Search results for: fracture detection

Authors: S. Ayeko, J. Milton, C. Hughes, K. Anderson, R. G. Middleton

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Background: Periprosthetic femoral fractures (PFF) in association with hip hemiarthroplasty or total hip arthroplasty is a common and serious complication. In the Vancouver Classification system algorithm, B1 fractures should be treated with Open Reduction and Internal Fixation (ORIF) and preferentially revised in combination with ORIF if B2 or B3. This study aims to assess patient outcomes after plate osteosynthesis alone for Vancouver B1 and B2 fractures. The main outcome is the 1-year re-revision rate, and secondary outcomes are 30-day and 1-year mortality. Method: This is a retrospective single-centre case-series review from January 2016 to June 2021. Vancouver B1 and B2, non-malignancy fractures in adults over 18 years of age treated with polyaxial Non-Contact Bridging plate osteosynthesis, have been included. Outcomes were gathered from electronic notes and radiographs. Results: There were 50 B1 and 64 B2 fractures. 26 B2 fractures were managed with ORIF and revision, 39 ORIF alone. Of the revision group, one died within 30 days (3.8%), one at one year (3.8%), and two were revised within one year (7.7). Of the B2 ORIF group, three died within 30-day mortality (7.96%), eight at one year (21.1%), and 0 were revised in 1 year. Conclusion: This study has demonstrated that satisfactory outcomes can be achieved with ORIF, excluding revision in the management of B2 fractures.

Keywords: arthroplasty, bridging plate, periprosthetic fracture, revision surgery

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Authors: Ogbuagu Josephat Okechukwu, Okeke Abuchi Princewill, Arinze Rosemary Uche, Tabugbo Ifeyinwa Blessing, Ogbuagu Adaora Stellamaris

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Phthalates or Phthalate esters (PAEs), categorized as an endocrine disruptor and persistent organic pollutants, are known for their environmental contamination and toxicological effects. In this study, the concentration of selected phthalates was determined across the sampling site to investigate their occurrence and the ecological and health risk assessment they pose to the environment. Water and sediment samples were collected following standard procedures. Solid phase and ultrasonic methods were used to extract seven different PAEs, which were analyzed by Gas Chromatography with Mass Detector (GCMS). The analytical average recovery was found to be within the range of 83.4% ± 2.3%. The results showed that PAEs were detected in six out of seven samples with a high percentage of detection rate in water. Di-n-butyl phthalate (DPB) and disobutyl phthalates (DiBP) showed a greater detection rate compared to other PAE monomers. The concentration of PEs was found to be higher in sediment samples compared to water samples due to the fact that sediments serve as a sink for most persistent organic pollutants. The concentrations of PAEs in water samples and sediments ranged from 0.00 to 0.23 mg/kg and 0.00 to 0.028 mg/l, respectively. Ecological risk assessment using the risk quotient method (RQ) reveals that the estimated environmental risk caused by phthalates lies within the moderate level as RQ ranges from 0.1 to 1.0, whereas the health risk assessment caused by phthalates on estimating the average daily dose reveals that the ingestion of phthalates was found to be approaching permissible limit which can cause serious carcinogenic occurrence in the human system with time due to excess accumulation.

Keywords: phthalates, endocrine disruptor, risk assessment, ecological risk, health risk

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Authors: R. K. Yadav, S. Baral, H. R. Paudel, R. Basnet

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The Public-Private Mix (PPM) approach is a strategic initiative that involves engaging all private and public healthcare providers in the fight against tuberculosis using international healthcare standards. For tuberculosis control in Nepal, the PPM approach could be a milestone. This study aimed to explore the barriers to a public-private mix approach in the management of tuberculosis cases in Nepal. A total of 20 respondents participated in the study. Barriers to PPM were identified in the following three themes: 1) Obstacles related to TB case detection, 2) Obstacles related to patients, and 3) Obstacles related to the healthcare system. PPM implementation was challenged by following subthemes that included staff turnover, low private sector participation in workshops, a lack of training, poor recording and reporting, insufficient joint monitoring and supervision, poor financial benefit, lack of coordination and collaboration, and non-supportive TB-related policies and strategies. The study concludes that numerous barriers exist in the way of effective implementation of the PPM approach, including TB cases detection barriers such as knowledge of TB diagnosis and treatment, HW attitude, workload, patient-related barriers such as knowledge of TB, self-medication practice, stigma and discrimination, financial status, and health system-related barriers such as staff turnover and poor engagement of the private sector in workshops, training, recording, and re-evaluation. Government stakeholders must work together with private sector stakeholders to perform joint monitoring and supervision. Private practitioners should receive training and orientation, and presumptive TB patients should be given adequate time and counseling as well as motivation to visit a government health facility.

