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In the current geopolitical configuration, generated by socio-economic and political-military events, with an impact on economic development, the public debt dynamics take on new dimensions. Public debt, as an indispensable concept for improving development strategies in an economic and geopolitical context, is influenced by the macroeconomic indicators’ variation, but also by the internal and external factors’ impact, which generate instability in a dynamic of unpredictable phenomena. Therefore, the study indicates proposals, based on multivariate regression, to capture the most sensitive variations of macroeconomic indicators on sustainable development at the European Union countries level. Moreover, the current European level configuration requires a detailed capture of the connection between public debt and economic growth, by using a VAR model (public debt, a concept that takes on new values in the current context, and economic growth, as the central pillar of sustainable development, analyzed from the perspective of the studied indicators fluctuation). Finally, I analyzed the public debt dynamics for the next period (2000 – 2022) to create a realistic picture, using the ARMA model. The results confirm the direct impact of each event on economic growth and development, and the limitation and elimination of negative effects vary depending on how it is managed and prevented. Also, the link between the economic growth and public debt is confirmed and, at the same time, it constitutes a benchmark that captures the importance of the decisions of the responsible factors regarding the financial-monetary instruments implemented or what is requested to be adopted. In other words, forecasting the public debt dynamics gives a certain stability, but also a strategic vision, offering viable solutions to support sustainable development efforts at European states level.

One measure that can be used to see the level of farmer welfare is the farmer exchange rate (NTP), which is a comparative calculation between the price index received by farmers (IJ) and the price index paid by farmers (IB), expressed as a percentage. In reality, NTP cannot explain the actual welfare situation of farmers because the ratio value has the potential to produce biased values. Another alternative that can be used to look at farmer welfare with less potential bias is to look at the difference between the sales index and the farmer purchasing index (ID). ID data forecasting can be a reference for developing and optimizing things that need to be improved in the agricultural sector. Despite the fact that a number of external factors, such as variations in the weather throughout the year, had a significant impact on the ID value, previous research used the ARIMA model to forecast without taking exogenous factors into account. Therefore, the goal of this research is to identify the optimal ARIMAX regression model for achieving accurate forecasting results with minimal error values. This research was carried out with limitations using data from the Central Statistics Agency and the Meteorological, Climatological, and Geophysical Agency in Central Java from 2008 to 2023. The first method in this research is to prepare the data, which involved collecting secondary data such as IJ and IB along with climate data such as rainfall, duration of sunlight, air pressure, wind speed, and rice prices. Next, calculate the difference between IJ and IB to determine the ID value. Then, verify the ID data's stationarity and perform AR and MA calculations. After determining the AR and MA values, construct an ARIMAX model that incorporates external factors, search for the optimal model, and utilize the optimal model to make future predictions. The results show that the accuracy of the ARIMAX model (1,1,0) has a better value than the accuracy of the ARIMA model (1,1,0), and the results obtained in this study are better than previous studies. The authors hope that the findings of this research will serve as a benchmark for the forecasting analysis of time series data in the agricultural sector, providing the local government with a foundation for policy decisions.

The edible insect sector has, in recent times, gained significant prominence and attention. Particularly, the government of Kenya has made remarkable steps to achieve a potentially large and valuable edible insect market, with a significant milestone being the passing of regulations on edible insects as a new source of proteins. However, research on the edible insect marketing environment is still indistinct. Therefore, the study sought to evaluate the determinants of the market outlets preferred or used by smallholder farmers and traders of domesticated and field-collected edible insects (including cricket, bees, winged termites, lake flies, and dung beetle) in Siaya and Vihiga counties. This cross-sectional study was done among 188 edible insects’ farmers and traders. Purposive sampling identified the study area, while snowball sampling reached the study participants. Data was collected using structured questionnaires and analyzed using multinomial logit regression to assess independent-dependent variable relationships, yielding marginal effects. Study findings showed that at 95% confidence interval, the yield was significant to the three outlets used by the farmers. Gender was significant to both institution and open-air markets outlets but insignificant to selling at the farm gate. Age, education level, and experience in marketing were insignificant to all the three market outlets. Marketing training was significant to the institution and open-air markets. The study also showed that farmers and traders had limited choices to sell their edible insect produce, which was majorly affected by yield and age variables. In view of these findings, enhancing edible insect marketing and training through initiatives that would increase production among farmers and breaking the attitudes toward open-air marketing among male farmers is pivotal to the thriving of the novel food enterprise towards achieving food security in the region.

Introduction Border bases are a generalization of Gröbner bases for zero-dimensional ideals which have attracted the interest of many researchers recently. More precisely, border bases provide a new method to find a structurally stable monomial basis for the residue class ring of a polynomial ideal and this yields a special generating set for the ideal possessing many nice properties. Given a finite set of points, finding the set of all polynomials vanishing on it (so-called either ideal of points or vanishing ideal of the set of points) has numerous applications in several fields in Mathematics and other sciences. In 1982, Buchberger and Möller proposed an algorithm to compute a Gröbner basis for an ideal of points. This algorithm proceeds by performing Gaussian elimination on a generalized Vandermonde matrix. In 2006, Farr and Gao presented an incremental algorithm to compute a Gröbner basis for an ideal of points. The main goal of their paper is to calculate a Gröbner basis for the vanishing ideal of any finite set of points under any monomial ordering, and for points with nontrivial multiplicities they adapt their algorithm to compute the vanishing ideal via Taylor expansions. The method of border bases is a beneficial tool to obtain a set of polynomial models identified by experimental design and regression. The utilization of Gröbner bases theory in experimental design was introduced by Pistone and Wynn. However, using Gröbner bases we cannot find all possible models which form structure of an order ideal for an experiment. For example, if we consider the design {(-1,1),(1,1),(0,0),(1,0),(0,-1)}, the model {} cannot be computed by Gröbner bases method. This fact is expected this method relies on monomial orderings. Material and methods In this paper, we first present the Buchberger-Möller and Farr-Gao algorithms and then by applying these algorithms, we describe an algorithm which computes a border basis for the ideal of points corresponding to the input set of points with nontrivial multiplicity. In addition, we focus on presenting different models related to an experiment by using the concept of monomial bases for the residue class ring of a polynomial ideal. Results and discussion As we mentioned earlier, Buchberger-Möller algorithm is an efficient algorithm to compute a Gröbner basis for an ideal of points. We describe a simpler presentation of this algorithm in which we use the function NormalForm which receives as input a linear polynomial p and a Grbner basis G = { , . . . , } of linear polynomials in , . . . , and returns f and q=[, . . . , ] where f is the remainder of the division of p by G and p=+· · ·++r. Furthermore, we compare the efficiency of this algorithm with the function VanishingIdeal of Maple. Given a finite set of points, we consider the case in which some points in the set have nontrivial multiplicity. Based on the Farr-Gao algorithm, we prepare an algorithm that computes a border basis for the vanishing ideal of the finite set of points by using Taylor expansions. Suppose that n is the number of factors in an experiment. An experimental design is a finite set of points. The set of all polynomials vanishing at the design is called a design ideal. Regression analysis is a useful statistical process for the investigation of relationships between a response (or dependent) variable and one or more predictor (or independent) variables. When there is more than one predictor variable in a regression model, the model is a multiple linear regression model which we can call polynomial model. Suppose a random sample of size n is given (then we have exactly n data points are observed from (Y,X). The expession error? is the model for multiple linear regression where 's are called slopes or regression coefficients. Also, representing the merged effects of the predictor variables on the response variable is called interaction effect. By using multiple linear regression, we can analyze models containing interaction effects. For example, let us consider the following model +error. By substituting and , we have a multiple linear regression as follows +error. In addition, multiple R-squared or R2 is a statistical measure that states the square of the relationship between the predicted response value and response value. It should be noted that multiple R-squared is always any value between 0 and 1, where a value closer to 1 informing that a greater proportion of variance is computed for the model. Statistically, a high multiple R-squared shows a well-fitting regression model. Also in multiple regression, tolerance is used as an indicator of multicollinearity. Tolerance may be said to be the opposite of the coefficient of determination and is obtained as . All other things equal, researchers desire higher levels of tolerance, as low levels of tolerance are known to affect adversely the results associated with a multiple regression analysis. The smaller the tolerance of a variable, the more redundant is its contribution to the regression (i.e., it is redundant with the contribution of other independent variables). In the regression equation, if the tolerance of any of the variables is equal to zero (or very close to zero), the regression equation cannot be evaluated (the matrix is said to be ill-conditioned in this case, and it cannot be inverted). Conclusion The following conclusions were drawn from this research. We present a simpler variant of Buchberger- Möller algorithm (which seems to be easier for the implementation issue) for computing a border basis for an ideal of points. We present an algorithm that incrementally computes a border basis for the vanishing ideal of any finite set of points in which some points have multiplicity. We provide good statistical polynomial models which are more suitable for practical applications due to the stability of border bases models compared with Gröbner bases models../files/site1/files/64/6.pdf

