Search results for: Fisher Scoring Algorithm

Authors: Kefaya Qaddoum

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The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: neural network, conformal prediction, cancer classification, regression

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Authors: Abraham Castellanos, Christophe Durville, Sophie Echenim

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In this article, a portfolio optimization problem is performed in a Solvency II context: it illustrates how advanced optimization techniques can help to tackle complex operational pain points around the monitoring, control, and stability of Solvency Capital Requirement (SCR). The market SCR of a portfolio is calculated as a combination of SCR sub-modules. These sub-modules are the results of stress-tests on interest rate, equity, property, credit and FX factors, as well as concentration on counter-parties. The market SCR is non convex and non differentiable, which does not make it a natural optimization criteria candidate. In the SCR formulation, correlations between sub-modules are fixed, whereas risk-driven portfolio allocation is usually driven by the dynamics of the actual correlations. Implementing a portfolio construction approach that is efficient on both a regulatory and economic standpoint is not straightforward. Moreover, the challenge for insurance portfolio managers is not only to achieve a minimal SCR to reduce non-invested capital but also to ensure stability of the SCR. Some optimizations have already been performed in the literature, simplifying the standard formula into a quadratic function. But to our knowledge, it is the first time that the standard formula of the market SCR is used in an optimization problem. Two solvers are combined: a bundle algorithm for convex non- differentiable problems, and a BFGS (Broyden-Fletcher-Goldfarb- Shanno)-SQP (Sequential Quadratic Programming) algorithm, to cope with non-convex cases. A market SCR minimization is then performed with historical data. This approach results in significant reduction of the capital requirement, compared to a classical Markowitz approach based on the historical volatility. A comparative analysis of different optimization models (equi-risk-contribution portfolio, minimizing volatility portfolio and minimizing value-at-risk portfolio) is performed and the impact of these strategies on risk measures including market SCR and its sub-modules is evaluated. A lack of diversification of market SCR is observed, specially for equities. This was expected since the market SCR strongly penalizes this type of financial instrument. It was shown that this direct effect of the regulation can be attenuated by implementing constraints in the optimization process or minimizing the market SCR together with the historical volatility, proving the interest of having a portfolio construction approach that can incorporate such features. The present results are further explained by the Market SCR modelling.

Keywords: financial risk, numerical optimization, portfolio management, solvency capital requirement

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Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

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Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

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Authors: Mansoureh Ghiasabadi Farahani, Samira Kalantary, Sara Taghi-Pour, Mahboubeh Shamsi

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Nowadays, the development of computers technology and its recent applications provide access to new types of data, which have not been considered by the traditional data analysts. Two particularly interesting characteristics of such data sets include their huge size and streaming nature .Incremental learning techniques have been used extensively to address the data stream classification problem. This paper presents a concise survey on the obstacles and the requirements issues classifying data streams with using decision tree. The most important issue is to maintain a balance between accuracy and efficiency, the algorithm should provide good classification performance with a reasonable time response.

Keywords: big data, data streams, classification, decision tree

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Authors: U. Yavas, M. Gokasan

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Control of diesel engine’s air path has drawn a lot of attention due to its multi input-multi output, closed coupled, non-linear relation. Today, precise control of amount of air to be combusted is a must in order to meet with tight emission limits and performance targets. In this study, passenger car size diesel engine is modeled by AVL Boost RT, and then simulated with standard, industry level PID controllers. Finally, linear model predictive control is designed and simulated. This study shows the importance of modeling and control of diesel engines with flexible algorithm development in computer based systems.

Keywords: predictive control, engine control, engine modeling, PID control, feedforward compensation

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Authors: Pedro Gomes, Luís Sá Castelo, António Lopes, Marta Maio, Pedro Mota, Adélia Avelar, António Marques Dias

