Suchergebnisse

The first phase of the dispute between China and Japan before the League of Nations began in September, 1931, when China, following upon the invasion of Manchuria by Japanese troops, invoked Article 11 of the Covenant and asked the Council to take measures to restore the territorial status quo and to determine the amount and the character of the compensation due to her as the result of the invasion of her territory. It ended in April, 1933, when, subsequently to the adoption by the Assembly of a report under Article 15 of the Covenant, it became clear that no effective action would be taken to implement the purposes of the Covenant. The charter of the community of nations organized in and through the League proved, to all appearances, to be of illusory value in its fundamental aspect, namely, in the undertaking to protect the members of the League from external violence and aggression. The prediction of the sceptics that the authority of the League would be unable to assert itself if challenged by one of the Great Powers seemed to have been amply confirmed. It seemed to have been fulfilled by a successful defiance of the Covenant so unprecedented in its magnitude, obviousness and persistence as to constitute a fair test case of the value and of the potentialities of the League.

Mr. J. Holmes had told us that the object of the study of law is to make
the prophecies of precedent more precise, to generalize them into a
thoroughly connected system; that that object is "the prediction of the
incidence of the public force through the instrumentalities of courts." The
framers of our constitutional jurisprudence were clearly concerned with the
incidence of just principles upon governmental powers. Kent declares that
when the United States ceased to be a part of the British Empire, and
assumed the character of an independent nation, they became subject to that
system of rules, which reason, morality and custom had established among the
civilized nations of Europe, as their public law. It was recognized that the
law of nations prescribed "what one nation may do without giving just cause
for war, and what of consequence, another may or ought to permit without
being considered as having sacrificed its honor, its dignity, or its
independence." Story avers that the general law of nations is "equally
obligatory upon all sovereigns and all states." It is "the umpire and
security of their rights and peace," declared Jefferson. It is a law which
"binds all nations," declared the Supreme Court of the United States in
1794.

Anastasiadou, M., Santos, V., & Dias, M. S. (2021). Evaluating Energy Performance Certificate Data with Data Science. In 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET) (pp. 1-5). IEEE. https://doi.org/10.1109/ICECET52533.2021.9698806 ; The related problems of improving existing buildings' energy performance, reducing energy consumption, and improving indoor comfort and their many consequences are well known. Considering increasing urbanization and climate change, governments define strategies to enhance and measure buildings' energy performance and energy efficiency. This work aims to contribute to the improvement of buildings' characteristics by conducting a thorough systematic literature review and adopting a data science approach to these problems, presenting initial results with an open-access energy performance certificate dataset from the Lombardy Region, in Italy. We provide a pre-processing method to the data, applicable for future research, aiming to address challenges such as automatic classification of existing buildings' energy performance certification, and predicting energy-efficient retrofit measures, using machine learning techniques. The analysis of this dataset is challenging because of the high variability and dimensionality of this dataset. For this purpose, a robust iterative process was developed. First, the data dimensionality was reduced with Pearson Correlation to find the best set of variables against the non-renewable global energy performance index (EPgl, nren). Then, the outliers were handled by utilizing Box Plot and Isolation Forest algorithms. The main contribution is to inform private and public building sectors on dealing with high dimensional data to achieve enhanced energy performance and predict energy-efficient retrofit measures to improve their energy performance. ; authorsversion ; published

Dramatic changes in the executive & legislative budget processes over the last ten years have elevated the US Office of Management & Budget (OMB) to a new position of power & importance. Within the executive branch, the budget process has become more highly centralized in the president's office; within the Congress, a similar centralization has occurred. In both branches, the OMB has become the key institution for coordinating the actions of the budget-making powers. At the same time, the much-discussed "iron triangles" explored by analysts of the budgetary system have lost much of their power to control the process. Although the OMB's active involvement in the congressional budget process diminished in 1983, the institutional relationships necessary for a more centralized budget process remain & can be reactivated to deal with the predicted enormous deficits. HA.

