Search results for: measuring accuracy

Authors: Gunilla Dahlgren, Per Liv, Fredrik Öhberg, Lisbeth Slunga Järvholm, Mikael Forsman, Börje Rehn

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Background: Accuracy of risk assessment tools in hand-repetitive work is important. This can support precision in the risk management process and for a sustainable working life for women and men equally. Musculoskeletal disorders, MSDs, from the hand, wrist, and forearm, are common in the working population. Women report a higher prevalence of MSDs in these regions. Objective: The objective of this study was to compare if women and men who performed the identical hand-intensive work task were rated equally using the Hand Activity Threshold Limit Value® (HA-TLV) when self-rated and observer-rated. Method: Fifty-six workers from eight companies participated, with various intensities in hand-repetitive work tasks. In total, 18 unique identical hand-intensive work tasks were executed in 28 pairs of a woman and a man. Hand activity and force levels were assessed. Each worker executed the work task for 15 minutes, which was also video recorded. Data was collected on workers who self-rated directly after the execution of the work task. Also, experienced observers performed ratings from videos of the same work tasks. For comparing means between women and men, paired samples t-tests were used. Results: The main results showed that there was no difference in self-ratings of hand activity level and force by women and men who executed the same work task. Further, there was no difference between observer ratings of hand activity level. However, the observer force ratings of women and men differed significantly (p=0.01). Conclusion: Hand activity and force levels are rated equally in women and men when self-rated, also by observers for hand activity. However, it is an observandum that observer force rating is rated higher for women and lower for men. This indicates the need of comparing force ratings with technical measures.

Keywords: gender, equity, sex differences, repetitive strain injury, cumulative trauma disorders, upper extremity, exposure assessment, workload, health risk assessment, observation, psychophysics

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

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Antimicrobial drugs have played an indispensable role in controlling illness and death associated with infectious diseases in animals and humans. However, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global healthcare problem. Many antibiotics had lost their effectiveness since the beginning of the antibiotic era because many bacteria have adapted defenses against these antibiotics. Rapid determination of antimicrobial susceptibility of a clinical isolate is often crucial for the optimal antimicrobial therapy of infected patients and in many cases can save lives. The conventional methods for susceptibility testing require the isolation of the pathogen from a clinical specimen by culturing on the appropriate media (this culturing stage lasts 24 h-first culturing). Then, chosen colonies are grown on media containing antibiotic(s), using micro-diffusion discs (second culturing time is also 24 h) in order to determine its bacterial susceptibility. Other methods, genotyping methods, E-test and automated methods were also developed for testing antimicrobial susceptibility. Most of these methods are expensive and time-consuming. Fourier transform infrared (FTIR) microscopy is rapid, safe, effective and low cost method that was widely and successfully used in different studies for the identification of various biological samples including bacteria; nonetheless, its true potential in routine clinical diagnosis has not yet been established. The new modern infrared (IR) spectrometers with high spectral resolution enable measuring unprecedented biochemical information from cells at the molecular level. Moreover, the development of new bioinformatics analyses combined with IR spectroscopy becomes a powerful technique, which enables the detection of structural changes associated with resistivity. The main goal of this study is to evaluate the potential of the FTIR microscopy in tandem with machine learning algorithms for rapid and reliable identification of bacterial susceptibility to antibiotics in time span of few minutes. The UTI E.coli bacterial samples, which were identified at the species level by MALDI-TOF and examined for their susceptibility by the routine assay (micro-diffusion discs), are obtained from the bacteriology laboratories in Soroka University Medical Center (SUMC). These samples were examined by FTIR microscopy and analyzed by advanced statistical methods. Our results, based on 700 E.coli samples, were promising and showed that by using infrared spectroscopic technique together with multivariate analysis, it is possible to classify the tested bacteria into sensitive and resistant with success rate higher than 90% for eight different antibiotics. Based on these preliminary results, it is worthwhile to continue developing the FTIR microscopy technique as a rapid and reliable method for identification antibiotic susceptibility.

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

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Authors: Fazl Ullah, Rahmat Ullah

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This paper shows a comprehensive learning focused on the optimization of gas production in shale gas reservoirs through hydraulic fracturing. Shale gas has emerged as an important unconventional vigor resource, necessitating innovative techniques to enhance its extraction. The key objective of this study is to examine the influence of fracture parameters on reservoir productivity and formulate strategies for production optimization. A sophisticated model integrating gas flow dynamics and real stress considerations is developed for hydraulic fracturing in multi-stage shale gas reservoirs. This model encompasses distinct zones: a single-porosity medium region, a dual-porosity average region, and a hydraulic fracture region. The apparent permeability of the matrix and fracture system is modeled using principles like effective stress mechanics, porous elastic medium theory, fractal dimension evolution, and fluid transport apparatuses. The developed model is then validated using field data from the Barnett and Marcellus formations, enhancing its reliability and accuracy. By solving the partial differential equation by means of COMSOL software, the research yields valuable insights into optimal fracture parameters. The findings reveal the influence of fracture length, diversion capacity, and width on gas production. For reservoirs with higher permeability, extending hydraulic fracture lengths proves beneficial, while complex fracture geometries offer potential for low-permeability reservoirs. Overall, this study contributes to a deeper understanding of hydraulic cracking dynamics in shale gas reservoirs and provides essential guidance for optimizing gas production. The research findings are instrumental for energy industry professionals, researchers, and policymakers alike, shaping the future of sustainable energy extraction from unconventional resources.