The animal–vehicle collisions (AVC) are serious risk of danger for animals, motorists and passengers onthe roads and there are various methods of study of AVCs on the highways. Methodology. The study reviewed of thearticle includes theoretical and empirical result about of identification location AVC on the such directions of research:1.)The speed limit and traffic volume; 2.) Temporal patterns; 3.) Spatial patterns; 4.) Effective mesh size; 5.) Thecorrelation and regression analysis; 6.) Mitigation measure. Results. For example we found out that more study shownthe next directions of research: spatial patterns, the correlation and regression analysis and mitigation measure. As aresult the analysis has shown that variations in landscape scale habitat composition of area were correlated withvariations in wildlife crossing rates at the landscape scale. The hot spots of AVC generally were associated withtopographic features that directed animals towards highways, the presence of habitat adjacent to highways, or foodresources that attracted animals. Practical value. There are correlations between the number and spatial distribution ofAVC and recorded wildlife measuring the distributions and populations of wildlife species. We found out some theunexplored the directions for our research in this field.

Background: This study aimed to screen a wide range of emotional and behavioural variables emerging during the first COVID-19 pandemic-lockdown in a sample of parents and children, residents in the southern part of Italy, and explore which variables could predict children’s wellbeing. We hypothesised that difficulties in adapting routines to pandemic restrictions, parents’ emotional wellbeing, and attitude towards the pandemic could influence the children’s behavioural attitudes. Methods: 221 parents completed the survey and gave information about 246 children. Ad hoc questionnaires were created and then exploratory reduced in factors. Strengths and Difficulties Questionnaire (SDQ) for parents assessed positive and negative behavioural attitudes in children. Depression Anxiety Stress Scale (Italian DASS-21) scored depression, anxiety and stress in parents. Results: Children presented higher emotional distress (Mean difference (Mdiff)=0.6, 95% C.I. 0.2, 0.9, p=0.013) and better prosocial behaviour (Mdiff=0.5, 95% C.I. 0.1, 0.9, p=0.011) than the Italian normative sample. Parents were more depressed than expected in the general population (Mdiff=1.0, 95% C.I. 0.3, 1.6, p=0.005). Having developed a morbid attachment to an adult (B=0.37, 95% CI 0.05, 0.69, p=0.024), a higher parental depression (B=0.1, 95% CI 0.02, 0.18, p=0.014), and children’s suffering from nightmares (B=0.35, 95% CI 0.03, 0.67, p=0.032) explained the 31.9% of the total variance in children’s emotional distress. Children’s anxiety was related to parents’ fear of the pandemic effects (r=0.32, p=0.001) and avoiding communicative approach (r=0.24, p=0.011). Conclusion: The first lockdown determined emotional distress and regressive mechanisms in children in the contest of higher parental discomfort, fear of the infection and avoidant communication. Following parents’ indications, it could be helpful to provide families with informative and age-appropriate support.

Cost estimation for any construction projects at the early stage is a significant attempt, which has a main role in the success of the construction projects, because estimation at the early stage before design is very desired for decision maker to decide whether to start or not according to available budget. All parties involved in construction work as (owners, engineers, contractors and others) pay a great attention to this stage where limited information is available with no drawings or designs even no specifications. The objective of this research is to derive a model relating the cost of project as awarded with several independent variables (parameters) which are; site area, building area, duration(days), earthwork, area of doors and windows, number of floors, number of columns, distance from the city center(km), by utilizing linear regression techniques. The research methodology consists of data collection of 52 school building projects from public sectors which carried out between 2007 and 2014 in Sulaimani governorate. The models have been developed by applying Excel program and Minitab 19 software, then the models have been summarized and best one has been selected. Also, several statistical procedures have been conducted such as R2, R2- adjusted and two sample t-test were used to select more reliable equation. To find out the accuracy of each developed model, the author calculated mean absolute percentage error (MAPE). The range was between 25.3% and 46%. The R2 was between 0.87 and 0.977 and also the ρ-value from two sample t- test was between 0.891 and 0.991.

Cost estimation for any construction projects at the early stage is a significant attempt, which has a main role in the success of the construction projects, because estimation at the early stage before design is very desired for decision maker to decide whether to start or not according to available budget. All parties involved in construction work as (owners, engineers, contractors and others) pay a great attention to this stage where limited information is available with no drawings or designs even no specifications. The objective of this research is to derive a model relating the cost of project as awarded with several independent variables (parameters) which are; site area, building area, duration(days), earthwork, area of doors and windows, number of floors, number of columns, distance from the city center(km), by utilizing linear regression techniques. The research methodology consists of data collection of 52 school building projects from public sectors which carried out between 2007 and 2014 in Sulaimani governorate. The models have been developed by applying Excel program and Minitab 19 software, then the models have been summarized and best one has been selected. Also, several statistical procedures have been conducted such as R2, R2- adjusted and two sample t-test were used to select more reliable equation. To find out the accuracy of each developed model, the author calculated mean absolute percentage error (MAPE). The range was between 25.3% and 46%. The R2 was between 0.87 and 0.977 and also the ρ-value from two sample t- test was between 0.891 and 0.991.

