taqman assay. Hardy Weinberg test was applied. The 10 SNPs were selected as genetic markers for this study (rs579459, rs1561198, rs2954029, rs1122608, rs17114036, rs9515203, rs10947789, rs7173743, rs2895811, rs2075650). Results: Mean age of the patient was 52 ± 11 years. Blood pressure and positive family history was found a significant risk factor for CAD. None of the selected SNPs showed significant association with coronary artery disease in our population (p>0.05). Conclusion: rs579459, rs1561198, rs2954029, rs1122608, rs17114036, rs9515203, rs10947789, rs7173743, rs2895811, rs2075650 are not significant genetic markers for CAD in our population.

Keywords: CAD, genetic markers, loci, risk factors

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Authors: Nur Aziza Luxfiati

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Clustering is partitioning data sets into sub-sets or groups in such a way that elements certain properties have shared property settings with a high level of similarity within one group and a low level of similarity between groups. . The K-Means algorithm is one of thealgorithmsclustering as a grouping tool that is most widely used in scientific and industrial applications because the basic idea of the kalgorithm is-means very simple. In this research, applying the technique of clustering using the k-means algorithm as a method of solving the problem of national development imbalances between regions in Central Java Province based on macroeconomic indicators. The data sample used is secondary data obtained from the Central Java Provincial Statistics Agency regarding macroeconomic indicator data which is part of the publication of the 2019 National Socio-Economic Survey (Susenas) data. score and determine the number of clusters (k) using the elbow method. After the clustering process is carried out, the validation is tested using themethodsBetween-Class Variation (BCV) and Within-Class Variation (WCV). The results showed that detection outlier using z-score normalization showed no outliers. In addition, the results of the clustering test obtained a ratio value that was not high, namely 0.011%. There are two district/city clusters in Central Java Province which have economic similarities based on the variables used, namely the first cluster with a high economic level consisting of 13 districts/cities and theclustersecondwith a low economic level consisting of 22 districts/cities. And in the cluster second, namely, between low economies, the authors grouped districts/cities based on similarities to macroeconomic indicators such as 20 districts of Gross Regional Domestic Product, with a Poverty Depth Index of 19 districts, with 5 districts in Human Development, and as many as Open Unemployment Rate. 10 districts.

Keywords: clustering, K-Means algorithm, macroeconomic indicators, inequality, national development

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Authors: Duaa Mughal, Syeda Areeba Nadeem, Shakil Ahmed, Ishtiaq Ahmed Khan

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The identification of porcine DNA in Halal food items is critical to ensuring compliance with dietary restrictions and religious beliefs. In Islam, Porcine is prohibited as clearly mentioned in Quran (Surah Al-Baqrah, Ayat 173). The purpose of this study was to compare two DNA extraction procedures for detecting 0.001% of porcine DNA in processed Halal food sample mixtures containing chicken, camel, veal, turkey and goat meat using the TaqMan Real-Time PCR technology. In this research, two different commercial kit protocols were compared. The processed sample mixtures were prepared by spiking known concentration of porcine DNA to non-porcine food matrices. Afterwards, TaqMan Real-Time PCR technique was used to target a particular porcine gene from the extracted DNA samples, which was quantified after extraction. The results of the amplification were evaluated for sensitivity, specificity, and reproducibility. The results of the study demonstrated that two DNA extraction techniques can detect 0.01% of porcine DNA in mixture of Halal food samples. However, as compared to the alternative approach, Eurofins| GeneScan GeneSpin DNA Isolation kit showed more effective sensitivity and specificity. Furthermore, the commercial kit-based approach showed great repeatability with minimal variance across repeats. Quantification of DNA was done by using fluorometric assay. In conclusion, the comparison of DNA extraction methods for detecting porcine DNA in Halal food sample mixes using the TaqMan Real-Time PCR technology reveals that the commercial kit-based approach outperforms the other methods in terms of sensitivity, specificity, and repeatability. This research helps to promote the development of reliable and standardized techniques for detecting porcine DNA in Halal food items, religious conformity and assuring nutritional.

