Search results for: linear predictive coding

Authors: Glenn Koonce

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There is a need to address the improvement of university educational leadership preparation programs through the processes of accreditation and continuous improvement. The program faculty in a university in the eastern part of the United States has incorporated an employer satisfaction focus group to address their national accreditation standard so that employers are satisfied with completers' preparation for the position of principal or assistant principal. Using the Council for the Accreditation of Educator Preparation (CAEP) required proficiencies, the following research questions are investigated: 1) what proficiencies do completers perform the strongest? 2) what proficiencies need to be strengthened? 3) what other strengths beyond the required proficiencies do completers demonstrate? 4) what other areas of responsibility beyond the required proficiencies do completers demonstrate? and 5) how can the program improve in preparing candidates for their positions? This study focuses on employers of one public school district that has a large number of educational leadership completers employed as principals and assistant principals. Central office directors who evaluate principals and principals who evaluate assistant principals are focus group participants. Construction of the focus group questions is a result of recommendations from an accreditation regulatory specialist, reviewed by an expert panel, and piloted by an experienced focus group leader. The focus group session was audio recorded, transcribed, and analyzed using the NVivo Version 14 software. After constructing folders in NVivo, the focus group transcript was loaded and skimmed by diagnosing significant statements and assessing core ideas for developing primary themes. These themes were aligned to address the research questions. From the transcript, codes were assigned to the themes and NVivo provided a coding hierarchy chart or graphical illustration for framing the coding. A final report of the coding process was designed using the primary themes and pertinent codes that were supported in excerpts from the transcript. The outcome of this study is to identify themes that can provide evidence that the educational leadership program is meeting its mission to improve PreK-12 student achievement through well-prepared completers who have achieved the position of principal or assistant principal. The considerations will be used to derive a composite profile of employers' satisfaction with program completers with the capacity to serve, influence, and thrive as educational leaders. Analysis of the idealized themes will result in identifying issues that may challenge university educational leadership programs to improve. Results, conclusions, and recommendations are used for continuous improvement, which is another national accreditation standard required for the program.

Keywords: educational leadership preparation, CAEP accreditation, principal & assistant principal evaluations, continuous improvement

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

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Authors: Rafael Zhindon Almeida

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Surface water quality is an important concern for the evaluation and prediction of water quality conditions. The objective of this study is to develop a statistical model that can accurately predict the water quality of the El Macho estuary in the city of Machala, El Oro province. The methodology employed in this study is of a basic type that involves a thorough search for theoretical foundations to improve the understanding of statistical modeling for water quality analysis. The research design is correlational, using a multivariate statistical model involving multiple linear regression and principal component analysis. The results indicate that water quality parameters such as fecal coliforms, biochemical oxygen demand, chemical oxygen demand, iron and dissolved oxygen exceed the allowable limits. The water of the El Macho estuary is determined to be below the required water quality criteria. The multiple linear regression model, based on chemical oxygen demand and total dissolved solids, explains 99.9% of the variance of the dependent variable. In addition, principal component analysis shows that the model has an explanatory power of 86.242%. The study successfully developed a statistical model to evaluate the water quality of the El Macho estuary. The estuary did not meet the water quality criteria, with several parameters exceeding the allowable limits. The multiple linear regression model and principal component analysis provide valuable information on the relationship between the various water quality parameters. The findings of the study emphasize the need for immediate action to improve the water quality of the El Macho estuary to ensure the preservation and protection of this valuable natural resource.

Keywords: statistical modeling, water quality, multiple linear regression, principal components, statistical models

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Authors: Tae Kyu An

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We demonstrate repurposing linear micropatterns on the CD as a master mold to fabricate TIPS-PEN microwires. From the micropatterns on CDs, we replicated polyurethane acrylate (PUA) templates which are robust and flexible until submicrometer scale patterns. Subsequently, 1.5 μm TIPS-PEN microwires separated by 1.5 μm were grown. Using crystal analysis tools with polarized optical microscopy and X-ray diffraction measurement, it was revealed that each TIPS-PEN microwires are highly crystalline and uniform compared to spin-coated films. It is attributed to the template-guided growth of TIPS-PEN crystals along the linear template, thus the OFETs comprised of TIPS-PEN microwires displayed the high field-effect mobility.