EVALUATES THE POSSIBILITIES FOR BRINGING INTO EXISTENCE AN ALL-EUROPE, THAT IS, A EUROPE THAT IS NO LONGER DIVIDED INTO EAST AND WEST. CONCLUDES THAT AS LONG AS THE CURRENT IDEOLOGICAL RIFT CONTINUES TO EXIST AND CURRENT POWER ARRANGEMENTS ARE MAINTAINED THERE IS LITTLE HOPE FOR SUCH A RECONCILIATION. SEES LITTLE REASON TO HOPE FOR CHANGE IN STATUS QUO IN THE FORESEEABLE FUTURE.

This study was funded by the Spanish Ministry of Health, the Institute of Health Carlos III (ISCIII), and the European Regional Development Fund (grants PS09/02272, PS09/02147, PS09/01095, PS09/00849, PS09/00461, and PI12-02755); the Andalusian Council of Health (grant PI-0569-2010); the Spanish Network of Primary Care Research, redIAPP (grant RD06/ 0018); the Aragon group (grant RD06/0018/0020); the Bizkaya group (grant RD06/0018/0018); the Castilla-Leon group (grant RD06/0018/0027); the Mental Health Barcelona Group (grant RD06/0018/0017); the Mental Health, Services and Primary Care Malaga group (grant RD06/0018/0039); and the projects "PI18/00238" and "PI18/00467" funded by the Institute of Health Carlos III (Co-funded by European Regional Development Fund/European Social Fund "A way tomake Europe"/"Investing in your future"). This study was performed as part of a PhD thesis conducted within the Official Doctoral Programme in Biomedicine of the University of Granada, Spain. Augusto Anguita-Ruiz was supported by a Ministry of Economy and Competitiveness and Institute of Health Carlos III fellowship (IFI17/00048). Juan Antonio Zarza-Rebollo received financial support from the Spanish Ministry of Economy and Competitiveness (BES-2017-082698). Ana M. Perez-Gutierrez was supported by a grant from the Ministry of Economy and Competitiveness and Institute of Health Carlos III (FI19/00228). Elena Lopez-Isac received financial support from the Spanish Ministry of Science and Innovation Juan de la Cierva Incorporacion Program (IJC2019040080-I), and Margarita Rivera was supported by the Ministry of Economy and Competitiveness Ramon y Cajal Program (RYC-2014-15774). The authors thank the Institute of Health Carlos III (ISCIII), the European Regional Development Fund (FEDER), the Andalusian Council of Health and Andalusian Health Service (SAS), the Primary Care Prevention and Health Promotion Research Network (redIAPP), the Biomedical Research Institute of Malaga (IBIMA), and the Biomedical Research Centre (CIBM) from the University of Granada for their economic and logistic support. The authors thank all the patients and General Practitioners who participated in the trial. ; Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesityassociated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals. ; Instituto de Salud Carlos III Spanish Government Institute of Health Carlos III (ISCIII) European Commission PS09/02272 PS09/02147 PS09/01095 PS09/00849 PS09/00461 PI12-02755 ; Andalusian Council of Health PI-0569-2010 ; Spanish Network of Primary Care Research, redIAPP RD06/ 0018 ; Gobierno de Aragon RD06/0018/0020 ; Bizkaya group RD06/0018/0018 ; Castilla-Leon group RD06/0018/0027 ; Mental Health Barcelona Group RD06/0018/0017 ; Mental Health, Services and Primary Care Malaga group RD06/0018/0039 ; Instituto de Salud Carlos III PI18/00238 PI18/00467 FI19/00228 ; European Regional Development Fund/European Social Fund "A way tomake Europe"/"Investing in your future" ; Ministry of Economy and Competitiveness ; Institute of Health Carlos III fellowship IFI17/00048 ; Spanish Government BES-2017-082698 ; Spanish Ministry of Science and Innovation Juan de la Cierva Incorporacion Program IJC2019040080-I ; Ministry of Economy and Competitiveness Ramon y Cajal Program RYC-2014-15774 ; Andalusian Council of Health ; Andalusian Health Service (SAS) ; Primary Care Prevention and Health Promotion Research Network (redIAPP) ; Biomedical Research Institute of Malaga (IBIMA) ; Biomedical Research Centre (CIBM) from the University of Granada ; European Commission