Keywords: fluid-solid coupling, apparent permeability, shale gas reservoir, fracture property, numerical simulation

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Authors: Haj Najafi Leila, Tehranizadeh Mohsen

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Dependencies between diverse factors involved in probabilistic seismic loss evaluation are recognized to be an imperative issue in acquiring accurate loss estimates. Dependencies among component damage costs could be taken into account considering two partial distinct states of independent or perfectly-dependent for component damage states; however, in our best knowledge, there is no available procedure to take account of loss dependencies in story level. This paper attempts to present a method called "modal cost superposition method" for decoupling story damage costs subjected to earthquake ground motions dealt with closed form differential equations between damage cost and engineering demand parameters which should be solved in complex system considering all stories' cost equations by the means of the introduced "substituted matrixes of mass and stiffness". Costs are treated as probabilistic variables with definite statistic factors of median and standard deviation amounts and a presumed probability distribution. To supplement the proposed procedure and also to display straightforwardness of its application, one benchmark study has been conducted. Acceptable compatibility has been proven for the estimated damage costs evaluated by the new proposed modal and also frequently used stochastic approaches for entire building; however, in story level, insufficiency of employing modification factor for incorporating occurrence probability dependencies between stories has been revealed due to discrepant amounts of dependency between damage costs of different stories. Also, more dependency contribution in occurrence probability of loss could be concluded regarding more compatibility of loss results in higher stories than the lower ones, whereas reduction in incorporation portion of cost modes provides acceptable level of accuracy and gets away from time consuming calculations including some limited number of cost modes in high mode situation.

Keywords: dependency, story-cost, cost modes, engineering demand parameter

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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 219

Authors: Kadri-Ann Kertsmik, Martin Talvik, Kimmo Lylykangas, Simo Ilomets, Targo Kalamees

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This study compares the building carbon footprint (CF) values for a case study of a private house located in a cold climate, using the Level(s) methodology. It provides a framework for measuring the environmental performance of buildings throughout their life cycle, taking into account various factors. The study presents the results of the three scenarios, comparing their carbon emissions and highlighting the benefits of circular material usage. The construction process was thoroughly documented, and all materials and components (including minuscule mechanical fasteners, each meter of cable, a kilogram of mortar, and the component of HVAC systems, among other things) delivered to the construction site were noted. Transportation distances of each delivery, the fuel consumption of construction machines, and electricity consumption for temporary heating and electrical tools were also monitored. Using the detailed data on material and energy resources, the CF was calculated for two scenarios: one where circular material usage was applied and another where virgin materials were used instead of reused ones. The results were compared with the CF calculated based on the building permit design model using the Level(s) methodology. To study the range of possible results in the early stage of CF assessment, the same building permit design was given to several experts. Results showed that embodied carbon values for a built scenario were significantly lower than the values predicted by the building permit stage as a result of more precise material quantities, as the calculation methodology is designed to overestimate the CF. Moreover, designers made an effort to reduce the building's CF by reusing certain materials such as ceramic tiles, lightweight concrete blocks, and timber during the construction process. However, in a cold climate context where operational energy (B6) continues to dominate, the total building CF value changes between the three scenarios were less significant. The calculation for the building permit project was performed by several experts, and CF results were in the same range. It alludes that, for the first estimation of preliminary building CF, using average values proves to be an appropriate method for the Estonian national carbon footprint estimation phase during building permit application. The study also identified several opportunities for reducing the carbon footprint of the building, such as reusing materials from other construction sites, preferring local material producers, and reducing wastage on site. The findings suggest that using circular materials can significantly reduce the carbon footprint of buildings. Overall, the study highlights the importance of using a comprehensive approach to measure the environmental performance of buildings, taking into account both the project and the actually built house. It also emphasises the need for ongoing monitoring for designing the building and construction site waste. The study also gives some examples of how to enable future circularity of building components and materials, e.g., building in layers, using wood as untreated, etc.

Keywords: carbon footprint, circular economy, sustainable construction, level(s) methodology

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Authors: Aurélie Michaud, Émilie Gaudet, Mónika Koós, Léna Nagy, Zsolt Demetrovics, Shane W. Kraus, Marc N. Potenza, Beáta Bőthe

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Pornography use is prevalent among adults worldwide. Prior studies have assessed the associations between pornography use frequency and sexual satisfaction, in cisgender and heterosexual individuals, with mixed results. However, measuring pornography use solely by pornography use frequency is problematic, as it can lead to disregarding important contextual factors that may be related to pornography use’s potential effects. Pornography use motivations (PUMs) represent key predictors of sexual behaviors. Yet, their associations with different indicators of sexual wellbeing have yet to be extensively studied. This cross-cultural study examined the links between the eight PUMs most often reported in the general population (i.e. sexual pleasure, sexual curiosity, emotional distraction or suppression, fantasy, stress reduction, boredom avoidance, lack of sexual satisfaction, and self-exploration) and sexual satisfaction in gender diverse and cisgender individuals. Given the lack of scientific data on associations between individuals’ PUMs and sexual satisfaction, these links were examined in an exploratory manner. A total of 43 countries from five continents were included in the International Sex Survey (ISS). A secure online platform was used to collect self-report, anonymous data from 82,243 participants (39.6% men, 57% women, 3.4% gender diverse individuals; M = 32.4 years, SD = 12.5). Gender-based differences in levels of sexual pleasure, sexual curiosity, emotional distraction, fantasy, stress reduction, boredom avoidance, lack of sexual satisfaction, and self-exploration PUMs were examined using one-way ANOVAs. Then, for each gender group, the associations between each PUM and sexual satisfaction were examined using multiple linear regression, controlling for frequency of masturbation. One-way ANOVAs indicated significant differences between men, women, and gender diverse individuals on all PUMs. For sexual pleasure, sexual curiosity, fantasy, boredom avoidance, lack of sexual satisfaction, emotional distraction, and stress reduction PUMs, men showed the highest scores, followed by gender-diverse individuals, and women. However, for self-exploration, gender-diverse individuals had higher average scores than men. For all PUMs, women’s average scores were the lowest. After controlling for frequency of masturbation, for all genders, sexual pleasure, sexual curiosity and boredom avoidance were significant positive predictors of sexual satisfaction, while lack of sexual satisfaction PUM was a significant negative predictor. Fantasy, stress reduction and self-exploration PUMs were positive significant predictors of sexual satisfaction, and fantasy was a negative significant predictor, but only for women. Findings highlight important gender differences in regards to the main motivations underlying pornography use and their relations to sexual satisfaction. While men and gender diverse individuals show similar motivation profiles, woman report a particularly unique experience, with fantasy, stress reduction and self-exploration being associated to their sexual satisfaction. This work outlines the importance of considering the role of pornography use motivations when studying the links between pornography viewing and sexual well-being, and may provide basis for gender-based considerations when working with individuals seeking help for their pornography use or sexual satisfaction.

