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Journal articles on the topic 'Multi variate regression'
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Luo, Chongliang, Jin Liu, Dipak K. Dey, and Kun Chen. "Canonical variate regression." Biostatistics 17, no. 3 (February 9, 2016): 468–83. http://dx.doi.org/10.1093/biostatistics/kxw001.
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Abstract In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an $F_2$ intercross mice study and an alcohol dependence study.
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Chen, Zexun, Bo Wang, and Alexander N. Gorban. "Multivariate Gaussian and Student-t process regression for multi-output prediction." Neural Computing and Applications 32, no. 8 (December 31, 2019): 3005–28. http://dx.doi.org/10.1007/s00521-019-04687-8.
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AbstractGaussian process model for vector-valued function has been shown to be useful for multi-output prediction. The existing method for this model is to reformulate the matrix-variate Gaussian distribution as a multivariate normal distribution. Although it is effective in many cases, reformulation is not always workable and is difficult to apply to other distributions because not all matrix-variate distributions can be transformed to respective multivariate distributions, such as the case for matrix-variate Student-t distribution. In this paper, we propose a unified framework which is used not only to introduce a novel multivariate Student-t process regression model (MV-TPR) for multi-output prediction, but also to reformulate the multivariate Gaussian process regression (MV-GPR) that overcomes some limitations of the existing methods. Both MV-GPR and MV-TPR have closed-form expressions for the marginal likelihoods and predictive distributions under this unified framework and thus can adopt the same optimization approaches as used in the conventional GPR. The usefulness of the proposed methods is illustrated through several simulated and real-data examples. In particular, we verify empirically that MV-TPR has superiority for the datasets considered, including air quality prediction and bike rent prediction. At last, the proposed methods are shown to produce profitable investment strategies in the stock markets.
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Åström, Oskar, Henrik Hedlund, and Alexandros Sopasakis. "Machine-Learning Approach to Non-Destructive Biomass and Relative Growth Rate Estimation in Aeroponic Cultivation." Agriculture 13, no. 4 (March 30, 2023): 801. http://dx.doi.org/10.3390/agriculture13040801.
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We train and compare the performance of two machine learning methods, a multi-variate regression network and a ResNet-50-based neural network, to learn and forecast plant biomass as well as the relative growth rate based onfrom a short sequence of temporal images from plants in aeroponic cultivation. The training dataset consists of images of 57 plants taken from two different angles every hour during a 5-day period. The results show that images taken from a top-down perspective produce better results for the multi-variate regression network, while images taken from the side are better for the ResNet-50 neural network. In addition, using images from both cameras improves the biomass estimates from the ResNet-50 network, but not those from the multivariatemulti-variatemultivariate regression. However, all relative growth rate estimates were improved by using images from both cameras. We found that the best biomass estimates are produced from the multi-variate regression model trained on top camera images using a moving average filter resulting in a root mean square error of 0.0466 g. The best relative growth rate estimates were produced from the ResNet-50 network training on images from both cameras resulting in a root mean square error of 0.1767 g/(g·day).
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de Laat, A. T. J., R. J. van der A, and M. van Weele. "Tracing the second stage of Antarctic ozone hole recovery with a "big data" approach to multi-variate regressions." Atmospheric Chemistry and Physics Discussions 14, no. 12 (July 14, 2014): 18591–640. http://dx.doi.org/10.5194/acpd-14-18591-2014.
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Abstract. This study presents a sensitivity analysis of multi-variate regressions of recent springtime Antarctic vortex ozone trends using a "big data" ensemble approach. Multi-variate regression methods are widely used for studying the variability and detection of ozone trends. Based on multi-variate regression analysis of total Antarctic springtime vortex ozone it has been suggested that the observed increase of ozone since the late 1990s is statistically significant and can be attributed to decreasing stratospheric halogens (Salby et al., 2011, 2012; Kuttippurath et al., 2013). We find that, when considering uncertainties that have not been addressed in these studies, this conclusion on ozone recovery is not warranted. An ensemble of regressions is constructed based on the analysis of uncertainties in the applied ozone record as well as of uncertainties in the various applied regressors. The presented combination of ensemble members spans up the uncertainty range with about 35 million different regressions. The poleward heat flux (Eliassen–Palm Flux) and the effective chlorine loading explain, respectively, most of the short-term and long-term variability in different Antarctic springtime total ozone records. The inclusion in the regression of stratospheric volcanic aerosols, solar variability, the Quasi-Biennial Oscillation and the Southern Annular Mode is shown to increase rather than to decrease the overall uncertainty in the attribution of Antarctic springtime ozone because of large uncertainties in their respective records. Calculating the trend significance for the ozone record from the late 1990s onwards solely based on the fit of the effective chlorine loading should be avoided, as this does not take fit residuals into account and thereby results in too narrow uncertainty intervals. When taking fit residuals into account, we find that less than 30% of the regressions in the full ensemble result in a statistically significant positive springtime ozone trend over Antarctica from the late 1990s to either 2010 or 2012. Analysis of choices and uncertainties in time series show that, depending on choices in time series and parameters, the fraction of statistically significant trends in parts of the ensemble can range from negligible to more than 90%. However, we were unable to detect a robust statistically significant positive trend in Antarctic springtime vortex ozone in the ensemble.
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Gholizadeh, Pouya, and Behzad Esmaeili. "Developing a Multi-variate Logistic Regression Model to Analyze Accident Scenarios: Case of Electrical Contractors." International Journal of Environmental Research and Public Health 17, no. 13 (July 6, 2020): 4852. http://dx.doi.org/10.3390/ijerph17134852.
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The ability to identify factors that influence serious injuries and fatalities would help construction firms triage hazardous situations and direct their resources towards more effective interventions. Therefore, this study used odds ratio analysis and logistic regression modeling on historical accident data to investigate the contributing factors impacting occupational accidents among small electrical contracting enterprises. After conducting a thorough content analysis to ensure the reliability of reports, the authors adopted a purposeful variable selection approach to determine the most significant factors that can explain the fatality rates in different scenarios. Thereafter, this study performed an odds ratio analysis among significant factors to determine which factors increase the likelihood of fatality. For example, it was found that having a fatal accident is 4.4 times more likely when the source is a “vehicle” than when it is a “tool, instrument, or equipment”. After validating the consistency of the model, 105 accident scenarios were developed and assessed using the model. The findings revealed which severe accident scenarios happen commonly to people in this trade, with nine scenarios having fatality rates of 50% or more. The highest fatality rates occurred in “fencing, installing lights, signs, etc.” tasks in “alteration and rehabilitation” projects where the source of injury was “parts and materials”. The proposed analysis/modeling approach can be applied among all specialty contracting companies to identify and prioritize more hazardous situations within specific trades. The proposed model-development process also contributes to the body of knowledge around accident analysis by providing a framework for analyzing accident reports through a multivariate logistic regression model.
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Hongchao, Ma, and Li Deren. "Enhancing group resolution of TM6 based on multi-variate regression model and semi-variogram function." Geo-spatial Information Science 4, no. 1 (January 2001): 43–49. http://dx.doi.org/10.1007/bf02826636.
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Merz, B., H. Kreibich, and U. Lall. "Multi-variate flood damage assessment: a tree-based data-mining approach." Natural Hazards and Earth System Sciences 13, no. 1 (January 11, 2013): 53–64. http://dx.doi.org/10.5194/nhess-13-53-2013.
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Abstract. The usual approach for flood damage assessment consists of stage-damage functions which relate the relative or absolute damage for a certain class of objects to the inundation depth. Other characteristics of the flooding situation and of the flooded object are rarely taken into account, although flood damage is influenced by a variety of factors. We apply a group of data-mining techniques, known as tree-structured models, to flood damage assessment. A very comprehensive data set of more than 1000 records of direct building damage of private households in Germany is used. Each record contains details about a large variety of potential damage-influencing characteristics, such as hydrological and hydraulic aspects of the flooding situation, early warning and emergency measures undertaken, state of precaution of the household, building characteristics and socio-economic status of the household. Regression trees and bagging decision trees are used to select the more important damage-influencing variables and to derive multi-variate flood damage models. It is shown that these models outperform existing models, and that tree-structured models are a promising alternative to traditional damage models.
