Search results for: data acquisition

Authors: Julia Wimmers Klick

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Interprofessional Education (IPE) is widely recognized as a valuable approach in healthcare education, despite the challenges it presents. This study explores IP surface anatomy lab sessions, with a focus on fostering trust and collaboration among healthcare students. The research is conducted within the context of rural healthcare settings in British Columbia (BC), where a medical school and a physical therapy (PT) program operate under the Faculty of Medicine at the University of British Columbia (UBC). While IPE sessions addressing soft skills have been implemented, the integration of hard skills, such as Anatomy, remains limited. To address this gap, a pilot feasibility study was conducted with a positive outcome, a follow-up study involved these IPE sessions aimed at exploring the influence of bonding and trust between medical and PT students. Data were collected through focus groups comprising participating students and faculty members, and a structured SWOC (Strengths, Weaknesses, Opportunities, and Challenges) analysis was conducted. The IPE sessions, 3 in total, consisted of a 2.5-hour lab on surface anatomy, where PT students took on the teaching role, and medical students were newly exposed to surface anatomy. The focus of the study was on the relationship-building process and trust development between the two student groups, rather than assessing the acquisition of surface anatomy skills. Results indicated that the surface anatomy lab served as a suitable tool for the application and learning of soft skills. Faculty members observed positive outcomes, including productive interaction between students, reversed hierarchy with PT students teaching medical students, practicing active listening skills, and using a mutual language of anatomy. Notably, there was no grade assessment or external pressure to perform. The students also reported an overall positive experience; however, the specific impact on the development of soft skill competencies could not be definitively determined. Participants expressed a sense of feeling respected, welcomed, and included, all of which contributed to feeling safe. Within the small group environment, students experienced becoming a part of a community of healthcare providers that bonded over a shared interest in health professions education. They enjoyed sharing diverse experiences related to learning across their varied contexts, without fear of judgment and reprisal that were often intimidating in single professional contexts. During a joint Christmas party for both cohorts, faculty members observed students mingling, laughing, and forming bonds. This emphasized the importance of early bonding and trust development among healthcare colleagues, particularly in rural settings. In conclusion, the findings emphasize the potential of IPE sessions to enhance trust and collaboration among healthcare students, with implications for their future professional lives in rural settings. Early bonding and trust development are crucial in rural settings, where healthcare professionals often rely on each other. Future research should continue to explore the impact of content-concentrated IPE on the development of soft skill competencies.

Keywords: interprofessional education, rural healthcare settings, trust, surface anatomy

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Authors: Frantisek Odvarka

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Based on the data from the Moravian Karst, the influence of the calcite speleothem growth by selected meteorological factors was evaluated. External temperature was determined as one of the main factors influencing speleothem growth in Moravian Karst. This factor significantly influences the CO₂ concentration in soil/epikarst, and cave atmosphere in the Moravian Karst and significantly contributes to the changes in the CO₂ partial pressure differences between soil/epikarst and cave atmosphere in Moravian Karst, which determines the drip water supersaturation with respect to the calcite and quantity of precipitated calcite in the Moravian Karst cave environment. External air temperatures and cave air temperatures were measured using a COMET S3120 data logger, which can measure temperatures in the range from -30 to +80 °C with an accuracy of ± 0.4 °C. CO₂ concentrations in the cave and soils were measured with a FT A600 CO₂H Ahlborn probe (value range 0 ppmv to 10,000 ppmv, accuracy 1 ppmv), which was connected to the data logger ALMEMO 2290-4, V5 Ahlborn. The soil temperature was measured with a FHA646E1 Ahlborn probe (temperature range -20 to 70 °C, accuracy ± 0.4 °C) connected to an ALMEMO 2290-4 V5 Ahlborn data logger. The airflow velocities into and out of the cave were monitored by a FVA395 TH4 Thermo anemometer (speed range from 0.05 to 2 m s⁻¹, accuracy ± 0.04 m s⁻¹), which was connected to the ALMEMO 2590-4 V5 Ahlborn data logger for recording. The flow was measured in the lower and upper entrance of the Imperial Cave. The data were analyzed in MS Office Excel 2019 and PHREEQC.

Keywords: speleothem growth, carbon dioxide partial pressure, Moravian Karst, external temperature

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Authors: Carine Cridelich, Pablo Juesas Cano, Emmanuel Ramasso, Noureddine Zerhouni, Bernd Weiler

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Safety is one of the most important considerations when buying a new car. While active safety aims at avoiding accidents, passive safety systems such as airbags and seat belts protect the occupant in case of an accident. In addition to legal regulations, organizations like Euro NCAP provide consumers with an independent assessment of the safety performance of cars and drive the development of safety systems in automobile industry. Those ratings are mainly based on injury assessment reference values derived from physical parameters measured in dummies during a car crash test. The components and sub-systems of a safety system are designed to achieve the required restraint performance. Sled tests and other types of tests are then carried out by car makers and their suppliers to confirm the protection level of the safety system. A Knowledge Discovery in Databases (KDD) process is proposed in order to minimize the number of tests. The KDD process is based on the data emerging from sled tests according to Euro NCAP specifications. About 30 parameters of the passive safety systems from different data sources (crash data, dummy protocol) are first analysed together with experts opinions. A procedure is proposed to manage missing data and validated on real data sets. Finally, a procedure is developed to estimate a set of rough initial parameters of the passive system before testing aiming at reducing the number of tests.