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Introduction: Total Knee Arthroplasty (TKA) has contributed to improvement of patient`s quality of life, although it has been associated with some complications including component loosening and polyethylene wear. To prevent these complications various fixation techniques have been employed. Hybrid TKA with cemented tibial and cementless femoral components have shown favourable outcomes, although it still lack of consensus in the literature. Objectives: To evaluate the clinical and radiographic results of hybrid versus cemented TKA with an average 5 years follow-up and analyse the survival rates. Methods: A retrospective study of 125 TKAs performed in 92 patients at our institution, between 2006 to 2008, with a minimum follow-up of 2 years. The same prosthesis was used in all knees. Hybrid TKA fixation was performed in 96 knees, with a mean follow-up of 4,8±1,7 years (range, 2–8,3 years) and 29 TKAs received fully cemented fixation with a mean follow-up of 4,9±1,9 years (range, 2-8,3 years). Selection for hybrid fixation was nonrandomized and based on femoral component fit. The Oxford Knee Score (OKS 0-48) was evaluated for clinical assessment and Knee Society Roentgenographic Evaluation Scoring System was used for radiographic outcome. The survival rate was calculated using the Kaplan-Meier method, with failures defined as revision of either the tibial or femoral component for aseptic failures and all-causes (aseptic and infection). Analysis of survivorship data was performed using the log-rank test. SPSS (v22) was the computer program used for statistical analysis. Results: The hybrid group consisted of 72 females (75%) and 24 males (25%), with mean age 64±7 years (range, 50-78 years). The preoperative diagnosis was osteoarthritis (OA) in 94 knees (98%), rheumatoid arthritis (RA) in 1 knee (1%) and Posttraumatic arthritis (PTA) in 1 Knee (1%). The fully cemented group consisted of 23 females (79%) and 6 males (21%), with mean age 65±7 years (range, 47-78 years). The preoperative diagnosis was OA in 27 knees (93%), PTA in 2 knees (7%). The Oxford Knee Scores were similar between the 2 groups (hybrid 40,3±2,8 versus cemented 40,2±3). The percentage of radiolucencies seen on the femoral side was slightly higher in the cemented group 20,7% than the hybrid group 11,5% p0.223. In the cemented group there were significantly more Zone 4 radiolucencies compared to the hybrid group (13,8% versus 2,1% p0,026). Revisions for all causes were performed in 4 of the 96 hybrid TKAs (4,2%) and 1 of the 29 cemented TKAs (3,5%). The reason for revision was aseptic loosening in 3 hybrid TKAs and 1 of the cemented TKAs. Revision was performed for infection in 1 hybrid TKA. The hybrid group demonstrated a 7 years survival rate of 93% for all-cause failures and 94% for aseptic loosening. No significant difference in survivorship was seen between the groups for all-cause failures or aseptic failures. Conclusions: Hybrid TKA yields similar intermediate-term results and survival rates as fully cemented total knee arthroplasty and remains a viable option in knee joint replacement surgery.

Keywords: hybrid, survival rate, total knee arthroplasty, orthopaedic surgery

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Authors: M. Altekin, R. F. Yükseler

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Geometrically nonlinear axisymmetric bending of a shallow spherical shell with a point support at the apex under linearly varying axisymmetric load was investigated numerically. The edge of the shell was assumed to be simply supported or clamped. The solution was obtained by the finite difference and the Newton-Raphson methods. The thickness of the shell was considered to be uniform and the material was assumed to be homogeneous and isotropic. Sensitivity analysis was made for two geometrical parameters. The accuracy of the algorithm was checked by comparing the deflection with the solution of point supported circular plates and good agreement was obtained.

Keywords: Bending, Nonlinear, Plate, Point support, Shell.

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Authors: R. Christopher, C. Brandt, N. Damons

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Background: The sequence of prevention model states that by constant assessment of injury, injury mechanisms and risk factors are identified, highlighting that collecting and recording of data is a core approach for preventing injuries. Several screening tools are available for use in the clinical setting. These screening techniques only recently received research attention, hence there is a dearth of inconsistent and controversial data regarding their applicability, validity, and reliability. Several systematic reviews related to common soccer injuries have been conducted; however, none of them addressed the screening tools for common soccer injuries. Objectives: The purpose of this study was to conduct a review of screening tools and their accuracy for common injuries in soccer. Methods: A systematic scoping review was performed based on the Joanna Briggs Institute procedure for conducting systematic reviews. Databases such as SPORT Discus, Cinahl, Medline, Science Direct, PubMed, and grey literature were used to access suitable studies. Some of the key search terms included: injury screening, screening, screening tool accuracy, injury prevalence, injury prediction, accuracy, validity, specificity, reliability, sensitivity. All types of English studies dating back to the year 2000 were included. Two blind independent reviewers selected and appraised articles on a 9-point scale for inclusion as well as for the risk of bias with the ACROBAT-NRSI tool. Data were extracted and summarized in tables. Plot data analysis was done, and sensitivity and specificity were analyzed with their respective 95% confidence intervals. I² statistic was used to determine the proportion of variation across studies. Results: The initial search yielded 95 studies, of which 21 were duplicates, and 54 excluded. A total of 10 observational studies were included for the analysis: 3 studies were analysed quantitatively while the remaining 7 were analysed qualitatively. Seven studies were graded low and three studies high risk of bias. Only high methodological studies (score > 9) were included for analysis. The pooled studies investigated tools such as the Functional Movement Screening (FMS™), the Landing Error Scoring System (LESS), the Tuck Jump Assessment, the Soccer Injury Movement Screening (SIMS), and the conventional hamstrings to quadriceps ratio. The accuracy of screening tools was of high reliability, sensitivity and specificity (calculated as ICC 0.68, 95% CI: 52-0.84; and 0.64, 95% CI: 0.61-0.66 respectively; I² = 13.2%, P=0.316). Conclusion: Based on the pooled results from the included studies, the FMS™ has a good inter-rater and intra-rater reliability. FMS™ is a screening tool capable of screening for common soccer injuries, and individual FMS™ scores are a better determinant of performance in comparison with the overall FMS™ score. Although meta-analysis could not be done for all the included screening tools, qualitative analysis also indicated good sensitivity and specificity of the individual tools. Higher levels of evidence are, however, needed for implication in evidence-based practice.