Keywords: pornography, sexual satifsaction, cross-cultural, gender diversity

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Authors: Nam Sze Cheng, P. C. Janita Chau

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Background: Eczema is a chronic inflammatory disease with high global prevalence rates in many childhood populations. It is also the most common paediatric skin problem. Although eczema education and proper skin care were effective in controlling eczema symptoms, the lack of both sufficient time for patient consultation and structured eczema education programme hindered the transferability of knowledge to patients and their parents. As a result, these young patients and their families suffer from a significant physical disability and psychological distress, which can substantially impair their quality of life. Objectives: This integrative review is to examine the effects of educational interventions for children with eczema and identify the core elements associated with an effective intervention. Methods: This integrative review targeted all articles published in 10 databases between May 2016 and February 2017 that reported the outcomes of disease interventions of any format for children and adolescents with the clinical diagnosis of eczema who were under 18 years of age. Five randomized controlled trials (RCT) and one systematic review of 10 RCTs were identified for review. All these publications had high methodological quality, except one study of web-based eczema education that was limited by selection bias and poor subject blinding. Findings: This review found that most studies adopted nurse-led or multi-disciplinary parental eczema education programme at the outpatient clinic setting. The format of these programmes included individual lectures, demonstration and group sharing, and the educational materials covered basic eczema knowledge and management as well as methods to interrupt itch-scratch cycle. The main outcome measures of these studies included severity of eczema symptoms, treatment adherence and quality of life of both patients and their families. Nine included studies reported statistically significant improvement in the primary outcome of symptom severity of these eczematous children. On the other hand, all these reviews failed to identify an effective dosage of intervention under these educational programmes that was attributed to the heterogeneity of the interventions. One study that was designed based on the social cognitive theory to guide the interventional content yielded statistically significant results. The systematic review recommended the importance of measuring parental self-efficacy. Implication: This integrative review concludes that structured educational programme can help nurses understand the theories behind different health interventions. They can then deliver eczema education to their patients in a consistent manner. These interventions also result in behavioral changes through patient education. Due to the lack of validated educational programmes in Chinese, it is imperative to conduct an RCT of eczema educational programme to investigate its effects on eczema severity, quality of life and treatment adherence in Hong Kong children as well as to promote the importance of parental self-efficacy.

Keywords: children, eczema, education, intervention

Procedia PDF Downloads 100

Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

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Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, Unmanned Aerial Vehicle (UAV), uniform, random, kriging

Procedia PDF Downloads 141

Authors: Anna Costanza Russo, Daniele Landi, Michele Germani

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Nowadays, the European Ecodesign Directive has emerged as a new approach to integrate environmental concerns into the product design and related processes. Ecodesign aims to minimize environmental impacts throughout the product life cycle, without compromising performances and costs. In addition, the recent Ecodesign Directives require products which are increasingly eco-friendly and eco-efficient, preserving high-performances. It is very important for producers measuring performances, for electric cooking ranges, hobs, ovens, and grills for household use, and a low power consumption of appliances represents a powerful selling point, also in terms of ecodesign requirements. The Ecodesign Directive provides a clear framework about the sustainable design of products and it has been extended in 2009 to all energy-related products, or products with an impact on energy consumption during the use. The European Regulation establishes measures of ecodesign of ovens, hobs, and kitchen hoods, and domestic use and energy efficiency of a product has a significant environmental aspect in the use phase which is the most impactful in the life cycle. It is important that the product parameters and performances are not affected by ecodesign requirements from a user’s point of view, and the benefits of reducing energy consumption in the use phase should offset the possible environmental impact in the production stage. Accurate measurements of cooking appliance performance are essential to help the industry to produce more energy efficient appliances. The development of ecodriven products requires ecoinnovation and ecodesign tools to support the sustainability improvement. The ecodesign tools should be practical and focused on specific ecoobjectives in order to be largely diffused. The main scope of this paper is the development, implementation, and testing of an innovative tool, which could be an improvement for the sustainable design of induction hobs. In particular, a prototypical software tool is developed in order to simulate the energy performances of the induction hobs. The tool is focused on a multiphysics model which is able to simulate the energy performances and the efficiency of induction hobs starting from the design data. The multiphysics model is composed by an electromagnetic simulation and a thermal simulation. The electromagnetic simulation is able to calculate the eddy current induced in the pot, which leads to the Joule heating of material. The thermal simulation is able to measure the energy consumption during the operational phase. The Joule heating caused from the eddy currents is the output of electromagnetic simulation and the input of thermal ones. The aims of the paper are the development of integrated tools and methodologies of virtual prototyping in the context of the ecodesign. This tool could be a revolutionary instrument in the field of industrial engineering and it gives consideration to the environmental aspects of product design and focus on the ecodesign of energy-related products, in order to achieve a reduced environmental impact.

Keywords: ecodesign, energy efficiency, induction hobs, virtual prototyping

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Authors: Vasantha Gouri Prathapagiri

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Innovations in ICT sector facilitate qualitative life style for citizens across the globe. Countries that facilitate usage of new techniques in ICT, i.e., big data analytics find it easier to fulfil the needs of their citizens. Big data is characterised by its volume, variety, and speed. Analytics involves its processing in a cost effective way in order to draw conclusion for their useful application. Big data also involves into the field of machine learning, artificial intelligence all leading to accuracy in data presentation useful for public policy making. Hence using data analytics in public policy making is a proper way to march towards all round development of any country. The data driven insights can help the government to take important strategic decisions with regard to socio-economic development of her country. Developed nations like UK and USA are already far ahead on the path of digitization with the support of Big Data analytics. India is a huge country and is currently on the path of massive digitization being realised through Digital India Mission. Internet connection per household is on the rise every year. This transforms into a massive data set that has the potential to improvise the public services delivery system into an effective service mechanism for Indian citizens. In fact, when compared to developed nations, this capacity is being underutilized in India. This is particularly true for administrative system in rural areas. The present paper focuses on the need for big data analytics adaptation in Indian rural administration and its contribution towards development of the country on a faster pace. Results of the research focussed on the need for increasing awareness and serious capacity building of the government personnel working for rural development with regard to big data analytics and its utility for development of the country. Multiple public policies are framed and implemented for rural development yet the results are not as effective as they should be. Big data has a major role to play in this context as can assist in improving both policy making and implementation aiming at all round development of the country.