Keywords: KDD process, passive safety systems, sled test, dummy injury assessment reference values, frontal impact

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Authors: Yafit Gabay

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Attention deficit hyperactivity disorder (ADHD) has been associated with both suboptimal functioning in the striatum and prefrontal cortex. Such abnormalities may impede the acquisition of perceptual categories, which are important for fundamental abilities such as object recognition and speech perception. Indeed, prior research has supported this possibility, demonstrating that children with ADHD have similar visual category learning performance as their neurotypical peers but use suboptimal learning strategies. However, much less is known about category learning processes in the auditory domain or among adults with ADHD in which prefrontal functions are more mature compared to children. Here, we investigated auditory and visual perceptual category learning in adults with ADHD and neurotypical individuals. Specifically, we examined learning of rule-based categories – presumed to be optimally learned by a frontal cortex-mediated hypothesis testing – and information-integration categories – hypothesized to be optimally learned by a striatally-mediated reinforcement learning system. Consistent with striatal and prefrontal cortical impairments observed in ADHD, our results show that across sensory modalities, both rule-based and information-integration category learning is impaired in adults with ADHD. Computational modeling analyses revealed that individuals with ADHD were slower to shift to optimal strategies than neurotypicals, regardless of category type or modality. Taken together, these results suggest that both explicit, frontally mediated and implicit, striatally mediated category learning are impaired in ADHD. These results suggest impairments across multiple learning systems in young adults with ADHD that extend across sensory modalities and likely arise from domain-general mechanisms.

Keywords: ADHD, category learning, modality, computational modeling

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Authors: Maria A. Castillo H., Andrés R. Leandro C.

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During the last decades, agricultural productivity in Latin America has increased with precision agriculture and more efficient agricultural technologies. The use of automated systems, satellite images, geographic information systems, and tools for data analysis, and artificial intelligence have contributed to making more effective strategic decisions. Twenty years ago, the state of Mérida, located in the Venezuelan Andes, reported the largest area covered by greenhouses in the country, where certified seeds of potatoes, vegetables, ornamentals, and flowers were produced for export and consumption in the central region of the country. In recent years, it is estimated that production under greenhouses has changed, and the area covered has decreased due to different factors, but there are few historical statistical data in sufficient quantity and quality to support this estimate or to be used for analysis and decision making. The objective of this study is to compare data collected about geoposition, use, and covered areas of the greenhouses in 2007 to data available in 2021, as support for the analysis of the current situation of horticultural production in the main municipalities of the state of Mérida. The document presents the development of the work in the diagnosis and integration of geographic coordinates in GIS and data analysis phases. As a result, an evaluation of the process is made, a dashboard is presented with the most relevant data along with the geographical coordinates integrated into GIS, and an analysis of the obtained information is made. Finally, some recommendations for actions are added, and works that expand the information obtained and its geographical traceability over time are proposed. This study contributes to granting greater certainty in the supporting data for the evaluation of social, environmental, and economic sustainability indicators and to make better decisions according to the sustainable development goals in the area under review. At the same time, the methodology provides improvements to the agricultural data collection process that can be extended to other study areas and crops.

Keywords: greenhouses, geographic information system, protected agriculture, data analysis, Venezuela

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Authors: Paul Campbell, Nicholas Biddle

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There is a complex, endogenous relationship between individual circumstances, attitudes, and behaviour. This study uses longitudinal panel data to assess changes in social and political attitudes over a 7-year period. Attitudes are captured with the question 'what is the most important issue facing Australia today', collected at multiple time points in a longitudinal survey of 2200 Australians. Consistency of attitudes, and factors predicting change over time, are assessed. The consistency of responses has methodological implications for data collection, specifically how often such questions ought to be asked of a population. When change in attitude is observed, this study assesses the extent to which individual demographic characteristics, personality traits, and broader societal events predict change.

Keywords: attitudes, longitudinal survey analysis, personality, social values

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Authors: Ana Mafalda Silva, Rebeca Fontes, Ana Paula Vaz, Carla Carreira, Ana Corte-Real

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Decades of research on the topic of interpersonal violence against minors highlight five main conclusions: 1) it causes harmful effects on children's development and health; 2) it is prevalent; 3) it violates children's rights; 4) it can be prevented and 5) parents are the main aggressors. The child abuse scenario is identified through clinical observation, administrative data and self-reports. The most used instruments are self-reports; however, there are no valid and reliable self-report instruments for minors, which consist of a retrospective interpretation of the situation by the victim already in her adult phase and/or by her parents. Clinical observation and collection of information, namely from the orofacial region, are essential in the early identification of these situations. The management of medical data, such as personal data, must comply with the General Data Protection Regulation (GDPR), in Europe, and with the General Law of Data Protection (LGPD), in Brazil. This review aims to answer the question: In a situation of medical assistance to minors, in the suspicion of interpersonal violence, due to mistreatment, is it necessary for the guardians to provide consent in the registration and sharing of personal data, namely medical ones. A scoping review was carried out based on a search by the Web of Science and Pubmed search engines. Four papers and two documents from the grey literature were selected. As found, the process of identifying and signaling child abuse by the health professional, and the necessary early intervention in defense of the minor as a victim of abuse, comply with the guidelines expressed in the GDPR and LGPD. This way, the notification in maltreatment scenarios by health professionals should be a priority and there shouldn’t be the fear or anxiety of legal repercussions that stands in the way of collecting and treating the data necessary for the signaling procedure that safeguards and promotes the welfare of children living with abuse.