Keywords: accuracy, screening tools, sensitivity, soccer injuries, specificity

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Authors: Minh Huynh, Aaron Heuser, Luke Patterson, Chris Zhang, Mason Miller, Daniel Wang, Sandeep Shetty, Mike Trinh, Abigail Miller, Adaeze Enekwechi, Tenille Daniels, Lu Huynh

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Machine learning and text analytics have been used to analyze child abuse, cyberbullying, domestic abuse and domestic violence, and hate speech. However, to the authors’ knowledge, no research to date has used these methods to study elder abuse in nursing homes or skilled nursing facilities from field inspection reports. We used machine learning and text analytics methods to analyze 356,000 inspection reports, which have been extracted from CMS Form-2567 field inspections of US nursing homes and skilled nursing facilities between 2016 and 2021. Our algorithm detected occurrences of the various types of abuse, including physical abuse, psychological abuse, verbal abuse, sexual abuse, and passive and active neglect. For example, to detect physical abuse, our algorithms search for combinations or phrases and words suggesting willful infliction of damage (hitting, pinching or burning, tethering, tying), or consciously ignoring an emergency. To detect occurrences of elder neglect, our algorithm looks for combinations or phrases and words suggesting both passive neglect (neglecting vital needs, allowing malnutrition and dehydration, allowing decubiti, deprivation of information, limitation of freedom, negligence toward safety precautions) and active neglect (intimidation and name-calling, tying the victim up to prevent falls without consent, consciously ignoring an emergency, not calling a physician in spite of indication, stopping important treatments, failure to provide essential care, deprivation of nourishment, leaving a person alone for an inappropriate amount of time, excessive demands in a situation of care). We further compare the prevalence of abuse before and after Covid-19 related restrictions on nursing home visits. We also identified the facilities with the most number of cases of abuse with no abuse facilities within a 25-mile radius as most likely candidates for additional inspections. We also built an interactive display to visualize the location of these facilities.

Keywords: machine learning, text analytics, elder abuse, elder neglect, nursing home abuse

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Authors: Amao Funmilayo Lanrewaju

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The study examined the socio-economic characteristics of the residents in selected urban fringes of Ibadan; identified and examined the housing and neighbourhood characteristics and evaluated housing quality in the study area. It analysed the relationship between the socio-economic characteristics of the residents, housing and neighbourhood characteristics as well as housing quality in the study area. This was with a view to providing information that would enhance the housing quality in urban fringes of Ibadan. Primary and secondary data were used for the study. A survey of eleven purposively selected communities from Oluyole and Egbeda local government areas in the urban fringes was conducted through a questionnaire administration and expert rating by five independent assessors (Qualified Architects) using penalty scoring within similar time-frames. The study employed a random sampling method to select a sample size of 480 houses representing 5% of the sampling frame of 9600 houses. Respondent in the first house was selected randomly and subsequently every 20th house in the streets involved was systematically selected for questionnaire administration, usually a household-head per building. The structured questionnaire elicited information on socio-economic characteristics of the residents, housing and neighbourhood characteristics, factors affecting housing quality and housing quality in the study area. Secondary data obtained for the study included the land-use plan of Ibadan from previous publications, housing demographics, population figures from relevant institutions and other published materials. The data collected were analysed using descriptive and inferential statistics such as frequency distribution, Cross tabulation, Correlation Analysis, Analysis of Variance (ANOVA) and Relative Importance Index (RII). The result of the survey revealed that respondents from the Yoruba ethnic group constituted the majority, comprising 439 (91.5%) of the 480 respondents from the two local government areas selected. It also revealed that the type of tenure status of majority of the respondents in the two local government areas was self-ownership (234, 48.8%), while 44.0% of the respondents acquired their houses through personal savings. Cross tabulation indicated that majority (67.1%, 322 out of 480) of the respondents were low-income earners. The study showed that both housing and neighbourhood services were not adequately provided across neighbourhoods in the study area. Correlation analysis indicated a significant relationship between respondents’ socio–economic status and their general housing quality (r=0.46; p-value of 0.01< 0.05). The ANOVA indicated that the relationship between socio-economic characteristics of the residents, housing and neighbourhood characteristics in the study area was significant (F=18.289, p=0.00; the coefficient of determination R2= 0.192). The findings from the study however revealed that there was no significant difference in the results obtained from users based evaluation and expert rating. The study concluded that housing quality in the urban fringes of Ibadan is generally poor and the socio-economic status of the residents significantly influenced the housing quality.