Keywords: barrier, tuberculosis, case finding, PPM, nepal

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Authors: Offer E. Edelstein, Iris Vered, Orly Sarid

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Background: Physical activity (PA) is considered as a major factor in bone density preservation and fracture prevention. Yet, gaps in understanding exist regarding how ethnocultural backgrounds might shape attitudes, intentions, and actual PA participation. Based on the theory of planned behavior (TPB) for predicting PA, the aims of the current study were: i) to compare attitudes, subjective norms, perceived control, intentions and knowledge, across two ethnocultural groups; ii) to evaluate the fit of the model across two ethnocultural groups of women: Israeli-born Jews and Ethiopian immigrants. Methods: Two hundred women (one hundred from each group), aged > 65, completed valid and reliable questionnaires assessing knowledge, TPB components, and actual PA. Results: The level of knowledge on osteoporosis was relatively low in both groups. Intention to participate in PA was the only variable that directly predicted actual PA. Intention to participate in PA served as a mediator among attitudes, subjective norms, perceived control, and actual PA. The TPB components mediated the link between knowledge and intention to participate in PA. Conclusion: It is important to understand and augment interventions that enhance PA, in the community, and with sensitivity concerning each ethnocultural group.

Keywords: attitudes, ethnocultural groups, knowledge, physical activity

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Authors: Adesuyi Ayodeji Steve, Zahn Munch

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This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

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Authors: Aanh Eduardo Dimate-Garcia, Diana Carolina Rodriguez-Romero, Edna Yuliana Gonzalez Rincon, Diana Marcela Pardo Lopez, Yessica Garibello Cubillos

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Musculoskeletal disorders (MD) are the new epidemic of chronic diseases, are multifactorial and affect the different productive sectors. Although there are multiple instruments to evaluate the static and dynamic load, the method of repetitive occupational action (OCRA) seems to be an attractive option. Objective: It is aimed to analyze the use of the OCRA method and the prevalence of MD in workers of various productive sectors according to the literature (2007-2018). Materials and Methods: A literature review (following the PRISMA statement) of studies aimed at assessing the level of biomechanical risk (OCRA) and the prevalence of MD in the databases Scielo, Science Direct, Scopus, ProQuest, Gale, PubMed, Lilacs and Ebsco was realized; 7 studies met the selection criteria; the majority are quantitative (cross section). Results: it was evidenced (gardening and flower-growers) in this review that 79% of the conditions related to the task require physical requirements and involve repetitive movements. In addition, of the high appearance of DM in the high-low back, upper and lower extremities that are produced by the frequency of the activities carried out (footwear production). Likewise, there was evidence of 'very high risks' of developing MD (salmon industry) and a medium index (OCRA) for repetitive movements that require special care (U-Assembly line). Conclusions: the review showed the limited use of the OCRA method for the detection of MD in workers from different sectors, and this method can be used for the detection of biomechanical risk and the appearance of MD.

Keywords: checklist, cumulative trauma disorders, musculoskeletal diseases, repetitive movements

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Authors: Swathi Gopakumar, Sruthi Krishna, Shivasubramani Krishnamoorthy

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Contactless, painless and radiation-free thermal imaging technology is one of the preferred screening modalities for detection of breast cancer. However, poor signal to noise ratio and the inexorable need to preserve edges defining cancer cells and normal cells, make the segmentation process difficult and hence unsuitable for computer-aided diagnosis of breast cancer. This paper presents key findings from a research conducted on the appraisal of two promising techniques, for the detection of breast cancer: (I) marker-controlled, Level-set segmentation of anisotropic diffusion filtered preprocessed image versus (II) Segmentation using marker-controlled level-set on a Gaussian-filtered image. Gaussian-filtering processes the image uniformly, whereas anisotropic filtering processes only in specific areas of a thermographic image. The pre-processed (Gaussian-filtered and anisotropic-filtered) images of breast samples were then applied for segmentation. The segmentation of breast starts with initial level-set function. In this study, marker refers to the position of the image to which initial level-set function is applied. The markers are generally placed on the left and right side of the breast, which may vary with the breast size. The proposed method was carried out on images from an online database with samples collected from women of varying breast characteristics. It was observed that the breast was able to be segmented out from the background by adjustment of the markers. From the results, it was observed that as a pre-processing technique, anisotropic filtering with level-set segmentation, preserved the edges more effectively than Gaussian filtering. Segmented image, by application of anisotropic filtering was found to be more suitable for feature extraction, enabling automated computer-aided diagnosis of breast cancer.