Keywords: Digital India Mission, public service delivery system, public policy, Indian administration

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Authors: Oscar E. Cariceo, Claudia V. Casal

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Machine learning offers a set of techniques to promote social work interventions and can lead to support decisions of practitioners in order to predict new behaviors based on data produced by the organizations, services agencies, users, clients or individuals. Machine learning techniques include a set of generalizable algorithms that are data-driven, which means that rules and solutions are derived by examining data, based on the patterns that are present within any data set. In other words, the goal of machine learning is teaching computers through 'examples', by training data to test specifics hypothesis and predict what would be a certain outcome, based on a current scenario and improve that experience. Machine learning can be classified into two general categories depending on the nature of the problem that this technique needs to tackle. First, supervised learning involves a dataset that is already known in terms of their output. Supervising learning problems are categorized, into regression problems, which involve a prediction from quantitative variables, using a continuous function; and classification problems, which seek predict results from discrete qualitative variables. For social work research, machine learning generates predictions as a key element to improving social interventions on complex social issues by providing better inference from data and establishing more precise estimated effects, for example in services that seek to improve their outcomes. This paper exposes the results of a classification algorithm to predict cyberbullying among adolescents. Data were retrieved from the National Polyvictimization Survey conducted by the government of Chile in 2017. A logistic regression model was created to predict if an adolescent would experience cyberbullying based on the interaction and behavior of gender, age, grade, type of school, and self-esteem sentiments. The model can predict with an accuracy of 59.8% if an adolescent will suffer cyberbullying. These results can help to promote programs to avoid cyberbullying at schools and improve evidence based practice.

Keywords: cyberbullying, evidence based practice, machine learning, social work research

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Authors: Venuka Kuruwita Arachchige Don, Mohamed Shaheen, Chris Goodier

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Aerated autoclaved concrete (AAC) is known for its lightweight, easy handling, high thermal insulation, and extremely porous structure. Investigation of pore behavior in AAC is crucial for characterizing the material, standardizing design and production techniques, enhancing the mechanical, durability, and thermal performance, studying the effectiveness of protective measures, and analyzing the effects of weather conditions. The significant details of pores are complicated to observe with acknowledged accuracy. The High-resolution Field Emission Scanning Electron Microscope (FESEM) image analysis is a promising technique for investigating the pore behavior and density of AAC, which is adopted in this study. Mercury intrusion porosimeter and gas pycnometer were employed to characterize porosity distribution and density parameters. The analysis considered three different densities of AAC blocks and three layers in the altitude direction within each block. A set of understandings was presented to extract and analyze the details of pore shape, pore size, pore connectivity, and pore percentages from FESEM images of AAC. Average pore behavior outcomes per unit area were presented. Comparison of porosity distribution and density parameters revealed significant variations. FESEM imaging offered unparalleled insights into porosity behavior, surpassing the capabilities of other techniques. The analysis conducted from a multi-staged approach provides porosity percentage occupied by various pore categories, total porosity, variation of pore distribution compared to AAC densities and layers, number of two-dimensional and three-dimensional pores, variation of apparent and matrix densities concerning pore behaviors, variation of pore behavior with respect to aluminum content, and relationship among shape, diameter, connectivity, and percentage in each pore classification.

Keywords: autoclaved aerated concrete, density, imaging technique, microstructure, porosity behavior

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Authors: Ranadhir Roy

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Inverse problems arise in medical (molecular) imaging. These problems are characterized by large in three dimensions, and by the diffusion equation which models the physical phenomena within the media. The inverse problems are posed as a nonlinear optimization where the unknown parameters are found by minimizing the difference between the predicted data and the measured data. To obtain a unique and stable solution to an ill-posed inverse problem, a priori information must be used. Mathematical conditions to obtain stable solutions are established in Tikhonov’s regularization method, where the a priori information is introduced via a stabilizing functional, which may be designed to incorporate some relevant information of an inverse problem. Effective determination of the Tikhonov regularization parameter requires knowledge of the true solution, or in the case of optical imaging, the true image. Yet, in, clinically-based imaging, true image is not known. To alleviate these difficulties we have applied the penalty/modified barrier function (PMBF) method instead of Tikhonov regularization technique to make the inverse problems well-posed. Unlike the Tikhonov regularization method, the constrained optimization technique, which is based on simple bounds of the optical parameter properties of the tissue, can easily be implemented in the PMBF method. Imposing the constraints on the optical properties of the tissue explicitly restricts solution sets and can restore uniqueness. Like the Tikhonov regularization method, the PMBF method limits the size of the condition number of the Hessian matrix of the given objective function. The accuracy and the rapid convergence of the PMBF method require a good initial guess of the Lagrange multipliers. To obtain the initial guess of the multipliers, we use a least square unconstrained minimization problem. Three-dimensional images of fluorescence absorption coefficients and lifetimes were reconstructed from contact and noncontact experimentally measured data.

Keywords: constrained minimization, ill-conditioned inverse problems, Tikhonov regularization method, penalty modified barrier function method

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Authors: Babatunde Dosumu, Obuks Ejohwomu, Akilu Yunusa-Kaltungo

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Cost estimation in construction is often difficult, particularly when dealing with risks and uncertainties, which are inevitable and peculiar to developing countries like Nigeria. Direct consequences of these are major deviations in cost, duration, and quality. The fundamental aim of this study is to develop a framework for assessing the impacts of risk on cost estimation, which in turn causes variabilities between contract sum and final account. This is very important, as initial estimates given to clients should reflect the certain magnitude of consistency and accuracy, which the client builds other planning-related activities upon, and also enhance the capabilities of construction industry professionals by enabling better prediction of the final account from the contract sum. In achieving this, a systematic literature review was conducted with cost variability and construction projects as search string within three databases: Scopus, Web of science, and Ebsco (Business source premium), which are further analyzed and gap(s) in knowledge or research discovered. From the extensive review, it was found that factors causing deviation between final accounts and contract sum ranged between 1 and 45. Besides, it was discovered that a cost estimation framework similar to Building Cost Information Services (BCIS) is unavailable in Nigeria, which is a major reason why initial estimates are very often inconsistent, leading to project delay, abandonment, or determination at the expense of the huge sum of money invested. It was concluded that the development of a cost estimation framework that is adjudged an important tool in risk shedding rather than risk-sharing in project risk management would be a panacea to cost estimation problems, leading to cost variability in the Nigerian construction industry by the time this ongoing Ph.D. research is completed. It was recommended that practitioners in the construction industry should always take into account risk in order to facilitate the rapid development of the construction industry in Nigeria, which should give stakeholders a more in-depth understanding of the estimation effectiveness and efficiency to be adopted by stakeholders in both the private and public sectors.