Keywords: housing quality, urban fringes, economic status, poverty

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Authors: V. Keim, J. Spachtholz, J. Hammer

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The importance of fibre reinforced plastics continually increases due to the excellent mechanical properties, low material and manufacturing costs combined with significant weight reduction. Today, components are usually designed and calculated numerically by using finite element methods (FEM) to avoid expensive laboratory tests. These programs are based on material models including material specific deformation characteristics. In this research project, material models for short glass fibre reinforced plastics are presented to simulate the visco-elasto-plastic deformation behaviour. Prior to modelling specimens of the material EMS Grivory HTV-5H1, consisting of a Polyphthalamide matrix reinforced by 50wt.-% of short glass fibres, are characterized experimentally in terms of the highly time dependent deformation behaviour of the matrix material. To minimize the experimental effort, the cyclic deformation behaviour under tensile and compressive loading (R = −1) is characterized by isothermal complex low cycle fatigue (CLCF) tests. Combining cycles under two strain amplitudes and strain rates within three orders of magnitude and relaxation intervals into one experiment the visco-elastic deformation is characterized. To identify visco-plastic deformation monotonous tensile tests either displacement controlled or strain controlled (CERT) are compared. All relevant modelling parameters for this complex superposition of simultaneously varying mechanical loadings are quantified by these experiments. Subsequently, two different material models are compared with respect to their accuracy describing the visco-elasto-plastic deformation behaviour. First, based on Chaboche an extended 12 parameter model (EVP-KV2) is used to model cyclic visco-elasto-plasticity at two time scales. The parameters of the model including a total separation of elastic and plastic deformation are obtained by computational optimization using an evolutionary algorithm based on a fitness function called genetic algorithm. Second, the 12 parameter visco-elasto-plastic material model by Launay is used. In detail, the model contains a different type of a flow function based on the definition of the visco-plastic deformation as a part of the overall deformation. The accuracy of the models is verified by corresponding experimental LCF testing.

Keywords: complex low cycle fatigue, material modelling, short glass fibre reinforced polyphthalamides, visco-elasto-plastic deformation

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Authors: Younghoon Kim, Seoung Bum Kim

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One-class classification plays an important role in detecting outlier and abnormality from normal observations. In the previous research, several attempts were made to extend the scope of application of the one-class classification techniques to statistical process control problems. For most previous approaches, such as support vector data description (SVDD) control chart, the design of the control limits is commonly based on the assumption that the proportion of abnormal observations is approximately equal to an expected Type I error rate in Phase I process. Because of the limitation of the one-class classification techniques based on convex optimization, we cannot make the proportion of abnormal observations exactly equal to expected Type I error rate: controlling Type I error rate requires to optimize constraints with integer decision variables, but convex optimization cannot satisfy the requirement. This limitation would be undesirable in theoretical and practical perspective to construct effective control charts. In this work, to address the limitation of previous approaches, we propose the one-class classification algorithm based on the mixed integer programming technique, which can solve problems formulated with continuous and integer decision variables. The proposed method minimizes the radius of a spherically shaped boundary subject to the number of normal data to be equal to a constant value specified by users. By modifying this constant value, users can exactly control the proportion of normal data described by the spherically shaped boundary. Thus, the proportion of abnormal observations can be made theoretically equal to an expected Type I error rate in Phase I process. Moreover, analogous to SVDD, the boundary can be made to describe complex structures by using some kernel functions. New multivariate control chart applying the effectiveness of the algorithm is proposed. This chart uses a monitoring statistic to characterize the degree of being an abnormal point as obtained through the proposed one-class classification. The control limit of the proposed chart is established by the radius of the boundary. The usefulness of the proposed method was demonstrated through experiments with simulated and real process data from a thin film transistor-liquid crystal display.

Keywords: control chart, mixed integer programming, one-class classification, support vector data description

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Authors: Anli Du Preez, James Bekker

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Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application.

Keywords: Data analytics, Industrial engineering, Machine learning, Value creation

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Authors: Somveer Singh, Vineet Kumar Singh

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We present a new model based on viscoelasticity for the Non-Newtonian fluids.We use a matrix formulated algorithm to approximate solutions of a class of partial integro-differential equations with the given initial and boundary conditions. Some numerical results are presented to simplify application of operational matrix formulation and reduce the computational cost. Convergence analysis, error estimation and numerical stability of the method are also investigated. Finally, some test examples are given to demonstrate accuracy and efficiency of the proposed method.