Keywords: real time PCR (qPCR), DNA extraction, porcine DNA, halal food authentication, religious conformity

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Authors: Saman Namvarrechi, Armin A. Dormeny, Javad Dargahi, Mojtaba Kahrizi

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Since last two decades, minimally invasive surgery (MIS) has grown significantly due to its advantages compared to the traditional open surgery like less physical pain, faster recovery time and better healing condition around incision regions; however, one of the important challenges in MIS is getting an effective sensing feedback within the patient’s body during operations. Therefore, surgeons need efficient tactile sensing like determining the hardness of contact tissue for investigating the patient’s health condition. In such a case, MIS tactile sensors are preferred to be able to provide force/pressure sensing, force position, lump detection, and softness sensing. Among different pressure sensor technologies, the piezoelectric operating principle is the fittest for MIS’s instruments, such as catheters. Using PVDF with its copolymer, TrFE, as a piezoelectric material, is a common method of design and fabrication of a tactile sensor due to its ease of implantation and biocompatibility. In this research, PVDF-TrFE polymer is deposited via spin-coating method and treated with various post-deposition processes to investigate its piezoelectricity and amount of electroactive β phase. These processes include different post thermal annealing, the effect of spin-coating speed, different layer of deposition, and the presence of additional hydrate salt. According to FTIR spectroscopy and SEM images, the amount of the β phase and porosity of each sample is determined. In addition, the optimum experimental study is established by considering every aspect of the fabrication process. This study clearly shows the effective way of deposition and fabrication of a tactile PVDF-TrFE based sensor and an enhancement methodology to have a higher β phase and piezoelectric constant in order to have a better sense of touch at the end effector of biomedical devices.

Keywords: β phase, minimally invasive surgery, piezoelectricity, PVDF-TrFE, tactile sensor

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Authors: Mabel Usunobun Olanipekun, Henry Ogbemudia Omoregbee

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Anomalies such as water pipeline and hydraulic or petrochemical pipeline network leakages and bursts have significant implications for economic conditions and the environment. In order to ensure pipeline systems are reliable, they must be efficiently controlled. Wireless Sensor Networks (WSNs) have become a powerful network with critical infrastructure monitoring systems for water, oil and gas pipelines. The loss of water, oil and gas is inevitable and is strongly linked to financial costs and environmental problems, and its avoidance often leads to saving of economic resources. Substantial repair costs and the loss of precious natural resources are part of the financial impact of leaking pipes. Pipeline systems experts have implemented various methodologies in recent decades to identify and locate leakages in water, oil and gas supply networks. These methodologies include, among others, the use of acoustic sensors, measurements, abrupt statistical analysis etc. The issue of leak quantification is to estimate, given some observations about that network, the size and location of one or more leaks in a water pipeline network. In detecting background leakage, however, there is a greater uncertainty in using these methodologies since their output is not so reliable. In this work, we are presenting a scalable concept and simulation where a pressure-driven model (PDM) was used to determine water pipeline leakage in a system network. These pressure data were collected with the use of acoustic sensors located at various node points after a predetermined distance apart. We were able to determine with the use of correlation difference to determine the leakage point locally introduced at a predetermined point between two consecutive nodes, causing a substantial pressure difference between in a pipeline network. After de-noising the signal from the sensors at the nodes, we successfully obtained the exact point where we introduced the local leakage using the correlation difference model we developed.

Keywords: leakage detection, acoustic signals, pipeline network, correlation, wireless sensor networks (WSNs)

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Authors: Wafa Jahouach-Rabai, Khouloud Ouerghi, Zohra Azzouz-Berriche, Faouzi Hosni

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Widely used organic products in the pharmaceutical industry have been detected in environmental systems, essentially carboxylic acids. In this purpose, the degradation efficiency of these contaminants was evaluated using an advanced oxidation process (AOP), namely ionization process as an alternative to conventional water treatment technologies. This process permitted the generation of radical reactions to directly degrade organic pollutants in wastewater. In fact, gamma irradiation of aqueous solutions produces several reactive radicals, essentially hydroxyl radical (OH), to destroy recalcitrant pollutants. Different concentrations of aqueous solutions of Fumaric acid (FA) were considered in this study (0.1-1 mmol/L), which were treated by irradiation doses from 1 to 15 kGy with 6.1 kGy/h rate by ionizing system in pilot scale (⁶⁰Co irradiator). Variations of main parameters influencing degradation efficiency versus absorbed doses were released in the aim to optimize total mineralization of considered pollutants. Preliminary degradation pathway until complete mineralization into CO₂ has been suggested based on detection of residual degradation derivatives using different techniques, namely high performance liquid chromatography (HPLC) and electron paramagnetic resonance spectroscopy (EPR). Results revealed total destruction of treated compound, which improve the efficiency of this process in water remediation. We investigated the reactivity of hydroxyl radicals generated by irradiation on dicarboxylic acid (FA) in aqueous solutions, leading to its degradation into other smaller molecules. In fact, gamma irradiation of FA leads to the formation of hydroxylated intermediates such as hydroxycarbonyl radical which were identified by EPR spectroscopy. Finally, pilot plant irradiation facilities improved the applicability of radiation technology on large scale.