Keywords: cost variability, construction projects, future studies, Nigeria

Procedia PDF Downloads 177

Authors: Abdullah S. Alghamdi, Khaled Alghamdi, Richard O. Jenkins, Parvez I. Haris

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Background: Quantity and quality of sleep have been reported to be significant risk factors for obesity and development of metabolic disorders such as type 2 diabetes mellitus (T2DM). The relationship between diabetes and sleep quantity was reported to be U-shaped, which means increased or decreased sleeping hours can increase the risk of diabetes. The plasma glucagon levels were found to continuously decrease during night-time sleep in healthy individuals, independently of blood glucose and insulin levels. The disturbance of the circadian rhythm is also important and has been linked with an increased the chance of diabetes incidence. There is a lack of research on sleep patterns on Saudis with T2DM and how this is affected by Ramadan fasting. Aim: To assess the sleeping patterns of Saudis with T2DM (before, during, and after Ramadan), using two different techniques and relate this to their HbA1c levels. Method: This study recruited 82 Saudi with T2DM, who chose to fast during Ramadan, from the Endocrine and Diabetic Centre of Al Iman General Hospital, Riyadh, Saudi Arabia. Ethical approvals for the study were obtained from De Montfort University and Saudi Ministry of Health. Their sleeping patterns were assessed by a self-administered questionnaire (before, during, and after Ramadan). The assessment included the daily total sleeping hours (DTSH), and total night-time sleeping hours (TNTSH) of the participants. In addition, sleeping patterns of 36 patients, randomly selected from the 82 participants, were further tracked during and after Ramadan by using Fitbit Flex 2™ accelerometer. Blood samples were collected in each period for measuring HbA1c. Results: Questionnaire analysis revealed that the sleeping patterns significantly changed between the periods, with shorter hours during Ramadan (P < 0.001 for DTSH, and P < 0.001 for TNTSH). These findings were confirmed by the Fitbit data, which also indicated significant shorter sleeping hours for the DTSH, and the TNTSH during Ramadan (P < 0.001 and P < 0.001, respectively). Although there were no significant correlations between the questionnaire and Fitbit data, the TNTSH were shorter among the participants in all periods by both techniques. The mean HbA1c significantly varied between periods, with lowest level during Ramadan. Although the statistical tests did not show significant variances in the mean HbA1c between the groups of participants regarding their hours of sleeping, the lowest mean HbA1c was observed in the group of participants who slept for 6-8 hours and had longer night-time sleeping hours. Conclusion: A short sleep duration, and absence of night-time sleep were significantly observed among the majority of the study population during Ramadan, which could suppress the full benefits of Ramadan fasting for diabetic patients. This study showed that there is a good agreement between the findings of the questionnaire and the Fitbit device for evaluating sleeping patterns in a Saudi population. A larger study is needed in the future to investigate the impact of Ramadan fasting on sleep quality and quantity and its relationship with health and disease.

Keywords: Diabetes, Fasting, Fitbit, HbA1c, IPAQ, Ramadan, Sleep

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Authors: Najeeb Hassan, Mahmudur Rahman

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Arial Khan River is one of the main south-eastward outlets of the River Padma. This river maintains a meander channel through its course and is erosional in nature. The specific objective of the research is to study and evaluate the hydrological characteristics in the form of assessing changes of cross-sections, discharge, water level and velocity profile in different stations and to create a hydrodynamic model of the Arial Khan River. Necessary data have been collected from Bangladesh Water Development Board (BWDB) and Center for Environment and Geographic Information Services (CEGIS). Satellite images have been observed from Google earth. In this study, hydrodynamic model of Arial Khan River has been developed using well known steady open channel flow code Hydrologic Engineering Centers and River Analysis System (HEC-RAS) using field surveyed geometric data. Cross-section properties at 22 locations of River Arial Khan for the years 2011, 2013 and 2015 were also analysed. 1-D HEC-RAS model has been developed using the cross sectional data of 2015 and appropriate boundary condition is being used to run the model. This Arial Khan River model is calibrated using the pick discharge of 2015. The applicable value of Mannings roughness coefficient (n) is adjusted through the process of calibration. The value of water level which ties with the observed data to an acceptable accuracy is taken as calibrated model. The 1-D HEC-RAS model then validated by using the pick discharges from 2009-2018. Variation in observed water level in the model and collected water level data is being compared to validate the model. It is observed that due to seasonal variation, discharge of the river changes rapidly and Mannings roughness coefficient (n) also changes due to the vegetation growth along the river banks. This river model may act as a tool to measure flood area in future. By considering the past pick flow discharge, it is strongly recommended to improve the carrying capacity of Arial Khan River to protect the surrounding areas from flash flood.

