Search results for: pressure-robust error estimate

Authors: Salwa S. Dawaki, Init Ithoi, Sa’adatu I. Yelwa

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Introduction: Parasitic infections are major public health problems worldwide, particularly in developing countries. Two third of the world population is infected while about 3 billion are at risk of parasitic infections. It is demonstrated that most parasitic infections occur as multiple infections especially among poor and rural communities of most countries in the tropical regions. Parasitic infections are endemic in Nigeria, yet multiple infections are rarely reported. The study aimed to estimate the prevalence and identify factors associating with multiple parasitic infections among rural population in Kano State Nigeria. Methodology: A cross-sectional survey was conducted from June to August 2013 in rural Kano State, Nigeria. Three samples stool, urine, and blood were collected from each of the 551 volunteers aged between one and ninety years old recruited for the survey. A pre-tested questionnaire was used to obtain epidemiological data. Data were analysed using appropriate descriptive, univariate and multivariate logistic regression methods. Major findings: The participants were 61.7% male, 38.3% female, and 69.0% were adults of 15 years and above. Overall, 463 (84%) were infected with parasitic infections among which 60.9% had multiple infections. A total of 15 parasitic species were recovered, and up to 8 different parasitic species were found concurrently in a single host. Plasmodium was the most common parasite followed by Blastocystis, Entamoeba species, and hookworms. It was found that presence of an infected family member (P = 0.017; OR = 1.52; 95% CI = 1.08, 2.13) and not wearing shoes outside home (P = 0.043; OR = 1.50; 95% CI = 1.01, 2.18) significantly associated with higher risk of having multiple parasitic infections among the studied population. Conclusion: Parasitic infections pose a public health challenge in the rural community of Kano. Multiple parasitic infections are highly prevalent and presence of an infected family member as well as not wearing proper foot wear outside home increases the risk of infection. Poor hygiene, unfavourable socioeconomic conditions, and culture promote survival and transmission of parasites. There is a need for implementation of integrated approach aimed at controlling or eliminating the infections with emphasis on public awareness.

Keywords: multiple infections, parasitic infections, poor hygiene, risk of infection

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Authors: Nasrin Amirrajab, Yasaman Razavi Ghahfarokhi, Zahra Tootak, Maryam Hadian, Fatemeh Abooali Shamshiri

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Urinary tract infections (UTIs) have been reported in children with nephrotic syndrome. However, the only causes for the infection reported to date are bacteria, but not many prior reported occurrences of fungi or yeast as causative organisms. Hence, the present study aimed to describe the epidemiology of urinary tract fungal infections in a tertiary care pediatric. A single-center cross-sectional study was conducted at the nephrology ward of Aboozar Pediatric Hospital between March 21, 2021, and April 28, 2022. Urine was collected aseptically from children, inoculated onto culture media, and incubated at 37 °C for 18–48 hours. Yeast was identified following standard procedures. Antifungal susceptibility testing was determined by the disk diffusion method according to the CLSI guideline. Descriptive statistics and logistical regressions were used to estimate the crude ratio with a 95% confidence interval. P-value < 0.05 was considered significant. Among 68 individuals referred to the mycology lab, the result of direct examination and culture of all patients approved for C.albicans. Of these, 38 individuals (55.8%) were male, and 30 (44.2%) were female. The patients' age ranges were between one month and an 18-year-old. In the study of infection intensity, the patients were classified into three levels such as few (73.5%), moderate (20.6%), and many (5.9%). In the present study, all the patients were sensitive to Posaconazole. Also, the eagle effect was found in Amphotericin B, Voriconazole, and Fluconazole with frequencies of 91.7%, 91.7%, and 83%, respectively. In addition, just 8.3% of isolates were resistant to Itraconazole. It has not shown resistance in other mentioned medicine. The patients showed an intermediate response to Itraconazole (91.7%), Fluconazole (17%), Voriconazole (8.3%), and Amphotericin B (8.3%). There is a high prevalence of yeast infections in children with suspected UTIs. Also, boys are more likely to get yeast infections, and the severity of the infection is higher than girls. The present study demonstrated the importance of diagnosing and selecting the appropriate drug for urinary tract fungal infections in hospitalized children.

Keywords: urinary tract infections, children, fungal infections, yeast, antifungal susceptibility

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Authors: Joshua W. Edefo, Shafiul Azam, Murray D. Smith

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Introduction: In April 2022, the Welsh Government introduced the six goals for urgent and emergency care programs. One of these goals was to provide access to clinically safe alternatives, leading to the establishment of the Same Day Emergency Care (SDEC) program. The SDEC initiative aims to offer viable options that maintain patient safety while avoiding unnecessary hospital stays. The aim of the study is to determine the duration of patient stay in SDEC and compare it with that of Emergency department (ED) stay to ascertain if one of the objectives of SDEC is achieved. Methods: Patient stays and attendance datasets were constructed from Withybush SDEC and ED patient records. These records were provided by Hywel Dda University Health Board Informatics. Some hypothetical pathways were identified, notably SDEC visits involving a single attendance and ED visits then immediately transferred to SDEC. Descriptive statistics were used to summarise the data, and hypothesis tests for mean differences used the student t-test. Propensity scoring was employed to match a set of ED patient stays to SDEC patient stays which were then used to determine the average treatment effect (ATE) to compare durations of stay in SDEC with ED. Regression methods were used to model the natural logarithm of the duration of SDEC attendance, and the level of statistical significance was set to 0.05. Results: SDEC visits involving a single attendance (170 of 384; 44.3%) is the most frequently observed pathway with patient length of stay at 256 minutes (95%CI 237.4 - 275.1). The next most frequently observed pathway of patient stay was SDEC attendance after presenting to ED (80 of 384; 20.8%) and gave the length of stay of 440 minutes (95%CI 351.6 - 529.2). Time spent in this pathway significantly increased by 184 minutes (95%CI 118.0 - 250.2, support for no difference p<0.001) compared to the most seen pathway. When SDEC data were compared with ED, the estimate for the ATE from SDEC single attendance was -272 minutes (95%CI -334.1 - -210.5; p<0.001), while that of ED then SDEC pathway was -50.6 min (95%CI -182.7-81.5; p=0.453). Conclusion: When patients are admitted to SDEC and successfully discharged, their stays are significantly shorter, approximately 4.5 hours, compared to patients who spend their entire stay in the Emergency Department. That difference vanishes when the patient stay includes a period spent previously in ED before transfer to SDEC.