Keywords: anisotropic diffusion, breast, Gaussian, level-set, thermograms

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Şeyma Özçirak Ergün, Ergün Şakalar, Emrah Yalazi̇, Nebahat Şahi̇n

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Food irradiation is a usage of exposing food to ionising radiation (IR) such as gamma rays. IR has been used to decrease the number of harmful microorganisms in the food such as spices. Excessive usage of IR can cause damage to both food and people who consuming food. And also it causes to damages on food DNA. Generally, IR detection techniques were utilized in literature for spices are Electron Spin Resonance (ESR), Thermos Luminescence (TL). Storage creates negative effect on IR detection method then analyses of samples have been performed without storage in general. In the experimental part, red pepper (Capsicum annuum) and mint (Mentha piperita) as spices were exposed to 0, 0.272, 0.497, 1.06, 3.64, 8.82, and 17.42 kGy ionize radiation. ESR was applied to samples irradiated. DNA isolation from irradiated samples was performed using GIDAGEN Multi Fast DNA isolation kit. The DNA concentration was measured using a microplate reader spectrophotometer (Infinite® 200 PRO-Life Science–Tecan). The concentration of each DNA was adjusted to 50 ng/µL. Genomic DNA was imaged by UV transilluminator (Gel Doc XR System, Bio-Rad) for the estimation of genomic DNA bp-fragment size after IR. Thus, agarose gel profiles of irradiated spices were obtained to determine the change of band profiles. Besides, samples were examined at three different time periods (0, 3, 6 months storage) to show the feasibility of developed method. Results of gel electrophoresis showed especially degradation of DNA of irradiated samples. In conclusion, this study with gel electrophoresis can be used as a basis for the identification of the dose of irradiation by looking at degradation profiles at specific amounts of irradiation. Agarose gel results of irradiated samples were confirmed with ESR analysis. This method can be applied widely to not only food products but also all biological materials containing DNA to predict radiation-induced damage of DNA.

Keywords: DNA, electrophoresis, gel electrophoresis, ionizeradiation

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Authors: Abdul Khalique Baloch, Ali Okatan

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The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.

Keywords: deep learning, computer vision, image processing, rotten fruit detection, fruits quality criteria, vegetables quality criteria

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Authors: Nadia El Alami El Hassani, Soukaina Motia, Benachir Bouchikhi, Nezha El Bari

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Doxycycline (DXy) is a cycline antibiotic, most frequently prescribed to treat bacterial infections in veterinary medicine. However, its broad antimicrobial activity and low cost, lead to an intensive use, which can seriously affect human health. Therefore, its spread in the food products has to be monitored. The scope of this work was to synthetize a sensitive and very selective molecularly imprinted polymer (MIP) for DXy detection in honey samples. Firstly, the synthesis of this biosensor was performed by casting a layer of carboxylate polyvinyl chloride (PVC-COOH) on the working surface of a gold screen-printed electrode (Au-SPE) in order to bind covalently the analyte under mild conditions. Secondly, DXy as a template molecule was bounded to the activated carboxylic groups, and the formation of MIP was performed by a biocompatible polymer by the mean of polyacrylamide matrix. Then, DXy was detected by measurements of differential pulse voltammetry (DPV). A non-imprinted polymer (NIP) prepared in the same conditions and without the use of template molecule was also performed. We have noticed that the elaborated biosensor exhibits a high sensitivity and a linear behavior between the regenerated current and the logarithmic concentrations of DXy from 0.1 pg.mL⁻¹ to 1000 pg.mL⁻¹. This technic was successfully applied to determine DXy residues in honey samples with a limit of detection (LOD) of 0.1 pg.mL⁻¹ and an excellent selectivity when compared to the results of oxytetracycline (OXy) as analogous interfering compound. The proposed method is cheap, sensitive, selective, simple, and is applied successfully to detect DXy in honey with the recoveries of 87% and 95%. Considering these advantages, this system provides a further perspective for food quality control in industrial fields.

Keywords: doxycycline, electrochemical sensor, food control, gold nanoparticles, honey, molecular imprinted polymer

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Authors: Rosli Mohd Yunus, A. K. M. Moshiul Alam, Mohammad Dalour Beg

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In the case of advance polymeric materials reinforcement and thermal stability of matrix is a focused arena of researchers. The distribution of carbon nanotubes (CNTs) in polymer matrix influences material properties. In this study, multi-walled carbon nanotubes (MWCNTs) have been dispersed in unsaturated polyester resin (UPR) through solution mixing and sonication techniques using tetra hydro furan (THF) solvent. Nanocomposites have been fabricated with solution mixing and without solution mixing. Viscosity, Fourier-transform infrared spectroscopy, Field emission scanning electron microscopy (FESEM) investigations have been conducted to study the distribution as well as interaction between matrix and MWCNT. The differential scanning calorimetry (DSC), thermogravimetric analyses (TGA) and pyrolysis behavior have been conducted to study the thermal degradation and stability of nanocomposites. In addition, the SEM micrographs of nanocomposite residual chars were exhibited more packed together. Incorporation of CNT enhances crystallinity and mechanical and thermal properties of the nanocomposites. Correlations among MWCNTs dispersion, nucleation, fracture morphology and various properties have been made.