Keywords: Legendre Wavelets, operational matrices, partial integro-differential equation, viscoelasticity

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Authors: J. Waita, S. Wamuhu, J. Makoyo, M. Rachel, T. Ngangari, W. Christine, M. Zipporah

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Contraceptive use is one of the key global strategies to alleviate maternal mortality. Global efforts through advocating for contraceptive uptake and service provision has led improved contraceptive prevalence. In Kenya maternal mortality rate has remained a challenged despites efforts by government and non-governmental organizations. Objective: To describe the uptake of contraceptives among women in Tunza Clinics, Kenya. Design and Methods: Ps Kenya through health care marketing fund is implementing a family planning program among its 350 Tunza fractional franchise facilities. Through private partnership, private owned facilities in low socio-economic areas are recruited and trained on contraceptive technology update. The providers are supported through facilitative supervision through a mobile based application Health Network Quality Improvement System (HNQIS) and interpersonal communication through 150 community based volunteers. The data analyzed in this paper was collected between January to July 2017 to show the uptake of modern Contraceptives among women in the Tunza franchise, method mix, age and distribution among the age bracket. Further analysis compares two different service delivery strategies; outreach and walk ins. Supportive supervision HNQIS scores was analyzed. Results: During the time period, a total of 132121 family planning clients were attended in 350 facilities. The average age of clients was 29.6 years. The average number of clients attended in the facilities per month was 18874. 73.7 %( n=132121) of the clients attended in the Tunza facilities were aged above 25 years while 22.1% 20-24 years and 4.2% 15-19 years. On contraceptive method mix, intra uterine device insertions clients contributed to 7.5%, implant insertions 15.3%, pills 11.2%, injections 62.7% while condoms and emergency pills had 2.7% and 0.6% respectively. Analysis of service delivery strategy indicated more than 79% of the clients were walk ins while 21% were attended to during outreaches. Uptake of long term contraceptive methods during outreaches was 73% of the clients while short term modern methods were 27%. Health Network Quality Improvement system assessment scores indicated 51% of the facilities scored over 90%, 25% scoring 80-89% while 21% scored below 80%. Conclusion: Preference for short term methods by women is possibly associated to cost as they are cheaper and easy to administer. When the cost of intra uterine device Implants is meant affordable during outreaches, the uptake is observed to increase. Making intra uterine device and implants affordable to women is a key strategy in increasing contraceptive prevalence hence averting maternal mortality.

Keywords: contraceptives, contraceptive uptake, low socio economic, supportive supervision

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Authors: H. A. Alzomor, B. K. Ouda, A. M. Eldeib

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Successful regional anesthesia depends upon precise location of the peripheral nerve or nerve plexus. Locating peripheral nerves is preferred to be done using nerve stimulation. In order to generate a nerve impulse by electrical means, a minimum threshold stimulus of current “rheobase” must be applied to the nerve. The technique depends on stimulating muscular twitching at a close distance to the nerve without actually touching it. Success rate of this operation depends on the accuracy of current intensity pulses used for stimulation. In this paper, we will discuss a circuit and algorithm for closed loop control for the current, theoretical analysis and test results and compare them with previous techniques.

Keywords: Close Loop Control (CLC), constant current, nerve locator, rheobase

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Authors: Nejah Ben Mabrouk, Bassem Jarboui, Habib Chabchoub

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Production and distribution planning is the most important part in supply chain management. In this paper, a NP-hard production-distribution problem for one product over a multi-period horizon is investigated. The aim is to minimize the sum of costs of three items: production setups, inventories and distribution, while determining, for each period, the amount produced, the inventory levels and the delivery trips. To solve this difficult problem, we propose a bi-phase approach based on a Variable Neighbourhood Search (VNS). This heuristic is tested on 90 randomly generated instances from the literature, with 20 periods and 50, 100, 200 customers. Computational results show that our approach outperforms existing solution procedures available in the literature

Keywords: logistic, production, distribution, variable neighbourhood search

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Authors: Chris Cadman, Marcel Strauss