Keywords: AOP, radiolysis, fumaric acid, gamma irradiation, hydroxyl radical, EPR, HPLC

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Authors: N. Noori, P. Taherkhani, A. Akhondzadeh Basti, H. Gandomi, M. Alimohammadi

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Research on natural antimicrobial agents, especially of plant origin, highly noticed in recent years and evaluation of antimicrobial effects of native plants such as Bunium persicum Boiss. is especially important. In the present study, sensory characteristics and microbiological properties of Gouda cheese affected by different concentrations of Bunium persicum Boiss. essential oil were investigated. Extraction of the essential oil was performed by hydro distillation. The oil was analyzed by GC using flame ionization (FID) and GC/ MS for detection. The antimicrobial effects were determined against various microbial groups (aerobic mesophilic bacteria, enterococci, mesophilic lactobacilli, enterobacteriaceae, lactococcus and yeasts). Microbial groups were counted during ripening period using plate count on specific culture media. Organoleptic evaluation including teture, flavor, odor, color and total acceptability were determined at the end of aging. According to results, the essential oil yield was 4/1 % ( W/ W). Twenty- six compounds were identified in the oil that concluded 99.7 % of the total oil. The major components of Bunium persicum Boiss. essential oil were γ- terpinene- 7- al (26.9 %) and cuminaldehyde (23.3 %). Generally, the increase of Black Cumin essential oil concentration led to reduction in microbial counts in different groups. The maximum antimicrobial effect was seen in yeast that reduced by 2 log compared to the control group at EO concentration of 4µl/ ml at day 90.The minimum reduction was observed in enterobacteriaceae that showed only 0.75 log decreese compared to the control at the same concentration of EO. Addition of EO improved organoleptic properties of Gouda cheese especially in the case of flavor and odor characteristic. However, no significant differences were observed in texture and color between treatment and control groups. Bunium persicum Boiss. essential oil could be used as preservative material and flavoring agent in some kinds of food such as cheese and also could be provided consumers health.

Keywords: Bunium persicum Boiss. essential oil, Microbiological properties, sensory evaluation, gouda cheese

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Authors: Radka Gorejova, Ivana Sisolakova, Jana Shepa, Frederika Chovancova, Renata Orinakova

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Diabetes mellitus (DM) is a serious metabolic disease characterized by chronic hyperglycemia. Often, the symptoms are not sufficiently observable at early stages, and so hyperglycemia causes pathological and functional changes before the diagnosis of the DM. Therefore, the development of an electrochemical sensor that will be fast, accurate, and instrumentally undemanding is currently needful. Screen-printed carbon electrodes (SPCEs) can be considered as the most suitable matrix material for insulin sensors because of the small size of the working electrode. It leads to the analyst's volume reduction to only 50 µl for each measurement. The surface of bare SPCE was modified by a combination of chitosan, multi-walled carbon nanotubes (MWCNTs), and zinc nanoparticles (ZnNPs) to obtain better electrocatalytic activity towards insulin oxidation. ZnNPs were electrochemically deposited on the chitosan-MWCNTs/SPCE surface using the pulse deposition method. Thereafter, insulin was determined on the prepared electrode using chronoamperometry and electrochemical impedance spectroscopy (EIS). The chronoamperometric measurement was performed by adding a constant amount of insulin in 0.1 M NaOH and PBS (2 μl) with the concentration of 2 μM, and the current response of the system was monitored after a gradual increase in concentration. Subsequently, the limit of detection (LOD) of the prepared electrode was determined via the Randles-Ševčík equation. The LOD was 0.47 µM. Prepared electrodes were studied also as the impedimetric sensors for insulin determination. Therefore, various insulin concentrations were determined via EIS. Based on the performed measurements, the ZnNPs/chitosan-MWCNTs/SPCE can be considered as a potential candidate for novel electrochemical sensor for insulin determination. Acknowledgments: This work has been supported by the projects Visegradfund project number 22020140, VEGA 1/0095/21 of the Slovak Scientific Grant Agency, and APVV-PP-COVID-20-0036 of the Slovak Research and Development Agency.