Keywords: child abuse, disease notifications, ethics, healthcare assistance

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Authors: Haitao Yang, Zhenjiang Ruan, Fei Xu, Lanting Xia

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Industry data centers often need to sync data changes reliably and instantly from a large-scale of heterogeneous autonomous relational databases accessed via the not-so-reliable Internet, for which a practical universal sync middle of low maintenance and operation costs is most wanted, but developing such a product and adapting it for various scenarios are a very sophisticated and continuous practice. The authors have been devising, applying, and optimizing a generic sync middleware system, named GSMS since 2006, holding the principles or advantages that the middleware must be SyncML-compliant and transparent to data application layer logic, need not refer to implementation details of databases synced, does not rely on host computer operating systems deployed, and its construction is light weighted and hence, of low cost. A series of ultimate experiments with GSMS sync performance were conducted for a persuasive example of a source relational database that underwent a broad range of write loads, say, from one thousand to one million intensive writes within a few minutes. The tests proved that GSMS has achieved an instant sync level of well below a fraction of millisecond per record sync, and GSMS’ smooth performances under ultimate write loads also showed it is feasible and competent.

Keywords: heterogeneous massive data, instantly sync intensive writes, Internet generic middleware design, optimization

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Authors: Penina Orenstein

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In today’s world, particularly with COVID-19 a constant worldwide threat, organizations need greater visibility over their supply chains more than ever before, in order to find areas for improvement and greater efficiency, reduce the chances of disruption and stay competitive. The concept of supply chain mapping is one where every process and route is mapped in detail between each vendor and supplier. The simplest method of mapping involves sourcing publicly available data including news and financial information concerning relationships between suppliers. An additional layer of information would be disclosed by large, direct suppliers about their production and logistics sites. While this method has the advantage of not requiring any input from suppliers, it also doesn’t allow for much transparency beyond the first supplier tier and may generate irrelevant data—noise—that must be filtered out to find the actionable data. The primary goal of this research is to build data maps of supply chains by focusing on a layered approach. Using these maps, the secondary goal is to address the question as to whether the supply chain is re-engineered to make improvements, for example, to lower the carbon footprint. Using a drill-down approach, the end result is a comprehensive map detailing the linkages between tier-one, tier-two, and tier-three suppliers super-imposed on a geographical map. The driving force behind this idea is to be able to trace individual parts to the exact site where they’re manufactured. In this way, companies can ensure sustainability practices from the production of raw materials through the finished goods. The approach allows companies to identify and anticipate vulnerabilities in their supply chain. It unlocks predictive analytics capabilities and enables them to act proactively. The research is particularly compelling because it unites network science theory with empirical data and presents the results in a visual, intuitive manner.

Keywords: data mining, supply chain, empirical research, data mapping

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Authors: Kenneth A. Rostowsky, Alexander S. Maher, Nahian F. Chowdhury, Andrei Irimia

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The clinical significance of cerebral microbleeds (CMBs) due to mild traumatic brain injury (mTBI) remains unclear. Here we use magnetic resonance imaging (MRI), diffusion tensor imaging (DTI) and connectomic analysis to investigate the statistical association between mTBI-related CMBs, post-TBI changes to the human connectome and neurological/cognitive deficits. This study was undertaken in agreement with US federal law (45 CFR 46) and was approved by the Institutional Review Board (IRB) of the University of Southern California (USC). Two groups, one consisting of 26 (13 females) mTBI victims and another comprising 26 (13 females) healthy control (HC) volunteers were recruited through IRB-approved procedures. The acute Glasgow Coma Scale (GCS) score was available for each mTBI victim (mean µ = 13.2; standard deviation σ = 0.4). Each HC volunteer was assigned a GCS of 15 to indicate the absence of head trauma at the time of enrollment in our study. Volunteers in the HC and mTBI groups were matched according to their sex and age (HC: µ = 67.2 years, σ = 5.62 years; mTBI: µ = 66.8 years, σ = 5.93 years). MRI [including T1- and T2-weighted volumes, gradient recalled echo (GRE)/susceptibility weighted imaging (SWI)] and gradient echo (GE) DWI volumes were acquired using the same MRI scanner type (Trio TIM, Siemens Corp.). Skull-stripping and eddy current correction were implemented. DWI volumes were processed in TrackVis (http://trackvis.org) and 3D Slicer (http://www.slicer.org). Tensors were fit to DWI data to perform DTI, and tractography streamlines were then reconstructed using deterministic tractography. A voxel classifier was used to identify image features as CMB candidates using Microbleed Anatomic Rating Scale (MARS) guidelines. For each peri-lesional DTI streamline bundle, the null hypothesis was formulated as the statement that there was no neurological or cognitive deficit associated with between-scan differences in the mean FA of DTI streamlines within each bundle. The statistical significance of each hypothesis test was calculated at the α = 0.05 level, subject to the family-wise error rate (FWER) correction for multiple comparisons. Results: In HC volunteers, the along-track analysis failed to identify statistically significant differences in the mean FA of DTI streamline bundles. In the mTBI group, significant differences in the mean FA of peri-lesional streamline bundles were found in 21 out of 26 volunteers. In those volunteers where significant differences had been found, these differences were associated with an average of ~47% of all identified CMBs (σ = 21%). In 12 out of the 21 volunteers exhibiting significant FA changes, cognitive functions (memory acquisition and retrieval, top-down control of attention, planning, judgment, cognitive aspects of decision-making) were found to have deteriorated over the six months following injury (r = -0.32, p < 0.001). Our preliminary results suggest that acute post-TBI CMBs may be associated with cognitive decline in some mTBI patients. Future research should attempt to identify mTBI patients at high risk for cognitive sequelae.