The forage palm is one of the main forages of ruminants in semiarid regions. Measurements of leaf area are required in agronomic studies because they are one of the main parameters used to evaluate plant growth. The objective of this study was to validate and define the best models for estimating the area and weight of Giant Sweet clone (Nopalea cochenillifera) forage cladodes in a non-destructive way based on the linear dimensions of length, width and thickness. There were 432 randomly measured cladodes at 550 days after planting. The length, width and thickness of each cladode were measured using a digital calliper. The cladodes were weighed individually. The cladode area was calculated by the gravimetric method. The power regression model was the most efficient method to explain the cladode area as a function of the product of length by width, while the gamma model was the most efficient method to explain the weight of cladodes as a function of the product of length by width and thickness. The power model, and gamma model, = 0.536T + 0.028LW, were used to determine the area and weight of Nopalea cochenillifera Giant Sweet clone cladodes, respectively, based on the values of linear dimensions measured independently of the order of the cladode.

Despite the potentials of dual reinforced particulate polymer composites to produce outstanding composite with enhanced wear properties, scholars have devoted insignificant attention to them. This paper introduces five diverse epoxy composites prepared in dual reinforcement blends. Using a mixed design L16 orthogonal array, Taguchi’s parametric optimization was conducted with some ratios between the wear parameters as a novel way of revealing the influence of their interrelatedness in the optimization process while the statistical modelling of the wear responses was pursued. Analysis of variance was also conducted as well as regression analysis. With experimental tests on the DIN abrasion tester, the (10OPP,15CSP)% composite obtained an optimal parametric setting of A1B2-3C3D1E4. The (10PKSP,15CSP), (10PSp,15ESP), (10OPP,15PSP) and (5PKSP,20ESP)% composites obtained optimal parameter settings of A3B2C3D3E2, A3B1-3,C3D1E4, A3B2C2D1E4 and A3B2C4D3E3, respectively. The correlation plots between the experimental and predicted values of the wear process and determination coefficient indicate a high level of accuracy of the models in predicting the wear behaviour of the composites.

Total Variation (TV) is an effective method of removing noise in digital image processing while preserving edges. The scaling or regularization parameter in the TV process defines the amount of denoising, with a value of zero giving a result equivalent to the input signal. The discrepancy principle is a classical method for regularization parameter selection whereby data is fit to a specified tolerance. The tolerance is often identified based on the fact that the least squares data fit is known to follow a χ2 distribution. However, this approach fails when the number of parameters is greater than or equal to the number of data. Typically, heuristics are employed to identify the tolerance in the discrepancy principle and this leads to oversmoothing. In this work we identify a χ2 test for TV regularization parameter selection assuming the blurring matrix is full rank. In particular, we prove that the degrees of freedom in the TV regularized residual is the number of data and this is used to identify the appropriate tolerance. The importance of this work lies in the fact that the χ2 test introduced here for TV automates the choice of regularization parameter selection and can straightforwardly be incorporated into any TV algorithm. Results are given for three test images and compared to results using the discrepancy principle and MAP estimates. [ABSTRACT FROM AUTHOR]

Many moving-camera video processing and analysis tasks require accurate estimation of homography across frames. Estimating homography between non-adjacent frames can be very challenging when their camera view angles show large difference. In this paper, we propose a new deep-learning based method for homography estimation along videos by exploiting temporal dynamics across frames. More specifically, we develop a recurrent convolutional regression network consisting of convolutional neural network (CNN) and recurrent neural network (RNN) with long short-term memory (LSTM) cells, followed by a regression layer for estimating the parameters of homography. In the experiments, we evaluate the proposed method on both the synthesized and real-world short videos. The experimental results verify that the proposed method can estimate the homographies along short videos better than several existing methods. [ABSTRACT FROM AUTHOR]

Food fraud can cause damage to consumer health and affect their confidence, destroy brands and generate large economic losses in the industry. Food authenticity allows to identify if food composition, geographical origin, genetic variety and farming system corresponds to what has been declared on the label. Although there are currently standardized methods to identify certain adulterants, the complexity of the food, the complexity of the supply chain and the appearance of new adulterants require the continuous development of analytical techniques to detect food fraud. NIR and Hyperspectral imaging (HSI) in tandem with chemometrics are non-destructive, non-invasive and accurate techniques for food authentication. This review focuses on NIR and HIS approaches to food authentication, including adulteration by substitution, geographical origin and farming system. In this context, the advances in NIR and HSI approaches reported since 2014 are discussed regarding their potential use in food authentication. Both techniques have shown to have efficiency, precision and selectivity to detect adulterants and identify geographic origin, genetic variety and farming system. Portability and remote access are shown as the next step for the industrialization of NIR and HSI devices.

To investigate the relationship between slope, aspect and elevation and quantitative characteristics of plant species, totally 125 sampling plots were selected in a block systematic design. In each sampling, plot presence, number, height, diameter at breast height (for trees) or root collar (for shrubs) and crown diameters were measured and recorded. Also, we recorded vegetation type according to the portion of species in the plot. Correlation analysis was conducted according to the scale of variables using Pierson, Spearman, Phi and Cramer and Eta coefficients. Results found that elevation is the most important factor affecting presence and density and also type in the study area. Altogether, Atlas Pistach (P. atlantica F. & M) and Wild Almond (A. scoparia Spach. ) in low elevations and Maple (A. cinerascens L.), Marsh-arrow-grass (A. lycioedes Spach.) and mountain almond (A. elaefnifolia Spach.) in higher elevations had better conditions. Slope had a negative correlation with presence of Wild Almond and Send Cherry (Ephedra spp), and positive correlation with the presence of other species. Also, Wild Pistach trees present in southern aspects showed more basal area and volume in these aspects. Results of this research can be used in the detection of suitable points for development of plant species. Regression analysis showed that frequency of almond and Send Cherry and average height of almond and wild Pistach were predictable according to elevation and slope. Of course, coefficient of determination was low in all cases.