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Background: In 2020, the University Hospital Wishaw in the United Kingdom became the centre for trauma and orthopaedics within its health board. This resulted in the majority of patients with suspected cauda equina syndrome (CES) being assessed and imaged at this site, putting an increased demand on MR imaging and displacing other previous activity. Following this transition, imaging requests for CES did not always follow national guidelines and would often be missing important clinical and safety information. There also appeared to be a very low positive scan rate compared with previously reported studies. In an attempt to improve patient selection and reduce the burden of CES imaging at this site clinical audit was performed. Methods: A total of 250 consecutive patients imaged to assess for CES were evaluated. Patients had to have presented to either the emergency or orthopaedic department acutely with a presenting complaint of suspected CES. Patients were excluded if they were not admitted acutely or were assessed by other clinical specialities. In total, 233 patients were included. Requests were assessed for appropriate clinical history, accurate and complete clinical assessment and MRI safety information. Clinical assessment was allocated a score of 1-6 based on information relating to history of pain, level of pain, dermatomes/myotomes affected, peri-anal paraesthesia/anaesthesia, anal tone and post-void bladder volume with each element scoring one point. Images were assessed for positive findings of CES, acquired spinal stenosis or nerve root compression. Results: Overall, 73% of requests had a clear clinical history of CES. The urgency of the request for imaging was given in 23% of cases. The mean clinical assessment score was 3.7 out of a total of 6. Overall, 2% of scans were positive for CES, 29% had acquired spinal stenosis and 30% had nerve root compression. For patients with CES, 75% had acute neurological signs compared with 68% of the study population. CES patients had a mean clinical history score of 5.3 compared with 3.7 for the study population. Overall, 95% of requests had appropriate MRI safety information. Discussion: it study included 233 patients who underwent specialist assessment and referral for MR imaging for suspected CES. Despite the serious nature of this condition, a large proportion of imaging requests did not have a clear clinical query of CES and the level of urgency was not given, which could potentially lead to a delay in imaging and treatment. Clinical examination was often also incomplete, which can make triaging of patients presenting with similar symptoms challenging. The positive rate for CES was only 2%, much below other studies which had positive rates of 6–40% with a large meta-analysis finding a mean positive rate of 19%. These findings demonstrate an opportunity to improve the quality of imaging requests for suspected CES. This may help to improve patient selection for imaging and result in a positive rate for CES imaging that is more in line with other centres.

Keywords: cauda equina syndrome, acute back pain, MRI, spine

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Authors: Moustapha H. Ibrahim, Dahir Abdourahman

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This document presents a short-circuit fault detection process in a photovoltaic (PV) system. The proposed method is developed in MATLAB/Simulink. It determines whatever the size of the installation number of the short circuit module. The proposed algorithm indicates the presence or absence of an abnormality on the power of the PV system through measures of hourly global irradiation, power output, and ambient temperature. In case a fault is detected, it displays the number of modules in a short circuit. This fault detection method has been successfully tested on two different PV installations.

Keywords: PV system, short-circuit, fault detection, modelling, MATLAB-Simulink

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Authors: Pushpendra Kumar, Sanjeev Kumar, R. Balasubramanian

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Estimation of optical flow from a sequence of images using variational methods is one of the most successful approach. Discontinuity between different motions is one of the challenging problem in flow estimation. In this paper, we design a new anisotropic diffusion operator, which is able to provide smooth flow over a region and efficiently preserve discontinuity in optical flow. This operator is designed on the basis of intensity differences of the pixels and isotropic operator using exponential function. The combination of these are used to control the propagation of flow. Experimental results on the different datasets verify the robustness and accuracy of the algorithm and also validate the effect of anisotropic operator in the discontinuity preserving.

Keywords: optical flow, variational methods, computer vision, anisotropic operator

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Authors: John Zacharias, Bin Wang

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Travel comfort is increasingly seen as crucial to effecting the switch from private motorized modes to public transit. Surveys suggest that travel comfort is closely related to perceived crowding, that may involve lack of available seating, difficulty entering and exiting, jostling and other physical contacts with strangers. As found in studies on environmental stress, other factors may moderate perceptions of crowding–in this case, we hypothesize that the ambient environment may play a significant role. Travel comfort was measured by applying a structured survey to randomly selected passengers (n=369) on 3 lines of the Beijing metro on workdays. Respondents were standing with all seats occupied and with car occupancy at 14 levels. A second research assistant filmed the metro car while passengers were interviewed, to obtain the total number of passengers. Metro lines 4, 6 and 10 were selected that travel through the central city north-south, east-west and circumferentially. Respondents evaluated the following factors: crowding, noise, smell, air quality, temperature, illumination, vibration and perceived safety as they experienced them at the time of interview, and then were asked to rank these 8 factors according to their importance for their travel comfort. Evaluations were semantic differentials on a 7-point scale from highly unsatisfactory (-3) to highly satisfactory (+3). The control variables included age, sex, annual income and trip purpose. Crowding was assessed most negatively, with 41% of the scores between -3 and -2. Noise and air quality were also assessed negatively, with two-thirds of the evaluations below 0. Illumination was assessed most positively, followed by crime, vibration and temperature, all scoring at indifference (0) or slightly positive. Perception of crowding was linearly and positively related to the number of passengers in the car. Linear regression tested the impact of ambient environmental factors on perception of crowding. Noise intensity accounted for more than the actual number of individuals in the car in the perception of crowding, with smell also contributing. Other variables do not interact with the crowding variable although the evaluations are distinct. In all, only one-third of the perception of crowding (R2=.154) is explained by the number of people, with the other ambient environmental variables accounting for two-thirds of the variance (R2=.316). However, when ranking the factors by their importance to travel comfort, perceived crowding made up 69% of the first rank, followed by noise at 11%. At rank 2, smell dominates (25%), followed by noise and air quality (17%). Commuting to work induces significantly lower evaluations of travel comfort with shopping the most positive. Clearly, travel comfort is particularly important to commuters. Moreover, their perception of crowding while travelling on metro is highly conditioned by the ambient environment in the metro car. Focussing attention on the ambient environmental conditions of the metro is an effective way to address the primary concerns of travellers with overcrowding. In general, the strongly held opinions on travel comfort require more attention in the effort to induce ridership in public transit.