Keywords: traumatic brain injury, magnetic resonance imaging, diffusion tensor imaging, connectomics

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Authors: N. Pegahfar, P. Ghafarian

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In this research, the synoptic weather conditions during the heavy flood of 10-12 January 2020 in the Sistan-va-Balouchestan Province of Iran will be analyzed. To this aim, reanalysis data from the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR), NCEP Global Forecasting System (GFS) analysis data, measured data from a surface station together with satellite images from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) have been used from 9 to 12 January 2020. Atmospheric parameters both at the lower troposphere and also at the upper part of that have been used, including absolute vorticity, wind velocity, temperature, geopotential height, relative humidity, and precipitation. Results indicated that both lower-level and upper-level currents were strong. In addition, the transport of a large amount of humidity from the Oman Sea and the Red Sea to the south and southeast of Iran (Sistan-va-Balouchestan Province) led to the vast and unexpected precipitation and then a heavy flood.

Keywords: Sistan-va-Balouchestn Province, heavy flood, synoptic, analysis data

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Authors: C. A. Ologunde

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Escherichia coli have been a major microorganisms associated with, and isolated from feacal samples either in adult or children all over the world. Strains of these organisms are resistant to cephalosporins and fluoroquinolone (FQ) antimicrobial agents among hospitalized patients and FQs are the most frequently prescribed antimicrobial class in hospitals, and the level of resistant of E. coli to these antimicrobial agents is a risk factor that should be assessed. Hence, this study was conducted to determine the prevalence and risk factors for colonization with fluoroquinolone (FQ)-resistant E. coli in hospitalized patients in Ado-Ekiti. Rectal swabs were obtained from patients in hospitals in the study area and FQ-resistant E. coli were isolated and identified by means of Nalidixic acid multi-disk and a 1-step screening procedure. Species identification and FQ resistance were confirmed by automated testing (Vitek, bioMerieux, USA). Individual colonies were subjected to pulse-field gel electrophoresis (PAGE) to determine macro-restriction polymorphism after digestion of chromosomal DNA. FQ-resistant E. coli was detected in the stool sample of 37(62%) hospitalized patient. With multivariable analyses, the use of FQ before hospitalization was the only independent risk factor for FQ-resistant E. coli carriage and was consistent for FQ exposures for the 3-12 months of study. Pulsed-field gel electrophoresis of FQ-resistant E. coli identified conal spread of 1(one) strain among 18 patients. Loss (9 patients) or acquisition (10 residents) of FQ-resistant E. coli was documented and was associated with de novo colonization with genetically distinct strains. It was concluded that FQ-resistant E. coli carriage was associated with clonal spread. The differential effects of individual fluoroquinolone on antimicrobial drug resistance are an important area for future study, as hospitals manipulate their formularies with regard to use of individual fluoroquinolone, often for economic reasons.

Keywords: E. coli, fluoroquinolone, risk factors, feacal carriage, hospitalized patients, Ado-Ekiti

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Authors: Amit Prakash Singh, Shyamli Singh, Chavi Srivastav

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This paper presents the idea of Internet of Thing (IoT) for the infrastructure of smart cities. Internet of Thing has been visualized as a communication prototype that incorporates myriad of digital services. The various component of the smart cities shall be implemented using microprocessor, microcontroller, sensors for network communication and protocols. IoT enabled systems have been devised to support the smart city vision, of which aim is to exploit the currently available precocious communication technologies to support the value-added services for function of the city. Due to volume, variety, and velocity of data, it requires analysis using Big Data concept. This paper presented the various techniques used to analyze big data using machine learning.

Keywords: IoT, smart city, embedded systems, sustainable environment

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Authors: Yingisani Chabalala, Elhadi Adam, Khalid Adem Ali

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Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapped spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit tree mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well-suited for accurate smallholder fruit plantation mapping.