Keywords: BWDB, CEGIS, HEC-RAS

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Authors: Soledad Carinelli, Iñigo Fernández, José Luis González-Mora, Pedro A. Salazar-Carballo

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Mastitis is the most relevant inflammatory disease in cattle, affecting the animal health and causing important economic losses on dairy farms. This disease takes place in the mammary gland or udder when some opportunistic microorganisms, such as Staphylococcus aureus, Streptococcus agalactiae, Corynebacterium bovis, etc., invade the teat canal. According to the severity of the inflammation, mastitis can be classified as sub-clinical, clinical and chronic. Standard methods for mastitis detection include counts of somatic cells, cell culture, electrical conductivity of the milk, and California test (evaluation of “gel-like” matrix consistency after cell lysed with detergents). However, these assays present some limitations for accurate detection of subclinical mastitis. Currently, haptoglobin, an acute phase protein, has been proposed as novel and effective biomarker for mastitis detection. In this work, an electrochemical biosensor based on polydopamine-modified magnetic nanoparticles (MNPs@pDA) for haptoglobin detection is reported. Thus, MNPs@pDA has been synthesized by our group and functionalized with hemoglobin due to its high affinity to haptoglobin protein. The protein was labeled with specific antibodies modified with alkaline phosphatase enzyme for its electrochemical detection using an electroactive substrate (1-naphthyl phosphate) by differential pulse voltammetry. After the optimization of assay parameters, the haptoglobin determination was evaluated in milk. The strategy presented in this work shows a wide range of detection, achieving a limit of detection of 43 ng/mL. The accuracy of the strategy was determined by recovery assays, being of 84 and 94.5% for two Hp levels around the cut off value. Milk real samples were tested and the prediction capacity of the electrochemical biosensor was compared with a Haptoglobin commercial ELISA kit. The performance of the assay has demonstrated this strategy is an excellent and real alternative as screen method for sub-clinical bovine mastitis detection.

Keywords: bovine mastitis, haptoglobin, electrochemistry, magnetic nanoparticles, polydopamine

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Authors: Roengchai Tansuchat, Woraphon Yamaka, Paravee Maneejuk

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From the past recent, the global financial crisis, economic instability, and large fluctuation in agricultural commodity price have led to increased concerns about the volatility transmission among them. The problem is further exacerbated by commodities volatility caused by other commodity price fluctuations, hence the decision on hedging strategy has become both costly and useless. Thus, this paper is conducted to analysis the volatility spillover effect among major agriculture including corn, soybeans, wheat and rice, to help the commodity suppliers hedge their portfolios, and manage the risk and co-volatility of them. We provide a switching regime approach to analyzing the issue of volatility spillovers in different economic conditions, namely upturn and downturn economic. In particular, we investigate relationships and volatility transmissions between these commodities in different economic conditions. We purposed a Copula-based multivariate Markov Switching GARCH model with two regimes that depend on an economic conditions and perform simulation study to check the accuracy of our proposed model. In this study, the correlation term in the cross-hedge ratio is obtained from six copula families – two elliptical copulas (Gaussian and Student-t) and four Archimedean copulas (Clayton, Gumbel, Frank, and Joe). We use one-step maximum likelihood estimation techniques to estimate our models and compare the performance of these copula using Akaike information criterion (AIC) and Bayesian information criteria (BIC). In the application study of agriculture commodities, the weekly data used are conducted from 4 January 2005 to 1 September 2016, covering 612 observations. The empirical results indicate that the volatility spillover effects among cereal futures are different, as response of different economic condition. In addition, the results of hedge effectiveness will also suggest the optimal cross hedge strategies in different economic condition especially upturn and downturn economic.

Keywords: agricultural commodity futures, cereal, cross-hedge, spillover effect, switching regime approach

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Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

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Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

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Authors: N. Manjunatha, K. T. Rangswamy, N. Nagaraju, H. A. Prameela, P. Rudraswamy, M. Krishnareddy

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The identification of virus in cowpea especially potyviruses is confusing. Even though there are several studies on viruses causing diseases in cowpea, difficult to distinguish based on symptoms and serological detection. The differentiation of potyviruses considering as a constraint, the present study is initiated for molecular characterization, host plant resistance and epidemiology of the BCMV infecting cowpea. The etiological agent causing cowpea mosaic was identified as Bean Common Mosaic Virus (BCMV) on the basis of RT-PCR and electron microscopy. An approximately 750bp PCR product corresponding to coat protein (CP) region of the virus and the presence of long flexuous filamentous particles measuring about 952 nm in size typical to genus potyvirus were observed under electron microscope. The characterized virus isolate genome had 10054 nucleotides, excluding the 3’ terminal poly (A) tail. Comparison of polyprotein of the virus with other potyviruses showed similar genome organization with 9 cleavage sites resulted in 10 functional proteins. The pairwise sequence comparison of individual genes, P1 showed most divergent, but CP gene was less divergent at nucleotide and amino acid level. A phylogenetic tree constructed based on multiple sequence alignments of the polyprotein nucleotide and amino acid sequences of cowpea BCMV and potyviruses showed virus is closely related to BCMV-HB. Whereas, Soybean variant of china (KJ807806) and NL1 isolate (AY112735) showed 93.8 % (5’UTR) and 94.9 % (3’UTR) homology respectively with other BCMV isolates. This virus transmitted to different leguminous plant species and produced systemic symptoms under greenhouse conditions. Out of 100 cowpea genotypes screened, three genotypes viz., IC 8966, V 5 and IC 202806 showed immune reaction in both field and greenhouse conditions. Single marker analysis (SMA) was revealed out of 4 SSR markers linked to BCMV resistance, M135 marker explains 28.2 % of phenotypic variation (R2) and Polymorphic information content (PIC) value of these markers was ranged from 0.23 to 0.37. The correlation and regression analysis showed rainfall, and minimum temperature had significant negative impact and strong relationship with aphid population, whereas weak correlation was observed with disease incidence. Path coefficient analysis revealed most of the weather parameters exerted their indirect contributions to the aphid population and disease incidence except minimum temperature. This study helps to identify specific gaps in knowledge for researchers who may wish to further analyse the science behind complex interactions between vector-virus and host in relation to the environment. The resistant genotypes identified are could be effectively used in resistance breeding programme.