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Benson, Roger B. J., and Philip D. Mannion. "Multi-variate models are essential for understanding vertebrate diversification in deep time." Biology Letters 8, no. 1 (June 22, 2011): 127–30. http://dx.doi.org/10.1098/rsbl.2011.0460.
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Statistical models are helping palaeontologists to elucidate the history of biodiversity. Sampling standardization has been extensively applied to remedy the effects of uneven sampling in large datasets of fossil invertebrates. However, many vertebrate datasets are smaller, and the issue of uneven sampling has commonly been ignored, or approached using pairwise comparisons with a numerical proxy for sampling effort. Although most authors find a strong correlation between palaeodiversity and sampling proxies, weak correlation is recorded in some datasets. This has led several authors to conclude that uneven sampling does not influence our view of vertebrate macroevolution. We demonstrate that multi-variate regression models incorporating a model of underlying biological diversification, as well as a sampling proxy, fit observed sauropodomorph dinosaur palaeodiversity best. This bivariate model is a better fit than separate univariate models, and illustrates that observed palaeodiversity is a composite pattern, representing a biological signal overprinted by variation in sampling effort. Multi-variate models and other approaches that consider sampling as an essential component of palaeodiversity are central to gaining a more complete understanding of deep time vertebrate diversification.
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Amos, Kanyesiga Johnson, and Bazinzi Natamba. "The Impact of Training and Development on Job Performance in Ugandan Banking Sector." Journal on Innovation and Sustainability. RISUS ISSN 2179-3565 6, no. 2 (August 10, 2015): 65. http://dx.doi.org/10.24212/2179-3565.2015v6i2p65-71.
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The study examined the impact of training and development on job performance in the Banking sector in Uganda among the selected four banks of Equity Bank, Bank of Africa, Barclays Bank Uganda and Centenary Bank and specifically looked at the relationship between training needs identification, training methods, monitoring, evaluation of training and job performance in the banking sector in Uganda. The study used correlation research design to address the relationship between variables. The study involved managers, heads of departments at each bank and employees. Data was collected using questionnaires to facilitate quantitative approaches in the study. Data was analyzed at three levels that is; univerariate, bi-variate and multi-variate. Univeriate analysis fetched descriptive statistics in form frequencies and percentages while bivariate analysis obtained correlations between variables. At multivariate level a logistic regression model was used to ascertain the magnitude of effect of each independent variable on the dependent variable. Study findings at a bi-variate level revealed a positive and significant relationship between the independent variables (identify training needs, identify training objectives, training content, on the job training technique, off the job training technique, skills application and Knowledge application) and the dependent variable (job performance). At the multi-variate level, it was revealed that all independent variables except knowledge application in the training and evaluation process explain 69% of job performance in the model. It was concluded that identification of training objectives, identification of training objectives and skills application have a positive significant effect on job performance in the banking sector in Uganda. It was therefore recommended that there is need to need to streamline the needs assessment process before the training process, endeavor to clearly define training objectives and have a strict monitoring and evaluation process on trainees.
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Barnes, R. J., M. S. Dhanoa, and Susan J. Lister. "Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra." Applied Spectroscopy 43, no. 5 (July 1989): 772–77. http://dx.doi.org/10.1366/0003702894202201.
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Particle size, scatter, and multi-collinearity are long-standing problems encountered in diffuse reflectance spectrometry. Multiplicative combinations of these effects are the major factor inhibiting the interpretation of near-infrared diffuse reflectance spectra. Sample particle size accounts for the majority of the variance, while variance due to chemical composition is small. Procedures are presented whereby physical and chemical variance can be separated. Mathematical transformations—standard normal variate (SNV) and de-trending (DT)—applicable to individual NIR diffuse reflectance spectra are presented. The standard normal variate approach effectively removes the multiplicative interferences of scatter and particle size. De-trending accounts for the variation in baseline shift and curvilinearity, generally found in the reflectance spectra of powdered or densely packed samples, with the use of a second-degree polynomial regression. NIR diffuse reflectance spectra transposed by these methods are free from multi-collinearity and are not confused by the complexity of shape encountered with the use of derivative spectroscopy.
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Beć, Krzysztof B., and Justyna Grabska. "Computer simulations of NIR spectra of thymol – Towards linking basic and analytical NIRS." NIR news 29, no. 7 (September 3, 2018): 13–16. http://dx.doi.org/10.1177/0960336018798913.
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Analytical near-infrared spectroscopy has been evolving rapidly over the last decades reaching a remarkable value for both industrial and institutional laboratories nowadays. Its growth has been strongly connected to focussed development of the instrumentation and multi-variate analytical methods. Multi-variate analysis gives near-infrared spectroscopy the desired analytical performance level but lacks the ability to provide physical insights into the analysed molecular system. Large amount of information carried in an NIR spectrum is omitted in analytical routines. In the present article, we review the latest accomplishments in cross-field research aimed at connecting the basic and analytical near-infrared spectroscopy. An example of thymol molecule, an important constituent of a traditional herbal medicine Thymi herba, is discussed. The key novelty in this case is computer simulation of NIR spectra which allows gaining better understanding of how spectra forming factors correspond to the partial least squares regression coefficients with special attention paid to the role of intermolecular interactions.
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دولتخواهی, زهرا, محمدرضا جوادی, and مهدی وفاخواه. "Using of Two and Multi Variate Regression Methods on Landslide Hazard Zonation (A Case Study: Northern Tehran Watershed)." journal of watershed management research 8, no. 15 (September 1, 2017): 171–79. http://dx.doi.org/10.29252/jwmr.8.15.171.
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Sundari V, Anusree M, Swetha U, and Divya Lakshmi R. "Crop recommendation and yield prediction using machine learning algorithms." World Journal of Advanced Research and Reviews 14, no. 3 (June 30, 2022): 452–59. http://dx.doi.org/10.30574/wjarr.2022.14.3.0581.
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Agriculture is the foundation of many countries' economies, particularly in India and Tamil Nadu. The young generation who are new to farming may confront the challenge of not understanding what to sow and what to reap benefit from. This is a problem that has to be addressed, and it is one that we are addressing. Predicting the proper crop and production will aid in making better decisions, reducing losses and managing the risk of price fluctuations. The existing system is not deployed, unlike ours, which is done by applying classification and regression algorithms to calculate crop type recommendations and yield predictions. Agricultural industries must use machine learning algorithms to anticipate the crop from a given dataset. The supervised machine learning technique is used to analyse a dataset in order to capture information from multiple sources, such as variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments, and so on. A comparison of machine learning algorithms was conducted in order to identify which algorithm was more accurate in predicting the best harvest. The results show that the proposed machine learning algorithm technique has the best accuracy when comparing entropy calculation, precision, Recall, F1 Score, Sensitivity, Specificity, and Entropy.
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HIYOSHI, YASUHIKO. "Prognostic factors of infantile acute blastic leukemia. Multi-variate analysis : analysis by Cox multiple regression type life table method." Rinsho yakuri/Japanese Journal of Clinical Pharmacology and Therapeutics 20, no. 1 (1989): 135–36. http://dx.doi.org/10.3999/jscpt.20.135.
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Moran, Valerie, and Rowena Jacobs. "Investigating the relationship between costs and outcomes for English mental health providers: a bi-variate multi-level regression analysis." European Journal of Health Economics 19, no. 5 (June 24, 2017): 709–18. http://dx.doi.org/10.1007/s10198-017-0915-5.
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LOPEZ-GUEDE, JOSE MANUEL, BORJA FERNANDEZ-GAUNA, and MANUEL GRAÑA. "STATE-ACTION VALUE FUNCTION MODELED BY ELM IN REINFORCEMENT LEARNING FOR HOSE CONTROL PROBLEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, supp02 (October 31, 2013): 99–116. http://dx.doi.org/10.1142/s0218488513400199.