RESUMEN: En vista de la creciente necesidad de implementar combustibles de origen fósil con una fracción de combustibles de base renovable en los motores de combustión interna, es necesario disponer de herramientas prácticas que verifiquen que las nuevas mezclas cumplan con las normas de regulación de combustibles. Variaciones de la Viscosidad Cinemática (VC) y la Temperatura de Punto de inflamación (TFP) del combustible podrían implicar modificaciones en el motor que garanticen su seguridad, rendimiento y emisiones. En la presente Tesis Doctoral se proponen métodos predictivos para la VC y la TFP de mezclas basados en interacción de grupos. El método para predecir la VC es aplicable a mezclas binarias y ternarias de sustancias puras conformadas por parafinas, alcoholes, aromáticos, naftenos y metilésteres. Los métodos para predecir la TFP son aplicables a mezclas complejas alcohol-diésel y alcohol-diésel-biodiesel de palma. El método propuesto para predecir la VC se basa en la Ecuación de Eyring, mientras que la energía libre de activación de exceso se obtuvo a partir del método UNIFAC del equilibrio líquido vapor. Fue necesario modificar sus parámetros de interacción de grupos para hacerlo aplicable a la viscosidad de mezclas. Para predecir la TFP se tomó como punto de partida la Ecuación de Liaw. Adicionalmente, se aplicó el método UNIFAC-Dortmund para predecir los coeficientes de actividad de las mezclas consideradas en este trabajo. Para conocer la constitución del diésel se propuso un método de caracterización que emplea una distribución tipo Gamma para cada familia en función del número de átomos de carbono de los constituyentes. También se propuso un método simplificado para el cálculo de la TFP basado en las ecuaciones de Liaw con Gibbs-Duhem, donde se empleó esta última para obtener un coeficiente de actividad representativo de las mezclas complejas diésel o diésel-biodiesel de palma. Al comparar con los datos experimentales, los resultados de VC y TFP obtenidos teóricamente en este trabajo arrojaron resultados satisfactorios. Además, la precisión y margen de aplicabilidad de estos métodos son mejores que aquellos disponibles a la fecha en la literatura. Al predecir la TFP de mezclas alcohol-diésel se obtuvo una máxima desviación promedio (AD) de -2.15°C, mientras que en mezclas alcohol-diésel-biodiesel la máxima AD fue 5.8°C. En la mayoría de los casos, el método menos preciso fue el que aplica solo la Ecuación de Liaw. El método para predecir la VC arrojó resultados satisfactorios, para la mayoría de grupos funcionales orgánicos, cuando se evaluó su versatilidad frente a la variación de la temperatura. Estos resultados fueron mejores que aquellos mencionados en la literatura que aplican métodos de contribución de grupo. Las mezclas alcohol-diésel y alcohol-diésel-biodiesel distan mucho del comportamiento ideal, por lo que hasta el momento era imposible predecir satisfactoriamente con los métodos conocidos. Se propone para trabajos futuros desarrollar un método para predecir la VC aplicable a mezclas complejas que tenga en cuenta la temperatura y los demás grupos funcionales de sustancias orgánicas que no se incluyeron en este trabajo. ; ABSTRACT: With the increasing necessity of applying new fuels with a renewable fraction in internal combustion engines. It is necessary to provide practical tools, which can predict critical properties of these complex new fuel blends. In such a way that laboratories from the fuel industry can verify if these new fuel blends meet governmental restrictions related with their use in internal combustion engines. Variations in Flash Point Temperature (FPT) and Kinematic Viscosity (KV) of fuel could imply modifications in the engine in order to prevent from explosion hazards and low performance, and increase fuel consumption and emissions. This Doctoral thesis proposes two-group contribution based methods to predict FPT and KV in alcohol/diesel and alcohol/diesel/biodiesel fuel blends. The FPT prediction method is applicable to alcohol-diesel and alcohol-diesel-palm biodiesel complex blends, while the KV prediction method is applicable to binary and ternary blends. The KV prediction method proposed in this work is based on Eyring equation. The traditional UNIFAC method, which has been widely used to obtain the vapor-liquid equilibrium interaction parameters, was modified to predict the viscosity interaction parameters as well as the excess activation energy. On the other hand, two FPT models were developed in this work. They are based on Liaw equation, and they use the UNIFAC-Dortmund method to predict the vapor-liquid equilibrium activity coefficients of the complex fuel blends tested in this work. The first of the two FPT proposed methods applies the Liaw equation to 54 and 62 diesel and diesel-biodiesel constituents, respectively. The second method applies the Liaw-Gibs-Duhem equation to any alcohol-surrogate blend (a surrogate of diesel or diesel-biodiesel). Diesel fuel is a complex blend of hydrocarbons; then a characterization was carried out to apply the FPT Liaw method. In this work, a novel diesel characterization method with a Gamma distribution of probability per diesel family was proposed. Comparisons of results from the theoretical methods proposed in this thesis with the experimental ones showed good agreement. Additionally, the precision and applicability of these methods were better than others available in literature. The FPT of alcohol-diesel fuel blends was predicted with a maximum average deviation (AD) of -2.15°C, while for alcohol-diesel-biodiesel the FPT exhibited a maximum AD of 5.8°C. In most cases, the first method based only in the Liaw equation showed lower accuracy in comparison with the Liaw-Gibbs-Duhem one. The KV prediction method developed in this work presented satisfactory results under several temperature and functional groups. KV of new interesting fuel surrogates containing KV data of functional groups, which were non-available in literature, were measured and predicted in this research work. Finally, the KV method proposed here was more confident in predicting KV when compared with other models available from literature. In summary, the FPT of alcohol-diesel and alcohol-diesel-palm biodiesel fuel blends does not correspond to ideal blends, and the two methods proposed in this thesis allowed to predict the FPT of complex fuel blends with good accuracy. Another contribution from this thesis is that based on second order group contribution method, the KV can be predicted for complex fuel blends under different temperatures. However, more research is necessary to enhance the KV model based on new interaction parameters and adding additional organic functional groups.