Keywords: ambient environment, mass rail transit, public transit, travel comfort

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Authors: Hayden R Schott, John M Felder III

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Introduction: Chronic lower extremity wounds in the diabetic and vasculopathic populations are associated with a high degree of morbidity.When wounds require more extensive treatment than can be offered by wound care centers, more aggressive solutions involve local tissue transfer and microsurgical free tissue transfer for achieving definitive soft tissue coverage. These procedures of autologous tissue transfer (ATT) offer resilient, soft tissue coverage of limb-threatening wounds and confer promising limb salvage rates. However, chronic osteomyelitis and recalcitrant soft tissue infections are common in severe diabetic foot wounds and serve to significantly complicate ATT procedures. Stimulan is a resorbable calcium sulfate antibiotic carrier. The use of stimulan antibiotic beads to treat chronic osteomyelitis is well established in the orthopedic and plastic surgery literature. In these procedures, the beads are placed beneath the skin flap to directly deliver antibiotics to the infection site. The purpose of this study was to quantify the success of Stimulan antibiotic beads in treating recalcitrant infections in patients with diabetic foot wounds receiving ATT. Methods: A retrospective review of clinical and demographic information was performed on patients who underwent ATT with the placement of Stimulan antibiotic beads for attempted limb salvage from 2018-21. Patients were analyzed for preoperative wound characteristics, demographics, infection recurrence, and adverse outcomes as a result of product use. The primary endpoint was 90 day infection recurrence, with secondary endpoints including 90 day complications. Outcomes were compared using basic statistics and Fisher’s exact tests. Results: In this time span, 14 patients were identified. At the time of surgery, all patients exhibited clinical signs of active infection, including positive cultures and erythema. 57% of patients (n=8) exhibited chronic osteomyelitis prior to surgery, and 71% (n=10) had exposed bone at the wound base. In 57% of patients (n=8), Stimulan beads were placed beneath a free tissue flap and beneath a pedicle tissue flap in 42% of patients (n=6). In all patients, Stimulan beads were only applied once. Recurrent infections were observed in 28% of patients (n=4) at 90 days post-op, and flap nonadherence was observed in 7% (n=1). These were the only Stimulan related complications observed. Ultimately, lower limb salvage was successful in 85% of patients (n=12). Notably, there was no significant association between the preoperative presence of osteomyelitis and recurrent infections. Conclusions: The use of Stimulanantiobiotic beads to treat recalcitrant infections in patients receiving definitive skin coverage of diabetic foot wounds does not appear to demonstrate unnecessary risk. Furthermore, the lack of significance between the preoperative presence of osteomyelitis and recurrent infections indicates the successful use of Stimulan to dampen infection in patients with osteomyelitis, as is consistent with the literature. Further research is needed to identify Stimulan as the significant contributor to infection treatment using future cohort and case control studies with more patients. Nonetheless, the use of Stimulan antibiotic beads in patients with diabetic foot wounds demonstrates successful infection suppression and maintenance of definitive soft tissue coverage.

Keywords: wound care, stimulan antibiotic beads, free tissue transfer, plastic surgery, wound, infection

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Authors: Nahid Parween Anwar, Sadia Muzaffar Bhutta, Takbir Ali

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Mentoring is a structured activity which is designed to facilitate engagement between mentor and mentee to enhance mentee’s professional capability as an effective teacher. Both mentor and mentee are important elements of the ‘mentoring equation’ and play important roles in nourishing this dynamic, collaborative and reciprocal relationship. Cluster-Based Mentoring Programme (CBMP) provides an indigenous example of a project which focused on development of primary school teachers in selected clusters with a particular focus on their classroom practice. A study was designed to examine the efficacy of CBMP as part of Strengthening Teacher Education in Pakistan (STEP) project. This paper presents results of one of the components of this study. As part of the larger study, a cross-sectional survey was employed to explore nature and patterns of mentoring process from mentees’ perspectives in the selected districts of Sindh and Balochistan. This paper focuses on the results of the study related to the question: What are mentees’ perceptions of their mentors’ support for enhancing their classroom practice during mentoring process? Data were collected from mentees (n=1148) using a 5-point scale -‘Mentoring for Effective Primary Teaching’ (MEPT). MEPT focuses on seven factors of mentoring: personal attributes, pedagogical knowledge, modelling, feedback, system requirement, development and use of material, and gender equality. Data were analysed using SPSS 20. Mentees perceptions of mentoring practice of their mentors were summarized using mean and standard deviation. Results showed that mean scale scores on mentees’ perceptions of their mentors’ practices fell between 3.58 (system requirement) and 4.55 (personal attributes). Mentees’ perceives personal attribute of the mentor as the most significant factor (M=4.55) towards streamlining mentoring process by building good relationship between mentor and mentees. Furthermore, mentees have shared positive views about their mentors efforts towards promoting gender impartiality (M=4.54) during workshop and follow up visit. Contrary to this, mentees felt that more could have been done by their mentors in sharing knowledge about system requirement (e.g. school policies, national curriculum). Furthermore, some of the aspects in high scoring factors were highlighted by the mentees as areas for further improvement (e.g. assistance in timetabling, written feedback, encouragement to develop learning corners). Mentees’ perceptions of their mentors’ practices may assist in determining mentoring needs. The results may prove useful for the professional development programme for the mentors and mentees for specific mentoring programme in order to enhance practices in primary classrooms in Pakistan. Results would contribute into the body of much-needed knowledge from developing context.