Keywords: attendance, emergency-department, patient-stay, same-day-emergency-care

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Authors: Ava Zarif Sanayei, Sedigheh Sina

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Interventional radiology (IR) uses imaging guidance, including X-rays and CT scans, to deliver therapy precisely. Most IR procedures are performed under local anesthesia and start with a small needle being inserted through the skin, which may be called pinhole surgery or image-guided surgery. There is increasing concern about radiation exposure during interventional radiology procedures due to procedure complexity. The basic aim of optimizing radiation protection as outlined in ICRP 139, is to strike a balance between image quality and radiation dose while maximizing benefits, ensuring that diagnostic interpretation is satisfactory. This study aims to estimate the equivalent doses to the main trunk of the body for the Interventional radiologist and Superintendent using LiF: Mg, Ti (TLD-100) chips at the IR department of a hospital in Shiraz, Iran. In the initial stage, the dosimeters were calibrated with the use of various phantoms. Afterward, a group of dosimeters was prepared, following which they were used for three months. To measure the personal equivalent dose to the body, three TLD chips were put in a tissue-equivalent batch and used under a protective lead apron. After the completion of the duration, TLDs were read out by a TLD reader. The results revealed that these individuals received equivalent doses of 387.39 and 145.11 µSv, respectively. The findings of this investigation revealed that the total radiation exposure to the staff was less than the annual limit of occupational exposure. However, it's imperative to implement appropriate radiation protection measures. Although the dose received by the interventional radiologist is a bit noticeable, it may be due to the reason for using conventional equipment with over-couch x-ray tubes for interventional procedures. It is therefore important to use dedicated equipment and protective means such as glasses and screens whenever compatible with the intervention when they are available or have them fitted to equipment if they are not present. Based on the results, the placement of staff in an appropriate location led to increasing the dose to the radiologist. Manufacturing and installation of moveable lead curtains with a thickness of 0.25 millimeters can effectively minimize the radiation dose to the body. Providing adequate training on radiation safety principles, particularly for technologists, can be an optimal approach to further decreasing exposure.

Keywords: interventional radiology, personal monitoring, radiation protection, thermoluminescence dosimetry

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Authors: Cristy Stagnar, Nina Hubig, Diana Ivankovic

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The epigenome is a compelling candidate for mediating long-term responses to environmental effects modifying disease risk. The main goal of this research is to develop a machine learning-based DNA methylation score, which will be valuable in delineating the unique contribution of paternal epigenetic modifications to the germline impacting childhood health outcomes. It will also be a useful tool in validating self-reports of nonsmoking and in adjusting epigenome-wide DNA methylation association studies for this early-life exposure. Using secondary data from two population-based methylation profiling studies, our DNA methylation score is based on CpG DNA methylation measurements from cord blood gathered from children whose fathers smoked pre- and peri-conceptually. Each child’s mother and father fell into one of three class labels in the accompanying questionnaires -never smoker, former smoker, or current smoker. By applying different machine learning algorithms to the accessible resource for integrated epigenomic studies (ARIES) sub-study of the Avon longitudinal study of parents and children (ALSPAC) data set, which we used for training and testing of our model, the best-performing algorithm for classifying the father smoker and mother never smoker was selected based on Cohen’s κ. Error in the model was identified and optimized. The final DNA methylation score was further tested and validated in an independent data set. This resulted in a linear combination of methylation values of selected probes via a logistic link function that accurately classified each group and contributed the most towards classification. The result is a unique, robust DNA methylation score which combines information on DNA methylation and early life exposure of offspring to paternal smoking during pregnancy and which may be used to examine the paternal contribution to offspring health outcomes.

Keywords: epigenome, health outcomes, paternal preconception environmental exposures, supervised machine learning

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Authors: Randa Alharbi, Vladislav Vyshemirsky

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Systems biology is an important field in science which focuses on studying behaviour of biological systems. Modelling is required to produce detailed description of the elements of a biological system, their function, and their interactions. A well-designed model requires selecting a suitable mechanism which can capture the main features of the system, define the essential components of the system and represent an appropriate law that can define the interactions between its components. Complex biological systems exhibit stochastic behaviour. Thus, using probabilistic models are suitable to describe and analyse biological systems. Continuous-Time Markov Chain (CTMC) is one of the probabilistic models that describe the system as a set of discrete states with continuous time transitions between them. The system is then characterised by a set of probability distributions that describe the transition from one state to another at a given time. The evolution of these probabilities through time can be obtained by chemical master equation which is analytically intractable but it can be simulated. Uncertain parameters of such a model can be inferred using methods of Bayesian inference. Yet, inference in such a complex system is challenging as it requires the evaluation of the likelihood which is intractable in most cases. There are different statistical methods that allow simulating from the model despite intractability of the likelihood. Approximate Bayesian computation is a common approach for tackling inference which relies on simulation of the model to approximate the intractable likelihood. Particle Markov chain Monte Carlo (PMCMC) is another approach which is based on using sequential Monte Carlo to estimate intractable likelihood. However, both methods are computationally expensive. In this paper we discuss the efficiency and possible practical issues for each method, taking into account the computational time for these methods. We demonstrate likelihood-free inference by performing analysing a model of the Repressilator using both methods. Detailed investigation is performed to quantify the difference between these methods in terms of efficiency and computational cost.

Keywords: Approximate Bayesian computation(ABC), Continuous-Time Markov Chains, Sequential Monte Carlo, Particle Markov chain Monte Carlo (PMCMC)

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Authors: Massimo Miccoli, Luca Marangoni, Alberto Aniello Scaringi, Alessandro Marceddu, Alessandro Amicone

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The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%.