Keywords: compact disk, macro patterning, OFET, soft lithography

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Authors: Luis N. Pereira, Marco P. Carrasco

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Dynamic pricing is a revenue management strategy in which hotel suppliers define, over time, flexible and different prices for their services for different potential customers, considering the profile of e-consumers and the demand and market supply. This means that the fundamentals of dynamic pricing are based on economic theory (price elasticity of demand) and market segmentation. This study aims to define a dynamic pricing strategy and a contextualized offer to the e-consumers profile in order to improve the number of reservations of an online distribution channel. Segmentation methods (hierarchical and non-hierarchical) were used to identify and validate an optimal number of market segments. A profile of the market segments was studied, considering the characteristics of the e-consumers and the probability of reservation a room. In addition, the price elasticity of demand was estimated for each segment using econometric models. Finally, predictive models were used to define rules for classifying new e-consumers into pre-defined segments. The empirical study illustrates how it is possible to improve the intelligence of an online distribution channel system through an optimal dynamic pricing strategy and a contextualized offer to the profile of each new e-consumer. A database of 11 million e-consumers of an online distribution channel was used in this study. The results suggest that an appropriate policy of market segmentation in using of online reservation systems is benefit for the service suppliers because it brings high probability of reservation and generates more profit than fixed pricing.

Keywords: dynamic pricing, e-consumers segmentation, online reservation systems, predictive analytics

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Authors: John Kabuba

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The study investigated the implementation of the Neural Network (NN) techniques for prediction of the loading of Cu ions onto clinoptilolite. The experimental design using analysis of variance (ANOVA) was chosen for testing the adequacy of the Neural Network and for optimizing of the effective input parameters (pH, temperature and initial concentration). Feed forward, multi-layer perceptron (MLP) NN successfully tracked the non-linear behavior of the adsorption process versus the input parameters with mean squared error (MSE), correlation coefficient (R) and minimum squared error (MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed that NN modeling techniques could effectively predict and simulate the highly complex system and non-linear process such as ion-exchange.

Keywords: clinoptilolite, loading, modeling, neural network

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Authors: Vedant Janapaty

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Estuaries are wetlands where fresh water from streams mixes with salt water from the sea. Also known as “kidneys of our planet”- they are extremely productive environments that filter pollutants, absorb floods from sea level rise, and shelter a unique ecosystem. However, eutrophication and loss of native species are ailing our wetlands. There is a lack of uniform data collection and sparse research on correlations between satellite data and in situ measurements. Remote sensing (RS) has shown great promise in environmental monitoring. This project attempts to use satellite data and correlate metrics with in situ observations collected at five estuaries. Images for satellite data were processed to calculate 7 bands (SIs) using Python. Average SI values were calculated per month for 23 years. Publicly available data from 6 sites at ELK was used to obtain 10 parameters (OPs). Average OP values were calculated per month for 23 years. Linear correlations between the 7 SIs and 10 OPs were made and found to be inadequate (correlation = 1 to 64%). Fourier transform analysis on 7 SIs was performed. Dominant frequencies and amplitudes were extracted for 7 SIs, and a machine learning(ML) model was trained, validated, and tested for 10 OPs. Better correlations were observed between SIs and OPs, with certain time delays (0, 3, 4, 6 month delay), and ML was again performed. The OPs saw improved R² values in the range of 0.2 to 0.93. This approach can be used to get periodic analyses of overall wetland health with satellite indices. It proves that remote sensing can be used to develop correlations with critical parameters that measure eutrophication in situ data and can be used by practitioners to easily monitor wetland health.

Keywords: estuary, remote sensing, machine learning, Fourier transform

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Authors: Salisu Ibrahim

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The paper studies the commutativity associated with fractional order linear time-varying systems (LTVSs), which is an important area of study in control systems engineering. In this paper, we explore the properties of these systems and their ability to commute. We proposed the necessary and sufficient condition for commutativity for fractional order LTVSs. Through a simulation and mathematical analysis, we demonstrate that these systems exhibit commutativity under certain conditions. Our findings have implications for the design and control of fractional order systems in practical applications, science, and engineering. An example is given to show the effectiveness of the proposed method which is been computed by Mathematica and validated by the use of MATLAB (Simulink).

Keywords: fractional differential equation, physical systems, equivalent circuit, analog control

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Authors: Salisu Ibrahim

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The paper studies the commutativity associated with fractional order linear time-varying systems (LTVSs), which is an important area of study in control systems engineering. In this paper, we explore the properties of these systems and their ability to commute. We proposed the necessary and sufficient condition for commutativity for fractional order LTVSs. Through a simulation and mathematical analysis, we demonstrate that these systems exhibit commutativity under certain conditions. Our findings have implications for the design and control of fractional order systems in practical applications, science, and engineering. An example is given to show the effectiveness of the proposed method which is been computed by Mathematica and validated by the use of Matlab (Simulink).