Keywords: zinc nanoparticles, insulin, chronoamperometry, electrochemical impedance spectroscopy

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Authors: Rose A. Yemson, Ping Jiang, Oyedeji L. Inumoh

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The deployment of wireless sensor network has rapidly increased, however with the increased capacity and diversity of sensors, and applications ranging from biological, environmental, military etc. generates tremendous volume of data’s where more attention is placed on the distributed sensing and little on how to manage, analyze, retrieve and understand the data generated. This makes it more quite difficult to process live sensor data, run concurrent control and update because sensor data are either heavyweight, complex, and slow. This work will focus on developing a web service platform for automatic detection of sensors, acquisition of sensor data, storage of sensor data into a database, processing of sensor data using reconfigurable software components. This work will also create a web service based sensor data management system to monitor physical movement of an individual wearing wireless network sensor technology (SunSPOT). The sensor will detect movement of that individual by sensing the acceleration in the direction of X, Y and Z axes accordingly and then send the sensed reading to a database that will be interfaced with an internet platform. The collected sensed data will determine the posture of the person such as standing, sitting and lying down. The system is designed using the Unified Modeling Language (UML) and implemented using Java, JavaScript, html and MySQL. This system allows real time monitoring an individual closely and obtain their physical activity details without been physically presence for in-situ measurement which enables you to work remotely instead of the time consuming check of an individual. These details can help in evaluating an individual’s physical activity and generate feedback on medication. It can also help in keeping track of any mandatory physical activities required to be done by the individuals. These evaluations and feedback can help in maintaining a better health status of the individual and providing improved health care.

Keywords: HTML, java, javascript, MySQL, sunspot, UML, web-based, wireless network sensor

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Authors: Areti Angeliki Veroniki, Sofia Tsokani, Evangelos Paraskevaidis, Dimitris Mavridis

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The plethora of diagnostic test accuracy (DTA) studies has led to the increased use of systematic reviews and meta-analysis of DTA studies. Clinicians and healthcare professionals often consult DTA meta-analyses to make informed decisions regarding the optimum test to choose and use for a given setting. For example, the human papilloma virus (HPV) DNA, mRNA, and cytology can be used for the cervical intraepithelial neoplasia grade 2+ (CIN2+) diagnosis. But which test is the most accurate? Studies directly comparing test accuracy are not always available, and comparisons between multiple tests create a network of DTA studies that can be synthesized through a network meta-analysis of diagnostic tests (DTA-NMA). The aim is to summarize the DTA-NMA methods for at least three index tests presented in the methodological literature. We illustrate the application of the methods using a real data set for the comparative accuracy of HPV DNA, HPV mRNA, and cytology tests for cervical cancer. A search was conducted in PubMed, Web of Science, and Scopus from inception until the end of July 2019 to identify full-text research articles that describe a DTA-NMA method for three or more index tests. Since the joint classification of the results from one index against the results of another index test amongst those with the target condition and amongst those without the target condition are rarely reported in DTA studies, only methods requiring the 2x2 tables of the results of each index test against the reference standard were included. Studies of any design published in English were eligible for inclusion. Relevant unpublished material was also included. Ten relevant studies were finally included to evaluate their methodology. DTA-NMA methods that have been presented in the literature together with their advantages and disadvantages are described. In addition, using 37 studies for cervical cancer obtained from a published Cochrane review as a case study, an application of the identified DTA-NMA methods to determine the most promising test (in terms of sensitivity and specificity) for use as the best screening test to detect CIN2+ is presented. As a conclusion, different approaches for the comparative DTA meta-analysis of multiple tests may conclude to different results and hence may influence decision-making. Acknowledgment: This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning 2014-2020» in the context of the project “Extension of Network Meta-Analysis for the Comparison of Diagnostic Tests ” (MIS 5047640).

Keywords: colposcopy, diagnostic test, HPV, network meta-analysis

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Authors: A. A. Adebayo, O. Osibanjo

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Informal electronic waste (e-waste) processing is a crude method of recycling, which is on the increase in Nigeria. The release of hazardous substances such as heavy metals (HMs) into the environment during informal e-waste processing has been a major concern. However, there is insufficient information on environmental contamination from e-waste recycling, associated ecological risk in Alaba International Market, a major electronic market in Lagos, Nigeria. The aims of this study were to determine the levels of HMs in soil, resulting from the e-waste recycling; and also assess associated ecological risks in Alaba international market. Samples of soils (334) were randomly collected seasonally for three years from fourteen selected e-waste activity points and two control sites. The samples were digested using standard methods and HMs analysed by inductive coupled plasma optical emission. Ecological risk was estimated using Ecological Risk index (ER), Potential Ecological Risk index (RI), Index of geoaccumulation (Igeo), Contamination factor (Cf) and degree of contamination factor (Cdeg). The concentrations range of HMs (mg/kg) in soil were: 16.7-11200.0 (Pb); 14.3-22600.0 (Cu); 1.90-6280.0 (Ni), 39.5-4570.0 (Zn); 0.79-12300.0 (Sn); 0.02-138.0 (Cd); 12.7-1710.0 (Ba); 0.18-131.0 (Cr); 0.07-28.0 (V), while As was below detection limit. Concentrations range in control soils were 1.36-9.70 (Pb), 2.06-7.60 (Cu), 1.25-5.11 (Ni), 3.62-15.9 (Zn), BDL-0.56 (Sn), BDL-0.01 (Cd), 14.6-47.6 (Ba), 0.21–12.2 (Cr) and 0.22-22.2 (V). The trend in ecological risk index was in the order Cu > Pb > Ni > Zn > Cr > Cd > Ba > V. The potential ecological risk index with respect to informal e-waste activities were: burning > dismantling > disposal > stockpiling. The index of geo accumulation indices revealed that soils were extremely polluted with Cd, Cu, Pb, Zn and Ni. The contamination factor indicated that 93% of the studied areas have very high contamination status for Pb, Cu, Ba, Sn and Co while Cr and Cd were in the moderately contaminated status. The degree of contamination decreased in the order of Sn > Cu > Pb >> Zn > Ba > Co > Ni > V > Cr > Cd. Heavy metal contamination of Alaba international market environment resulting from informal e-waste processing was established. Proper management of e-waste and remediation of the market environment are recommended to minimize the ecological risks.