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This paper addresses the problem of efficiency in reinforcement learning of Single Robot Hose Transport (SRHT) by training an Extreme Learning Machine (ELM) from the state-action value Q-table, obtaining large reduction in data space requirements because the number of ELM parameters is much less than the Q-table's size. Moreover, ELM implements a continuous map which can produce compact representations of the Q-table, and generalizations to increased space resolution and unknown situations. In this paper we evaluate empirically three strategies to formulate ELM learning to provide approximations to the Q-table, namely as classification, multi-variate regression and several independent regression problems.
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Paudel, Puja, Shital Bhandary, and Jayandra Byanju. "Factors affecting menopause in Nepalese women." Journal of Patan Academy of Health Sciences 4, no. 2 (November 15, 2017): 33–38. http://dx.doi.org/10.3126/jpahs.v4i2.24582.
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Introductions: Menopause is a physiological event that indicates the end of reproductive period in woman’s life. It has many health issues and morbidity. There are multiple factors that influence age of onset of menopause and we aim to find these associated factors.
Methods: The 2011 ‘Nepal demographic and health survey’ data was used in this study. Logistic regression was used to find the association between the dependent and independent variables using bi-variate and multi-variate analysis.
Results: The bivariate analysis showed the association of age, wealth index, education, marital status, employment, use of oral contraceptives and smoking with menopause. The multivariate analysis showed the independent association of age, education, employment and oral contraceptives.
Conclusions: The employment, marital status and use of oral contraceptives were found to be independently associated with the age of onset of menopause on multivariate analysis.
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Okwir, Gustavio, Sharma Pramod Kumar, Hongkai Gao, Juma Rajabu Selemani, and Karoli N. Njau. "Multi-variate regression analysis of lake level variability: A case of semi-closed, shallow rift valley lake in Northern Tanzania." Environmental Challenges 7 (April 2022): 100533. http://dx.doi.org/10.1016/j.envc.2022.100533.
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Providence, Alimasi Mongo, Chaoyu Yang, Tshinkobo Bukasa Orphe, Anesu Mabaire, and George K. Agordzo. "Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms." Electronics 11, no. 19 (October 1, 2022): 3167. http://dx.doi.org/10.3390/electronics11193167.
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Multi-variable time series (MTS) information is a typical type of data inference in the real world. Every instance of MTS is produced via a hybrid dynamical scheme, the dynamics of which are often unknown. The hybrid species of this dynamical service are the outcome of high-frequency and low-frequency external impacts, as well as global and local spatial impacts. These influences impact MTS’s future growth; hence, they must be incorporated into time series forecasts. Two types of normalization modules, temporal and spatial normalization, are recommended to accomplish this. Each boosts the original data’s local and high-frequency processes distinctly. In addition, all components are easily incorporated into well-known deep learning techniques, such as Wavenet and Transformer. However, existing methodologies have inherent limitations when it comes to isolating the variables produced by each sort of influence from the real data. Consequently, the study encompasses conventional neural networks, such as the multi-layer perceptron (MLP), complex deep learning methods such as LSTM, two recurrent neural networks, support vector machines (SVM), and their application for regression, XGBoost, and others. Extensive experimental work on three datasets shows that the effectiveness of canonical frameworks could be greatly improved by adding more normalization components to how the MTS is used. This would make it as effective as the best MTS designs are currently available. Recurrent models, such as LSTM and RNN, attempt to recognize the temporal variability in the data; however, as a result, their effectiveness might soon decline. Last but not least, it is claimed that training a temporal framework that utilizes recurrence-based methods such as RNN and LSTM approaches is challenging and expensive, while the MLP network structure outperformed other models in terms of time series predictive performance.
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Helfrich-Schkarbanenko, Andreas, and Nathalie Verné. "Online Tests and Predictive Analytics." International Journal of Emerging Technologies in Learning (iJET) 17, no. 23 (December 8, 2022): 89–93. http://dx.doi.org/10.3991/ijet.v17i23.36459.
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We present a framework that estimates students’ exam performance in the context of mathematics lectures. For the prediction, we apply a multi-variate nonlinear regression implemented in MATLAB based on the results of five tests acting as independent data. The electronic tests were implemented by means of online assessment system STACK. The tests cover all major topics and are evenly distributed throughout the lecture period. The performance of the approach is quantitatively tested on student groups. In the future, the insights gained in this way will serve as a starting point for prespective analytics.
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Nykvist, F., M. Hurme, H. Alaranta, and ML Miettinen. "Social factors and outcome in a five-year follow-up study of 276 patients with sciatica." Journal of Rehabilitation Medicine 23, no. 1 (April 29, 2020): 19–26. http://dx.doi.org/10.2340/1650197791231926.
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Social factors of 179 operated and 97 non-operated patients one year after hospitalization due to low back pain and sciatica were tested by multi-variate regression analysis in relation to the five-year outcome evaluated according to the WHO Handicap Classification. For operated men a subjective working incapacity (relative risk RR = 4.6) and co-morbidity (RR = 2.7) predicted a poor outcome. For operated women the predictive factors were subjective working incapacity (RR = 3.2) and older age (RR = 1.9). For non-operated men an increased occurrence of occupational hazards (RR = 3.6) and for non-operated women co-morbidity (RR = 7.1) indicated a poor outcome
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Vališ, David, and Libor Žák. "Assessment of Off-Line Diagnostic Oil Data with Using Selected Mathematical Tools." Applied Mechanics and Materials 772 (July 2015): 141–46. http://dx.doi.org/10.4028/www.scientific.net/amm.772.141.
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The paper deals with assessment of oil filed data from heavy off-road vehicle. The oil sample is collected off-line and processed consequently in a tribolaboratory. We call the outcomes from tribolaboratory as oil field data. Firstly we apply selected regression functions for description of the most interesting oil particles generation. It is the vehicle engine and its metal – ferrum, lead or cooper – oil data which are explored for further utilisation. We apply and present methods of multi-variate regression analysis to model the metal – Fe and Pb – data and provide outcomes + estimations for system operation so far and also proposals for system further operation. The novelty is to providing inputs for soft failure identification, to helping to change the life cycle costing, to change the system of maintenance policy, system operation and mission planning.
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Sawada, Kimi, Yukari Takemi, Nobuko Murayama, and Hiromi Ishida. "Relationship between rice consumption and body weight gain in Japanese workers: white versus brown rice/multigrain rice." Applied Physiology, Nutrition, and Metabolism 44, no. 5 (May 2019): 528–32. http://dx.doi.org/10.1139/apnm-2018-0262.
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Increasing obesity rates have driven research into dietary support for body weight control, but previous studies have only assessed changes in body weight of ±3 kg. We investigated the relationships between white or brown/multi-grain rice consumption and 1-year body weight gain ≥3 kg in Japanese factory workers (n = 437). Routine medical check-up data from a 1-year nutrition and lifestyle cohort study were analysed. Participants were divided into white rice and brown/multi-grain rice consumption groups and further classified by tertile of rice consumption. Multiple logistic regression analyses were performed by tertile. At 1 year, high white rice consumption was significantly associated with increased risk of body weight gain ≥3 kg compared with low white rice consumption, maintained after adjustment for age, sex, and consumption of other obesogenic foods (p = 0.034). In the brown/multi-grain rice consumption group, however, there was no significant difference in risk between high and low consumption, even after multi-variate adjustment (p = 0.387). The consumption of white rice, but not brown rice/multi-grain rice, was positively correlated with the risk of a 1-year body weight gain of 3 kg or more. This suggests that brown rice/multi-grain rice consumption is useful for body weight control among Japanese workers.
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Karsten, Keller, and Engelhardt Martin. "IMPACT OF CARTILAGE DAMAGE ON ARTHROGENIC MUSCLE INHIBITION IN PATIENTS WITH MENISCUS INJURIES." Journal of Musculoskeletal Research 19, no. 01 (March 2016): 1650001. http://dx.doi.org/10.1142/s0218957716500019.