Chrzan Elżbieta, Chrzan Paweł, Zukow Walery, Napierała Marek, Eksterowicz Jerzy. Rola pielęgniarki w profilaktyce i leczeniu odleżyn = The role of the nurse in the prevention and treatment of pressure ulcers. Journal of Education, Health and Sport. 2016;6(12):123-170. eISSN 2391-8306. DOI http://dx.doi.org/10.5281/zenodo.192461 http://ojs.ukw.edu.pl/index.php/johs/article/view/4041 The journal has had 7 points in Ministry of Science and Higher Education parametric evaluation. Part B item 755 (23.12.2015). 755 Journal of Education, Health and Sport eISSN 2391-8306 7 © The Author (s) 2016; This article is published with open access at Licensee Open Journal Systems of Kazimierz Wielki University in Bydgoszcz, Poland Open Access. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. This is an open access article licensed under the terms of the Creative Commons Attribution Non Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited. This is an open access article licensed under the terms of the Creative Commons Attribution Non Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited. The authors declare that there is no conflict of interests regarding the publication of this paper. Received: 02.11.2016. Revised 22.11.2016. Accepted: 03.12.2016. Rola pielęgniarki w profilaktyce i leczeniu odleżyn The role of the nurse in the prevention and treatment of pressure ulcers Elżbieta Chrzan1, Paweł Chrzan1, Walery Zukow2, Marek Napierała2, Jerzy Eksterowicz2 1Wydział Nauk o Zdrowiu, Radomska Szkoła Wyższa, Radom, Polska 2Wydział Kultury Fizycznej, Zdrowia i Turystyki, Uniwersytet Kazimierza Wielkiego, Bydgoszcz, Polska Słowa kluczowe: rola pielęgniarki, odleżyny, regresja. Key words: the role of nurses, bed sores, regression. Streszczenie Profilaktyka i leczenie odleżyn stanowią niezmiernie istotny problem w opiece pielęgniarskiej. Odleżyny są powodem dodatkowych komplikacji zdrowotnych pacjenta, są źródłem bólu i cierpienia, a także często kłopotów finansowych. Celem niniejszej pracy jest rozpoznanie roli, jaką pełni pielęgniarka w zapobieganiu i leczeniu odleżyn i odpowiedź na postawione pytania problemowe: • Problem główny: Jakie znaczenie ma opieka pielęgniarska w profilaktyce i leczeniu odleżyn? • Problemy szczegółowe: • Jaki zakres czynności prewencyjnych może podjąć pielęgniarka w zakresie zapobiegania odleżynom? • Jaką rolę pełni pielęgniarka w leczeniu odleżyn? • Czy płeć, wiek i rozpoznanie mają znaczenie w leczeniu odleżyn? • Czy długość pobytu pacjenta w szpitalu ma wpływ na postępy w leczeniu odleżyn? • Co wnosi standaryzacja profilaktyki przeciwodleżynowej w opiece stacjonarnej? W odpowiedzi na powyższe pytania przyjęłam następujące hipotezy: • Hipoteza główna: Pielęgniarka pełni wiodącą rolę w profilaktyce przeciw odleżynowej i leczeniu odleżyn. • Hipotezy szczegółowe: • Rzetelna na wysokim poziomie opieka pielęgniarska skutecznie zapobiega wystąpieniu u chorych odleżyn. • Pielęgniarka ma znaczący wpływ na efekty terapeutyczne w procesie leczenia odleżyn. • Wiek, płeć i rozpoznanie u pacjenta są zmiennymi, mającymi istotny wpływ na leczenie odleżyn. • Długość hospitalizacji, rozumiana jako czas specjalistycznego, kompleksowego, indywidualnego i wielostronnego leczenia odleżyn, ma korzystny wpływ na postępy w leczeniu odleżyn. • Standaryzacja opieki pielęgniarskiej w zakresie profilaktyki i leczenia odleżyn podnosi jakość świadczonych usług medycznych. W pracy zastosowano 3 metody: metodę analizy literatury medycznej oraz metodę analizy dokumentów medycznych, tj. zbiorczej karty profilaktyki odleżynowej, prowadzonej na Oddziale Medycyny Paliatywnej w Szpitalu Specjalistycznym w Puławach w roku 2010. W analizie statystycznej ujęto 124 pacjentów zagrożonych odleżynami. Główną metoda badawcza zastosowano Metodę Wnioskowania statystycznego, model regresji liniowej wielokrotnej. W podsumowaniu końcowym dokonano weryfikacji hipotez. Wszystkie hipotezy są prawdziwe z wyjątkiem hipotezy 3. Wiek i płeć są zmiennymi, które nie mają wpływu na leczenie odleżyn, zaś zmienna rozpoznanie jest statystycznie istotna. We wnioskach końcowych ustalono, że: • Pielęgniarki doskonale realizują proces pielęgnowania w zakresie profilaktyki i leczenia odleżyn. • Wprowadzenie standardów profilaktyki przeciwodleżynowej przynosi znaczną poprawę jakości świadczonych usług medycznych. • Monitorowanie profilaktyki przeciwodleżynowej w postaci rzetelnie prowadzonej dokumentacji zabezpiecza pielęgniarki przed skutkami roszczeń pacjentów. • Ogólnie przyjęte zasady leczenia odleżyn przynoszą coraz lepsze wyniki terapeutyczne. • Programy profilaktyki i leczenia odleżyn powinny być wdrażane we wszystkich szpitalach, zakładach opiekuńczo-leczniczych i domach opieki. • Konieczne jest wprowadzenie kompleksowej edukacji przeciwodleżynowej w środowisku rodzinnym pacjenta, gdyż większość odleżyn powstaje w domu chorego. • Świadomość ponoszonych kosztów leczenia odleżyn powinna skłonić menadżerów służby zdrowia do zwiększenia środków finansowych na prewencję odleżyn i ich leczenie według najnowszych osiągnięć nauki. • Wyraźnie brakuje w literaturze naukowej pielęgniarskich prac empirycznych z zakresu problematyki i leczenia odleżyn, które pozwoliłyby na weryfikację skuteczności sposobów i metod postępowania w praktyce zawodowej. Summary Prevention and treatment of pressure ulcers are extremely significant problem in nursing care. Bed sores are a source of additional health complications of the patient, are a source of pain and suffering, as well as common financial problems. The purpose of this work is to identify the role played by the nurse in the prevention and treatment of pressure ulcers and the response to the questions of concern: the root Problem: the importance of nursing care in the prevention and treatment of pressure ulcers? Specific problems: what range of preventive activities may take a nurse in preventing pressure ulcers? What is the role of the nurse in the treatment of pressure ulcers? Whether gender, age and diagnosis are important in the treatment of pressure ulcers? Whether the patient's length of stay in hospital has an impact on the progress in the treatment of pressure ulcers? What brings standardization against bed sores stationary care prevention? In response to the above questions it took the following hypothesis: Hypothesis: Nurse full leading role in in prevention against bed sores. and treatment of pressure ulcers. Hypothesis: Sound at a high level of nursing care effectively prevents patients with pressure ulcers. Nurse has a significant impact on therapeutic effects in the process of treatment of pressure ulcers. Age, sex and diagnosis of the patient are variables, which have a significant impact on the treatment of pressure ulcers. Duration of hospitalization, understood as a time of specialized, comprehensive, individual and multilateral treatment of pressure ulcers, has a beneficial effect on progress in the treatment of pressure ulcers. Standardization of nursing care in prevention and treatment of pressure ulcers raises the quality of medical services. In the work uses 3 methods: method medical literature analysis and method of analysis of medical documents. cumulative bed sores prevention card, conducted in palliative medicine in a Hospital Specialized in Puławy in 2010. In the statistical analysis included 124 patients at risk of pressure ulcer. The main test method uses a method of statistical inference, multiple linear regression model. In summary, the final was made the verification of hypotheses. All hypotheses are true except for hypothesis 3. Age and gender are variables, which do not affect the treatment of pressure ulcers, and diagnosis of variable is statistically significant. In the conclusions it was found that: Nurse perfectly carry out the process of nurture in the prevention and treatment of pressure ulcers. The introduction of the standards of prevention against bed sores brings a significant improvement in the quality of medical services. Monitoring the prevention against bed sores in the form of fairly conducted documentation protects nurses against the claims of patients. Generally accepted rules for the treatment of pressure ulcers to bring you more and better therapeutic results. Prevention programs and treatment of pressure ulcers should be implemented in all educational establishments, hospitals and nursing homes. It is necessary to introduce a comprehensive education against bed sores in the family environment of the patient, as most pressure ulcers arise in the House. Awareness of the costs of treatment of pressure ulcers should prompt health care managers to increase financial resources for prevention of pressure ulcers and their treatment according to the latest scientific advances. Clearly missing in the scientific literature of empirical work nursing from the scope of the problems and treatment of pressure ulcers, that would allow for the verification of the effectiveness of ways and practices in professional practice.