Keywords: char, multiwall carbon nanotubes, nano composite, pyrolysis

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Authors: Miladi Yasmine, Bouhlel Salah, Garnit Hechmi, David Banks

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The Jebel Mecella F (Ba-Pb-Zn) ore deposits of the Zaghouan district are located in northeastern Tunisia, 60 km south of Tunis. The host rocks belong to the Ressas Formation of Kimmeridgian-Tithonian age and lower Cretaceous layers. Mineralisations occur as stratiform lenses and fracture fillings. The ore mineral assemblage is composed of fluorite, barite, sphalerite galena, and quartz. Primary fluid inclusions in sphalerite have homogenization temperatures ranging from 129 to 145°C final melting temperature range from -14.9 to -10.0, corresponding to salinities of 14.0 to 17.7 wt% NaCl equivalent. Fluid inclusions in fluorite homogenize to the liquid phase between 116 and 160°C. The final ice melting temperature ranges from -23 to -15 °C, corresponding to salinities between 17 and 24 wt% NaCl equivalent. The LAICP-MS analyses of the fluid inclusions in fluorite show that these fluids are dominated by Na>K>Mg. Furthermore, the high K/Na values from fluid inclusions suggest the brine interacted with K-rich rocks in the basement or in siliciclastic sediments in the basins. The ore fluids in Jebel Mecella are highly saline and Na-K dominated with lower Mg concentrations, and come from the leaching of the dolomitic host rocks. These results are compatible with Mississippi-Valley-type mineralizing fluids.

Keywords: Jebel Mecella, fluid inclusions, micro thermometry, LA-ICP-MS

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Authors: Subodh N. Patel, Abhijit Pusty, Manashi Adhikary, Sandip Bhattacharyya

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The steam superheater (SH) is a coil type heat exchanger which is used to produce superheated steam or to convert the wet steam to dry steam (69.6 kg/cm² and 495°C), generated by a boiler. There were two superheaters in the system, SH I and SH II. SH II is a set of tubes that faces the initial interaction with flue gas at high temperature followed by SH I tubes. After a service life of 2100 hours, a tube in the SH II found to be punctured. Dye penetrant test revealed that out of 50 such tubes, 14 more tubes had severe cracks at a similar location. The failure was investigated in detail. The materials and scale were characterized by optical microscope and advance characterization technique. Scale, observed on fracture surface, was characterized under scanning electron microscope and Raman spectroscopy. Stresses acting on the tubes in working condition were analyzed by finite element method software, ANSYS. Cyclic stresses were observed in the simulation at the same prone location due to restriction in expansion of tubes. Based on scale characterization and stress analysis, it was concluded that the tube failed in thermo-mechanical fatigue. Finally, prevention and control measures were taken to avoid such failure in the future.

Keywords: finite element analysis, oxide scale, superheater tube, thermomechanical fatigue

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Authors: Melissa Ramirez, Mohammad Awal

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The Inflow Performance Relationship (IPR) of a well is a relation between the oil production rate and flowing bottom-hole pressure. This relationship is an important tool for petroleum engineers to understand and predict the well performance. In the petroleum industry, IPR correlations are used to design and evaluate well completion, optimizing well production, and designing artificial lift. The most commonly used IPR correlations models are Vogel and Wiggins, these models are applicable to homogeneous and isotropic reservoir data. In this work, a new IPR model is developed to determine inflow performance relationship of oil wells in a naturally fracture reservoir. A 3D black-oil reservoir simulator is used to develop the oil mobility function for the studied reservoir. Based on simulation runs, four flow rates are run to record the oil saturation and calculate the relative permeability for a naturally fractured reservoir. The new method uses the result of a well test analysis along with permeability and pressure-volume-temperature data in the fluid flow equations to obtain the oil mobility function. Comparisons between the new method and two popular correlations for non-fractured reservoirs indicate the necessity for developing and using an IPR correlation specifically developed for a fractured reservoir.

Keywords: inflow performance relationship, mobility function, naturally fractured reservoir, well test analysis

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Authors: Vaibhav Budhiraja, Chandra Mouli Pandey

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The ultrasonic synthesis of nanostructured conducting copolymer is an effective technique to synthesize polymer with desired chemical properties. This tailored nanostructure, shows tremendous improvement in sensitivity and stability to detect a variety of analytes. The present work reports ultrasonically synthesized nanostructured conducting poly(o-phenylenediamine)-co-poly(1-naphthylamine) (POPD-co-PNA). The synthesized material has been characterized using Fourier transform infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy, transmission electron microscopy, X-ray diffraction and cyclic voltammetry. FTIR spectroscopy confirmed random copolymerization, while UV-visible studies reveal the variation in polaronic states upon copolymerization. High crystallinity was achieved via ultrasonic synthesis which was confirmed by X-ray diffraction, and the controlled morphology of the nanostructures was confirmed by transmission electron microscopy analysis. Cyclic voltammetry shows that POPD-co-PNA has rather high electrochemical activity. This behavior was explained on the basis of variable orientations adopted by the conducting polymer chains. The synthesized material was electrophoretically deposited at onto indium tin oxide coated glass substrate which is used as cathode and parallel platinum plate as the counter electrode. The fabricated bioelectrode was further used for detection of glucose by crosslinking of glucose oxidase in the PODP-co-PNA film. The bioelectrode shows a surface-controlled electrode reaction with the electron transfer coefficient (α) of 0.72, charge transfer rate constant (ks) of 21.77 s⁻¹ and diffusion coefficient 7.354 × 10⁻¹⁵ cm²s⁻¹.