Keywords: fractional differential equation, physical systems, equivalent circuit, and analog control

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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Authors: Nouira Mariem, Ben Rayana Hazem, Ennigrou Samir

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Background and aim: During the past decade, the frequency of extrapulmonary forms of tuberculosis has increased. These forms are under-diagnosed using conventional tests. The aim of this study was to evaluate the performance of the Tuberculin Skin Test (TST) for the diagnosis of tuberculosis, using the ROC curve and Fagan’s Nomogram methodology. Methods: This was a case-control, multicenter study in 11 anti-tuberculosis centers in Tunisia, during the period from June to November2014. The cases were adults aged between 18 and 55 years with confirmed tuberculosis. Controls were free from tuberculosis. A data collection sheet was filled out and a TST was performed for each participant. Diagnostic accuracy measures of TST were estimated using ROC curve and Area Under Curve to estimate sensitivity and specificity of a determined cut-off point. Fagan’s nomogram was used to estimate its predictive values. Results: Overall, 1053 patients were enrolled, composed of 339 cases (sex-ratio (M/F)=0.87) and 714 controls (sex-ratio (M/F)=0.99). The mean age was 38.3±11.8 years for cases and 33.6±11 years for controls. The mean diameter of the TST induration was significantly higher among cases than controls (13.7mm vs.6.2mm;p=10-6). Area Under Curve was 0.789 [95% CI: 0.758-0.819; p=0.01], corresponding to a moderate discriminating power for this test. The most discriminative cut-off value of the TST, which were associated with the best sensitivity (73.7%) and specificity (76.6%) couple was about 11 mm with a Youden index of 0.503. Positive and Negative predictive values were 3.11% and 99.52%, respectively. Conclusion: In view of these results, we can conclude that the TST can be used for tuberculosis diagnosis with a good sensitivity and specificity. However, the skin induration measurement and its interpretation is operator dependent and remains difficult and subjective. The combination of the TST with another test such as the Quantiferon test would be a good alternative.

Keywords: tuberculosis, tuberculin skin test, ROC curve, cut-off

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Authors: Iyai Davies, Olivier L. C. Haas

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In this paper, the problem of stability and stabilization for neutral delay-differential systems with infinite delay is investigated. Using Lyapunov method, new delay-independent sufficient condition for the stability of neutral systems with infinite delay is obtained in terms of linear matrix inequality (LMI). Memory-less state feedback controllers are then designed for the stabilization of the system using the feasible solution of the resulting LMI, which are easily solved using any optimization algorithms. Numerical examples are given to illustrate the results of the proposed methods.

Keywords: infinite delays, Lyapunov method, linear matrix inequality, neutral systems, stability

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Authors: Reza Hedayati, Meysam Jahanbakhshi

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Elastic energy absorbers which consist of a ring-liked plate and springs can be a good choice for increasing the impact duration during an accident. In the current project, an energy absorber system is optimized using four optimizing methods Kuhn-Tucker, Sequential Linear Programming (SLP), Concurrent Subspace Design (CSD), and Pshenichny-Lim-Belegundu-Arora (PLBA). Time solution, convergence, Programming Length and accuracy of the results were considered to find the best solution algorithm. Results showed the superiority of PLBA over the other algorithms.

Keywords: Concurrent Subspace Design (CSD), Kuhn-Tucker, Pshenichny-Lim-Belegundu-Arora (PLBA), Sequential Linear Programming (SLP)

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Authors: Bongs Lainjo

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The evolution and escalation of bike-sharing programs (BSP) continue unabated. Since the sixties, many countries have introduced different models and strategies of BSP. These include variations ranging from dockless models to electronic real-time monitoring systems. Reasons for using this BSP include recreation, errands, work, etc. And there is all indication that complex, and more innovative rider-friendly systems are yet to be introduced. The objective of this paper is to analyze current variables established by different operators and streamline them identifying the most compelling ones using analytics. Given the contents of available databases, there is a lack of uniformity and common standard on what is required and what is not. Two factors appear to be common: user type (registered and unregistered, and duration of each trip). This article uses historical data provided by one operator based in the greater Washington, District of Columbia, USA area. Several variables including categorical and continuous data types were screened. Eight out of 18 were considered acceptable and significantly contribute to determining a useful and reliable predictive model. Bike-sharing systems have become popular in recent years all around the world. Although this trend has resulted in many studies on public cycling systems, there have been few previous studies on the factors influencing public bicycle travel behavior. A bike-sharing system is a computer-controlled system in which individuals can borrow bikes for a fee or free for a limited period. This study has identified unprecedented useful, and pragmatic parameters required in improving BSP ridership dynamics.