Response Surface Methodology (RSM) is an optimization tool that can identify interrelationship between variables as being adopted by experiment/ research studies in food and herbal plants extraction niche area. This review discusses the optimization approach through utilization of research surface methodology either using central composite design or Box-Behnken method specifically in extraction processes. The use of analysis of variance (ANOVA) to evaluate the degree of accuracy held by the derived model is based on several responses. RSM helps to determine the best experimental design in order to identify the relationship between variables. This paper also discusses on the utilization of RSM to derive a model equation that later can be applied for response prediction and the determination of optimal conditions.

Inflation has become a problem in the Indonesian economy. There are several ways the government has attempted to overcome the economic crisis. The government has often used tight money policies by raising interest rates in Indonesia. For that reason, interest rates are important, because they can be used to evaluate expectations of inflation. This analysis uses regression and cointegration to test the short-run and the long-run relationships between inflation spreads and interest rate spreads. Based on the results, the spreads which can explain inflation expectations in the short-run are the 12-1 month interest-rate spread; the 12-3 month interest rate spread; the 12-6 month interest rate spread; the 6-1 month interest rate spread; and the 6-3 month interest rate spread. In the long-run, only one interest rate spread is found to significantly explain the expected inflation path, the 12-3 month interest rate spread. Because of that, it is suggested that the government or economic participants in Indonesia use the 12-3 month interest rate spread to analyze expected inflation in Indonesia, since this spread can be used in both the short and long-run. Abstract in Bahasa Indonesia : Inflasi telah menjadi masalah besar dalam perekonomian Indonesia. Ada berbagai cara dilakukan oleh pemerintah untuk mengatasi krisis yang terjadi. Pemerintah Indonesia sering menggunakan kebijakan uang ketat dengan menaikkan suku bunga. Karena itu, tingkat suku bunga menjadi penting, karena bisa digunakan untuk menganalisa ekspektasi inflasi. Analisa ini menggunakan regresi dan kointegrasi untuk menguji hubungan jangka pendek maupun jangka panjang antara spread inflasi dengan spread suku bunga. Dari hasil yang diperoleh untuk jangka pendek, spread yang mampu menjelaskan ekspektasi inflasi adalah spread suku bunga deposito 12-1 bulan; spread deposito 12-3 bulan; spread deposito 12-6 bulan; spread deposito 6-1 bulan; dan spread deposito 6-3 bulan. Sedangkan untuk jangka panjang hanya ada satu spread deposito yang dapat menjelaskan pergerakan ekspektasi inflasi, yaitu spread deposito 12-3 bulan. Karena itu, disarankan agar pemerintah maupun para pelaku ekonomi di Indonesia menggunakan spread suku bunga deposito 12-3 bulan untuk menganalisa ekspektasi inflasi di Indonesia, karena spread tersebut dapat digunakan dalam jangka pendek maupun dalam jangka panjang. Kata kunci: inflasi, suku bunga, ekspektasi, kointegrasi, regresi.

This study has the purpose of determining which source of borrowing (rupiah or US dollars) is more profitable for a company wishing to expand its business. Two types of borrowing are examined: fixed loan and fixed principal loan, for each currency. Regression analysis and forecsting are used to determine the rupiah exchange rate for borrowed dollars. The results show that for both currencies, it is more profitable for a company to choose to borrow using the fixed loan compared to the fixed principal loan. From the point of view of interest rates, it is more profitable to borrow dollars because the US dollar interest rate is lower, but from the point of view of total payment in rupiah, it is clear that borrowing in rupiah is more profitable rather than US dollars. This is caused by the fact that a company choosing to borrow dollars will face exchange rate risk. Abstract in Bahasa Indonesia : Penelitian ini bertujuan untuk menentukan sumber dana pinjaman rupiah atau dolar US yang lebih menguntungkan perusahaan. Dua bentuk pinjaman yang diteliti adalah Fixed Loan serta pinjaman angsuran tetap untuk masing-masing mata uang. Analisa regresi dan forecasting digunakan untuk menentukan nilai tukar rupiah dalam dolar US untuk pinjaman dolar. Hasil menunjukkan bahwa untuk kedua mata uang, sebuah perusahaan lebih beruntung kalau memilih pinjaman yang dihitung dengan jenis Fixed Loan dibanding dengan jenis hitungan pinjaman angsuran tetap. Dari sudut bunga, memang lebih menguntungkan jika memilih pinjaman dolar US karena bunganya lebih kecil, tetapi jika dilihat dari sudut total pembayaran dalam rupiah, ternyata pinjaman rupiah lebih menguntungkan daripada pinjaman dolar US. Hal ini disebabkan karena jika memilih pinjaman dolar US, perusahaan akan menghadapi risiko nilai tukar. Kata kunci: pinjaman, bunga, kurs, rupiah, dolar, regresi.