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Background: Knee traumata are associated with arthrogenic muscle inhibition (AMI). We aimed to identify impact factors on AMI. Methods: A total of 37 patients with meniscus injuries were interviewed and examined for maximum isometric knee extensor force preoperatively. We analyzed AMI as relative maximum isometric force between healthy and injured legs. Regression analyses were computed to evaluate associations between an AMI with muscle-strength reduction [Formula: see text]% and several parameters and between cartilage damage[Formula: see text][Formula: see text][Formula: see text]stadium 3 and several parameters. ROC curves were calculated to investigate effectiveness of age and pain at rest for prediction of cartilage lesions [Formula: see text][Formula: see text]stadium 3. Results: Meniscus injuries lead to distinct AMI with reduced strength of 26.6% in mean. In multi-variate logistic regressions, an AMI with muscle weakness [Formula: see text]% was associated with higher severity of cartilage lesions (OR3.267, 95% CI 1.059–10.078, [Formula: see text]). In uni-variate regression analyses, pain at rest (OR1.398, 95%CI 1.071–1.824, [Formula: see text]) and patients’ age (per year) (OR1.145, 95%CI 1.042–1.257, [Formula: see text]) were associated with cartilage damage[Formula: see text][Formula: see text][Formula: see text]stadium 3. Optimal cut-off values for patients’ age and pain at rest to predict cartilage damage stadium[Formula: see text][Formula: see text] were 44.5 years and VAS-scale-value 4.5 with good effectiveness (AUC 0.855 and 0.732), respectively. Conclusions: Meniscus injuries lead to distinct AMI with 26.6% reduction in muscle strength. Concomitant cartilage damage is an important cofactor for development of distinct AMI. Moderate to severe pain at rest and age [Formula: see text] years were indicators for concomitant higher cartilage damage in patients with meniscal lesions.
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Msosa, Steven Kayambazinthu. "The impact of Perceived Justice on Students’ Negative Emotional Responses during Service Recovery." International Journal of Higher Education 9, no. 5 (July 30, 2020): 230. http://dx.doi.org/10.5430/ijhe.v9n5p230.
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This study sought to evaluate the impact of perceived justice on students’ negative emotional responses during service recovery. Quantitative, descriptive multi-variate regression analysis and a cross-sectional study were undertaken using a judgmental sample of 430 students drawn from three public Higher Education Institutions in South Africa. The results of this study showed that all the dimensions of justice, viz. procedural, distributive and interactional justice, have a negative and significant impact on negative emotions. The findings of this study could assist Higher Education institutional managers to interrogate the fairness of the processes used in Higher Education Institutions to address student grievances because they have a negative and significant impact on students’ negative emotions.
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Kaltenthaler, Karl C., and Christopher J. Anderson. "The Changing Political Economy of Inflation." Journal of Public Policy 20, no. 2 (August 2000): 109–31. http://dx.doi.org/10.1017/s0143814x00000787.
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A type of conventional wisdom has developed among many scholars that industrialized countries with independent central banks produce lower relative inflation rates than countries that do not have these institutions. We argue that the relative importance of central bank independence for fighting inflation changed fundamentally from the 1970s to the 1980s as a result of experiences in the advanced industrialized democracies, which led both Right and Left governments to move toward more neo-liberal macroeconomic policies. As governments made price stability more of a priority, the anti-inflationary effects of independent central banks would become much less pronounced. This hypothesis is tested and confirmed in the study in a multi-variate regression analysis using data from eighteen industrialized democracies.
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Mohanto, Sumant, and Debasis Deb. "Prediction of Plastic Damage Index for Assessing Rib Pillar Stability in Underground Metal Mine Using Multi-variate Regression and Artificial Neural Network Techniques." Geotechnical and Geological Engineering 38, no. 1 (September 28, 2019): 767–90. http://dx.doi.org/10.1007/s10706-019-01065-y.
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Shrestha, Ira, Puja Paudel, Milli Joshi, and Shital Bhandary. "Iron-folic acid supplementation and its differentials among Nepalese women of reproductive age." Journal of General Practice and Emergency Medicine of Nepal 4, no. 6 (June 30, 2015): 6–10. http://dx.doi.org/10.59284/jgpeman121.
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Introduction: Iron-folic acid (IFA) supplementations protect mothers and their infants against perinatal and maternal mortality. Ministry of health and population advises pregnant women and lactating mother to take IFA supplements everyday for certain period. However, not all women follow this.
Methods: 2011 Nepal Demographic and Health Survey (NDHS) data was used. Dependent variable was created using women with last child born live in the last five years and taking at least 1 tablet/syrup of IFA and women not taking any tablet/syrup during the study period. Key background variables of women and their last child born during last five years were used as independent variables. Logistic regression was used to find the association between the dependent and independent variables without controlling (bi-variate) and controlling (multi-variate) for other factors during analysis.
Results: About 60% of the women took/bought IFA supplements. Although bivariate analysis showed the effects of perceived child size and type of place of residence on maternal intake of IFA, multivariate analysis did not show this effect. However, mother’s age, residence at hill region, education level and wealth index showed their effect on maternal IFA supplements intake in both bivariate and multivariate analysis.
Conclusion: The mothers from Dalit, Janjati and other marginalized group, living in hill region, with no education and higher age were less likely to take IFA supplements. Therefore, such groups should be counseled to increase the use of IFA.
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Chen, X., Z. Hao, N. Devineni, and U. Lall. "Climate information based streamflow and rainfall forecasts for Huai River basin using hierarchical Bayesian modeling." Hydrology and Earth System Sciences 18, no. 4 (April 29, 2014): 1539–48. http://dx.doi.org/10.5194/hess-18-1539-2014.
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Abstract. A Hierarchal Bayesian model is presented for one season-ahead forecasts of summer rainfall and streamflow using exogenous climate variables for east central China. The model provides estimates of the posterior forecasted probability distribution for 12 rainfall and 2 streamflow stations considering parameter uncertainty, and cross-site correlation. The model has a multi-level structure with regression coefficients modeled from a common multi-variate normal distribution resulting in partial pooling of information across multiple stations and better representation of parameter and posterior distribution uncertainty. Covariance structure of the residuals across stations is explicitly modeled. Model performance is tested under leave-10-out cross-validation. Frequentist and Bayesian performance metrics used include receiver operating characteristic, reduction of error, coefficient of efficiency, rank probability skill scores, and coverage by posterior credible intervals. The ability of the model to reliably forecast season-ahead regional summer rainfall and streamflow offers potential for developing adaptive water risk management strategies.
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Leonard, Ahuejere, and Ishmael Kalule. "CONTEXTUAL DETERMINANTS OF FERTILITY TRANSITIONS AMONG BLACK SOUTH AFRICAN WOMEN: A MULTI-LEVEL ANALYSIS." SOCIETY AND CULTURE DEVELOPMENT IN INDIA 2, no. 2 (2022): 247–71. http://dx.doi.org/10.47509/scdi.2022.v02i02.02.
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Fertility declines (transitions) and their contextual determinants are concerns to the government and other stakeholders in South Africa. These are so, especially among the Black South African women of childbearing age. The South African Demographic Health Survey (SADHS) 2016 data was used to examine the role of three hierarchical layers of variables (individual, household and community level characteristics) in determining fertility transitions among Black South African women of childbearing age. Based on the Social-Ecological (SEM) and the Easterlin’s micro-economic models, the chi-squared test and multilevel logistic regression were performed at the bi-variate and multivariate levels of analysis. The multilevel logistic regression was performed using the generalised linear and latent mixed model (GLLAMM) to obtain fixed and random effects. Findings suggest that close to half (48.1%) of these women had low fertility levels (1-2 children) in South Africa. Those in rural areas had a higher fertility level (4 plus children), compared to those in urban areas. Factors such as mother’s age, employment and wealth status, owning a house with water and electricity, access and distance to health facilities and workers, etc., were strongly associated (significant) at different hierarchical model levels (p<0.05). Results of random effect revealed a non-existance (0.00%) of variations in their log odds of predicting fertility transitions across the communities (clusters/layers). The study recommends that these findings be considered in all programme and policy developments around the issue in South Africa.