Keywords: adversarial attacks, malicious images detector, binary classifier, multimodal transformer autoencoder

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Authors: Samuel J. Ahmad, Michael Zhu, Andrew J. Kobets

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Segmentation tools continue to advance, evolving from manual methods to automated contouring technologies utilizing convolutional neural networks. These techniques have evaluated ventricular and hemorrhagic volumes in the past but may be applied in novel ways to assess posterior fossa-associated pathologies such as Chiari malformations. Herein, we summarize literature pertaining to segmentation in the context of this and other posterior fossa-based diseases such as trigeminal neuralgia, hemifacial spasm, and posterior fossa syndrome. A literature search for volumetric analysis of the posterior fossa identified 27 papers where semi-automated, automated, manual segmentation, linear measurement-based formulas, and the Cavalieri estimator were utilized. These studies produced superior data than older methods utilizing formulas for rough volumetric estimations. The most commonly used segmentation technique was semi-automated segmentation (12 studies). Manual segmentation was the second most common technique (7 studies). Automated segmentation techniques (4 studies) and the Cavalieri estimator (3 studies), a point-counting method that uses a grid of points to estimate the volume of a region, were the next most commonly used techniques. The least commonly utilized segmentation technique was linear measurement-based formulas (1 study). Semi-automated segmentation produced accurate, reproducible results. However, it is apparent that there does not exist a single semi-automated software, open source or otherwise, that has been widely applied to the posterior fossa. Fully-automated segmentation via such open source software as FSL and Freesurfer produced highly accurate posterior fossa segmentations. Various forms of segmentation have been used to assess posterior fossa pathologies and each has its advantages and disadvantages. According to our results, semi-automated segmentation is the predominant method. However, atlas-based automated segmentation is an extremely promising method that produces accurate results. Future evolution of segmentation technologies will undoubtedly yield superior results, which may be applied to posterior fossa related pathologies. Medical professionals will save time and effort analyzing large sets of data due to these advances.

Keywords: chiari, posterior fossa, segmentation, volumetric

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Authors: N. Haj Salem, M. Belhadj, S. Ben Jomâa, R. Dhouieb, S. Saadi, M. A. Mesrati, A. Chadly

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Introduction: The hyoid bone is considered as one of many bones used to identify a missed person. There is a specificity of each population group in human identifications. Objective: To analyze the relationship between age, sex and metric parameters of hyoid bone in Tunisian population sample, using CT-scan. Materials and Methods: A prospective study was conducted in the Department of Forensic Medicine of FattoumaBourguiba Hospital of Monastir-Tunisia during 4 years. A total of 240 samples of hyoid bone were studied. The age of cases ranged from 18 days to 81 years. The specimens were collected only from the deceased of known age. Once dried, each hyoid bone was scanned using CT scan. For each specimen, 10 measurements were taken using a computer program. The measurements consisted of 6 lengths and 4 widths. A regression analysis was used to estimate the relationship between age, sex, and different measurements. For age estimation, a multiple logistic regression was carried out for samples ≤ 35 years. For sex determination, ROC curve was performed. Discriminant value finally retained was based on the best specificity with the best sensitivity. Results: The correlation between real age and estimated age was good (r²=0.72) for samples aged 35 years or less. The unstandardised canonical function equation was estimated using three variables: maximum length of the right greater cornua, length from the middle of the left joint space to the middle of the right joint space and perpendicular length from the centre point of a line between the distal ends of the right and left greater cornua to the centre point of the anterior view of the body of the hyoid bone. For sex determination, the ROC curve analysis reveals that the area under curve was at 81.8%. Discriminant value was 0.451 with a specificity of 73% and sensibility of 79%. The equation function was estimated based on two variables: maximum length of the greater cornua and maximum length of the hyoid bone. Conclusion: The findings of the current study suggest that metric analysis of the hyoid bone may predict the age ≤ 35 years. Sex estimation seems to be more reliable. Further studies dealing with the fusion of the hyoid bone and the current study could help to achieve more accurate age estimation rates.

Keywords: anthropology, age estimation, CT scan, sex determination, Tunisia

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Authors: Mehwish Durrani, Rubina Nazli, Muhammad Abubakr, Muhammad Shafiq

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Objectives: To evaluate the high socio-economic status, urbanization, and decrease ambulation can lead to early osteoporosis in women reporting from Peshawar region. Study Design: Descriptive cross-sectional study was done. Sample size was 100 subjects, using 30% proportion of osteoporosis, 95% confidence level, and 9% margin of error under WHO software for sample size determination. Place and Duration of study: This study was carried out in the tertiary referral health care facilities of Peshawar viz PGMI Hayatabad Medical Complex, Peshawar, Khyber Pakhtunkhwa Province, Pakistan. Ethical approval for the study was taken from the Institutional Ethical Research board (IERD) at Post Graduate Medical Institute, Hayatabad Medical Complex, and Peshawar.The study was done in six months time period. Patients and Methods: Levels of CTX1 as a marker of bone degradation in radiographically assessed perimenopausal women was determined. These females were randomly selected and screened for osteoporosis. Hemoglobin in gm/dl, ESR by Westergren method as millimeter in 1 hour, Serum Ca mg/dl, Serum alkaline Phosphatase international units per liter radiographic grade of osteoporosis according to Singh index as 1-6 and CTX 1 level in pg/ml. Results: High levels of CTX1 was observed in perimenopausal osteoporotic women which were radiographically diagnosed as osteoporotic patients. The High socio-economic class also predispose to osteoporosis. Decrease ambulation another risk factor showed significant association with the increased levels of CTX1. Conclusion: The results of this study propose that minimum ambulation and high socioeconomic class both had significance association with the increase levels of serum CTX1, which in turn will lead to osteoporosis and to its complications.