Keywords: sharing program, historical data, parameters, ridership dynamics, trip duration

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Authors: Saba Rahman, Arvind K. Jain, S. D. Bharti, T. K. Datta

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The present study compares the semi-empirical wake-oscillator models that are used to predict vortex-induced vibration of structures. These models include those proposed by Facchinetti, Farshidian, and Dolatabadi, and Skop and Griffin. These models combine a wake oscillator model resembling the Van der Pol oscillator model and a single degree of freedom oscillation model. In order to use these models for estimating the top displacement of chimneys, the first mode vibration of the chimneys is only considered. The modal equation of the chimney constitutes the single degree of freedom model (SDOF). The equations of the wake oscillator model and the SDOF are simultaneously solved using an iterative procedure. The empirical parameters used in the wake-oscillator models are estimated using a newly developed approach, and response is compared with experimental data, which appeared comparable. For carrying out the iterative solution, the ode solver of MATLAB is used. To carry out the comparative study, a tall concrete chimney of height 210m has been chosen with the base diameter as 28m, top diameter as 20m, and thickness as 0.3m. The responses of the chimney are also determined using the linear model proposed by E. Simiu and the deterministic model given in Eurocode. It is observed from the comparative study that the responses predicted by the Facchinetti model and the model proposed by Skop and Griffin are nearly the same, while the model proposed by Fashidian and Dolatabadi predicts a higher response. The linear model without considering the aero-elastic phenomenon provides a less response as compared to the non-linear models. Further, for large damping, the prediction of the response by the Euro code is relatively well compared to those of non-linear models.

Keywords: chimney, deterministic model, van der pol, vortex-induced vibration

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Authors: A. Van Staden, N. Purcell

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Spelling development is complex and multifaceted and relies on several cognitive-linguistic processes. This paper explored the spelling difficulties of English second language learners with auditory processing delays. This empirical study aims to address these issues by means of an intervention design. Specifically, the objectives are: (a) to develop and implement a multi-sensory spelling program for second language learners with auditory processing difficulties (APD) for a period of 6 months; (b) to assess the efficacy of the multi-sensory spelling program and whether this intervention could significantly improve experimental learners' spelling, phonological awareness, and processing (PA), rapid automatized naming (RAN), working memory (WM), word reading and reading comprehension; and (c) to determine the relationship (or interplay) between these cognitive and linguistic skills (mentioned above), and how they influence spelling development. Forty-four English, second language learners with APD were sampled from one primary school in the Free State province. The learners were randomly assigned to either an experimental (n=22) or control group (n=22). During the implementation of the spelling program, several visual, tactile and kinesthetic exercises, including the utilization of fingerspelling were introduced to support the experimental learners’ (N = 22) spelling development. Post-test results showed the efficacy of the multi-sensory spelling program, with the experimental group who were trained in utilising multi-sensory coding and fingerspelling outperforming learners from the control group on the cognitive-linguistic, spelling and reading measures. The results and efficacy of this multi-sensory spelling program and the utilisation of fingerspelling for hearing second language learners with APD open up innovative perspectives for the prevention and targeted remediation of spelling difficulties.

Keywords: English second language spellers, auditory processing delays, spelling difficulties, multi-sensory intervention program

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Authors: Abhishek Soni, A. Kumaraswamy, M. S. Mahesh

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Bullet penetration in steel plate is investigated with the help of three-dimensional, non-linear, transient, dynamic, finite elements analysis using explicit time integration code LSDYNA. The effect of large strain, strain-rate and temperature at very high velocity regime was studied from number of simulations of semi-spherical nose shape bullet penetration through single layered circular plate with 2 mm thickness at impact velocities of 500, 1000, and 1500 m/s with the help of Johnson Cook material model. Mie-Gruneisen equation of state is used in conjunction with Johnson Cook material model to determine pressure-volume relationship at various points of interests. Two material models viz. Plastic-Kinematic and Johnson- Cook resulted in different deformation patterns in steel plate. It is observed from the simulation results that the velocity drop and loss of kinetic energy occurred very quickly up to perforation of plate, after that the change in velocity and changes in kinetic energy are negligibly small. The physics behind this kind of behaviour is presented in the paper.