Keywords: Alaba international market, ecological risk, electronic waste, heavy metal contamination

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Authors: Natalia Rudeli, Elisabeth Viles, Adrian Santilli

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Delays in the construction industry are a global phenomenon. Many construction projects experience extensive delays exceeding the initially estimated completion time. The main purpose of this study is to identify construction projects typical behaviors in order to develop a prognosis and management tool. Being able to know a construction projects schedule tendency will enable evidence-based decision-making to allow resolutions to be made before delays occur. This study presents an innovative approach that uses Cluster Analysis Method to support predictions during Earned Value Analyses. A clustering analysis was used to predict future scheduling, Earned Value Management (EVM), and Earned Schedule (ES) principal Indexes behaviors in construction projects. The analysis was made using a database with 90 different construction projects. It was validated with additional data extracted from literature and with another 15 contrasting projects. For all projects, planned and executed schedules were collected and the EVM and ES principal indexes were calculated. A complete linkage classification method was used. In this way, the cluster analysis made considers that the distance (or similarity) between two clusters must be measured by its most disparate elements, i.e. that the distance is given by the maximum span among its components. Finally, through the use of EVM and ES Indexes and Tukey and Fisher Pairwise Comparisons, the statistical dissimilarity was verified and four clusters were obtained. It can be said that construction projects show an average delay of 35% of its planned completion time. Furthermore, four typical behaviors were found and for each of the obtained clusters, the interim milestones and the necessary rhythms of construction were identified. In general, detected typical behaviors are: (1) Projects that perform a 5% of work advance in the first two tenths and maintain a constant rhythm until completion (greater than 10% for each remaining tenth), being able to finish on the initially estimated time. (2) Projects that start with an adequate construction rate but suffer minor delays culminating with a total delay of almost 27% of the planned time. (3) Projects which start with a performance below the planned rate and end up with an average delay of 64%, and (4) projects that begin with a poor performance, suffer great delays and end up with an average delay of a 120% of the planned completion time. The obtained clusters compose a tool to identify the behavior of new construction projects by comparing their current work performance to the validated database, thus allowing the correction of initial estimations towards more accurate completion schedules.

Keywords: cluster analysis, construction management, earned value, schedule

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Authors: Windy Rakhmawati, Suryani Suryani, Sri Hendrawati, Nenden Nur Asriyani Maryam

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Tuberculosis (TB) is one of the leading causes of death in the world. Fear of COVID-19 has made people reluctant to visit health facilities, leading to disruptions to childhood TB control programs, which may increase household transmission and delay diagnosis and treatment. This study aimed to describe parents' knowledge, attitudes, and health-seeking behaviour towards childhood TB during the COVID-19 pandemic. This cross-sectional study was performed on 392 parents with TB children in three provinces with the highest proportion of TB cases in Indonesia. This study was conducted from February to December 2022. The inclusion criteria of respondents were parents with a child aged 0-14 years old with TB diagnosis who live with their parents. Data were collected using the Knowledge, Attitude, and Practice (KAP) survey guidelines from the World Health Organization and analyzed descriptively, as well as Spearman’s correlation. Overall, 392 parents of children with TB had poor knowledge (51.8%) including about causes, risk factors, transmission, symptoms, treatment, and prevention, which about 52.3%, 55.1%, 61.2%, 69.6%, 100%, 59.2%, respectively. Parents' health service-seeking behaviour towards Child TB was not normally distributed (P < 0.05) with knowledge test results (.000) and Seeking Health Services (.000). Health-seeking behaviour of parents in pediatric TB care was self-medication or self-treatment (86.2%), Traditional health seeking behaviour (4.8%), and modern health seeking behaviour (8.9%). The correlation between knowledge and seeking health services (Sig= .609) means there is no correlation between knowledge about TB and parents' health-seeking behaviour. Furthermore, 60.2% of the respondents would be shocked if their child had TB. More than half of the families in this study have poor knowledge and did self-medication or self-treatment regarding health-seeking behaviour for TB disease. Therefore, health workers, especially nurses, must provide TB-related education and health promotion and emphasize the importance of early detection. Health workers can also optimize their role in caring for and providing care to patients by increasing their trust in health workers, which will impact health-seeking behaviour in the future.