Keywords: osteoporosis, CTX1, perimenopausal women, Hayatabad Medical Complex, Khyberpuktoonkhwa

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Authors: Srikanth Pandurangi, Basheer Mohammed, Nezar Al Sayegh

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In order to meet the increasing demand for housing care for Kuwaiti citizens, the government authorities in Kuwait are undertaking a series of projects in the form of new large cities, outside the current urban area. Al Mutlaa City located to the north-west of the Kuwait Metropolitan Area is one such project out of the 15 planned new cities. The city accommodates a wide variety of residential developments, employment opportunities, commercial, recreational, health care and institutional uses. This paper examines the application of comprehensive transportation demand modeling works undertaken in VISUM platform to understand the future intracity and intercity travel distribution patterns in Kuwait. The scope of models developed varied in levels of detail: strategic model update, sub-area models representing future demand of Al Mutlaa City, sub-area models built to estimate the demand in the residential neighborhoods of the city. This paper aims at offering model update framework that facilitates easy integration between sub-area models and strategic national models for unified traffic forecasts. This paper presents the transportation demand modeling results utilized in informing the planning of multi-modal transportation system for Al Mutlaa City. This paper also presents the household survey data collection efforts undertaken using GPS devices (first time in Kuwait) and notebook computer based digital survey forms for interviewing representative sample of citizens and residents. The survey results formed the basis of estimating trip generation rates and trip distribution coefficients used in the strategic base year model calibration and validation process.

Keywords: innovative methods in transportation data collection, integrated public transportation system, traffic forecasts, transportation modeling, travel behavior

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Authors: Ahmad B. Habieb, Gabriele Milani, Tavio Tavio, Federico Milani

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Housings in developing countries have often inadequate seismic protection, particularly for masonry. People choose this type of structure since the cost and application are relatively cheap. Seismic protection of masonry remains an interesting issue among researchers. In this study, we develop a low-cost seismic isolation system for masonry using fiber reinforced elastomeric isolators. The elastomer proposed consists of few layers of rubber pads and fiber lamina, making it lower in cost comparing to the conventional isolators. We present a finite element (FE) analysis to predict the behavior of the low cost rubber isolators undergoing moderate deformations. The FE model of the elastomer involves a hyperelastic material property for the rubber pad. We adopt a Yeoh hyperelasticity model and estimate its coefficients through the available experimental data. Having the shear behavior of the elastomers, we apply that isolation system onto small masonry housing. To attach the isolators on the building, we model the shear behavior of the isolation system by means of a damped nonlinear spring model. By this attempt, the FE analysis becomes computationally inexpensive. Several ground motion data are applied to observe its sensitivity. Roof acceleration and tensile damage of walls become the parameters to evaluate the performance of the isolators. In this study, a concrete damage plasticity model is used to model masonry in the nonlinear range. This tool is available in the standard package of Abaqus FE software. Finally, the results show that the low-cost isolators proposed are capable of reducing roof acceleration and damage level of masonry housing. Through this study, we are also capable of monitoring the shear deformation of isolators during seismic motion. It is useful to determine whether the isolator is applicable. According to the results, the deformations of isolators on the benchmark one story building are relatively small.

Keywords: masonry, low cost elastomeric isolator, finite element analysis, hyperelasticity, damped non-linear spring, concrete damage plasticity

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Authors: Mustapha Ouardouz, Mina Amharref, Abdessamed Bernoussi

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The future energy, for limited energy resources countries, goes through renewable energies (solar, wind etc.). The renewable energies constitute a major component of the energy strategy to cover a substantial part of the growing needs and contribute to environmental protection by replacing fossil fuels. Indeed, sustainable development involves the promotion of renewable energy and the preservation of the environment by the use of clean energy technologies to limit emissions of greenhouse gases and reducing the pressure exerted on the forest cover. So the impact studies, of the energy use on the environment and farm-related risks are necessary. For that, a global approach integrating all the various sectors involved in such project seems to be the best approach. In this paper we present an approach based on the multi criteria analysis and the realization of one pilot to achieve the development of an innovative geo-intelligent environmental platform. An implementation of this platform will collect, process, analyze and manage environmental data in connection with the nature of used energy in the studied region. As an application we consider a region in the north of Morocco characterized by intense agricultural and industrials activities and using diverse renewable energy. The strategic goals of this platform are; the decision support for better governance, improving the responsiveness of public and private companies connected by providing them in real time with reliable data, modeling and simulation possibilities of energy scenarios, the identification of socio-technical solutions to introduce renewable energies and estimate technical and implantable potential by socio-economic analyzes and the assessment of infrastructure for the region and the communities, the preservation and enhancement of natural resources for better citizenship governance through democratization of access to environmental information, the tool will also perform simulations integrating environmental impacts of natural disasters, particularly those linked to climate change. Indeed extreme cases such as floods, droughts and storms will be no longer rare and therefore should be integrated into such projects.

Keywords: renewable energies, decision aided tool, environment, simulation

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Authors: Navah Z. Ratzon, Anat Keren, Shlomit Y. Greenberg

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Car accidents are a problem worldwide. Adolescents’ involvement in car accidents is higher in comparison to the overall driving population. Researchers estimate the risk of accidents among adolescents with symptoms of attention-deficit/hyperactivity disorder (ADHD) to be 1.2 to 4 times higher than that of their peers. Individuals with ADHD exhibit unique patterns of eye movements and attentional shifts that play an important role in driving. In addition, deficiencies in cognitive and executive functions among adolescents with ADHD is likely to put them at greater risk for car accidents. Fifteen adolescents with ADHD and 17 matched controls participated in the study. Individuals from both groups attended local public schools and did not have a driver’s license. Participants’ mean age was 16.1 (SD=.23). As part of the experiment, they all completed a driving simulation session, while their eye movements were monitored. Data were recorded by an eye tracker: The entire driving session was recorded, registering the tester’s exact gaze position directly on the screen. Eye movements and simulator data were analyzed using Matlab (Mathworks, USA). Participants’ cognitive and metacognitive abilities were evaluated as well. No correlation was found between saccade properties, regions of interest, and simulator performance in either group, although participants with ADHD allocated more visual scan time (25%, SD = .13%) to a smaller segment of dashboard area, whereas controls scanned the monitor more evenly (15%, SD = .05%). The visual scan pattern found among participants with ADHD indicates a distinct pattern of engagement-disengagement of spatial attention compared to that of non-ADHD participants as well as lower attention flexibility, which likely affects driving. Additionally the lower the results on the cognitive tests, the worse driving performance was. None of the participants had prior driving experience, yet participants with ADHD distinctly demonstrated difficulties in scanning their surroundings, which may impair driving. This stresses the need to consider intervention programs, before driving lessons begin, to help adolescents with ADHD acquire proper driving habits, avoid typical driving errors, and achieve safer driving.