Keywords: AISI 4340 steel, ballistic impact simulation, bullet penetration, non-linear FEM

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Authors: Alfredo Reyes-Salazar, Mario D. Llanes-Tizoc, Eden Bojorquez, Federico Valenzuela-Beltran, Juan Bojorquez, Jose R. Gaxiola-Camacho, Achintya Haldar

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Seismic analysis of steel buildings is usually based on the use of the concentrated mass (ML) matrix and the Rayleigh damping matrix (C). Similarly, the initial stiffness matrix (KO) and the first two modes associated with lateral vibrations are commonly used to develop matrix C. The evaluation of the accuracy of these practices for the particular case of steel buildings with moment-resisting steel frames constitutes the main objective of this research. For this, the non-linear seismic responses of three models of steel frames, representing low-, medium- and high-rise steel buildings, are considered. Results indicate that if the ML matrix is used, shears and bending moments in columns are underestimated by up to 30% and 65%, respectively when compared to the corresponding results obtained with the consistent mass matrix (MC). It is also shown that if KO is used in C instead of the tangent stiffness matrix (Kt), axial loads in columns are underestimated by up to 80%. It is concluded that the consistent mass matrix should be used in the structural modelling of moment-resisting steel frames and that the tangent stiffness matrix should be used to develop the Rayleigh damping matrix.

Keywords: moment-resisting steel frames, consistent and concentrated mass matrices, non-linear seismic response, Rayleigh damping

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Authors: Ewa M. Laskowska, Jorn Vatn

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Industry 4.0 focuses on digital optimization of industrial processes. The idea is to use extracted data in order to build a decision support model enabling use of those data for real time decision making. In terms of predictive maintenance, the desired decision support tool would be a model enabling prognostics of system's health based on the current condition of considered equipment. Within area of system prognostics and health management, a commonly used health indicator is Remaining Useful Lifetime (RUL) of a system. Because the RUL is a random variable, it has to be estimated based on available health indicators. Health indicators can be of different types and come from different sources. They can be process variables, equipment performance variables, data related to number of experienced failures, etc. The aim of this study is the analysis of performance variables of emergency shutdown valves (ESV) used in oil and gas industry. ESV is inspected periodically, and at each inspection torque and time of valve operation are registered. The data will be analyzed by means of machine learning or statistical analysis. The purpose is to investigate whether the available data could be used as a health indicator for a prognostic purpose. The second objective is to examine what is the most efficient way to incorporate the data into predictive model. The idea is to check whether the data can be applied in form of explanatory variables in Markov process or whether other stochastic processes would be a more convenient to build an RUL model based on the information coming from registered data.

Keywords: emergency shutdown valves, health indicator, prognostics, remaining useful lifetime, RUL

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Authors: Nicholas Jensen

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As the effort to create new drugs to treat rare conditions cost-effectively intensifies, there is a need to ensure maximum efficiency in the manufacturing process. This includes the creation of ultracompact treatment forms, which can best be achieved via applications of fundamental laws of physics. This paper reports an experiment exploring the relationship between the forms of Newton's 2ⁿᵈ Law appropriate to linear motion and to transversal architraves. The moment of inertia of three discs was determined by experiments and compared with previous data derived from a theoretical relationship. The method used was to attach the discs to a moment arm. Comparing the results with those obtained from previous experiments, it is found to be consistent with the first law of thermodynamics. It was further found that Newton's 2ⁿᵈ law violates the second law of thermodynamics. The purpose of this experiment was to explore the relationship between the forms of Newton's 2nd Law appropriate to linear motion and to apply torque to a twisting force, which is determined by position vector r and force vector F. Substituting equation alpha in place of beta; angular acceleration is a linear acceleration divided by radius r of the moment arm. The nevrological analogy of Newton's 2nd Law states that these findings can contribute to a fuller understanding of thermodynamics in relation to viscosity. Implications for the pharmaceutical industry will be seen to be fruitful from these findings.