Keywords: attitude, child, health seeking behaviour, knowledge, tuberculosis

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Authors: Nina Ponikvar, Katja Zajc Kejžar

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While the positive impacts of heritage on a broad societal spectrum have long been recognized and measured, the economic effects of the heritage sector are often less visible and frequently underestimated. At macro level, economic effects are usually studied based on one of the two mainstream approach, i.e. either the multiplier approach or the value chain approach. Consequently, there is limited comparability of the empirical results due to the use of different methodological approach in the literature. Furthermore, it is also not clear on which criteria the used approach was selected. Our aim is to bring the attention to the difference in the scope of effects that are encompassed by the two most frequent methodological approaches to valuation of economic effects of cultural heritage on macroeconomic level, i.e. the multiplier approach and the value chain approach. We show that while the multiplier approach provides a systematic, theory-based view of economic impacts but requires more data and analysis, the value chain approach has less solid theoretical foundations and depends on the availability of appropriate data to identify the contribution of cultural heritage to other sectors. We conclude that the multiplier approach underestimates the economic impact of cultural heritage, mainly due to the narrow definition of cultural heritage in the statistical classification and the inability to identify part of the contribution of cultural heritage that is hidden in other sectors. Yet it is not possible to clearly determine whether the value chain method overestimates or underestimates the actual economic impact of cultural heritage since there is a risk that the direct effects are overestimated and double counted, but not all indirect and induced effects are considered. Accordingly, these two approaches are not substitutes but rather complementary. Consequently, a direct comparison of the estimated impacts is not possible and should not be done due to the different scope. To illustrate the difference of the impact assessment of the cultural heritage, we apply both approaches to the case of Slovenia in the 2015-2022 period and measure the economic impact of cultural heritage sector in terms of turnover, gross value added and employment. The empirical results clearly show that the estimation of the economic impact of a sector using the multiplier approach is more conservative, while the estimates based on value added capture a much broader range of impacts. According to the multiplier approach, each euro in cultural heritage sector generates an additional 0.14 euros in indirect effects and an additional 0.44 euros in induced effects. Based on the value-added approach, the indirect economic effect of the “narrow” heritage sectors is amplified by the impact of cultural heritage activities on other sectors. Accordingly, every euro of sales and every euro of gross value added in the cultural heritage sector generates approximately 6 euros of sales and 4 to 5 euros of value added in other sectors. In addition, each employee in the cultural heritage sector is linked to 4 to 5 jobs in other sectors.

Keywords: economic value of cultural heritage, multiplier approach, value chain approach, indirect effects, slovenia

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Authors: Edward Holupka, John Rossman, Tye Morancy, Joseph Aronovitz, Irving Kaplan

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A common modality for the treatment of early stage prostate cancer is the implantation of radioactive seeds directly into the prostate. The radioactive seeds are positioned inside the prostate to achieve optimal radiation dose coverage to the prostate. These radioactive seeds are positioned inside the prostate using Transrectal ultrasound imaging. Once all of the planned seeds have been implanted, two dimensional transaxial transrectal ultrasound images separated by 2 mm are obtained through out the prostate, beginning at the base of the prostate up to and including the apex. A common deep neural network, called DetectNet was trained to automatically determine the position of the implanted radioactive seeds within the prostate under ultrasound imaging. The results of the training using 950 training ultrasound images and 90 validation ultrasound images. The commonly used metrics for successful training were used to evaluate the efficacy and accuracy of the trained deep neural network and resulted in an loss_bbox (train) = 0.00, loss_coverage (train) = 1.89e-8, loss_bbox (validation) = 11.84, loss_coverage (validation) = 9.70, mAP (validation) = 66.87%, precision (validation) = 81.07%, and a recall (validation) = 82.29%, where train and validation refers to the training image set and validation refers to the validation training set. On the hardware platform used, the training expended 12.8 seconds per epoch. The network was trained for over 10,000 epochs. In addition, the seed locations as determined by the Deep Neural Network were compared to the seed locations as determined by a commercial software based on a one to three months after implant CT. The Deep Learning approach was within \strikeout off\uuline off\uwave off2.29\uuline default\uwave default mm of the seed locations determined by the commercial software. The Deep Learning approach to the determination of radioactive seed locations is robust, accurate, and fast and well within spatial agreement with the gold standard of CT determined seed coordinates.