The Indonesian government's decision to erase BULOG's sugar monopoly and apply a zero percent import tariff for sugar in January 1998 caused the local sugar industry to be threatened. The fact that imported sugar was cheaper than local sugar showed the inefficiency of the local sugar industry in Indonesia. This caused many sugar factories to nearly go bankrupt because they could not compete with the imported sugar. After much protest, the government retracted its decision to free the sugar market applied a 25% import tariff on sugar. This research shows that sugar importers, owners of sugar factories and the government enjoy the benefits of the tariff but that consumers and farmers do not. This study evaluates demand and supply of sugar in Indonesia before and after the government applied the import tariff. Results of welfare analysis using consumer and producer surplus show that it is better for the government to erase the tariff to make the sugar industry more efficient and protect consumers from loss. Abstract in Bahasa Indonesia : Keputusan pemerintah Indonesia untuk mencabut monopoli BULOG dalam pengadaan gula dan menerapkan tarif impor gula sebesar nol persen pada bulan Januari 1998 mengakibatkan industri gula lokal terancam. Kenyataan bahwa saat itu gula impor lebih murah daripada harga gula lokal menunjukkan ketidakefisienan dari industri gula lokal di Indonesia. Hal ini menyebabkan banyak pabrik gula domestik terancam bangkrut karena tidak dapat bersaing dengan gula impor. Setelah terjadi protes, pemerintah Indonesia mencabut keputusan tersebut dan menggantikannya dengan tarif impor gula sebesar 25%. Penelitian ini menunjukkan bahwa hanya importir gula, pemililk pabrik gula serta pemerintah yang menikmati keuntungan dari kebijakan tarif tersebut tetapi sebaliknya para konsumen dan para petani tebu tidak mengalami keuntungan. Studi ini menganalisa permintaan dan penawaran gula di Indonesia sebelum dan sesudah diterapkan tarif impor gula oleh pemerintah. Hasil penelitian ini menunjukkan bahwa demi kesejahteraan negara secara keseluruhan, sebaiknya pemerintah mencabut kebijakan tarif tersebut supaya industri gula domestik menjadi lebih efisien dan para konsumen tidak mengalami kerugian. Kata kunci: gula, tarif, surplus konsumen, surplus produsen, regresi.

We propose a procedure associated with the idea of the E-M algorithm for model selection in the presence of missing data. The idea extends the concept of parameters to include both the model and the parameters under the model, and thus allows the model to be part of the E-M iterations. We develop the procedure, known as the E-MS algorithm, under the assumption that the class of candidate models is finite. Some special cases of the procedure are considered, including E-MS with the generalized information criteria (GIC), and E-MS with the adaptive fence (AF; Jiang et al.). We prove numerical convergence of the E-MS algorithm as well as consistency in model selection of the limiting model of the E-MS convergence, for E-MS with GIC and E-MS with AF. We study the impact on model selection of different missing data mechanisms. Furthermore, we carry out extensive simulation studies on the finite-sample performance of the E-MS with comparisons to other procedures. The methodology is also illustrated on a real data analysis involving QTL mapping for an agricultural study on barley grains. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

Abstract: A spirometer is used for basic lung function test for preliminary diagnosis of respiratory diseases. There are significant amount of calculations and graphical analysis required to transform the raw spirometric data into meaningful parameters. This parameters and graphs help the physicians in preliminary patient diagnosis for respiratory disorders like asthma, chronic obstructive pulmonary disease, etc. This study was undertaken for the development of a software system which can be used with any spirometric instrument to automate the calculations of pulmonary dead space volumes and analysis of raw data. The clinician can feed the raw data from patient testing into the easy to use graphical user interface of the software which will be analyzed instantly and all the parameters, regression slopes, shape analysis plots and the results will be displayed graphically. The estimation of the vital parameters and regression slopes are based on standard protocols and equations. This system will eliminate presently practiced time consuming manual calculations and graphical analysis; will have increased precision, be considerably faster and more versatile. [Copyright &y& Elsevier]

BACKGROUND Recently, we reported that transient treatment of genetically hypertensive rats with high-dose angiotensin receptor blocker (ARB) causes regression of established hypertension. In this study, we investigated whether treatment with candesartan or nifedipine controlled-release (CR) resulted in a sustained regression of hypertension in humans. METHODS Patients aged 30 to 59 years with untreated stage 1 essential hypertension and a family history of hypertension were treated with the antihypertensive agents candesartan (n = 124) or nifedipine CR (n = 120). After 1 year of treatment (phase 1), the medications were tapered and discontinued (phase 2). During phase 2, home and office blood pressures were monitored for another year to assess posttreatment reoccurrence of stage 1 hypertension. RESULTS In phase 1, after 1 year of treatment a similarly substantial BP decrease was seen in the candesartan (−24.5/16.1mm Hg) and nifedipine (−26.8/18.0mm Hg) groups. In phase 2 there was a substantial reoccurrence of hypertension; at the study end, only 1 patient was able to continue without antihypertensive medication. However, a Kaplan-Meier analysis revealed a significant delay of reoccurrence of hyper tension (P = 0.0001) in the candesartan group. CONCLUSIONS One year of treatment with candesartan or nifedipine CR was not associated with marked regression of hypertension in humans at the standard doses used in this trial. However, withdrawal of candesartan was associated with a slightly longer delay before restarting medications. Further studies with larger doses of candesartan given over a longer time are required to determine whether such a regimen may induce sustainable and clinically relevant reversal of hypertension and alteration in its natural history. [ABSTRACT FROM PUBLISHER]

This paper examines the relationships between the mean residual life functions of parallel and k-out-of-n systems with the regression of order statistics. Using these relationships, the results and properties about the mean residual life function of those systems can be used for the regression of order statistics and vice versa. Finally, the paper proposes a definition for the mean residual life function of a k-out-of-n system when the number of failed components of the system is known. [ABSTRACT FROM AUTHOR]

This paper considers regression models for cross-section data that exhibit cross- section dependence due to common shocks, such as macroeconomic shocks. The paper analyzes the properties of least squares (LS) estimators in this context, The results of the paper allow for any form of cross-section dependence and heterogeneity across population units. The probability limits of the LS estimators are determined, and necessary and sufficient conditions are given for consistency. The asymptotic distributions of the estimators are found to be mixed normal after recentering and scaling. The t, Wald, and F statistics are found to have asymptotic standard normal, x2 and scaled x2 distributions, respectively, under the null hypothesis when the conditions required for consistency of the parameter under test hold. However, the absolute values of t, Wald, and F statistics are found to diverge to infinity under the null hypothesis when these conditions fail. Confidence intervals exhibit similarly dichotomous behavior. Hence, common shocks are found to be innocuous in some circumstances, but quite problematic in others. Models with factor structures for errors and regressors are considered. Using the general results, conditions are determined under which consistency of the LS estimators holds and fails in models with factor structures. The results are extended to cover heterogeneous and functional factor structures in which common factors have different impacts on different population units. [ABSTRACT FROM AUTHOR]

A new regression estimator viewed as the solution of a strictly convex quadratic programming problem is introduced in this paper. Two recurrent neural networks in continuous-time and discrete-time respectively are proposed to solve the quadratic programming problem in real time. The continuous-time neural network is shown to have a global stability, including the global asymptotic and exponential stability. The discrete-time neural network is shown to have a global convergence with a fixed step length. This fixed step length can be independent of the regression problem size by scaling a design parameter. Since the sizes of the proposed neural networks depend only on the constraints of the optimization problems, the proposed new regression estimator can obtained by two novel neural networks with lower implementation costs than the conventional methods. Our simulation results confirm that the proposed neural networks are effective in solving various kind of regression problems. [ABSTRACT FROM AUTHOR]