Keywords: conducting, electrophoretic, glucose, poly (o-phenylenediamine), poly (1-naphthylamine), ultrasonic

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Authors: David Polcari, Samuel C. Perry, Loredano Pollegioni, Matthias Geissler, Janine Mauzeroll

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ᴅ-serine acts as an endogenous co-agonist for N-methyl-ᴅ-aspartate receptors in neuronal synapses. This makes it a key component in the development and function of a healthy brain, especially given its role in several neurodegenerative diseases such as Alzheimer’s disease and dementia. Despite such clear research motivations, the primary site and mechanism of ᴅ-serine release is still currently unclear. For this reason, we are developing a biosensor for the detection of ᴅ-serine utilizing a microelectrode in combination with a ᴅ-amino acid oxidase enzyme, which produces stoichiometric quantities of hydrogen peroxide in response to ᴅ-serine. For the fabrication of a biosensor with good selectivity, we use a permselective poly(meta-phenylenediamine) film to ensure only the target molecule is reacted, according to the size exclusion principle. In this work, we investigated the effect of the electrodeposition conditions used on the biosensor’s response time and selectivity. Careful optimization of the fabrication process allowed for enhanced biosensor response time. This allowed for the real time sensing of ᴅ-serine in a bulk solution, and also provided in means to map the efflux of ᴅ-serine in real time. This was done using scanning electrochemical microscopy (SECM) with the optimized biosensor to measure localized release of ᴅ-serine from an agar filled glass capillary sealed in an epoxy puck, which acted as a model system. The SECM area scan simultaneously provided information regarding the rate of ᴅ-serine flux from the model substrate, as well as the size of the substrate itself. This SECM methodology, which provides high spatial and temporal resolution, could be useful to investigate the primary site and mechanism of ᴅ-serine release in other biological samples.

Keywords: ᴅ-serine, enzymatic biosensor, microelectrode, scanning electrochemical microscopy

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Authors: Behrooz Movahedi

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Fe-based amorphous feedstock powders are used as the matrix into which various ratios of hard B4C nanoparticles (0, 5, 10, 15, 20 vol.%) as reinforcing agents were prepared using a planetary high-energy mechanical milling. The ball-milled nanocomposite feedstock powders were also sprayed by means of high-velocity oxygen fuel (HVOF) technique. The characteristics of the powder particles and the prepared coating depending on their microstructures and nanohardness were examined in detail using nanoindentation tester. The results showed that the formation of the Fe-based amorphous phase was noticed over the course of high-energy ball milling. It is interesting to note that the nanocomposite coating is divided into two regions, namely, a full amorphous phase region and homogeneous dispersion of B4C nanoparticles with a scale of 10–50 nm in a residual amorphous matrix. As the B4C content increases, the nanohardness of the composite coatings increases, but the fracture toughness begins to decrease at the B4C content higher than 20 vol.%. The optimal mechanical properties are obtained with 15 vol.% B4C due to the suitable content and uniform distribution of nanoparticles. Consequently, the changes in mechanical properties of the coatings were attributed to the changes in the brittle to ductile transition by adding B4C nanoparticles.

Keywords: Fe-based amorphous, B₄C nanoparticles, nanocomposite coating, HVOF

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Authors: Niloufar Khoshbakht, Peggi L. Clouston, Sanjay R. Arwade, Alexander C. Schreyer

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Laminated veneer bamboo (LVB) is a structural engineered composite made from glued layers of bamboo. A relatively new building product, LVB is currently employed in similar sizes and applications as dimensional lumber. This study describes the results of a 3D elastic Finite Element model for halfhole specimens when loaded in compression parallel-to-grain per ASTM 5764. The model simulates LVB fracture initiation due to shear stresses in the dowel joint and predicts displacement at failure validated through comparison with experimental results. The material fails at 1mm displacement due to in-plane shear stresses. The paper clarifies the complex interactive state of in-plane shear, tension perpendicular-to-grain, and compression parallel-to-grain stresses that form different distributions in the critical zone beneath the bolt hole for half-hole specimens. These findings are instrumental in understanding key factors and fundamental failure mechanisms that occur in LVB dowel connections to help devise safe standards and further LVB product adoption and design.