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Mereu, Luigi, Simona Scollo, Antonella Boselli, Giuseppe Leto, Ricardo Zanmar Sanchez, Costanza Bonadonna, and Frank Silvio Marzano. "Dual-Wavelength Polarimetric Lidar Observations of the Volcanic Ash Cloud Produced during the 2016 Etna Eruption." Remote Sensing 13, no. 9 (April 29, 2021): 1728. http://dx.doi.org/10.3390/rs13091728.
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Lidar observations are very useful to analyse dispersed volcanic clouds in the troposphere mainly because of their high range resolution, providing morphological as well as microphysical (size and mass) properties. In this work, we analyse the volcanic cloud of 18 May 2016 at Mt. Etna, in Italy, retrieved by polarimetric dual-wavelength Lidar measurements. We use the AMPLE (Aerosol Multi-Wavelength Polarization Lidar Experiment) system, located in Catania, about 25 km from the Etna summit craters, pointing at a thin volcanic cloud layer, clearly visible and dispersed from the summit craters at the altitude between 2 and 4 km and 6 and 7 km above the sea level. Both the backscattering and linear depolarization profiles at 355 nm (UV, ultraviolet) and 532 nm (VIS, visible) wavelengths, respectively, were obtained using different angles at 20°, 30°, 40° and 90°. The proposed approach inverts the Lidar measurements with a physically based inversion methodology named Volcanic Ash Lidar Retrieval (VALR), based on Maximum-Likelihood (ML). VALRML can provide estimates of volcanic ash mean size and mass concentration at a resolution of few tens of meters. We also compared those results with two methods: Single-variate Regression (SR) and Multi-variate Regression (MR). SR uses the backscattering coefficient or backscattering and depolarization coefficients of one wavelength (UV or VIS in our cases). The MR method uses the backscattering coefficient of both wavelengths (UV and VIS). In absence of in situ airborne validation data, the discrepancy among the different retrieval techniques is estimated with respect to the VALR ML algorithm. The VALR ML analysis provides ash concentrations between about 0.1 μg/m3 and 1 mg/m3 and particle mean sizes of 0.1 μm and 6 μm, respectively. Results show that, for the SR method differences are less than <10%, using the backscattering coefficient only and backscattering and depolarization coefficients. Moreover, we find differences of 20–30% respect to VALR ML, considering well-known parametric retrieval methods. VALR algorithms show how a physics-based inversion approaches can effectively exploit the spectral-polarimetric Lidar AMPLE capability.
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Ziman, Nathan, Laura P. Sands, Christopher Tang, Jiafeng Zhu, and Jacqueline M. Leung. "Does postoperative delirium following elective noncardiac surgery predict long-term mortality?" Age and Ageing 49, no. 6 (March 31, 2020): 1020–27. http://dx.doi.org/10.1093/ageing/afaa047.
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Abstract Objective to determine whether incident postoperative delirium in elective older surgical patient was associated with increased risk for mortality, controlling for covariates of 5-year mortality. Design secondary analysis of prospective cohort studies. Setting academic Medical Center. Subjects patients ≥65 years of age undergoing elective non-cardiac surgery. Outcomes postoperative assessments of delirium measured using the Confusion Assessment Method (CAM), mortality within 5 years of the index surgery was determined from National Death Index records. Results postoperative delirium occurred in 332/1,315 patients (25%). Five years after surgery, 175 patients (13.3%) were deceased. Older age was associated with an increased odds of mortality [odds ratio (OR) 1.90, 95% confidence interval (CI) 1.20–2.70] for those aged 70–79 years compared to those aged <70 years, and OR 3.29, 95% CI 2.14–5.06 for those aged >80 years. Other variables associated with 5-year mortality on bi-variate analyses were white race, self-rated functional status, lower preoperative cognitive status, higher risk score as measured by the American Society of Anesthesiologists (ASA) classification, higher surgical risk score, history of congestive heart failure, myocardial infarction, renal disease, cancer, peripheral vascular disease and postoperative delirium. However, postoperative delirium was not associated with 5-year mortality on multi-variate logistic regression (OR 1.18, 95% CI 0.85–1.65). Conclusions our results showed that delirium was not associated with 5-year mortality in elective surgical patients after consideration of co-variates of mortality. Our results suggest the importance of accounting for known preoperative risks for mortality when investigating the relationship between delirium and long-term mortality.
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Schmidt, Luisa, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra. "Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties." Biogeosciences 20, no. 5 (March 16, 2023): 1027–46. http://dx.doi.org/10.5194/bg-20-1027-2023.
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Abstract. Vegetation attenuates the microwave emission from the land surface. The strength of this attenuation is quantified in models in terms of the parameter vegetation optical depth (VOD) and is influenced by the vegetation mass, structure, water content, and observation wavelength. Earth observation satellite sensors operating in the microwave frequencies are used for global VOD retrievals, enabling the monitoring of vegetation at large scales. VOD has been used to determine above-ground biomass, monitor phenology, or estimate vegetation water status. VOD can be also used for constraining land surface models or modelling wildfires at large scales. Several VOD products exist, differing by frequency/wavelength, sensor, and retrieval algorithm. Numerous studies present correlations or empirical functions between different VOD datasets and vegetation variables such as the normalized difference vegetation index, leaf area index, gross primary production, biomass, vegetation height, or vegetation water content. However, an assessment of the joint impact of land cover, vegetation biomass, leaf area, and moisture status on the VOD signal is challenging and has not yet been done. This study aims to interpret the VOD signal as a multi-variate function of several descriptive vegetation variables. The results will help to select VOD at the most suitable wavelength for specific applications and can guide the development of appropriate observation operators to integrate VOD with large-scale land surface models. Here we use VOD from the Land Parameter Retrieval Model (LPRM) in the Ku, X, and C bands from the harmonized Vegetation Optical Depth Climate Archive (VODCA) dataset and L-band VOD derived from Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) sensors. The leaf area index, live-fuel moisture content, above-ground biomass, and land cover are able to explain up to 93 % and 95 % of the variance (Nash–Sutcliffe model efficiency coefficient) in 8-daily and monthly VOD within a multi-variable random forest regression. Thereby, the regression reproduces spatial patterns of L-band VOD and spatial and temporal patterns of Ku-, X-, and C-band VOD. Analyses of accumulated local effects demonstrate that Ku-, X-, and C-band VOD are mostly sensitive to the leaf area index, and L-band VOD is most sensitive to above-ground biomass. However, for all VODs the global relationships with vegetation properties are non-monotonic and complex and differ with land cover type. This indicates that the use of simple global regressions to estimate single vegetation properties (e.g. above-ground biomass) from VOD is over-simplistic.
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Cheng, Lu-Yun (Vivian), Huifen (Helen) Cai, and Zhongqi Jin. "The effect of parental opportunism, IJV's autonomy and tacit knowledge on IJV instability: A comparison of multi-variate regression and fuzzy-set qualitative comparative analysis." Journal of Business Research 69, no. 11 (November 2016): 5203–9. http://dx.doi.org/10.1016/j.jbusres.2016.04.113.
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Mirzaei, Mojgan, Stefania Bertazzon, and Isabelle Couloigner. "Modeling Wildfire Smoke Pollution by Integrating Land Use Regression and Remote Sensing Data: Regional Multi-Temporal Estimates for Public Health and Exposure Models." Atmosphere 9, no. 9 (August 27, 2018): 335. http://dx.doi.org/10.3390/atmos9090335.
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To understand the health effects of wildfire smoke, it is important to accurately assess smoke exposure over space and time. Particulate matter (PM) is a predominant pollutant in wildfire smoke. In this study, we develop land-use regression (LUR) models to investigate the impact that a cluster of wildfires in the northwest USA had on the level of PM in southern Alberta (Canada), in the summer of 2015. Univariate aerosol optical depth (AOD) and multivariate AOD-LUR models were used to estimate the level of PM2.5 in urban and rural areas. For epidemiological studies, it is also important to distinguish between wildfire-related PM2.5 and PM2.5 originating from other sources. We therefore subdivided the study period into three sub-periods: (1) Pre-fire, (2) during-fire, and (3) post-fire. We then developed separate models for each sub-period. With this approach, we were able to identify different predictors significantly associated with smoke-related PM2.5 verses PM2.5 of different origin. Leave-one-out cross-validation (LOOCV) was used to evaluate the models’ performance. Our results indicate that model predictors and model performance are highly related to the level of PM2.5, and the pollution source. The predictive ability of both uni- and multi-variate models were higher in the during-fire period than in the pre- and post-fire periods.