Keywords: prostate, deep neural network, seed implant, ultrasound

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Authors: Yi-Shuan ShaoShao, Yen-An Tsai, Chia-Huang Chang, Kai-Wei Liao, Ming-Song Tsai, Mei-Lien Chen

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Organophosphate pesticides (OPs) is an environmental hormone with proven endocrine-disrupting effects that may affect the growth and development in human. A large amount of organophosphate pesticides (OPs) is used throughout Taiwan, and human may be exposed through dietary intake or residential use. During pregnancy, OPs can be transferred to the blood stream reaching the fetus through the placenta. The aim of this study was to explore the association between maternal OPs exposure levels and fetal developments and birth outcomes. A birth cohort was follow-up. Maternal urine sample were collected at the first, second, and third gestational trimester. Fetal growth characteristics were measured by ultrasonic scan and birth outcomes were assessed by pediatrician. Urinary metabolite of organophosphate pesticides were assessed using gas chromatography-mass spectrometry. The analytes included dimethylphosphate (DMP), dimethylthiophosphate (DMTP), dimethyldithiophosphates (DMDTP), diethylphosphate (DEP), diethylthiophosphate (DETP), and diethyldithiophosphate (DEDTP). We found that all of urine samples in each trimester were detected at least one metabolite for dialkyl phosphate (DAP). The detection rate range of OP urinary metabolites were from the lowest 22% DEDTP to the highest 100% DMP and DMTP. And to compared geometric means (GM) of urinary metabolites with three trimesters, that third trimester had the highest concentration for DMPs, DEPs, and DAPs in pregnant women were 368.01, 169.85 and 543.75 nmol/g creatinine, respectively. We observed that DAPs concentration in first and second trimester were significantly negative association with head circumference. DMPs in first trimester was significantly negative association with thoracic circumference (p=0.05) by spearman correlation. Our results support associations with prenatal OPs exposure with fetal head circumference and thoracic circumference. It provided that maternal OPs exposure might affect birth outcomes. Thus, prenatal exposure to OPs and health risk worthy of attention and concern.

Keywords: DAPs, birth outcomes, organophosphate pesticides, prenatal

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Authors: Fuyuan Ma, Yuhan Wang, Junhe Zhang, Ying Wang

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Identifying the sources of information dissemination is a pivotal task in the study of collective behaviors in networks, enabling us to discern and intercept the critical pathways through which information propagates from its origins. This allows for the control of the information’s dissemination impact in its early stages. Numerous methods for source detection rely on pre-existing, underlying propagation models as prior knowledge. Current models that eschew prior knowledge attempt to harness label propagation algorithms to model the statistical characteristics of propagation states or employ Graph Neural Networks (GNNs) for deep reverse modeling of the diffusion process. These approaches are either deficient in modeling the propagation patterns of information or are constrained by the over-smoothing problem inherent in GNNs, which limits the stacking of sufficient model depth to excavate global propagation patterns. Consequently, we introduce the ODESI model. Initially, the model employs a label propagation algorithm to delineate the distribution density of infected states within a graph structure and extends the representation of infected states from integers to state vectors, which serve as the initial states of nodes. Subsequently, the model constructs a deep architecture based on GNNs-coupled Ordinary Differential Equations (ODEs) to model the global propagation patterns of continuous propagation processes. Addressing the challenges associated with solving ODEs on graphs, we approximate the analytical solutions to reduce computational costs. Finally, we conduct simulation experiments on two real-world social network datasets, and the results affirm the efficacy of our proposed ODESI model in source identification tasks.

Keywords: source identification, ordinary differential equations, label propagation, complex networks

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Authors: Eirini Konstanta, Cedric Gouedard, Aggeliki Delimitsou, Stefania Patera, Samuel Murray