Keywords: composite, dowel connection, embedment strength, failure behavior, finite element analysis, Moso bamboo

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Authors: Salvatore Luongo, Carlo Luongo

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This paper discusses the implementation solutions to reduce the computational load for the Traffic Situational Awareness with Alerts (TSAA) application, based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology. In 2008, there were 23 total mid-air collisions involving general aviation fixed-wing aircraft, 6 of which were fatal leading to 21 fatalities. These collisions occurred during visual meteorological conditions, indicating the limitations of the see-and-avoid concept for mid-air collision avoidance as defined in the Federal Aviation Administration’s (FAA). The commercial aviation aircraft are already equipped with collision avoidance system called TCAS, which is based on classic transponder technology. This system dramatically reduced the number of mid-air collisions involving air transport aircraft. In general aviation, the same reduction in mid-air collisions has not occurred, so this reduction is the main objective of the TSAA application. The major difference between the original conflict detection application and the TSAA application is that the conflict detection is focused on preventing loss of separation in en-route environments. Instead TSAA is devoted to reducing the probability of mid-air collision in all phases of flight. The TSAA application increases the flight crew traffic situation awareness providing alerts of traffic that are detected in conflict with ownship in support of the see-and-avoid responsibility. The relevant effort has been spent in the design process and the code generation in order to maximize the efficiency and performances in terms of computational load and memory consumption reduction. The TSAA architecture is divided into two high-level systems: the “Threats database” and the “Conflict detector”. The first one receives the traffic data from ADS-B device and provides the memorization of the target’s data history. Conflict detector module estimates ownship and targets trajectories in order to perform the detection of possible future loss of separation between ownship and each target. Finally, the alerts are verified by additional conflict verification logic, in order to prevent possible undesirable behaviors of the alert flag. In order to reduce the computational load, a pre-check evaluation module is used. This pre-check is only a computational optimization, so the performances of the conflict detector system are not modified in terms of number of alerts detected. The pre-check module uses analytical trajectories propagation for both target and ownship. This allows major accuracy and avoids the step-by-step propagation, which requests major computational load. Furthermore, the pre-check permits to exclude the target that is certainly not a threat, using an analytical and efficient geometrical approach, in order to decrease the computational load for the following modules. This software improvement is not suggested by FAA documents, and so it is the main innovation of this work. The efficiency and efficacy of this enhancement are verified using fast-time and real-time simulations and by the execution on a real device in several FAA scenarios. The final implementation also permits the FAA software certification in compliance with DO-178B standard. The computational load reduction allows the installation of TSAA application also on devices with multiple applications and/or low capacity in terms of available memory and computational capabilities

Keywords: traffic situation awareness, general aviation, aircraft conflict detection, computational load reduction, implementation solutions, software certification

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Authors: Rafik Baouche, Rabah Chaouchi

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This work reports the first comprehensive stress field interpretation from the eleven recently drilled wells in the Berkaoui Basin, Algerian Sahara. A cumulative length of 7000+m acoustic image logs from 06 vertical wells were investigated, and a mean NW-SE (128°-145° N) maximum horizontal stress (SHMax) orientation is inferred from the B-D quality wellbore breakouts. The study integrates log-based approach with the downhole measurements to infer pore pressure, in-situ stress magnitudes. Vertical stress (Sv), interpreted from the bulk-density profiles, has an average gradient of 22.36 MPa/km. The Ordovician and Cambrian reservoirs have a pore pressure gradient of 13.47-13.77 MPa/km, which is more than the hydrostatic pressure regime. A 17.2-18.3 MPa/km gradient of minimum horizontal stress (Shmin) is inferred from the fracture closure pressure in the reservoirs. Breakout widths constrained the SHMax magnitude in the 23.8-26.5 MPa/km range. Subsurface stress distribution in the central Saharan Algeria indicates that the present-day stress field in the Berkaoui Basin is principally strike-slip faulting (SHMax > Sv > Shmin). Inferences are drawn on the regional stress pattern and drilling and reservoir development.

Keywords: stress, imagery, breakouts, sahara

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Authors: Peter Jurči, Jana Ptačinová, Mária Hudáková, Mária Dománková, Martin Kusý, Martin Sahul

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The strength, hardness, and toughness (ductility) are in strong conflict for the metallic materials. The only possibility how to make their simultaneous improvement is to provide the microstructural refinement, by cold deformation, and subsequent recrystallization. However, application of this kind of treatment is impossible for high-carbon high-alloyed ledeburitic tool steels. Alternatively, it has been demonstrated over the last few years that sub-zero treatment induces some microstructural changes in these materials, which might favourably influence their complex of mechanical properties. Commercially available PM ledeburitic steel Vanadis 6 has been used for the current investigations. The paper demonstrates that sub-zero treatment induces clear refinement of the martensite, reduces the amount of retained austenite, enhances the population density of fine carbides, and makes alterations in microstructural development that take place during tempering. As a consequence, the steel manifests improved wear resistance at higher toughness and fracture toughness. Based on the obtained results, the key question “can the wear performance be improved by sub-zero treatment simultaneously with toughness” can be answered by “definitely yes”.