Keywords: cluster-based mentoring programme, mentoring for effective primary teaching (MEPT), professional development, survey

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Authors: Tatsuya Takino, Izuru Nomura, Yuji Kageyama, Shin Nagata, Hiroyuki Kamata

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We propose a method of detecting the craters from the image of the lunar surface. This proposal assumes that it is applied to SLIM (Smart Lander for Investigating Moon) working group aiming at the pinpoint landing on the lunar surface and investigating scientific research. It is difficult to equip and use high-performance computers for the small space probe. So, it is necessary to use a small computer with an exclusive hardware such as FPGA. We have studied the crater detection using principal component analysis (PCA), In this paper, We implement detection algorithm into the FPGA, and the detection is performed on the data that was captured from the CMOS camera.

Keywords: crater detection, PCA, FPGA, image processing

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Authors: J. P. P. Andrade, V. A. F. Campos

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This work presents an application of Linear Matrix Inequalities (LMI) for the robust control of an F-16 aircraft through an algorithm ensuring the damping factor to the closed loop system. The results show that the zero and gain settings are sufficient to ensure robust performance and stability with respect to various operating points. The technique used is the pole placement, which aims to put the system in closed loop poles in a specific region of the complex plane. Test results using a dynamic model of the F-16 aircraft are presented and discussed.

Keywords: F-16 aircraft, linear matrix inequalities, pole placement, robust control

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Authors: T. Padma, Jayashree S. Pillai

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Random entities are an essential component in any cryptographic application. The suitability of a number theory based novel pseudorandom sequence called Pseudorandom Partial Quotient Sequence (PPQS) generated from the continued fraction expansion of irrational numbers, in cryptographic applications, is analyzed in this paper. An approach to build the algorithm around a hard mathematical problem has been considered. The PQ sequence is tested for randomness and its suitability as a cryptographic key by performing randomness analysis, key sensitivity and key space analysis, precision analysis and evaluating the correlation properties is established.

Keywords: pseudorandom sequences, key sensitivity, correlation, security analysis, randomness analysis, sensitivity analysis

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Authors: Michael Danilov, Sergei Iskakov, Vladimir Mazurenko

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The present paper describes a high-performance parallel realization of an exact diagonalization solver for quantum-electron models in a shared memory computing system. The proposed algorithm contains a storage format for efficient computing eigenvalues and eigenvectors of a quantum electron Hamiltonian matrix. The results of the test calculations carried out for 15 sites Hubbard model demonstrate reduction in the required memory and good multiprocessor scalability, while maintaining performance of the same order as compressed row storage.

Keywords: sparse matrix, compressed format, Hubbard model, Anderson model

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Authors: Zdenek Silar, Martin Dobrovolny

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This article deals with the obstacle detection on a railway crossing (clearance detection). Detection is based on the optical flow estimation and classification of the flow vectors by K-means clustering algorithm. For classification of passing vehicles is used optical flow direction determination. The optical flow estimation is based on a modified Lucas-Kanade method.

Keywords: background estimation, direction of optical flow, K-means clustering, objects detection, railway crossing monitoring, velocity vectors

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Authors: Gaelle Candel, David Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

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Authors: Ismail Haddad, Djamel Herbadji, Aissa Belmeguenai, Selma Boumerdassi

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in this paper, we provide a novel approach for image encryption that employs the Fibonacci matrix and an enhanced fractional order chaotic map. The enhanced map overcomes the drawbacks of the classical map, especially the limited chaotic range and non-uniform distribution of chaotic sequences, resulting in a larger encryption key space. As a result, this strategy improves the encryption system's security. Our experimental results demonstrate that our proposed algorithm effectively encrypts grayscale images with exceptional efficiency. Furthermore, our technique is resistant to a wide range of potential attacks, including statistical and entropy attacks.

Keywords: image encryption, logistic map, fibonacci matrix, grayscale images

Procedia PDF Downloads 318