Keywords: ADHD, attentional shifting, driving simulator, eye movements

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Authors: Vishal Pareek, Shubham Gupta, Sushil Chandra Jain

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With increasing high-speed computation demand the power consumption, heat dissipation and chip size issues are posing challenges for logic design with conventional technologies. Recovery of bit loss and bit errors is other issues that require reversibility and fault tolerance in the computation. The reversible computing is emerging as an alternative to conventional technologies to overcome the above problems and helpful in a diverse area such as low-power design, nanotechnology, quantum computing. Bit loss issue can be solved through unique input-output mapping which require reversibility and bit error issue require the capability of fault tolerance in design. In order to incorporate reversibility a number of combinational reversible logic based circuits have been developed. However, very few sequential reversible circuits have been reported in the literature. To make the circuit fault tolerant, a number of fault model and test approaches have been proposed for reversible logic. In this paper, we have attempted to incorporate fault tolerance in sequential reversible building blocks such as D flip-flop, T flip-flop, JK flip-flop, R-S flip-flop, Master-Slave D flip-flop, and double edge triggered D flip-flop by making them parity preserving. The importance of this proposed work lies in the fact that it provides the design of reversible sequential circuits completely testable for any stuck-at fault and single bit fault. In our opinion our design of reversible building blocks is superior to existing designs in term of quantum cost, hardware complexity, constant input, garbage output, number of gates and design of online testable D flip-flop have been proposed for the first time. We hope our work can be extended for building complex reversible sequential circuits.

Keywords: parity preserving gate, quantum computing, fault tolerance, flip-flop, sequential reversible logic

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Authors: Lubna M. A. Hassan, Nasir Brema, Abdallah Mamy, Insaf Yahya, Tanzil A. G., Ahmed M. M. Hasoba, Omer A. Suliman

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The Nubian ibex species has been categorized as vulnerable by the International Union for Conservation of Nature (IUCN) due to a lack of population data in specific regions within their habitat. This species faces numerous challenges, including habitat loss caused by agricultural practices, livestock rearing, mining activity, and infrastructure development. Additionally, competition with non-native species and hunting pose significant threats to their survival. Unfortunately, studies on the distribution, conservation status, ecology, and health of the ibex are limited and primarily descriptive in nature. In order to bridge this knowledge gap, recent surveys were conducted in the Red Sea State of Sudan during specific periods in 2015, 2016, 2019, and 2021. These surveys have provided valuable insights into the distribution, habitats, and conservation status of the Nubian ibex in the Red Sea State. The findings indicate that the Capra nubiana ibex can be found across more than 17 mountains in the Red Sea State. However, the total population estimate from recent years suggests that there are fewer than 250 individuals remaining. The study has also identified the highest altitude at which the Nubian ibex habitats existed in Sudan's Red Sea State, measuring 1675 m. This area harbors a diverse array of Nubian ibex habitats, encompassing a total of 21 wild plant species from 10 distinct families. The region experiences an average annual temperature ranging from 20.64°C in January to 33.30°C in August. Precipitation occurs in November and December, although it is characterized by unreliability and erratic patterns. It is important to note that these population estimates were obtained through surveys conducted in collaboration with rangers and local communities, and adjustments to survey methods are necessary to accommodate the challenging mountainous terrain, such as utilizing aerial surveys. To effectively address these threats, it is imperative to establish comprehensive long-term monitoring programs.

Keywords: Nubian ibex, distribution, population, habitats

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Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

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Authors: Marjan Moradi fard, Hossein Rassi, Masoud Houshmand

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Aim: Breast cancer is one of the most common cancers affecting the morbidity and mortality of Iranian women. This disease is a result of collective alterations of oncogenes and tumor suppressor genes. Studies have produced conflicting results concerning the role of p53 codon 72 polymorphism (G>C) and miR-146a rs2910164 polymorphism (G>C) on the risk of several cancers; therefore, a research was performed to estimate the association between the p53 codon 72 polymorphism and miR-146a rs2910164 polymorphism in breast cancer. Methods and Materials: A total of 45 archival breast cancer samples from Khatam hospital and 40 healthy samples were collected. Verification of each cancer reported in a relative was sought through the pathology reports of the hospital records. Then, DNA extracted from all samples by standard methods and p53 codon 72 polymorphism genotypes and miR-146a rs2910164 polymorphism genotypes were analyzed using multiplex PCR. The tubules, mitotic activity, necrosis, polymorphism and grade of breast cancer were staged by Nottingham histological grading and immunohistochemical staining of the sections from the paraffin wax embedded tissues for the expression of ER, PR and p53 was carried out using a standard method. Finally, data analysis was performed using the 7 version of the Epi Info(TM) 2012 software and test chi-square(x2) for trend. Results: Successful DNA extraction was assessed by PCR amplification of b-actin gene (99 bp). According to the results, p53 GG genotype and miR-146a rs2910164 CC genotype was significantly associated with increased risk of breast cancer in the study population. In this study, we established that tumors of p53 GG genotype and miR-146a rs2910164 CC genotype exhibited higher mitotic activity, higher polymorphism, lower necrosis, lower tubules, higher ER- and PR-negatives and lower TP53-positives than the other genotypes. Conclusion: The present study provided preliminary evidence that a p53 GG genotype may effect breast cancer risk in the study population, interacting synergistically with miR-146a rs2910164 CC genotype. Our results demonstrate that the testing of p53 codon 72 polymorphism genotypes and miR-146a rs2910164 polymorphism genotypes in combination with clinical parameters can serve as major risk factors in the early identification of breast cancers.