Keywords: ledeburitic tool steels, microstructure, sub-zero treatment, mechanical properties

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Authors: Eliza. E. Camaso, Guiller. B. Damian, Miguelito. F. Isip, Ronaldo T. Alberto

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Land-cover maps were important for many scientific, ecological and land management purposes and during the last decades, rapid decrease of soil fertility was observed to be due to land use practices such as rice cultivation. High-precision land-cover maps are not yet available in the area which is important in an economy management. To assure   accurate mapping of land cover to provide information, remote sensing is a very suitable tool to carry out this task and automatic land use and cover detection. The study did not only provide high precision land cover maps but it also provides estimates of rice production area that had undergone chemical degradation due to fertility decline. Land-cover were delineated and classified into pre-defined classes to achieve proper detection features. After generation of Land-cover map, of high intensity of rice cultivation, soil fertility degradation assessment in rice production area due to fertility decline was created to assess the impact of soils used in agricultural production. Using Simple spatial analysis functions and ArcGIS, the Land-cover map of Municipality of Quezon in Nueva Ecija, Philippines was overlaid to the fertility decline maps from Land Degradation Assessment Philippines- Bureau of Soils and Water Management (LADA-Philippines-BSWM) to determine the area of rice crops that were most likely where nitrogen, phosphorus, zinc and sulfur deficiencies were induced by high dosage of urea and imbalance N:P fertilization. The result found out that 80.00 % of fallow and 99.81% of rice production area has high soil fertility decline.

Keywords: aerial image, landcover, LiDAR, soil fertility degradation

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Gaurav Bhanjana, Ganga Ram Chaudhary, Sandeep Kumar, Neeraj Dilbaghi

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Main sources of endocrine disrupting compounds in the ecosystem are hormones, pesticides, phthalates, flame retardants, dioxins, personal-care products, coplanar polychlorinated biphenyls (PCBs), bisphenol A, and parabens. These endocrine disrupting compounds are responsible for learning disabilities, brain development problems, deformations of the body, cancer, reproductive abnormalities in females and decreased sperm count in human males. Although discharge of these chemical compounds into the environment cannot be stopped, yet their amount can be retarded through proper evaluation and detection techniques. The available techniques for determination of these endocrine disrupting compounds mainly include high performance liquid chromatography (HPLC), mass spectroscopy (MS) and gas chromatography-mass spectrometry (GC–MS). These techniques are accurate and reliable but have certain limitations like need of skilled personnel, time consuming, interference and requirement of pretreatment steps. Moreover, these techniques are laboratory bound and sample is required in large amount for analysis. In view of above facts, new methods for detection of endocrine disrupting compounds should be devised that promise high specificity, ultra sensitivity, cost effective, efficient and easy-to-operate procedure. Nowadays, electrochemical sensors/biosensors modified with nanomaterials are gaining high attention among researchers. Bioelement present in this system makes the developed sensors selective towards analyte of interest. Nanomaterials provide large surface area, high electron communication feature, enhanced catalytic activity and possibilities of chemical modifications. In most of the cases, nanomaterials also serve as an electron mediator or electrocatalyst for some analytes.

Keywords: electrochemical, endocrine disruptors, microscopy, nanoparticles, sensors

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Authors: Bahar Ayhan

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The present paper is concerned with the numerical modeling of the inelastic behavior of the anisotropically damaged ductile materials, which are based on a generalized macroscopic theory within the framework of continuum damage mechanics. Kinematic decomposition of the strain rates into elastic, plastic and damage parts is basis for accomplishing the structure of continuum theory. The evolution of the damage strain rate tensor is detailed with the consideration of anisotropic effects. Helmholtz free energy functions are constructed separately for the elastic and inelastic behaviors in order to be able to address the plastic and damage process. Additionally, the constitutive structure, which is based on the standard dissipative material approach, is elaborated with stress tensor, a yield criterion for plasticity and a fracture criterion for damage besides the potential functions of each inelastic phenomenon. The finite element method is used to approximate the linearized variational problem. Stress and strain outcomes are solved by using the numerical integration algorithm based on operator split methodology with a plastic and damage (multiplicator) variable separately. Numerical simulations are proposed in order to demonstrate the efficiency of the formulation by comparing the examples in the literature.

Keywords: anisotropic damage, finite element method, plasticity, coupling

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Authors: Wen Ma, Yong Peng, Zhixiang Li

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Security journey is a vital focus on the field of Rail Transportation. Accidents caused by the damage of the high-speed train windshield have occurred many times and have given rise to terrible consequences. Train windshield consists of tempered glass and polyvinyl butyral (PVB) film. In this work, the quasi-static tests and the split Hopkinson pressure bar (SHPB) tests were carried out first to obtain the mechanical properties and constitutive model for the tempered glass and PVB film. These tests results revealed that stress and Young’s modulus of tempered glass were wake-sensitive to strain rate, but stress and Young’s modulus of PVB film were strong-sensitive to strain rate. Then impact experiment of the windshield was carried out to investigate dynamic response and failure characteristics of train windshield. In addition, a finite element model based on the combined finite element method was proposed to investigate fracture and fragmentation responses of train windshield under different-velocity impact. The results can be used for further design and optimization of the windshield for high-speed train application.