Keywords: Newtonian physics, inertia, viscosity, pharmaceutical applications

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Authors: Vandna Raghuvanshi, Vikrant Thakur, Anupam Jhobta

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Objective: To design HRCT criterion to forecast the threat of pulmonary tuberculosis. Material and methods: This was a prospective study of 69 patients with clinical suspicion of pulmonary tuberculosis. We studied their medical characteristics, numerous separate HRCT-results, and a combination of HRCT findings to foresee the danger for PTB by utilizing univariate and multivariate investigation. Temporary HRCT diagnostic criteria were planned in view of these outcomes to find out the risk of PTB and tested these criteria on our patients. Results: The results of HRCT chest were analyzed, and Rank was given from 1 to 4 according to the HRCT chest findings. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated. Rank 1: Highly suspected PTB. Rank 2: Probable PTB Rank 3: Nonspecific or difficult to differentiate from other diseases Rank 4: Other suspected diseases • Rank 1 (Highly suspected TB) was present in 22 (31.9%) patients, all of them finally diagnosed to have pulmonary tuberculosis. The sensitivity, specificity, and negative likelihood ratio for RANK 1 on HRCT chest was 53.6%, 100%, and 0.43, respectively. • Rank 2 (Probable TB) was present in 13 patients, out of which 12 were tubercular, and 1 was non-tubercular. • The sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of the combination of Rank 1 and Rank 2 was 82.9%, 96.4%, 23.22, and 0.18, respectively. • Rank 3 (Non-specific TB) was present in 25 patients, and out of these, 7 were tubercular, and 18 were non-tubercular. • When all these 3 ranks were considered together, the sensitivity approached 100% however, the specificity reduced to 35.7%. The positive likelihood ratio and negative likelihood ratio were 1.56 and 0, respectively. • Rank 4 (Other specific findings) was given to 9 patients, and all of these were non-tubercular. Conclusion: HRCT is useful in selecting individuals with greater chances of pulmonary tuberculosis.

Keywords: pulmonary, tuberculosis, multivariate, HRCT

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Authors: Marco Furinghetti, Alberto Pavese, Michele Rinaldi

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Concave Surface Slider devices have been more and more used in real applications for seismic protection of both bridge and building structures. Several research activities have been carried out, in order to investigate the lateral response of such a typology of devices, and a reasonably high level of knowledge has been reached. If radial analysis is performed, the frictional force is always aligned with respect to the restoring force, whereas under bidirectional seismic events, a bi-axial interaction of the directions of motion occurs, due to the step-wise projection of the main frictional force, which is assumed to be aligned to the trajectory of the isolator. Nonetheless, if non-linear time history analyses have to be performed, standard codes provide precise rules for the definition of an averagely spectrum-compatible set of accelerograms in radial conditions, whereas for bidirectional motions different combinations of the single components spectra can be found. Moreover, nowadays software for the adjustment of natural accelerograms are available, which lead to a higher quality of spectrum-compatibility and to a smaller dispersion of results for radial motions. In this endeavor a simplified design procedure is defined, for building structures, base-isolated by means of Concave Surface Slider devices. Different case study structures have been analyzed. In a first stage, the capacity curve has been computed, by means of non-linear static analyses on the fixed-base structures: inelastic fiber elements have been adopted and different direction angles of lateral forces have been studied. Thanks to these results, a linear elastic Finite Element Model has been defined, characterized by the same global stiffness of the linear elastic branch of the non-linear capacity curve. Then, non-linear time history analyses have been performed on the base-isolated structures, by applying seven bidirectional seismic events. The spectrum-compatibility of bidirectional earthquakes has been studied, by considering different combinations of single components and adjusting single records: thanks to the proposed procedure, results have shown a small dispersion and a good agreement in comparison to the assumed design values.

Keywords: concave surface slider, spectrum-compatibility, bidirectional earthquake, base isolation

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Authors: Kiurski S. Jelena, Aksentijević M. Snežana, Oros B. Ivana, Kecić S. Vesna, Djogo Z. Maja

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The paper investigates the effect of light intensity on the formation of two ketones, acetone and methyl ethyl ketone, in working premises of five pad printing departments in Novi Sad, Serbia. Multiple linear regression analysis examined the form of interdependency concentrations of methyl ethyl ketone, acetone and light intensity in five printing presses at seven sampling points, using Statistica software package version 10th. The results show an average stacking variation investigated variable and can be presented by the general regression model: y = b0 + b1xi1 + b2xi2.