Keywords: smallholder agriculture, fruit trees, data fusion, precision agriculture

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Authors: Sombol Mokhles

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Tackling climate change requires expanding networking opportunities between a diverse range of cities to accelerate climate actions. Existing climate city networks have limitations in actively engaging “ordinary” cities in networking processes between cities, as they encourage a few powerful cities to be followed by the many “ordinary” cities. To reimagine the networking opportunities between cities beyond global cities, this paper incorporates “cosmopolitan comparison” to expand our knowledge of a diverse range of cities using a data-driven approach. Through a cosmopolitan perspective, a framework is presented on how to utilise large data to expand knowledge of cities beyond global cities to reimagine the existing hierarchical networking practices. The contribution of this framework is beyond urban climate governance but inclusive of different fields which strive for a more inclusive and cosmopolitan comparison attentive to the differences across cities.

Keywords: cosmopolitan city comparison, data-driven approach, climate city networking, urban climate governance

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Authors: Muhammad Shuaibu Birniwa

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Automobile Independent Workshop Service Technicians (popularly called roadside mechanics) are technical personals that repairs most of the automobile vehicles in Nigeria. Majority of these mechanics acquired their skills through apprenticeship training. Modern vehicle imported into the country posed greater challenges to the present automobile technicians particularly in the area of carrying out maintenance repairs of these latest automobile vehicles (autotronics vehicle) due to their inability to possessed autotronic skills competency. To source for solution to the above mentioned problems, therefore a research is carried out in North – Western region of Nigeria to produce a suitable maintenance training modules that can be used to train the technicians for them to upgrade/acquire the needed competencies for successful maintenance repair of the autotronic vehicles that were running everyday on the nation’s roads. A cluster sampling technique is used to obtain a sample from the population. The population of the study is all autotronic inclined lecturers, instructors and independent workshop service technicians that are within North – Western region of Nigeria. There are seven states (Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto and Zamfara) in the study area, these serves as clusters in the population. Five (5) states were randomly selected to serve as the sample size. The five states are Jigawa, Kano, Katsina, Kebbi and Zamfara, the entire population of the five states which serves as clusters is (183), lecturers (44), instructors (49) and autotronic independent workshop service technicians (90), all of them were used in the study because of their manageable size. 183 copies of autotronic maintenance training module questionnaires (AMTMQ) with 174 and 149 question items respectively were administered and collected by the researcher with the help of an assistants, they are administered to 44 Polytechnic lecturers in the department of mechanical engineering, 49 instructors in skills acquisition centres/polytechnics and 90 master craftsmen of an independent workshops that are autotronic inclined. Data collected for answering research questions 1, 3, 4 and 5 were analysed using SPSS software version 22, Grand Mean and standard deviation were used to answer the research questions. Analysis of Variance (ANOVA) was used to test null hypotheses one (1) to three (3) and t-test statistical tool is used to analyzed hypotheses four (4) and five (5) all at 0.05 level of significance. The research conducted revealed that; all the objectives, contents/tasks, facilities, delivery systems and evaluation techniques contained in the questionnaire were required for the development of the autotronic maintenance training modules for independent workshop service technicians in the north – western zone of Nigeria. The skills upgrade training conducted by federal government in collaboration with SURE-P, NAC and SMEDEN was not successful because the educational status of the target population was not considered in drafting the needed training modules. The mode of training used does not also take cognizance of the theoretical aspect of the trainees, especially basic science which rendered the programme ineffective and insufficient for the tasks on ground.

Keywords: autotronics, roadside, mechanics, technicians, independent

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

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Authors: Muhammad Usman

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Pattern extraction has been done in past to extract hidden and interesting patterns from large datasets. Recently, advancements are being made in these techniques by providing the ability of multi-level mining, effective dimension reduction, advanced evaluation and visualization support. This paper focuses on reviewing the current techniques in literature on the basis of these parameters. Literature review suggests that most of the techniques which provide multi-level mining and dimension reduction, do not handle mixed-type data during the process. Patterns are not extracted using advanced algorithms for large datasets. Moreover, the evaluation of patterns is not done using advanced measures which are suited for high-dimensional data. Techniques which provide visualization support are unable to handle a large number of rules in a small space. We present a theoretical model to handle these issues. The implementation of the model is beyond the scope of this paper.

Keywords: association rule mining, data mining, data warehouses, visualization of association rules

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Authors: I. N. Nwafia, U. C. Ozumba, M. E. Ohanu, S. O. Ebede