Keywords: cowpea, epidemiology, genotypes, virus

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Authors: K. S. Vedasingha, K. K. M. T. Perera, K. I. Hathurusinghe, H. W. I. Akalanka, Nelum Chathuranga Amarasena, Nalaka R. Dissanayake

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Railways provide the most convenient and economically beneficial mode of transportation, and it has been the most popular transportation method among all. According to the past analyzed data, it reveals a considerable number of accidents which occurred at railways and caused damages to not only precious lives but also to the economy of the countries. There are some major issues which need to be addressed in railways of South Asian countries since they fall under the developing category. The goal of this research is to minimize the influencing aspect of railway level crossing accidents by developing the “railway process automation system”, as there are high-risk areas that are prone to accidents, and safety at these places is of utmost significance. This paper describes the implementation methodology and the success of the study. The main purpose of the system is to ensure human safety by using the Internet of Things (IoT) and image processing techniques. The system can detect the current location of the train and close the railway gate automatically. And it is possible to do the above-mentioned process through a decision-making system by using past data. The specialty is both processes working parallel. As usual, if the system fails to close the railway gate due to technical or a network failure, the proposed system can identify the current location and close the railway gate through a decision-making system, which is a revolutionary feature. The proposed system introduces further two features to reduce the causes of railway accidents. Railway track crack detection and motion detection are those features which play a significant role in reducing the risk of railway accidents. Moreover, the system is capable of detecting rule violations at a level crossing by using sensors. The proposed system is implemented through a prototype, and it is tested with real-world scenarios to gain the above 90% of accuracy.

Keywords: crack detection, decision-making, image processing, Internet of Things, motion detection, prototype, sensors

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

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Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

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Authors: Luis Alvarado, Victor Poblete, Isaac Gonzalez, Yetzabeth Gonzalez

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In MIREX 2016 (http://www.music-ir.org/mirex), the deep neural network (DNN)-Deep Chroma Extractor, proposed by Korzeniowski and Wiedmer, reached the highest score in an audio chord recognition task. In the present paper, this tool is assessed under acoustic reverberant environments and distinct source-microphone distances. The evaluation dataset comprises The Beatles and Queen datasets. These datasets are sequentially re-recorded with a single microphone in a real reverberant chamber at four reverberation times (0 -anechoic-, 1, 2, and 3 s, approximately), as well as four source-microphone distances (32, 64, 128, and 256 cm). It is expected that the performance of the trained DNN will dramatically decrease under these acoustic conditions with signals degraded by room reverberation and distance to the source. Recently, the effect of the bio-inspired Locally-Normalized Cepstral Coefficients (LNCC), has been assessed in a text independent speaker verification task using speech signals degraded by additive noise at different signal-to-noise ratios with variations of recording distance, and it has also been assessed under reverberant conditions with variations of recording distance. LNCC showed a performance so high as the state-of-the-art Mel Frequency Cepstral Coefficient filters. Based on these results, this paper proposes a variation of locally-normalized triangular filters called Locally-Normalized Quarter Tone (LNQT) filters. By using the LNQT spectrogram, robustness improvements of the trained Deep Chroma Extractor are expected, compared with classical triangular filters, and thus compensating the music signal degradation improving the accuracy of the chord recognition system.

Keywords: chord recognition, deep neural networks, feature extraction, music information retrieval

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Authors: Irsa Ejaz, Siyang He, Wei Li, Naiyue Hu, Chaochen Tang, Songbo Li, Meng Li, Boubacar Diallo, Guanghui Xie, Kang Yu

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Background: Near-infrared spectroscopy (NIR) is a non-destructive, fast, and low-cost method to measure the grain quality of different cereals. Previously reported NIR model calibrations using the whole grain spectra had moderate accuracy. Improved predictions are achievable by using the spectra of whole grains, when compared with the use of spectra collected from the flour samples. However, the feasibility for determining the critical biochemicals, related to the classifications for food, feed, and fuel products are not adequately investigated. Objectives: To evaluate the feasibility of using NIRS and the influence of four sample types (whole grains, flours, hulled grain flours, and hull-less grain flours) on the prediction of chemical components to improve the grain sorting efficiency for human food, animal feed, and biofuel. Methods: NIR was applied in this study to determine the eight biochemicals in four types of sorghum samples: hulled grain flours, hull-less grain flours, whole grains, and grain flours. A total of 20 hybrids of sorghum grains were selected from the two locations in China. Followed by NIR spectral and wet-chemically measured biochemical data, partial least squares regression (PLSR) was used to construct the prediction models. Results: The results showed that sorghum grain morphology and sample format affected the prediction of biochemicals. Using NIR data of grain flours generally improved the prediction compared with the use of NIR data of whole grains. In addition, using the spectra of whole grains enabled comparable predictions, which are recommended when a non-destructive and rapid analysis is required. Compared with the hulled grain flours, hull-less grain flours allowed for improved predictions for tannin, cellulose, and hemicellulose using NIR data. Conclusion: The established PLSR models could enable food, feed, and fuel producers to efficiently evaluate a large number of samples by predicting the required biochemical components in sorghum grains without destruction.

Keywords: FT-NIR, sorghum grains, biochemical composition, food, feed, fuel, PLSR

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Authors: Ismaila Olabisi Ogundiji, Hakeem Mayowa Olujide, Qasim Usamot

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The main reasons for site-specific management for agricultural inputs are to increase the profitability of crop production, to protect the environment and to improve products’ quality. Information about the variability of different soil attributes within a field is highly essential for the decision-making process. Lack of fast and accurate acquisition of soil characteristics remains one of the biggest limitations of precision agriculture due to being expensive and time-consuming. Adaptive sampling has been proven as an accurate and affordable sampling technique for planning within a field for site-specific management of agricultural inputs. This study employed spatial uncertainty of soil apparent electrical conductivity (ECa) estimates to identify adaptive re-survey areas in the field. The original dataset was grouped into validation and calibration groups where the calibration group was sub-grouped into three sets of different measurements pass intervals. A conditional simulation was performed on the field ECa to evaluate the ECa spatial uncertainty estimates by the use of the geostatistical technique. The grouping of high-uncertainty areas for each set was done using image segmentation in MATLAB, then, high and low area value-separate was identified. Finally, an adaptive re-survey was carried out on those areas of high-uncertainty. Adding adaptive re-surveying significantly minimized the time required for resampling whole field and resulted in ECa with minimal error. For the most spacious transect, the root mean square error (RMSE) yielded from an initial crude sampling survey was minimized after an adaptive re-survey, which was close to that value of the ECa yielded with an all-field re-survey. The estimated sampling time for the adaptive re-survey was found to be 45% lesser than that of all-field re-survey. The results indicate that designing adaptive sampling through spatial uncertainty models significantly mitigates sampling cost, and there was still conformity in the accuracy of the observations.