Keywords: acetone, methyl ethyl ketone, multiple linear regression analysis, pad printing

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Authors: Jiashu Shen

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Objectives: Spirometry is a basic reference for health evaluation which is widely used in clinical. Previous reference of spirometry is not applicable because of drastic changes of social and natural circumstance in China. A new reference values for the spirometry of the Chinese population is extremely needed. Method: Spirometric reference value was established using the statistical modeling method Generalized Additive Models for Location, Scale and Shape for forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC, and maximal mid-expiratory flow (MMEF). Results: Data from 236,606 healthy non-smokers aged 4–90 years was collected from the MJ Health Check database. Spirometry equations for FEV1, FVC, MMEF, and FEV1/FVC were established, including the predicted values and lower limits of normal (LLNs) by sex. The predictive equations that were developed for the spirometric results elaborated the relationship between spirometry and age, and they eliminated the effects of height as a variable. Most previous predictive equations for Chinese spirometry were significantly overestimated (to be exact, with mean differences of 22.21% in FEV1 and 31.39% in FVC for males, along with differences of 26.93% in FEV1 and 35.76% in FVC for females) or underestimated (with mean differences of -5.81% in MMEF and -14.56% in FEV1/FVC for males, along with a difference of -14.54% in FEV1/FVC for females) the results of lung function measurements as found in this study. Through cross-validation, our equations were established as having good fit, and the means of the measured value and the estimated value were compared, with good results. Conclusions: Our study updates the spirometric reference equations for Chinese people of all ages and provides comprehensive values for both physical examination and clinical diagnosis.

Keywords: Chinese, GAMLSS model, reference values, spirometry

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

Abstract:

In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: Shweda Mohan, J. L. Nandagopal, S. Amritha

Abstract:

This paper focuses on the dynamic modelling of unicycle robot. Two main concepts used for balancing unicycle robot are: reaction wheel pendulum and inverted pendulum. The pitch axis is modelled as inverted pendulum and roll axis is modelled as reaction wheel pendulum. The unicycle yaw dynamics is not considered which makes the derivation of dynamics relatively simple. For the roll controller, sliding-mode controller has been adopted and optimal methods are used to minimize switching-function chattering. For pitch controller, an LQR controller has been implemented to drive the unicycle robot to follow the desired velocity trajectory. The pitching and rolling balance could be achieved by two DC motors. Unicycle robot is a non-holonomic, non-linear, static unbalance system that has the minimal number of point contact to the ground, therefore, it is a perfect platform for researchers to study motion and balance control. These real-time solutions will be a viable solution for advanced robotic systems and controls.

Keywords: decoupled dynamics, linear quadratic regulator (LQR) control, Lyapunov function sliding mode control, unicycle robot, velocity and trajectory control

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Authors: Prof. Ammal Mokhtar Metwally, Samia M. Sami, Nihad A. Ibrahim, Fatma A. Shaaban, Iman I. Salama

Abstract:

Objectives: Reducing neonatal mortality by 2030 is still a challenging goal in developing countries. low birth weight (LBW) is a significant contributor to this, especially where weighing newborns is not possible routinely. The present study aimed to determine a simple, easy, reliable anthropometric measure(s) that can predict LBW) and neonatal mortality. Methods: A prospective cohort study of 570 babies born in districts of El Menia governorate, Egypt (where most deliveries occurred at home) was examined at birth. Newborn weight, length, head, chest, mid-arm, and thigh circumferences were measured. Follow up of the examined neonates took place during their first four weeks of life to report any mortalities. The most predictable anthropometric measures were determined using the statistical package of SPSS, and multiple Logistic regression analysis was performed.: Results: Head and chest circumferences with cut-off points < 33 cm and ≤ 31.5 cm, respectively, were the significant predictors for LBW. They carried the best combination of having the highest sensitivity (89.8 % & 86.4 %) and least false negative predictive value (1.4 % & 1.7 %). Chest circumference with a cut-off point ≤ 31.5 cm was the significant predictor for neonatal mortality with 83.3 % sensitivity and 0.43 % false negative predictive value. Conclusion: Using chest circumference with a cut-off point ≤ 31.5 cm is recommended as a single simple anthropometric measurement for the prediction of both LBW and neonatal mortality. The predicted measure could act as a substitute for weighting newborns in communities where scales to weigh them are not routinely available.