Keywords: breast cancer, miR-146a rs2910164 polymorphism, p53 codon 72 polymorphism, tumors, pathology reports

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Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

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Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

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Authors: Michael A. Sprayberry, Vincent C. Paquit

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Process parameter optimization in metal powder bed electron beam melting (MPBEBM) is crucial to ensure the technology's repeatability, control, and industry-continued adoption. Despite continued efforts to address the challenges via the traditional design of experiments and process mapping techniques, there needs to be more successful in an on-the-fly optimization framework that can be adapted to MPBEBM systems. Additionally, data-intensive physics-based modeling and simulation methods are difficult to support by a metal AM alloy or system due to cost restrictions. To mitigate the challenge of resource-intensive experiments and models, this paper introduces a Multi-Objective Reinforcement Learning (MORL) methodology defined as an optimization problem for MPBEBM. An off-policy MORL framework based on policy gradient is proposed to discover optimal sets of beam power (P) – beam velocity (v) combinations to maintain a steady-state melt pool depth and phase transformation. For this, an experimentally validated Eagar-Tsai melt pool model is used to simulate the MPBEBM environment, where the beam acts as the agent across the P – v space to maximize returns for the uncertain powder bed environment producing a melt pool and phase transformation closer to the optimum. The culmination of the training process yields a set of process parameters {power, speed, hatch spacing, layer depth, and preheat} where the state (P,v) with the highest returns corresponds to a refined process parameter mapping. The resultant objects and mapping of returns to the P-v space show convergence with experimental observations. The framework, therefore, provides a model-free multi-objective approach to discovery without the need for trial-and-error experiments.

Keywords: additive manufacturing, metal powder bed fusion, reinforcement learning, process parameter optimization

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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: Anu S. Nair, V. Anantha Subramanian

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This paper presents a surface development and fairing technique combining the features of a modern computer-aided design tool namely the Non-Uniform Rational Basis Spline (NURBS) with an algorithm to obtain a rapidly faired hull form. Some of the older series based designs give sectional area distribution such as in the Wageningen-Lap Series. Others such as the FORMDATA give more comprehensive offset data points. Nevertheless, this basic data still requires fairing to obtain an acceptable faired hull form. This method uses the input of sectional area distribution as an example and arrives at the faired form. Characteristic section shapes define any general ship hull form in the entrance, parallel mid-body and run regions. The method defines a minimum of control points at each section and using the Golden search method or the bisection method; the section shape converges to the one with the prescribed sectional area with a minimized error in the area fit. The section shapes combine into evolving the faired surface by NURBS and typically takes 20 iterations. The advantage of the method is that it is fast, robust and evolves the faired hull form through minimal iterations. The curvature criterion check for the hull lines shows the evolution of the smooth faired surface. The method is applicable to hull form from any parent series and the evolved form can be evaluated for hydrodynamic performance as is done in more modern design practice. The method can handle complex shape such as that of the bulbous bow. Surface patches developed fit together at their common boundaries with curvature continuity and fairness check. The development is coded in MATLAB and the example illustrates the development of the method. The most important advantage is quick time, the rapid iterative fairing of the hull form.

Keywords: computer-aided design, methodical series, NURBS, ship design

Procedia PDF Downloads 155

Authors: Kholoud Abdulaziz, Kholoud Al-Najdi, Abdullah Kadri, Konstantinos E. Kakosimos

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The Middle East area is highly affected by air pollution induced by anthropogenic and natural phenomena. There is evidence that air pollution, especially particulates, greatly affects the population health. Many studies have raised a warning of the high concentration of particulates and their affect not just around industrial and construction areas but also in the immediate working and living environment. One of the methods to study air quality is continuous and periodic monitoring using active or passive samplers. Active monitoring and sampling are the default procedures per the European and US standards. However, in many cases they have been inefficient to accurately capture the spatial variability of air pollution due to the small number of installations; which eventually is attributed to the high cost of the equipment and the limited availability of users with expertise and scientific background. Another alternative has been found to account for the limitations of the active methods that is the passive sampling. It is inexpensive, requires no continuous power supply, and easy to assemble which makes it a more flexible option, though less accurate. This study aims to investigate and evaluate the use of passive sampling for particulate matter pollution monitoring in dry tropical climates, like in the Middle East. More specifically, a number of field measurements have be conducted, both indoors and outdoors, at Qatar and the results have been compared with active sampling equipment and the reference methods. The samples have been analyzed, that is to obtain particle size distribution, by applying existing laboratory techniques (optical microscopy) and by exploring new approaches like the white light interferometry to. Then the new parameters of the well-established model have been calculated in order to estimate the atmospheric concentration of particulates. Additionally, an extended literature review will investigate for new and better models. The outcome of this project is expected to have an impact on the public, as well, as it will raise awareness among people about the quality of life and about the importance of implementing research culture in the community.

Keywords: air pollution, passive samplers, interferometry, indoor, outdoor

Procedia PDF Downloads 387

Authors: Ali Ahmadi, Maryam Abbasi, Mohammad Mehdi Parsaei

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Background: Stress in the workplace today is one of the most important public health concerns and a serious threat to the health of the workforce worldwide. Occupational stress can cause occupational events and reduce quality of life. As a result, it has a very undesirable impact on the performance of organizations, companies, and their human resources. This study aimed to evaluate the relationship between job stress dimensions and occupational accidents in industrial factories in Southwest Iran. Materials and Methods: This cross-sectional study was conducted among 200 workers in the summer of 2023 in the Southwest of Iran. To select participants, we used a convenience sampling method. The research tools in this study were the Health and Safety Executive (HSE) stress questionnaire with 35 questions and 7 dimensions and demographic information. A high score on this questionnaire indicates that there is low job stress and pressure. All workers completed the informed consent form. Univariate analysis was performed using chi-square and T-test. Multiple regression analysis was used to estimate the odds ratios (OR) and 95% confidence interval (CI) for the association of stress-related factors with job accidents in participants. Stata 14.0 software was used for analysis. Results: The mean age of the participants was 39.81(6.36) years. The prevalence of job accidents was 28.0% (95%CI: 21.0, 34.0). Based on the results of the multiple logistic regression with the adjustment of the effect of the confounding variables, one increase in the score of the demand dimension had a protective impact on the risk of job accidents(aOR=0.91,95%CI:0.85-0.95). Additionally, an increase in one of the scores of the managerial support (aOR=0.89, 95% CI: 0.83-0.95) and peer support (aOR=0.76, 95%CI: 0.67-87) dimensions was associated with a lower number of job accidents. Among dimensions, an increase in the score of relationship (aOR=0.89, 95%CI: 0.80-0.98) and change (aOR=0.86, 95%CI: 0.74-0.96) reduced the odds of the accident's occurrence among the workers by 11% and 16%, respectively. However, there was no significant association between role and control dimensions and the job accident (p>0.05). Conclusions: The results show that the prevalence of job accidents was alarmingly high. Our results suggested that an increase in scores of dimensions HSE questioners is significantly associated with a decrease the accident occurrence in the workplace. Therefore, planning to address stressful factors in the workplace seems necessary to prevent occupational accidents.