To compare posterior capsule opacification (PCO) after cataract surgery with implantation of two silicone intraocular lenses (IOLs) with different designs.We carried out a prospective, clinical study of 116 patients randomized to standardized phacoemulsification with implantation of CeeOn Edge (n = 57) or SI40NB (n = 59) IOLs. The follow-up period was 3 years. To evaluate PCO morphologically, digital images were obtained and analysed using evaluation of posterior capsule opacification computer software (epco). The neodymium:YAG (Nd:YAG) capsulotomy rate was recorded.At 2 and 3 years, the eyes with SI40NB IOLs had significantly more PCO than those with the CeeOn Edge IOLs (p = 0.00014 and p = 0.002). Nine Nd:YAG capsulotomies were performed in the SI40NB group and none in the CeeOn Edge group. This difference was statistically significant (p = 0.003). In some patients a regression of PCO was noticed and confirmed usingepco. Statistically less PCO was noted when the capsulorhexis rim was placed so that it covered all 360 degrees of the optic of the IOL.A clinically and statistically significant difference in PCO development between CeeOn Edge and SI40NB IOLs at 2 and 3 years postoperatively was found. These findings support earlier studies indicating that a sharp edge of the optic is a more important factor in IOL design than IOL material in the prevention of PCO. [ABSTRACT FROM AUTHOR]

Two elderly patients with prolonged depression and prominent pseudohysterical symptoms were successively treated using electroconvulsive therapy (ECT). Patient 1, a 71-year-old woman, developed depression in anticipation of her husband's retirement from his post as a college president and his receiving of a civilian decoration.She became bedridden because of the adverse effects of the antidepressants, and she was admitted to our hospital. Initial treatment was efficacious to some extent; however, after moving from a private room to an eight-person ward, she began to exhibit passive-aggressive behavior and seemed to seek attention. Her diagnosis and treatment was difficult, but ECT relieved her symptoms. Patient 2, a 74-year-old man, developed depression after experiencing domestic trivialities. Treatment as an outpatient produced numerous adverse effects and was of little effect, so the patient was admitted to hospital. As was seen in patient 1, this patient was moved to a ward room after he showed an initial improvement, but his condition deteriorated and he began to show passive-aggressive behavior; ECT also relieved his symptoms. A multidisciplinary strategy, including ECT during the early period of treatment, might be effective for the treatment of senile depression. [ABSTRACT FROM AUTHOR]

To examine the external validity of DSM-IV childhood disintegrative disorder (CDD), 10 children (M = 8.2 yrs) with CDD and 152 gender- and age-matched children with autistic disorder (AD) were compared on 24 variables. The CDD children had a significantly higher rate of epilepsy, significantly less uneven intellectual functioning, and a tendency of greater abnormality in auditory responsiveness than AD children, to validate CDD externally. Their short-term outcome, as shown in the degree of retardation, was not worse than the AD children, which is in disagreement with previous studies reporting worse outcomes in CDD than autism. These results need to be verified by a long-term prospective study that compares CDD and AD patients from infancy. [ABSTRACT FROM AUTHOR]

The increase in the numbers enrolling in university computer courses makes huge demands on resources, and maintaining standards of teaching and tutorial support is almost impossible without a massive increase in staff. To overcome this situation, the authors have developed RoboProf, an automated learning environment which, as well as generating and assessing programming exercises, provides ongoing assistance and feedback to students without extra demands on lecturer and tutors' time. This system also contains a technique for detecting plagiarism, an increasing problem in computing courses worldwide. For this research, RoboProf was used to teach Java programming to a class containing nearly 300 students in the first year of a computing degree. Use of the system by students was monitored and recorded on log files in order to investigate the extent to which usage patterns influence achieved programming skill. An analysis shows that students who complete the set of RoboProf exercises perform significantly better than those who do not. The timeliness in which these exercises are completed relative to other students is significant: early solvers get higher marks, and students solving the problems with fewer attempts get higher marks. Not surprisingly, plagiarists achieve a lower score than those who do their own work. Other factors that were found to influence programming performance included entry standards and gender. Entry qualifications impacted positively on performance, and males performed significantly better than females. There was a significant positive correlation between the score achieved in the RoboProf course and the performance in a subsequent computing course administered in the traditional manner. [ABSTRACT FROM AUTHOR]

Markov chain marginal bootstrap (MCMB) is a new method for constructing confidence intervals or regions for maximum likelihood estimators of certain parametric models and for a wide class of M estimators of linear regression. The MCMB method distinguishes itself from the usual bootstrap methods in two important aspects: it involves solving only one-dimensional equations for parameters of any dimension and produces a Markov chain rather than a (conditionally) independent sequence. It is designed to alleviate computational burdens often associated with bootstrap in high-dimensional problems. The validity of MCMB is established through asymptotic analyses and illustrated with empirical and simulation studies for linear regression and generalized linear models. [ABSTRACT FROM AUTHOR]

We consider the component analysis problem for a regression model with an additive structure. The problem is to test whether some of the additive components, are of polynomial structure (e.g., linear) without specifying the structure of the remaining components. A particular case is the problem of selecting the significant covariates. The method that we present is based on the wavelet transform using the Haar basis, which allows for applications under mild conditions on the design and smoothness of the regression function. The results demonstrate that each component of the model can he tested with the rate corresponding to the case if all of the remaining components were known. The proposed procedure is also computationally straightforward. Simulation results and a real data example about female labor supply demonstrate the test's good performance. [ABSTRACT FROM AUTHOR]

The shape of a regression curve can to a large extent be characterized by the succession of structural features like extrema, inflection points, and so on. When analyzing a sample of regression curves, it is often important to know at an early stage of data analysis which structural features are occurring consistently in each curve of the sample. Such a definition is usually not easy due to substantial interindividual variation both in the x and the y axis and due to the influence of noise. A method is proposed for identifying typical features without relying on an a priori specified functional model for the curves. The approach is based on the frequencies of occurrence of structural features, as, for example, maxima in the curve sample along the x axis. Important tools are nonparametric regression and differentiation and kernel density estimation. Apart from a theoretical foundation, the usefulness of the method is documented by application to two interesting biomedical areas: growth and development, and neurophysiology. [ABSTRACT FROM AUTHOR]

When a simultaneous confidence band for a multiple regression function is to be constructed, one encounters the problem of choosing a band from among those available. The following notion of optimality for bands is introduced. Suppose that the band is constructed with the purpose of giving confidence intervals for evaluations x[sub l]beta[sup l], of the regression function at future settings x[sub j] (I = 1, . . . , n) of the predictor variables. The settings are assumed to be unknown at the time the band is to be chosen, but they are assumed to lie within a given bounded set, which is referred to as the constraint set. Once a particular band is chosen and the predictor variable settings become known, the "regret" for the procedure can be defined as the difference between the average width of the n intervals and the smallest possible width attainable by a band constructed using knowledge of the predictor variable settings. Thus the regret measures the cost of not knowing the settings when the band is chosen. It is reasonable to want a minimax regret band, that is, a band minimizing maximum regret over all possible settings of the predictor variables. For a standard multiple regression model the unique taut minimax regret band is characterized and described in a simple manner. Formulas for computing critical points for minimax regret bands are given for two special cases: for simple linear regression, when the predictor variable is constrained to lie in an interval, and for (balanced) multiple regression with intercept, when the constraint region is one of those defined by Halperin and Gurian (1968). Tables of critical points are presented for balanced simple linear regression over a finite interval. The following application is presented to illustrate the use of these results. A fitted simple linear regression model based on data from... [ABSTRACT FROM AUTHOR]