Keywords: soil electrical conductivity, adaptive sampling, conditional simulation, spatial uncertainty, site-specific management

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Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: Shohreh Ghasemi

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Introduction: Managing oral and maxillofacial trauma is a multifaceted challenge, as it can have life-threatening consequences and significant functional and aesthetic impact. Navigation techniques have been introduced to improve surgical precision to meet this challenge. A machine learning algorithm was also developed to support clinical decision-making regarding treating oral and maxillofacial trauma. Given these advances, this systematic meta-analysis aims to assess the efficacy of navigational techniques in treating oral and maxillofacial trauma and explore the impact of machine learning on their management. Methods: A detailed and comprehensive analysis of studies published between January 2010 and September 2021 was conducted through a systematic meta-analysis. This included performing a thorough search of Web of Science, Embase, and PubMed databases to identify studies evaluating the efficacy of navigational techniques and the impact of machine learning in managing oral and maxillofacial trauma. Studies that did not meet established entry criteria were excluded. In addition, the overall quality of studies included was evaluated using Cochrane risk of bias tool and the Newcastle-Ottawa scale. Results: Total of 12 studies, including 869 patients with oral and maxillofacial trauma, met the inclusion criteria. An analysis of studies revealed that navigation techniques effectively improve surgical accuracy and minimize the risk of complications. Additionally, machine learning algorithms have proven effective in predicting treatment outcomes and identifying patients at high risk for complications. Conclusion: The introduction of navigational technology has great potential to improve surgical precision in oral and maxillofacial trauma treatment. Furthermore, developing machine learning algorithms offers opportunities to improve clinical decision-making and patient outcomes. Still, further studies are necessary to corroborate these results and establish the optimal use of these technologies in managing oral and maxillofacial trauma

Keywords: trauma, machine learning, navigation, maxillofacial, management

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Authors: Matthew D. Baffa

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Infrared thermography is a non-destructive test method used to estimate surface temperatures based on the amount of electromagnetic energy radiated by building envelope components. These surface temperatures are indicators of various qualitative building envelope deficiencies such as locations and extent of heat loss, thermal bridging, damaged or missing thermal insulation, air leakage, and moisture presence in roof, floor, and wall assemblies. Although infrared thermography is commonly used for qualitative deficiency detection in buildings, this study assesses its use as a quantitative method to estimate the overall thermal conductance value (U-value) of the exterior above-grade walls of a study home. The overall U-value of exterior above-grade walls in a home provides useful insight into the energy consumption and thermal comfort of a home. Three methodologies from the literature were employed to estimate the overall U-value by equating conductive heat loss through the exterior above-grade walls to the sum of convective and radiant heat losses of the walls. Outdoor infrared thermography field measurements of the exterior above-grade wall surface and reflective temperatures and emissivity values for various components of the exterior above-grade wall assemblies were carried out during winter months at the study home using a basic thermal imager device. The overall U-values estimated from each methodology from the literature using the recorded field measurements were compared to the nominal exterior above-grade wall overall U-value calculated from materials and dimensions detailed in architectural drawings of the study home. The nominal overall U-value was validated through calendarization and weather normalization of utility bills for the study home as well as various estimated heat loss quantities from a HOT2000 computer model of the study home and other methods. Under ideal environmental conditions, the estimated overall U-values deviated from the nominal overall U-value between ±2% to ±33%. This study suggests infrared thermography can estimate the overall U-value of exterior above-grade walls in low-rise residential homes with a fair amount of accuracy.

Keywords: emissivity, heat loss, infrared thermography, thermal conductance

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Authors: Rasmila Kawan, Sanjay Juvekar, Sandeep Salvi, Gufran Beig, Rainer Sauerborn

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Background: Indoor air pollution especially from the use of biomass fuels, remains a potentially large global health threat. The inefficient use of such fuels in poorly ventilated conditions results in high levels of indoor air pollution, most seriously affecting women and young children. Objectives: The main aim of this study was to measure and compare the lung function of the women exposed in the biomass fuels and LPG fuels and relate it to the indoor emission measured using a structured questionnaire, spirometer and filter based low volume samplers respectively. Methodology: This cross-sectional comparative study was conducted among the women (aged > 18 years) living in rural villages of Pune district who were not diagnosed of chronic pulmonary diseases or any other respiratory diseases and using biomass fuels or LPG for cooking for a minimum period of 5 years or more. Data collection was done from April to June 2017 in dry season. Spirometer was performed using the portable, battery-operated ultrasound Easy One spirometer (Spiro bank II, NDD Medical Technologies, Zurich, Switzerland) to determine the lung function over Forced expiratory volume. The primary outcome variable was forced expiratory volume in 1 second (FEV1). Secondary outcome was chronic obstruction pulmonary disease (post bronchodilator FEV1/ Forced Vital Capacity (FVC) < 70%) as defined by the Global Initiative for Obstructive Lung Disease. Potential confounders such as age, height, weight, smoking history, occupation, educational status were considered. Results: Preliminary results showed that the lung function of the women using Biomass fuels (FEV1/FVC = 85% ± 5.13) had comparatively reduced lung function than the LPG users (FEV1/FVC = 86.40% ± 5.32). The mean PM 2.5 mass concentration in the biomass user’s kitchen was 274.34 ± 314.90 and 85.04 ± 97.82 in the LPG user’s kitchen. Black carbon amount was found higher in the biomass users (black carbon = 46.71 ± 46.59 µg/m³) than LPG users (black carbon=11.08 ± 22.97 µg/m³). Most of the houses used separate kitchen. Almost all the houses that used the clean fuel like LPG had minimum amount of the particulate matter 2.5 which might be due to the background pollution and cross ventilation from the houses using biomass fuels. Conclusions: Therefore, there is an urgent need to adopt various strategies to improve indoor air quality. There is a lacking of current state of climate active pollutants emission from different stove designs and identify major deficiencies that need to be tackled. Moreover, the advancement in research tools, measuring technique in particular, is critical for researchers in developing countries to improve their capability to study the emissions for addressing the growing climate change and public health concerns.

Keywords: black carbon, biomass fuels, indoor air pollution, lung function, particulate matter

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