Keywords: HSE, Iran, job stress occupational accident, safety, occupational health

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Authors: Sudhakara Naidu Neppalli, Tejas S. Bhosale

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It is well known that the block copolymer (BCP) can form a complex molecule, through non-covalent bonds such as hydrogen bond, ionic bond and co-ordination bond, with low molecular weight compound as well as with macromolecules, which provide vast applications, includes the alteration of morphology and properties of polymers. Hence we covered the research that, the importance of non-covalent bonds in increasing the non-favourable segmental interactions of the blocks was well examined by attaching and detaching the bonds between the BCP and additive. We also monitored the phase transition of block copolymer and effective interaction parameter (χeff) for Li-doped polymers using small angle x-ray scattering and transmission electron microscopy. The effective interaction parameter (χeff) between two block components was evaluated using Leibler theory based on the incompressible random phase approximation (RPA) for ionized BCP in a disordered state. Furthermore, conductivity experiments demonstrate that the ionic conductivity in the samples quenched from the different structures is morphology-independent, while it increases with increasing ion salt concentration. Morphological transitions, interaction parameter, and thermal stability also examined in quarternized block copolymer. D-spacing was used to estimate effective interaction parameter (χeff) of block components in weak and strong segregation regimes of ordered phase. Metal-containing polymer has been the topic of great attention in recent years due to their wide range of potential application. Similarly, metal- ligand complex is used as a supramolecular linker between the polymers giving rise to a ‘Metallo-Supramolecule assembly. More precisely, functionalized polymer end capped with 2, 2’:6’, 2”- terpyridine ligand can be selectively complexed with wide range of transition metal ions and then subsequently attached to other terpyridine terminated polymer block. In compare to other supramolecular assembly, BCP involved metallo-supramolecule assembly offers vast applications such as optical activity, electrical conductivity, luminescence and photo refractivity.

Keywords: band gap, block copolymer, conductivity, interaction parameter, phase transition

Procedia PDF Downloads 150

Authors: Iuri Salukvadze

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Development of Georgian Economy largely depends on its effective use of its transit country potential. The value of Georgia as the part of Europe-Asia corridor has increased; this increases the interest of western and eastern countries to Georgia as to the country that laid on the transit axes that implies transit infrastructure creation and development in Georgia. It is important to use compacted concrete with the additive in modern road construction industry. Even in the 21-century, concrete remains as the main vital constructive building material, therefore innovative, economic and environmentally protected technologies are needed. Georgian construction market requires the use of concrete of new generation, adaptation of nanotechnologies to the local realities that will give the ability to create multifunctional, nano-technological high effective materials. It is highly important to research their physical and mechanical states. The study of compacted concrete with the additives is necessary to use in the road construction in the future and to increase hardness of roads in Georgia. The aim of the research is to study the physical-mechanical properties of the compacted concrete with the additives based on the local materials. Any experimental study needs large number of experiments from one side in order to achieve high accuracy and optimal number of the experiments with minimal charges and in the shortest period of time from the other side. To solve this problem in practice, it is possible to use experiments planning static and mathematical methods. For the materials properties research we will use distribution hypothesis, measurements results by normal law according to which divergence of the obtained results is caused by the error of method and inhomogeneity of the object. As the result of the study, we will get resistible compacted concrete with additives for the motor roads that will improve roads infrastructure and give us saving rate while construction of the roads and their exploitation.

Keywords: construction, seismic protection systems, soil, motor roads, concrete

Procedia PDF Downloads 223

Authors: Thomai Voulgari, Vasilis Vasilopoulos, Antonis Skamnakis

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The aim of this study is to investigate the effect of the European election campaigns (May 23-26, 2019) on Twitter achieving with artificial intelligence tools such as troll factories and automated inauthentic accounts. Our research focuses on the last European Parliamentary elections that took place between 23 and 26 May 2019 specifically in Italy, Greece, Germany and France. It is difficult to estimate how many Twitter users are actually bots (Echeverría, 2017). Detection for fake accounts is becoming even more complicated as AI bots are made more advanced. A political bot can be programmed to post comments on a Twitter account for a political candidate, target journalists with manipulated content or engage with politicians and artificially increase their impact and popularity. We analyze variables related to 1) the scope of activity of automated bots accounts and 2) degree of coherence and 3) degree of interaction taking into account different factors, such as the type of content of Twitter messages and their intentions, as well as the spreading to the general public. For this purpose, we collected large volumes of Twitter accounts of party leaders and MEP candidates between 10th of May and 26th of July based on content analysis of tweets based on hashtags while using an innovative network analysis tool known as MediaWatch.io (https://mediawatch.io/). According to our findings, one of the highest percentage (64.6%) of automated “bot” accounts during 2019 European election campaigns was in Greece. In general terms, political bots aim to proliferation of misinformation on social media. Targeting voters is a way that it can be achieved contribute to social media manipulation. We found that political parties and individual politicians create and promote purposeful content on Twitter using algorithmic tools. Based on this analysis, online political advertising play an important role to the process of spreading misinformation during elections campaigns. Overall, inauthentic accounts and social media algorithms are being used to manipulate political behavior and public opinion.