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TUNALILAR, SECKIN, and ONUR DEMIRORS. "AN EXPLORATION OF FUNCTIONAL SIZE BASED EFFORT ESTIMATION MODELS." International Journal of Software Engineering and Knowledge Engineering 21, no. 03 (May 2011): 413–29. http://dx.doi.org/10.1142/s0218194011005347.
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A number of methods have been proposed to build a relationship between effort and size. These models are generally based on regression analysis and a widely accepted model is not yet available. Although in some sizing methods, such as MKII and IFPUG, different multipliers for the base functional components (BFC) exist, their origin and the purpose of their usage are undefined. The COSMIC method does not treat components separately and assigns the same measurement unit to each of them. In this study we used the Artificial Neural Network and regression based methods to create effort estimation models that take the four components of the COSMIC method into consideration. In the research we compared several functional size based effort models in terms of accuracy using a reliable company dataset. These models comprised not only the generic models proposed in the literature or currently in use, but also specific models that we generated using our dataset with a single and multi-variate regression analysis and the ANN method. We also explored the effect of functional similarity (FS) using our specific models. We found that using BFC instead of total size improved effort estimation models and the ANN method is a useful approach to calibrate these components according to the company characteristics.
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Jin, Yun-Tae, Ha-Yeon Kim, Min Jhon, Ju-Wan Kim, Hee-Ju Kang, Ju-Yeon Lee, Sung-Wan Kim, Il-Seon Shin, and Jae-Min Kim. "Prediction of 12-Week Remission by Psychopharmacological Treatment Step in Patients With Depressive Disorders." Psychiatry Investigation 19, no. 10 (October 25, 2022): 866–71. http://dx.doi.org/10.30773/pi.2022.0160.
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Objective To investigate the predictors of remission by 4 treatment steps in depressive outpatients receiving 12-week psychopharmacotherapy.Methods Patients were consecutively recruited at a university hospital in South Korea from March 2012 to April 2017. At baseline, 1,262 patients were evaluated for sociodemographic and clinical data including assessments scales, and were received antidepressant monotherapy. For patients with an insufficient response or uncomfortable side effects, next treatment steps (1, 2, 3, and 4) with alternative strategies (switching, augmentation, combination, and mixtures of these approaches) were administered considering measurements and patient preference at every 3 weeks in the acute treatment phase (3, 6, 9, and 12 weeks). Remission was defined as a Hamilton Depression Rating Scale score of ≤7.Results In the multi-variate logistic regression analyses, remission was predicted by higher functional levels in patients received Step 1 and 2 treatment; by lower life stressors in Step 1; by higher social support in Step 3 and 4; and by lower suicidality in Step 1–3.Conclusion Differential associations were found between symptoms or functions and treatment steps, which suggested that multi-faceted evaluations at baseline could predict remission by treatment steps.
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Stikliene, Aida. "Research on the Relationship Between Teachers’ Professional Skills and Students’ Expectations for Improving the Study Environment." New Trends and Issues Proceedings on Humanities and Social Sciences 4, no. 8 (January 6, 2018): 109. http://dx.doi.org/10.18844/prosoc.v4i8.2984.
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The teacher's attitude towards the teaching process and communication skills is of particular importance and plays a crucial role in today’s rapidly changing world. It has to go together, raising consciousness and awareness of individuals on study environment issues and ensuring that they contribute to solutions of learning problems. The research was conducted with 405 prospective professionals from the Faculty of Forest Sciences and Ecology, Aleksandras Stulginskis University. An interactive questionnaire ‘Study subject in student’s eyes’ (SSSE) developed at Aleksandras Stulginskis University (2014–2017) was used as the data collection tool. This article analyses the teachers’ pedagogical work from the students’ point of view. The multi-variate analysis and regression tree model were used in the interpretation of results. The results confirmed the hypothesis that hard working students better evaluate teachers’ professional skills. It seems that elder course students with age have higher expectations from the teaching environment. Keywords:
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Saad, Mostafa M., Ramanunni Parakkal Menon, and Ursula Eicker. "Supporting Decision Making for Building Decarbonization: Developing Surrogate Models for Multi-Criteria Building Retrofitting Analysis." Energies 16, no. 16 (August 17, 2023): 6030. http://dx.doi.org/10.3390/en16166030.
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Decarbonizing buildings is crucial in addressing pressing climate change issues. Buildings significantly contribute to global greenhouse gas emissions, and reducing their carbon footprint is essential to achieving sustainable and low-carbon cities. Retrofitting buildings to become more energy efficient constitutes a solution. However, building energy retrofits are complex processes that require a significant number of simulations to investigate the possible options, which limits comprehensive investigations that become infeasible to carry out. Surrogate models can be vital in addressing computational inefficiencies by emulating physics-based models and predicting building performance. However, there is a limited focus on investigating feature engineering and selection methods and their effect on the model’s performance and optimization. Feature selection methods are considered effective with interpretable models such as multi-variate linear regression (MVLR) and multiple adaptive regression splines (MARS) for achieving stable prediction stability. This study proposes a modelling framework to create, optimize, and improve the performance of surrogate predictive models for energy consumption, carbon emissions, and the associated cost of building energy retrofit processes. The investigated feature selection methods are wrapper and embedded methods such as backward-stepwise feature selection (BSFS), recursive feature elimination (RFE), and Elastic Net embedded regularization in order to provide insights into the model’s behavior and optimize the model’s performance. The most accurate surrogate models developed achieved a mean absolute percentage error (MAPE) of 0.2–1.8% compared to the used test data. In addition, when calculated for a million samples, all developed surrogate models reduced the computational time by one-thousand-fold compared to physics-based models. The study’s findings pave the way towards low-computational accurate models that can comprehensively predict building performance in near real-time, ultimately leading to identifying decarbonization measures at scale.
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Atakla, Hugues Ghislain, Fatoumata Lounceny Barry, Mahugnon Maurel Ulrich Dénis Noudohounsi, Benjamin Bekoe Ofosu, Ummi Sulaimi Sulemana, and Dismand Stephan Houinato. "Prognostic Indicators in Patients with Intracerebral Hematoma in an urban clinical setting of a resource limited Country." Nepal Journal of Neuroscience 18, no. 4 (November 30, 2021): 33–38. http://dx.doi.org/10.3126/njn.v18i4.36701.
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Context and Objective: Hemorrhagic Cerebrovascular Accidents represent 10 to 15% of all strokes and are often related to the spontaneous rupture of small vessels weakened by chronic arterial hypertension or amyloid angiopathy. The aim of this work was to study the prognostic determinants of intracerebral haematomas at the neurology department of Conakry University Hospital.
Patients and Method: This was a retrospective analytical study conducted on all patients who were hospitalized with intracerebral hematoma over the 24-month period. Only the records of patients in whom intracerebral hematoma was confirmed by brain imaging were included in this study. Logistic regression (uni-variate and multi-variate) identified prognostic determinants of intracerebral hematoma at p < 0.05. The data were entered using Epi Info software version 7.1.4.0 then analysed using STATA / SE software version 11.2.
Results: This study found 21% of cases of intracerebral hematomas during the study period, with a male predominance of 60% and a sex ratio of 1.50. The study was conducted in the presence of a male patient. Hypertension was the cause found in 89.52% of patients; followed by arteriovenous malformations in 6.67% of patients, 2.86% of cases of amyloid angiopathy and 0.95% of unknown cause. Nevertheless, we still recorded 20% of deaths during hospitalization.
Conclusion: Previous quality of life and co-morbidities also modify the prognosis and should be taken into account in the prediction of disability and future quality of life of patients with intracerebral haematoma.