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Antibiotic resistance is increasing globally and has become a major health challenge. Extended-spectrum beta-lactamase is clinically important because the ESBL gene are mostly plasmid encoded and these plasmids frequently carry genes encoding resistance to other classes of antimicrobials thereby limiting antibiotic options in the treatment of infections caused by these organisms. The specific objectives of this study were to determine the prevalence of ESBLs production in Escherichia coli, to determine the antibiotic susceptibility pattern of ESBLs producing Escherichia coli, to detect TEM, SHV and CTX-M genes and the risk factors to acquisition of ESBL producing Escherichia coli. The protocol of the study was approved by Health Research and Ethics committee of the University of Nigeria Teaching Hospital (UNTH), Enugu. It was a descriptive cross-sectional study that involved all hospitalized patients in UNTH from whose specimens Escherichia coli was isolated during the period of the study. The samples analysed were urine, wound swabs, blood and cerebrospinal fluid. These samples were cultured in 5% sheep Blood agar and MacConkey agar (Oxoid Laboratories, Cambridge UK) and incubated at 35-370C for 24 hours. Escherichia coli was identified with standard biochemical tests and confirmed using API 20E auxanogram (bioMerieux, Marcy 1'Etoile, France). The antibiotic susceptibility testing was done by disc diffusion method and interpreted according to the Clinical and Laboratory Standard Institute guideline. ESBL production was confirmed using ESBL Epsilometer test strips (Liofilchem srl, Italy). The ESBL bla genes were detected with polymerase chain reaction, after extraction of DNA with plasmid mini-prep kit (Jena Bioscience, Jena, Germany). Data analysis was with appropriate descriptive and inferential statistics. One hundred and six isolates (53.00%) out of the 200 were from urine, followed by isolates from different swabs specimens 53(26.50%) and the least number of the isolates 4(2.00) were from blood (P value = 0.096). Seventy (35.00%) out of the 200 isolates, were confirmed positive for ESBL production. Forty-two (60.00%) of the isolates were from female patients while 28(40.00%) were from male patients (P value = 0.13). Sixty-eight (97.14%) of the isolates were susceptible to imipenem while all of the isolates were resistant to ampicillin, chloramphenicol and tetracycline. From the 70 positive isolates the ESBL genes detected with polymerase chain reaction were blaCTX-M (n=26; 37.14%), blaTEM (n=7; 10.00%), blaSHV (n=2; 2.86%), blaCTX-M/TEM (n=7; 10.0%), blaCTX-M/SHV (n=14; 20.0%) and blaCTX-M/TEM/SHV (n=10; 14.29%). There was no gene detected in 4(5.71%) of the isolates. The most associated risk factors to infections caused by ESBL producing Escherichia coli was previous antibiotics use for the past 3 months followed by admission in the intensive care unit, recent surgery, and urinary catheterization. In conclusion, ESBLs was detected in 4 of every 10 Escherichia coli with the predominant gene detected being CTX-M. This knowledge will enable appropriate measures towards improvement of patient health care, antibiotic stewardship, research and infection control in the hospital.

Keywords: antimicrobial, Escherichia coli, extended spectrum beta lactamase, resistance

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Authors: Zhan Panpan, Lu Lan, Sun Yong, He Xiongwen, Yan Dong, Gu Ming

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The function of the two spacecraft docking network, the communication and control of a docking target with various spacecrafts is realized in the space lab data management system. In order to solve the problem of the complex data communication mode between the space lab and various spaceships, and the problem of software reuse caused by non-standard protocol, a data management software system supporting rendezvous and docking with various spaceships has been designed. The software system is based on CCSDS Spcecraft Onboard Interface Service(SOIS). It consists of Software Driver Layer, Middleware Layer and Appliaction Layer. The Software Driver Layer hides the various device interfaces using the uniform device driver framework. The Middleware Layer is divided into three lays, including transfer layer, application support layer and system business layer. The communication of space lab plaform bus and the docking bus is realized in transfer layer. Application support layer provides the inter tasks communitaion and the function of unified time management for the software system. The data management software functions are realized in system business layer, which contains telemetry management service, telecontrol management service, flight status management service, rendezvous and docking management service and so on. The Appliaction Layer accomplishes the space lab data management system defined tasks using the standard interface supplied by the Middleware Layer. On the basis of layered architecture, rendezvous and docking tasks and the rendezvous and docking management service are independent in the software system. The rendezvous and docking tasks will be activated and executed according to the different spaceships. In this way, the communication management functions in the independent flight mode, the combination mode of the manned spaceship and the combination mode of the cargo spaceship are achieved separately. The software architecture designed standard appliction interface for the services in each layer. Different requirements of the space lab can be supported by the use of standard services per layer, and the scalability and flexibility of the data management software can be effectively improved. It can also dynamically expand the number and adapt to the protocol of visiting spaceships. The software system has been applied in the data management subsystem of the space lab, and has been verified in the flight of the space lab. The research results of this paper can provide the basis for the design of the data manage system in the future space station.

Keywords: space lab, rendezvous and docking, data management, software system

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Authors: Javier De La Torre Costa, Belen Rubio Ballester, Martina Maier, Paul F. M. J. Verschure

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After a stroke, a great number of patients experience persistent motor impairments such as hemiparesis or weakness in one entire side of the body. As a result, the lack of use of the paretic limb might be one of the main contributors to functional loss after clinical discharge. We aim to reverse this cycle by promoting the use of the paretic limb during activities of daily living (ADLs). To do so, we describe the key components of a system that is composed of a wearable bracelet (i.e., a smartwatch) and a mobile phone, designed to bring a set of neurorehabilitation principles that promote acquisition, retention and generalization of skills to the home of the patient. A fundamental question is whether the loss in motor function derived from learned–non–use may emerge as a consequence of decision–making processes for motor optimization. Our system is based on well-established rehabilitation strategies that aim to reverse this behaviour by increasing the reward associated with action execution as well as implicitly reducing the expected cost associated with the use of the paretic limb, following the notion of the reinforcement–induced movement therapy (RIMT). Here we validate an accelerometer–based measure of arm use, and its capacity to discriminate different activities that require increasing movement of the arm. We also show how the system can act as a personalized assistant by providing specific goals and adjusting them depending on the performance of the patients. The usability and acceptance of the device as a rehabilitation tool is tested using a battery of self–reported and objective measurements obtained from acute/subacute patients and healthy controls. We believe that an extension of these technologies will allow for the deployment of unsupervised rehabilitation paradigms during and beyond the hospitalization time.