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Introduction: Quality reference DNA standards (QRS) for molecular testing by next-generation sequencing (NGS) are essential for accurate quantitation of copy number variations (CNV) for germline and variant allelic frequencies (VAF) for somatic analyses. Objectives: Presently, several molecular analytics for oncology patients are reliant upon quantitative metrics. Test validation and standardisation are also reliant upon the availability of surrogate control materials allowing for understanding test LOD (limit of detection), sensitivity, specificity. We have developed a dual calibration platform allowing for QRS pairs to be included in analysed DNA samples, allowing for accurate quantitation of CNV and VAF metrics within and between patient samples. Methods: QRS™ blocks up to 500nt were designed for common NGS panel targets incorporating ≥ 2 identification tags (IDTDNA.com). These were analysed upon spiking into gDNA, somatic, and ctDNA using a proprietary CalSuite™ platform adaptable to common LIMS. Results: We demonstrate QRS™ calibration reproducibility spiked to 5–25% at ± 2.5% in gDNA and ctDNA. Furthermore, we demonstrate CNV and VAF within and between samples (gDNA and ctDNA) with the same reproducibility (± 2.5%) in a clinical sample of lung cancer and HBOC (EGFR and BRCA1, respectively). CNV analytics was performed with similar accuracy using a single pair of QRS calibrators when using multiple single targeted sequencing controls. Conclusion: Dual paired QRS™ calibrators allow for accurate and reproducible quantitative analyses of CNV, VAF, intrinsic sample allele measurement, inter and intra-sample measure not only simplifying NGS analytics but allowing for monitoring clinically relevant biomarker VAF across patient ctDNA samples with improved accuracy.

Keywords: calibrator, CNV, gene copy number, VAF

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Authors: Barun K. De

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Acquired immunodeficiency syndrome (AIDS) is caused by two types of human immunodeficiency viruses, collectively designated HIV. HIV infection is spreading globally particularly in developing countries. Before an individual is diagnosed with HIV, the disease goes through different phases. First there is an acute early phase that is followed by an established or chronic phase. Subsequently, there is a latency period after which the individual becomes immunodeficient. It is in the acute phase that an individual is highly infectious due to a high viral load. Presently, HIV diagnosis involves use of tests that do not detect the acute phase infection during which both the viral RNA and p24 antigen are expressed. Instead, these less sensitive tests detect antibodies to viral antigens which are typically sero-converted later in the disease process following acute infection. These antibodies are detected in both asymptomatic HIV-infected individuals as well as AIDS patients. Studies indicate that early diagnosis and treatment of HIV infection can reduce medical costs, improve survival, and reduce spreading of infection to new uninfected partners. Newer 4th generation combination antigen/antibody tests are highly sensitive and specific for detection of acute and established HIV infection (HIV1 and HIV2) enabling immediate linkage to care. The CDC (Center of Disease Control, USA) recently recommended an algorithm involving three different tests to screen and diagnose acute and established infections of HIV-1 and HIV-2 in a general population. Initially a 4th generation combo test detects a viral antigen p24 and specific antibodies against HIV -1 and HIV-2 envelope proteins. If the test is positive it is followed by a second test known as a differentiation assay which detects antibodies against specific HIV-1 and HIV-2 envelope proteins confirming established infection of HIV-1 or HIV-2. However if it is negative then another test is performed that measures viral load confirming an acute HIV-1 infection. Screening results of a Phoenix area population detected 0.3% new HIV infections among which 32.4% were acute cases. Studies in the U.S. indicate that this algorithm effectively reduces HIV infection through immediate treatment and education following diagnosis.

Keywords: new algorithm, HIV, diagnosis, infection

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Authors: Alvaro Figueira, Bruno Cabral

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Nowadays, students are used to be assessed through an online platform. Educators have stepped up from a period in which they endured the transition from paper to digital. The use of a diversified set of question types that range from quizzes to open questions is currently common in most university courses. In many courses, today, the evaluation methodology also fosters the students’ online participation in forums, the download, and upload of modified files, or even the participation in group activities. At the same time, new pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted using an eLearning system for that purpose. However, although there can be a lot of feedback from these activities to student’s, usually it is restricted to the assessments of online well-defined tasks. In this article, we propose an automatic system that informs students of abnormal deviations of a 'correct' learning path in the course. Our approach is based on the fact that by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student’s current online actions towards the course. Our goal is to prevent situations that have a significant probability to lead to a poor grade and, eventually, to failing. In the major learning management systems (LMS) currently available, the interaction between the students and the system itself is registered in log files in the form of registers that mark beginning of actions performed by the user. Our proposed system uses that logged information to derive new one: the time each student spends on each activity, the time and order of the resources used by the student and, finally, the online resource usage pattern. Then, using the grades assigned to the students in previous years, we built a learning dataset that is used to feed a machine learning meta classifier. The produced classification model is then used to predict the grades a learning path is heading to, in the current year. Not only this approach serves the teacher, but also the student to receive automatic feedback on her current situation, having past years as a perspective. Our system can be applied to online courses that integrate the use of an online platform that stores user actions in a log file, and that has access to other student’s evaluations. The system is based on a data mining process on the log files and on a self-feedback machine learning algorithm that works paired with the Moodle LMS.

Keywords: data mining, e-learning, grade prediction, machine learning, student learning path

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