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Terfa, Zelalem G., Sayem Ahmed, Jahangir Khan, and Louis W. Niessen. "Household Microenvironment and Under-Fives Health Outcomes in Uganda: Focusing on Multidimensional Energy Poverty and Women Empowerment Indices." International Journal of Environmental Research and Public Health 19, no. 11 (May 30, 2022): 6684. http://dx.doi.org/10.3390/ijerph19116684.
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Young children in low- and middle-income countries (LMICs) are vulnerable to adverse effects of household microenvironments. The UN Sustainable Development Goals (SDGs)—specifically SDG 3 through 7—urge for a comprehensive multi-sector approach to achieve the 2030 goals. This study addresses gaps in understanding the health effects of household microenvironments in resource-poor settings. It studies associations of household microenvironment variables with episodes of acute respiratory infection (ARI) and diarrhoea as well as with stunting among under-fives using logistic regression. Comprehensive data from a nationally representative, cross-sectional demographic and health survey (DHS) in Uganda were analysed. We constructed and applied the multidimensional energy poverty index (MEPI) and the three-dimensional women empowerment index in multi-variate regressions. The multidimensional energy poverty was associated with higher risk of ARI (OR = 1.32, 95% CI 1.10 to 1.58). Social independence of women was associated with lower risk of ARI (OR= 0.91, 95% CI 0.84 to 0.98), diarrhoea (OR = 0.93, 95% CI 0.88 to 0.99), and stunting (OR = 0.83, 95% CI 0.75 to 0.92). Women’s attitude against domestic violence was also significantly associated with episodes of ARI (OR = 0.88, 95% CI 0.82 to 0.93) and diarrhoea (OR = 0.89, 95% CI 0.84 to 0.93) in children. Access to sanitation facilities was associated with lower risk of ARI (OR = 0.55, 95% CI 0.45 to 0.68), diarrhoea (OR = 0.83, 95% CI 0.71 to 0.96), and stunting (OR = 0.64, 95% CI 0.49 to 0.86). Investments targeting synergies in integrated energy and water, sanitation and hygiene, and women empowerment programmes are likely to contribute to the reduction of the burden from early childhood illnesses. Research and development actions in LMICs should address and include multi-sector synergies.
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Thomas, Mervin Joe, Mithun M. Sanjeev, A. P. Sudheer, and Joy M.L. "Comparative study of various machine learning algorithms and Denavit–Hartenberg approach for the inverse kinematic solutions in a 3-PPSS parallel manipulator." Industrial Robot: the international journal of robotics research and application 47, no. 5 (June 13, 2020): 683–95. http://dx.doi.org/10.1108/ir-11-2019-0233.
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Purpose This paper aims to use different machine learning (ML) algorithms for the prediction of inverse kinematic solutions in parallel manipulators (PMs) to overcome the computational difficulties and approximations involved with the analytical methods. The results obtained from the ML algorithms and the Denavit–Hartenberg (DH) approach are compared with the experimental results to evaluate their performances. The study is performed on a novel 6-degree of freedom (DoF) PM that offers precise motions with a large workspace for the end effector. Design/methodology/approach The kinematic model for the proposed 3-PPSS PM is obtained using the modified DH approach and its inverse kinematic solutions are determined using the Levenberg–Marquardt algorithm. Various prediction algorithms such as the multiple linear regression, multi-variate polynomial regression, support vector, decision tree, random forest regression and multi-layer perceptron networks are applied to predict the inverse kinematic solutions for the manipulator. The data set required to train the network is generated experimentally by recording the poses of the end effector for different instantaneous positions of the slider using the concept of ArUco markers. Findings This paper fully demonstrates the possibility to use artificial intelligence for the prediction of inverse kinematic solutions especially for complex geometries. Originality/value As the analytical models derived from the geometrical method, Screw theory or numerical techniques involve approximations and needs more computational power, it is not advisable for real-time control of the manipulator. In addition, the data set obtained from the derived inverse kinematic equations to train the network may lead to inaccuracies in the predicted results. This error may generate significant deviations in the end-effector position from the desired position. The present work attempts to resolve this issue by proposing a camera-based approach that uses ArUco library and ML algorithms to create the data set experimentally and predict the inverse kinematic solutions accurately.
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Shin, Jong Gye, and Jang Hyun Lee. "Nondimensionalized Relationship Between Heating Conditions and Residual Deformations in the Line Heating Process." Journal of Ship Research 46, no. 04 (December 1, 2002): 229–38. http://dx.doi.org/10.5957/jsr.2002.46.4.229.
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The nature of a line heating process is very complex since a variety of factors affects the amount of residual deformation. A linear relationship between input and output parameters, which has been derived from simple experiments, is successively used. This relationship, however, is very limited since it does not include important parameters and the line heating process is not linear. A rigorous approach is presented here in an attempt to obtain new relationships between input parameters and final deformations during the line heating process. The residual deformations are investigated by using a thermal elastic-plastic analysis based on finite-element analysis (FEA). Experiments are carried out in order to verify the validity of the FEA results. The nondimensional input parameters are then determined by the dimensional analysis. The relationships between the input parameters and the residual deformations are developed by using multi-variate analysis (MVA) and multiple-regression methods. The final form of the relationships is nonlinear and includes relevant information.
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Fayyoumi, Ebaa, and Sahar Idwan. "Factors impacting Jordanian women in computing case study: Hashemite University." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (November 1, 2021): 1130. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp1130-1140.
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<p>We consider pursuing the Jordanian women their graduate studies in Information Technology disciplines as an indicator of socio-economic development and empowering women in Jordan. This paper presents the first study of multi-variate stereotypes that shape the problem by addressing the following factors: travel abroad, family matters, skills and experience, traditional and cultural differences, scholarship opportunities, financial matters, and language complications. These factors were extensively studied, and their effects were estimated by applying the linear-regression, one-way ANOVA, and Scheffe tests. The scholarship opportunity (R<sup>2</sup> = 0:354), travel abroad (R<sup>2</sup> = 0:281), and financial matters (R<sup>2</sup> = 0:226) were the most influential factors on Jordanian women’s decision in pursuing their graduate studies. On the other hand, skills and experience stereotype (R<sup>2</sup> = 0:076) has the least influence.</p>
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Gertz, Autumn, Benjamin Rader, Kara Sewalk, and John S. Brownstein. "Emerging Socioeconomic Disparities in COVID-19 Vaccine Second-Dose Completion Rates in the United States." Vaccines 10, no. 1 (January 14, 2022): 121. http://dx.doi.org/10.3390/vaccines10010121.
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Although COVID-19 vaccination plans acknowledge a need for equity, disparities in two-dose vaccine initiation have been observed in the United States. We aim to assess if disparity patterns are emerging in COVID-19 vaccination completion. We gathered (n = 843,985) responses between February and November 2021 from a web survey. Individuals self-reported demographics and COVID-19 vaccination status. Dose initiation and completion rates were calculated incorporating survey weights. A multi-variate logistic regression assessed the association between income and completing vaccination, accounting for other demographics. Overall, 57.4% initiated COVID-19 vaccination, with 84.5% completing vaccination. Initiation varied by income, and we observed disparities in completion by occupation, race, age, and insurance. Accounting for demographics, higher incomes are more likely to complete vaccination than lower incomes. We observe disparities in completion across annual income. Differences in COVID-19 vaccination completion may lead to two tiers of protection in the population, with certain sub-groups being better protected from future infection.
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Bórawski, Piotr, Adam Pawlewicz, Andrzej Parzonko, Jayson, K. Harper, and Lisa Holden. "Factors Shaping Cow’s Milk Production in the EU." Sustainability 12, no. 1 (January 6, 2020): 420. http://dx.doi.org/10.3390/su12010420.
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The milk market in the European Union (EU) is adjusting rapidly to the removal of dairy quotas. The most important changes include increased milk yield per cow, increased total milk production, decreased number of cows, and the decreased milk consumption. The main aim of the paper is to examine the milk production changes in the EU. We investigated the dynamics of changes in farm milk production during the period from 1998–2017 in the EU. Moreover, we investigated the impact of the removal of quotas on the production of milk on farms in the EU countries for the period from 2015–2017. Milk production in the EU increased from 151 million tons in 1998 to 165 million tons in 2017 (a 10% increase). A multi-variate regression model was to test which explanatory variables have an impact on milk production in the EU. The most important factors were a gross domestic product, final household consumption expenditure (current prices, million euro), and population (number).