Keywords: stroke, wearables, learned non use, hemiparesis, ADLs

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Authors: Youhang Zhou, Weimin Zeng, Qi Xie

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Abstract: In order to solve the wear recognition problem of the machine tool guide surface, a new machine tool guide surface recognition method based on the radar-graph barycentre feature is presented in this paper. Firstly, the gray mean value, skewness, projection variance, flat degrees and kurtosis features of the guide surface image data are defined as primary characteristics. Secondly, data Visualization technology based on radar graph is used. The visual barycentre graphical feature is demonstrated based on the radar plot of multi-dimensional data. Thirdly, a classifier based on the support vector machine technology is used, the radar-graph barycentre feature and wear original feature are put into the classifier separately for classification and comparative analysis of classification and experiment results. The calculation and experimental results show that the method based on the radar-graph barycentre feature can detect the guide surface effectively.

Keywords: guide surface, wear defects, feature extraction, data visualization

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Authors: Min Kyung An

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In Wireless Sensor Networks which consist of tiny wireless sensor nodes with limited battery power, one of the most fundamental applications is data aggregation which collects nearby environmental conditions and aggregates the data to a designated destination, called a sink node. Important issues concerning the data aggregation are time efficiency and energy consumption due to its limited energy, and therefore, the related problem, named Minimum Latency Aggregation Scheduling (MLAS), has been the focus of many researchers. Its objective is to compute the minimum latency schedule, that is, to compute a schedule with the minimum number of timeslots, such that the sink node can receive the aggregated data from all the other nodes without any collision or interference. For the problem, the two interference models, the graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR), have been adopted with different power models, uniform-power and non-uniform power (with power control or without power control), and different antenna models, omni-directional antenna and directional antenna models. In this survey article, as the problem has proven to be NP-hard, we present and compare several state-of-the-art approximation algorithms in various models on the basis of latency as its performance measure.

Keywords: data aggregation, convergecast, gathering, approximation, interference, omni-directional, directional

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Authors: Ebrahim Alrashed

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Wireless sensor networks are vulnerable to attacks from adversaries attempting to disrupt their operations. Sink-hole attacks are a type of attack where an adversary node drops data forwarded through it and hence affecting the reliability and accuracy of the network. Since sensor nodes have limited battery power, it is essential that any solution to the sinkhole attack problem be very energy-aware. In this paper, we present a reliable and energy efficient scheme to forward data from source nodes to the base station while under sink-hole attack. The scheme also detects sink-hole attack nodes and avoid paths that includes them.

Keywords: energy-aware routing, reliability, sink-hole attack, WSN

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Authors: Helen K. Buteme, Rebecca Axelsson-Robertson, Moses L. Joloba, Henry W. Boom, Gunilla Kallenius, Markus Maeurer

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Background: The Ugandan population is heavily affected by infectious diseases and Human leukocyte antigen (HLA) diversity plays a crucial role in the host-pathogen interaction and affects the rates of disease acquisition and outcome. The identification of HLA class 1 alleles and determining which alleles are associated with tuberculosis (TB) outcomes would help in screening individuals in TB endemic areas for susceptibility to TB and to predict resistance or progression to TB which would inevitably lead to better clinical management of TB. Aims: To be able to determine the HLA class 1 phenotype distribution in a Ugandan TB cohort and to establish the relationship between these phenotypes and active and latent TB. Methods: Blood samples were drawn from 32 HIV negative individuals with active TB and 45 HIV negative individuals with latent MTB infection. DNA was extracted from the blood samples and the DNA samples HLA typed by the polymerase chain reaction-sequence specific primer method. The allelic frequencies were determined by direct count. Results: HLA-A*02, A*01, A*74, A*30, B*15, B*58, C*07, C*03 and C*04 were the dominant phenotypes in this Ugandan cohort. There were differences in the distribution of HLA types between the individuals with active TB and the individuals with LTBI with only HLA-A*03 allele showing a statistically significant difference (p=0.0136). However, after FDR computation the corresponding q-value is above the expected proportion of false discoveries (q-value 0.2176). Key findings: We identified a number of HLA class I alleles in a population from Central Uganda which will enable us to carry out a functional characterization of CD8+ T-cell mediated immune responses to MTB. Our results also suggest that there may be a positive association between the HLA-A*03 allele and TB implying that individuals with the HLA-A*03 allele are at a higher risk of developing active TB.