Keywords: constitutive model, impact response, mechanism properties, PVB film, tempered glass

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Authors: Parvaneh Eskandari, Rachel O'Reilly

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In the last years, controlling the self-assembly behavior of stimuli-responsive nano-objects, including micelles, vesicles, worm-like, etc., at different conditions is considered a pertinent challenge in the polymer community. The aim of the project was to synthesize aggregation-induced emission (AIE)-active stimuli-responsive polymeric nano-objects to control the self-assemblies morphologies of the prepared nano-objects. Two types of nanoobjects, micelle and vesicles, including PDMAEMA-b-P(BzMA-TPEMA) [PDMAEMA: poly(N,Ndimethylaminoethyl methacrylate); P(BzMA-TPEMA): poly[benzyl methacrylate-co- tetraphenylethene methacrylate]] were synthesized by using reversible addition−fragmentation chain-transfer (RAFT)- mediated polymerization-induced self-assembly (PISA), which combines polymerization and self-assembly in a single step. Transmission electron microscope and dynamic light scattering (DLS) analysis were used to confirm the formed self-assemblies morphologies. The controlled self-assemblies were applied as nitrophenolic compounds (NPCs) adsorbents from wastewater, thanks to their CO2-responsive part, PDMAEMA. Moreover, the fluorescence-active part of the prepared nano-objects, P(BzMA-TPEMA), played a key role in the detection of the NPCs at the aqueous solution. The optical properties of the prepared nano-objects were studied by UV/Vis and fluorescence spectroscopies. For responsivity investigations, the hydrodynamic diameter and Zeta-potential (ζ-potential) of the sample's aqueous solution were measured by DLS. In the end, the prepared nano-objects were used for the detection and adsorption of different NPCs.

Keywords: aggregation-induced emission polymers, stimuli-responsive polymers, reversible addition−fragmentation chain-transfer polymerization, polymerization-induced self-assembly, wastewater treatment

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Authors: Valeriy M. Kondratev, Ekaterina A. Vyacheslavova, Talgat Shugabaev, Alexander S. Gudovskikh, Alexey D. Bolshakov

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Modern sensorics imposes strict requirements on the biosensors characteristics, especially technological feasibility, and selectivity. There is a growing interest in the analysis of human health biological markers, which indirectly testifying the pathological processes in the body. Such markers are acids and alkalis produced by the human, in particular - ammonia and hydrochloric acid, which are found in human sweat, blood, and urine, as well as in gastric juice. Biosensors based on modern nanomaterials, especially low dimensional, can be used for this markers detection. Most classical adsorption sensors based on metal and silicon oxides are considered non-selective, because they identically change their electrical resistance (or impedance) under the action of adsorption of different target analytes. This work demonstrates a feasible frequency-resistive method of electrical impedance spectroscopy data analysis. The approach allows to obtain of selectivity in adsorption sensors of a resistive type. The method potential is demonstrated with analyzis of impedance spectra of silicon nanowires in the presence of NH3 and HCl vapors with concentrations of about 125 mmol/L (2 ppm) and water vapor. We demonstrate the possibility of unambiguous distinction of the sensory signal from NH3 and HCl adsorption. Moreover, the method is found applicable for analysis of the composition of ammonia and hydrochloric acid vapors mixture without water cross-sensitivity. Presented silicon sensor can be used to find diseases of the gastrointestinal tract by the qualitative and quantitative detection of ammonia and hydrochloric acid content in biological samples. The method of data analysis can be directly translated to other nanomaterials to analyze their applicability in the field of biosensory.

Keywords: electrical impedance spectroscopy, spectroscopy data analysis, selective adsorption sensor, nanotechnology

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Authors: Nikhat Iqbal Tamboli

Abstract:

INTRODUCTION: road traffic accidents (RTAs) are one of the leading and common causes of ocular injuries especially in developing countries like India which are preventable with certain measures and so it is of public health importance. AIM: To study incidence and clinical presentation of ocular morbidity in road traffic accidents. METHOD: Prospective cross-sectional study was conducted on 360 patients reported in department of ophthalmology. Detailed ocular examination and relevant investigations done. RESULTS: Incidence of ocular injuries is 23%. male:female ratio is 4.5:1.Cases having Sub conjunctival haemorrhage [74].eccymosis[217]. lid lcerations [164]orbital fracture[12] corneal tear [7]corneal abrasion[2] sclera tear[6] hyphaema[4] traumatic mydriasis [7]traumatic cataract [2]vitreous haemorrhage [1]traumatic optic neuropathy[1].Maximum cases in age group 20-40 years, with two wheeler vehicles 94.7% .Under influence of alcohol 13.3%. CONCLUSION: Younger age group with male preponderance is involved in ocular trauma due to road traffic accidents .maximum cases reported are with anterior segment injuries. Alcohol and two wheeler vehicles are common risk factors. Injuries involving cornea had bad prognosis and involving retina had worst prognosis.

Keywords: ocular morbidity, eye trauma, RTA, eye injury

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