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Omotayo, Osibanjo Adewale, Abolaji Joachim Abiodun, and Akinrole Olumuyiwa Fadugba. "Executives Perception of the Impact of Flexitime on Organizational Performance." International Journal of Applied Behavioral Economics 1, no. 3 (July 2012): 16–27. http://dx.doi.org/10.4018/ijabe.2012070102.
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The paper investigates the perception of Nigerian executives on the impact of flexitime on organizational performance. Effort is made to explore the attitudinal disposition of employees towards flexitime and how gender affects employee satisfaction with flexitime. The study, based on administered questionnaires as the main medium for data collection from managers in private sector of the Nigerian economy, utilizes correlations and multi-variate regression analysis to determine variables that significantly contribute to manager’s satisfaction with flexible work arrangement. The study finds that marital status and gender exert significant negative impact on level of satisfaction with flexitime. In addition, gender, marital status and motivation capabilities of flexitime were found to be a significant determinant of satisfaction with flexitime. Therefore, given the cultural context of the study it does appear that organizations might found it profitable to adopt a flexitime policy so as to relieve their employees some family or domestic burden with the attendant motivation benefit that increases employee performances.
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Gannon, P. O., I. H. Koumakpayi, C. Le Page, M. Alam Fahmy, A. Mes-Masson, and F. Saad. "High KI67 expression is associated, in a multi-variate model, with lower risk of biochemical recurrence in prostate cancer patients following radical prostatectomy." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 21112. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.21112.
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21112 Background: The prediction of disease progression in prostate cancer patients following radical prostatectomy (RP) remains problematic. The use of molecular markers could offer a better stratification of patients more at risk of progression. As such, we recently reported that nuclear ErbB3 was associated with Gleason score and hormone-refractory status. The objective of this study was to evaluate whether ErbB3 could predict overall biochemical recurrence (BCR). In addition, we evaluated if three nuclear markers known to be associated with progression (Cyclin D1, Ki67 and androgen receptor) were more significant predictors of BCR than ErbB3 alone or in combination. Methods: Using immunohistochemistry, we analyzed a tissue microarray containing 373 cores from 63 RP specimens. No patient had received hormone therapy prior to surgery and prior to BCR. The quantitative analysis of nuclear staining was measured by two independent observers (ErbB3, Cyclin D1 and AR) or with the ImagePro Plus softwareTM (Ki67). Marker expressions were categorized as either positive or negative according to the median expression. Results: Of the four markers analyzed, Ki67 alone was the strongest predictor of overall BCR. In a multi-variate Cox regression model (backward conditional), while controlling for the pre-operative PSA, Gleason score and lymph node invasion at time of surgery, KI67 was found to be an independent predictor of BCR with a KI67+ patients having lower risk of BCR (HR=-2.51, p=0.015, CI 95%: 1.19–5.29). We then analyzed if different marker combinations could predict BCR. Patients positive for nuclear AR or AR+/Cyclin D1+ double positive were found to have lower risk of BCR (Kaplan-Meier, p=0.047 and p=0.026, respectively). However, in the multi- variate model, the combinations of Cyclin D1+/AR+ (HR=-2.28, p=0.053, CI 95%: 0.94–5.49), ErbB3+/Ki67+ (HR=-2.43, p=0.034, CI 95%: 1.07- 5.52) and AR+/Ki67+ (HR=-2.32, p=0.049, CI 95%: 1.01–5.35) could not improve on the predictive value of KI67 alone. Conclusions: The major new finding of the study is that patients positive for KI67 expression were at a lower risk of developing BCR, which contrast previously published results, and warrants further investigations. No significant financial relationships to disclose.
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Dumitra, Sinziana, Rebecca A. Nelson, Vasilena Zheleva, Mustafa Raoof, and Lily L. Lai. "Minimally invasive surgery to improve time to chemotherapy in colon cancer: Compliance to the American College of Surgeons Commission on Cancer quality metrics." Journal of Clinical Oncology 35, no. 4_suppl (February 1, 2017): 712. http://dx.doi.org/10.1200/jco.2017.35.4_suppl.712.
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712 Background: One of the American College of Surgeons Commission on Cancer (ACS CoC) quality measures in colon cancer is receipt of chemotherapy (CT) in Stage III disease within 120 days of diagnosis. Minimally invasive surgery (MIS) has been associated with faster recovery times. The aim of this study is to assess whether MIS improves compliance to this metric. Methods: Stage III colon cancer patients 80 years old and younger from 2010 to 2012 were identified in the National Cancer Database. Demographic, tumor and treatment characteristics were evaluated including receipt of CT and surgical approach. Uni- and multi-variate logistic regression was used to assess factors associated with CT compliance. Results: Of the 19,963 patients identified, 14,901(74.6%) were compliant while 5,062 (25.3%) were not. Of the patients who were non-compliant, 956 (4.8%) received CT after 120 days. Surgical approach was significantly different between CT compliant and non-compliant groups (MIS 28% vs 32%,p < 0.000). Uni- and multi-variate analyses identified MIS as a significant factor associated with improved compliance to CT with an OR of 1.31 (95%CI 1.22-1.41). Other factors associated with CT compliance were nodal and tumor stage and treatment in an academic program. Non-compliance was associated with age 50-64 (OR 0.76; 95%CI 0.68-0.86), age 65-79 (OR 0.49; 95%CI 0.43-0.56) and increased co-morbidities (OR 0.60; 95%CI 0.53-0.67). Lack of insurance (OR 0.69; 95%CI 0.58-0.81) or Medicaid (OR 0.54; 95%CI 0.47-0.62) and Medicare (OR 0.69; 95%CI 0.63-0.77) as well as distance to hospital of more than 44 miles were also associated with non-compliance to CT (OR 0.86; 95%CI 0.76-0.97). Conclusions: This is the first study to demonstrate that MIS for Stage III colon cancer improves compliance to receipt of CT within the 120 days. Given the potential survival benefits as a result of adherence to ACS CoC cancer care quality metrics, MIS may benefit patients not only in faster return to recovery but also in improved cancer outcomes.
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Gebremickael, Abinet, Tsegaye Yohanes, Nega Chufamo, and Belay Boda. "Assessment of Knowledge and Associated Factors towards Congenital Anomalies among Pregnant Women Visiting Antenatal Care Clinic at Arba Minch General Hospital, Gamo Zone, Southern Ethiopia." OMO International Journal of Sciences 4, no. 1 (June 25, 2021): 64–75. http://dx.doi.org/10.59122/135a4d3.
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Knowledge of Congenital anomalies (CAs) among the public, especially reproductive age women have a significant role in reducing the incidence. However, there is a dearth of studies conducted on this issue in our country. This study was aimed to assess the pregnant women’s knowledge of CAs at the antenatal care clinic of Arba Minch General Hospital. Institution based cross-sectional study was conducted between December 2017 and September 2018. Semi-structured questionnaire was used to collect the data. Data were cleaned, entered and analysed by using SPSS version- 20 software packages. Besides descriptive statistics, Bi-variate and Multi-variate logistic regression analyses were done to explore the predictors of women’s level of knowledgetoward CAs. P-value < 0.05 was considered as statistically significant. A total of 392 pregnant women had participated in the present study. From total respondents, only 11.0% of the pregnant women have known that many of CAs are of genetic origin, and a significant proportion of the women had believed that CA is a disease acquired by pregnant women (39.0%), and it occurs in a baby due to the sin of families (48.5%). Only 189 (48.2%) women had adequate overall knowledge about CAs. The participants had good knowledge of the risk factors than their specific knowledge of CAs. The level of education and occupation were significantly associated (P<0.05) with the women’s overall knowledge of CAs. In conclusion, the women’s knowledge of CAs in this study was found less. Appropriate strategies should be designed and implemented to improve women’s knowledge of congenital anomalies.