Keywords: HLA, phenotype, tuberculosis, Uganda

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Authors: Abdelaziz Fellah, Allaoua Maamir

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We propose a domain-independent merging-cluster filter approach complemented with a set of algorithms for identifying approximate duplicate entities efficiently and accurately within a single and across multiple data sources. The near-optimal merging-cluster filter (MCF) approach is based on the Monge-Elkan well-tuned algorithm and extended with an affine variant of the Smith-Waterman similarity measure. Then we present constant, variable, and function threshold algorithms that work conceptually in a divide-merge filtering fashion for detecting near duplicates as hierarchical clusters along with their corresponding representatives. The algorithms take recursive refinement approaches in the spirit of filtering, merging, and updating, cluster representatives to detect approximate duplicates at each level of the cluster tree. Experiments show a high effectiveness and accuracy of the MCF approach in detecting approximate duplicates by outperforming the seminal Monge-Elkan’s algorithm on several real-world benchmarks and generated datasets.

Keywords: data mining, data cleaning, approximate duplicates, near-duplicates detection, data mining applications and discovery

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Authors: S. H. Liao, J. M. Huang

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The delivery service business model is the final link in logistics for both online-and-offline businesses. The online-and-offline business model focuses on the entire customer purchasing process online and offline, placing greater emphasis on the importance of data to optimize overall retail operations. For the retail industry, it is an important task of information and management to strengthen the collection and investigation of consumers' online and offline purchasing data to better understand customers and then recommend products. This study implements two-stage data mining analytics for clustering and association rules analysis to investigate Taiwanese consumers' (n=2,209) preferences for delivery service. This process clarifies online-and-offline purchasing behaviors and preferences to find knowledge profiles/patterns/rules for cross-channel collaborative recommendations. Finally, theoretical and practical implications for methodology and enterprise are presented.

Keywords: delivery service, online-and-offline purchasing, retail cross-channel, collaborative recommendations, data mining analytics

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Authors: Najmeh Abedzadeh, Matthew Jacobs

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An intrusion detection system (IDS) is a software application that monitors malicious activities and generates alerts if any are detected. However, most network activities in IDS datasets are normal, and the relatively few numbers of attacks make the available data imbalanced. Consequently, cyber-attacks can hide inside a large number of normal activities, and machine learning algorithms have difficulty learning and classifying the data correctly. In this paper, a comprehensive literature review is conducted on different types of algorithms for both implementing the IDS and methods in correcting the imbalanced IDS dataset. The most famous algorithms are machine learning (ML), deep learning (DL), synthetic minority over-sampling technique (SMOTE), and reinforcement learning (RL). Most of the research use the CSE-CIC-IDS2017, CSE-CIC-IDS2018, and NSL-KDD datasets for evaluating their algorithms.

Keywords: IDS, imbalanced datasets, sampling algorithms, big data

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Authors: Pappas Theodoros, Mihail Diakomihalis

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Measuring the economic impact of tourism in a benchmark economy is a global concern, with previous measurements being partial and not fully integrated. Tourism is a phenomenon that requires individual consumption of visitors and which should be observed and measured to reveal, thus, the overall contribution of tourism to an economy. The Tourism Satellite Account (TSA) is a critical tool for assessing the annual growth of tourism, providing reliable measurements. This article introduces a system of TSA information that encompasses all the works of the TSA, including input, storage, management, and analysis of data, as well as additional future functions and enhances the efficiency of tourism data management and TSA collection utility. The methodology and results presented offer insights into the development and implementation of TSA.

Keywords: tourism satellite account, information system, data-based tourist account, relation database

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Authors: Bulcha Belay Etana, Benny Malengier, Janarthanan Krishnamoorthy, Timothy Kwa, Lieva VanLangenhove

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Electromyography measures the electrical activity of muscles using surface electrodes or needle electrodes to monitor various disease conditions. Recent developments in the signal acquisition of electromyograms using textile electrodes facilitate wearable devices, enabling patients to monitor and control their health status outside of healthcare facilities. Here, we have developed and tested wearable textile electrodes to acquire electromyography signals from patients suffering from Parkinson’s disease and incorporated a feedback-control system to relieve muscle cramping through thermal stimulus. In brief, the textile electrodes made of stainless steel was knitted into a textile fabric as a sleeve, and their electrical characteristic, such as signal-to-noise ratio, was compared with traditional electrodes. To relieve muscle cramping, a heating element made of stainless-steel conductive yarn sewn onto cotton fabric, coupled with a vibration system, was developed. The system integrated a microcontroller and a Myoware muscle sensor to activate the heating element as well as the vibration motor when cramping occurs, and at the same time, the element gets deactivated when the muscle cramping subsides. An optimum therapeutic temperature of 35.5 °C is regulated by continuous temperature monitoring to deactivate the heating system when this threshold value is reached. The textile electrode exhibited a signal-to-noise ratio of 6.38dB, comparable to that of the traditional electrode’s value of 7.05 dB. For a given 9 V power supply, the rise time was about 6 minutes for the developed heating element to reach an optimum temperature.

Keywords: smart textile system, wearable electronic textile, electromyography, heating textile, vibration therapy, Parkinson’s disease

Procedia PDF Downloads 106