Keywords: low birth weight, neonatal mortality, anthropometric measures, practical screening

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Authors: Francesco Pantano, Marta Fogolari, Michele Iuliani, Sonia Simonetti, Silvia Cavaliere, Marco Russano, Fabrizio Citarella, Bruno Vincenzi, Silvia Angeletti, Giuseppe Tonini

Abstract:

Background: Although immune checkpoint inhibitors (ICIs) have changed the treatment paradigm of non–small cell lung cancer (NSCLC), these drugs fail to elicit durable responses in the majority of NSCLC patients. The gut microbiota, able to regulate immune responsiveness, is emerging as a promising, modifiable target to improve ICIs response rates. Since the oral microbiome has been demonstrated to be the primary source of bacterial microbiota in the lungs, we investigated its composition as a potential predictive biomarker to identify and select patients who could benefit from immunotherapy. Methods: Thirty-five patients with stage IV squamous and non-squamous cell NSCLC eligible for an anti-PD-1/PD-L1 as monotherapy were enrolled. Saliva samples were collected from patients prior to the start of treatment, bacterial DNA was extracted using the QIAamp® DNA Microbiome Kit (QIAGEN) and the 16S rRNA gene was sequenced on a MiSeq sequencing instrument (Illumina). Results: NSCLC patients were dichotomized as “Responders” (partial or complete response) and “Non-Responders” (progressive disease), after 12 weeks of treatment, based on RECIST criteria. A prevalence of the phylum Candidatus Saccharibacteria was found in the 10 responders compared to non-responders (abundance 5% vs 1% respectively; p-value = 1.46 x 10-7; False Discovery Rate (FDR) = 1.02 x 10-6). Moreover, a higher prevalence of Saccharibacteria Genera Incertae Sedis genus (belonging to the Candidatus Saccharibacteria phylum) was observed in "responders" (p-value = 6.01 x 10-7 and FDR = 2.46 x 10-5). Finally, the patients who benefit from immunotherapy showed a significant abundance of TM7 Phylum Sp Oral Clone FR058 strain, member of Saccharibacteria Genera Incertae Sedis genus (p-value = 6.13 x 10-7 and FDR=7.66 x 10-5). Conclusions: These preliminary results showed a significant association between oral microbiota and ICIs response in NSCLC patients. In particular, the higher prevalence of Candidatus Saccharibacteria phylum and TM7 Phylum Sp Oral Clone FR058 strain in responders suggests their potential immunomodulatory role. The study is still ongoing and updated data will be presented at the congress.

Keywords: oral microbiota, immune checkpoint inhibitors, non-small cell lung cancer, predictive biomarker

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Authors: Onur Karaman, A. Gunes Tanir

Abstract:

Photon radiation therapy used to treat cancer is one of the most important methods. However, photon beam collimator materials in Linear Accelerator (LINAC) head generally contains heavy elements is used and the interaction of bremsstrahlung photon with such heavy nuclei, the neutron can be produced inside the treatment rooms. In radiation therapy, neutron contamination contributes to the risk of secondary malignancies in patients, also physicians working in this field. Since the neutron is more dangerous than photon, it is important to determine neutron dose during radiotherapy treatment. In this study, it is aimed to analyze the effect of field size, distance from axis and depth on the amount of in-field and out-field neutron contamination for ElektaVmat accelerator with 18 MV nominal energy. The photon spectra at the distance of 75, 150, 225, 300 cm from target and on the isocenter of beam were scored for 5x5, 10x10, 20x20, 30x30 and 40x40 cm2 fields. Results demonstrated that the neutron spectra and dose are dependent on field size and distances. Beyond 225 cm of isocenter, the dependence of the neutron dose on field size is minimal. As a result, it is concluded that as the open field increases, neutron dose determined decreases. It is important to remember that when treating with high energy photons, the dose from contamination neutrons must be considered as it is much greater than the photon dose.

Keywords: radiotherapy, neutron contamination, linear accelerators, photon

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Authors: Aouaouche Elmaouhab, Hassina Beggar

Abstract:

Solving multi-objective discrete optimization applications has always been limited by the resources of one machine: By computing power or by memory, most often both. To speed up the calculations, the grid computing represents a primary solution for the treatment of these applications through the parallelization of these resolution methods. In this work, we are interested in the study of some methods for solving multiple objective integer linear programming problem based on Branch-and-Bound and the study of grid computing technology. This study allowed us to propose an implementation of the method of Abbas and Al on the grid by reducing the execution time. To enhance our contribution, the main results are presented.

Keywords: multi-objective optimization, integer linear programming, grid computing, parallel computing

Procedia PDF Downloads 477