Keywords: artificial intelligence tools, human-bot interactions, political manipulation, social networking, troll factories

Procedia PDF Downloads 123

Authors: Mark P. Mayala, Henry Mayala, Khuzeima Khanbhai

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Background: Heart failure has been a rising concern in Tanzania. New drugs have been introduced, including the group of drugs called Angiotensin receptor Neprilysin Inhibitor (ARNI), but due to their high cost, angiotensin-converting enzymes inhibitors (ACEIs) and Angiotensin receptor blockers (ARBs) have been mostly used in Tanzania. However, according to our knowledge, the efficacy comparison of the two groups is yet to be studied in Tanzania. The aim of this study was to compare the efficacy of ACEIs and ARBs among patients with heart failure. Methodology: This was a hospital-based prospective cohort study done at Jakaya Kikwete Cardiac Institution (JKCI), Tanzania, from June to December 2020. Consecutive enrollment was done until fulfilling the inclusion criteria. Clinical details were measured at baseline. We assessed the relationship between ARBs and ACEIs users with N-terminal pro-brain natriuretic peptide (NT pro-BNP) levels at admission and at 1-month follow-up using a chi-square test. A Kaplan-Meier curve was used to estimate the survival time of the two groups. Results: 155 HF patients were enrolled, with a mean age of 48 years, whereby 52.3% were male, and their mean left ventricular ejection fraction (LVEF) was 37.3%. 52 (33.5%) heart failure patients were on ACEIs, 57 (36.8%) on ARBs, and 46 (29.7%) were neither using ACEIs nor ARBs. At least half of the patients did not receive a guideline-directed medical therapy (GDMT), with only 82 (52.9%) receiving a GDMT. A drop in NT pro-BNP levels was observed during admission and at 1-month follow-up on both groups, from 6389.2 pg/ml to 4000.1 pg/ml for ARB users and 5877.7 pg/ml to 1328.2 pg/ml for the ACEIs users. There was no statistical difference between the two groups when estimated by the Kaplan-Meier curve, though more deaths were observed in those who were neither on ACEIs nor ARBs, with a calculated P value of 0.01. Conclusion: This study demonstrates that ACEIs have more efficacy and overall better clinical outcome than ARBs, but this should be taken under the patient-based case, considering the side effects of ACEIs and patients’ adherence.

Keywords: angiotensin converting enzymes inhibitors, angiotensin receptor blockers, guideline direct medical therapy, N-terminal pro-brain natriuretic peptide

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Authors: Abdelhalim. N. Mohammed, Mohammed. Y. Esmail

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In the course of recent decades, medical imaging has been dominated by the use of costly film media for review and archival of medical investigation, however due to developments in networks technologies and common acceptance of a standard digital imaging and communication in medicine (DICOM) another approach in light of World Wide Web was produced. Web technologies successfully used in telemedicine applications, the combination of web technologies together with DICOM used to design a web-based and open source DICOM viewer. The Web server allowance to inquiry and recovery of images and the images viewed/manipulated inside a Web browser without need for any preinstalling software. The dynamic site page for medical images visualization and processing created by using JavaScript and HTML5 advancements. The XAMPP ‘apache server’ is used to create a local web server for testing and deployment of the dynamic site. The web-based viewer connected to multiples devices through local area network (LAN) to distribute the images inside healthcare facilities. The system offers a few focal points over ordinary picture archiving and communication systems (PACS): easy to introduce, maintain and independently platforms that allow images to display and manipulated efficiently, the system also user-friendly and easy to integrate with an existing system that have already been making use of web technologies. The wavelet-based image compression technique on which 2-D discrete wavelet transform used to decompose the image then wavelet coefficients are transmitted by entropy encoding after threshold to decrease transmission time, stockpiling cost and capacity. The performance of compression was estimated by using images quality metrics such as mean square error ‘MSE’, peak signal to noise ratio ‘PSNR’ and compression ratio ‘CR’ that achieved (83.86%) when ‘coif3’ wavelet filter is used.

Keywords: DICOM, discrete wavelet transform, PACS, HIS, LAN

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Authors: Sofiya Rao, Sagnik Dey

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Aerosol-cloud-precipitation interaction continues to be one of the largest sources of uncertainty in quantifying the aerosol climate forcing. The uncertainty is increasing from global to regional scale. This problem remains unresolved due to the large discrepancy in the representation of cloud processes in the climate models. Most of the studies on aerosol-cloud-climate interaction and aerosol-cloud-precipitation over Indian Ocean (like INDOEX, CAIPEEX campaign etc.) are restricted to either particular to one season or particular to one region. Here we developed a theoretical framework to quantify aerosol indirect radiative forcing using Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol and cloud products of 15 years (2000-2015) period over the Indian Ocean. This framework relies on the observationally constrained estimate of the aerosol-induced change in cloud albedo. We partitioned the change in cloud albedo into the change in Liquid Water Path (LWP) and Effective Radius of Clouds (Reff) in response to an aerosol optical depth (AOD). Cloud albedo response to an increase in AOD is most sensitive in the range of LWP between 120-300 gm/m² for a range of Reff varying from 8-24 micrometer, which means aerosols are most sensitive to this range of LWP and Reff. Using this framework, aerosol forcing during a transition from indirect to semi-direct effect is also calculated. The outcome of this analysis shows best results over the Arabian Sea in comparison with the Bay of Bengal and the South Indian Ocean because of heterogeneity in aerosol spices over the Arabian Sea. Over the Arabian Sea during Winter Season the more absorbing aerosols are dominating, during Pre-monsoon dust (coarse mode aerosol particles) are more dominating. In winter and pre-monsoon majorly the aerosol forcing is more dominating while during monsoon and post-monsoon season meteorological forcing is more dominating. Over the South Indian Ocean, more or less same types of aerosol (Sea salt) are present. Over the Arabian Sea the Aerosol Indirect Radiative forcing are varying from -5 ± 4.5 W/m² for winter season while in other seasons it is reducing. The results provide observationally constrained estimates of aerosol indirect forcing in the Indian Ocean which can be helpful in evaluating the climate model performance in the context of such complex interactions.

Keywords: aerosol-cloud-precipitation interaction, aerosol-cloud-climate interaction, indirect radiative forcing, climate model

Procedia PDF Downloads 155