The minimum norm quadratic unbiased estimator type (MINQUE type) of estimates considered in this article are obtained by requiring identical values for the ratios of the a priori variances to the a priori error variance and letting this common value tend to infinity. The resulting estimates are invariant quadratic unbiased estimators with certain parametric and nonparametric optimality properties: assuming normally distributed random effects the efficiency of the proposed estimates to the minimum variance quadratic unbiased estimates (MIVQUE's) approaches unity when the true variance ratios are identical and tend to infinity. Assuming nonnormal effect distributions in the model with two variance components, the estimates are asymptotically efficient: in a sequence of designs where the number of classes and the number of observations on each class approach infinity, it is shown that the asymptotic variances of the estimates are equivalent to the theoretical minimum variances for invariant quadratic unbiased estimators. The result is interesting and useful since the usual analysis of variance (ANOVA) estimate of between-classes variance has strictly larger asymptotic variance for the unbalanced one-way model. Commonly considered estimates result from this procedure; the usual residual mean square (assuming that all non-error effects are fixed) is the resulting estimate of the error variance. In the one-way model the resulting estimates coincide with estimates considered by Thomas and Hultquist (1978), Burdick and Graybill (1984), Ahrens, Kleffe, and Tenzler (1981), and Kaplan (1982). In particular, the convergence of the MINQUE estimates was proved in the latter two papers in the context of the unbalanced one-way model. The procedure yields computationally convenient estimates in the general mixed ANOVA model. Computing formulas are... [ABSTRACT FROM AUTHOR]

In the forward selection of independent variables for a linear regression model, the next best-fitting variable for inclusion is the variable with largest F-to-enter. We consider the evaluation of the p value for this selection. [ABSTRACT FROM AUTHOR]

The law school validity studies are primarily concerned with the prediction of first-year average in law school from Law School Aptitude Test score and undergraduate grade point average. Traditionally, a separate admitting equation is estimated in each law school by the method of least squares based on data from students who attended the law school in recent years. These least squares equations can fluctuate rather wildly from year to year. This study employs empirical Bayes techniques to obtain admitting equations that are better than the least squares admitting equations in two ways: for each law school, the empirical Bayes admitting equations are more stable in time than the least squares admitting equations; and the empirical Bayes admitting equations predict student performance more accurately than the least squares admitting equations. [ABSTRACT FROM AUTHOR]

The limiting distribution of the regression coefficients calculated from a correlation matrix that has been corrected for attenuation is obtained. Methods of estimating the covariance matrix of the vector of regression coefficients are presented. Nonnormal regression variables and nondiagonal error matrices are considered. The procedures are illustrated with data on the socioeconomic career. [ABSTRACT FROM AUTHOR]

This article is a generalization of the procedures developed by Steinhorst and Bowden (1971) for constructing confidence bands for the 100 beta th percentiles in the general linear model. Since the original procedure built these bands about a biased estimator of the 100 beta th precentile line, some other estimators were tried. While some improvement was noted, the results are not conclusive, suggesting that the best procedure has not been found yet. [ABSTRACT FROM AUTHOR]

The effects of the optimistic bias of R[SUP2] on oral health data were investigated by means of a cross validation study in which the number of predictors in a regression model was manipulated. The use of too many predictors increased positive bias and reduced the true predictive quality of a regression model and should be avoided through the use of appropriate stepwise procedures. [ABSTRACT FROM AUTHOR]

The power of a backcross design to detect genetic variation associated with a single chromosome was investigated. A simple chromosomal test was suggested in which the phenotypic observations are regressed onto genotypic information from multiple markers. It was shown that the optimum marker spacing depends on the underlying genetic structure and chromosome length. A sparse marker map, with markers approximately every 50 cm, is sufficient to detect chromosomal variation if the nature of the genetic variance is coupled polygenes, whereas the optimum marker spacing to detect a single QTL somewhere on the chromosome is slightly denser, about 20–40 cM. Although the method was demonstrated for line crosses, it can equally be applied to other populations, for example four-way crosses and half-sib designs. [ABSTRACT FROM AUTHOR]

Inference for structural parameters that are common to several regressions is a frequent problem of statistical practice. In the unlikely case that the residual error variances are equal for all regressions, the inference problem usually has a closed-form solution within the standard regression framework. In the case of unequal and unknown residual error variances, the problem falls into the category of Behrens-Fisher problems. This article describes a comprehensive solution to this inference problem using the concept of a generalized p value introduced by Tsui and Weerahandi in 1989. An example investigating the effect on stock return due to a firm's inclusion in the Standard and Poor's 500 illustrates the method. [ABSTRACT FROM AUTHOR]

ABSTRACT Age and growth rate studies of blue pine, Pinus wallichiana (A.B. Jacks.) were carried out in Kuldana reserve forest of Tehsil Murree, Pakistan. Forest was divided into ten stands and cores from fifty trees were taken. Fifty samples (cores) were extracted from this forest by using Swedish increment borers. Velmex Measuring System was used in measuring tree ring widths. Diameter at breast height (Dbh) was measured and GPS (Global Positioning System) reading of each sample was also noted. Crossdating of every sample was done manually by this system. The oldest tree was observed 454 years of age and youngest was 65 years of age. Most of the trees were more than 130 years of age in the study area. Overall, a good growth rate was observed which indicated the climate conditions favored the growth of species in this site. Regression analysis was performed in between Dbh (inches) and age, (years) and Dbh (inches) and growth rate (inches). Maximum value (R2=0.8272) was observed from mid-point to endpoint of Forest which showed significant positive correlation and no significant correlation was observed in both marked points between growth rate (inches) and Dbh (inches) at this site. [ABSTRACT FROM AUTHOR]

Chronic diseases often cause several medical complications. This paper aims to predict multiple complications among patients with a chronic disease. The literature uses single-task learning algorithms to predict complications independently and assumes no correlation among complications of chronic diseases. We propose two methods (independent prediction of complications with single-task learning and concurrent prediction of complications with multi-task learning) and show that medical complications of chronic diseases can be correlated. We use a case study and compare the performance of these two methods by predicting complications of hypertrophic cardiomyopathy on 106 predictors in 1078 electronic medical records from April 2009-April 2017, inclusive. The methods are implemented using logistic regression, artificial neural networks, decision trees, and support vector machines. The results show multi-task learning with logistic regression improves the performance of predictions in terms of both discrimination and calibration. [ABSTRACT FROM AUTHOR]