The occurrence and fate of residues from the therapeutic use of the non-steroidal anti-inflammatory drug metamizole have been studied in investigations of sewage effluents from a military hospital, municipal sewers and a sewage treatment plant (STP) in Berlin, Germany. The loads of the metabolites aminoantipyrin (AA), 4-acetylaminoantipyrin (AAA) and 4-formyl-aminoantipyrin (FAA), rapidly formed after the application of metamizole, were predicted from pharmacokinetic data and based on the evaluation of extensive data sets of on the administration in hospitals and private households. In parallel, the actual concentrations were measured within three field trials. For the military hospital, the estimated average annual discharges of AA/AAA and FAA were 10.5 and 3.2 kg, respectively. For the STP, annual loads of 333 and 133 kg were determined for AA/AAA and FAA, respectively. During sewage treatment, an average decrease of 26% of the loads was measured for AA/AAA whereas no changes were observed for FAA. Generally, the prediction of the loads resulted in an overestimation of the residue levels compared to those measured in the respective sewers. Thus, modeling of predicted loads or concentrations alone will not be sufficient for a realistic assessment. Concerns for human or other mammals' health are not expected from the occurrence of metamizole residues in the aquatic system measured at concentrations up to 7 mu g l(-1) in STIR effluents. However, a rest of uncertainty remains as it was not possible to derive a no observed effect level for the induction of rare but potentially fatal toxicological side effects reported for metamizole. (C) 2007 Elsevier Ltd. All rights reserved

Submitted 2020-07-23 | Accepted 2020-08-16 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.265-268The study was performed on 136 pig carcasses representing the Croatian pig population with regards to the breed structure. The carcasses were dissected according to the EU reference method and lean meat percentage was estimated using the Hennessy Grading Probe (HGP7) device and the "Two points" (ZP) method. Comparison of lean meat percentage obtained by dissection and two prediction methods showed significant differences in estimating the lean meat percentage of pig carcasses (P <0.05). The distribution of carcasses according to SEUROP system showed a difference in the classification depending on the applied method, indicating a need for adjustment of current formulae for lean meat percentage estimation in Croatia.Keywords: pig, carcass, lean meat percentage, dissection, EU reference methodReferencesCauseur, D., Daumas, G., Dhorne, T., Engel, B., Fonti-Furnols, M., Hojsgaard, S. (2003). Statistical handbook for assessing pig classification methods: recommendations from the 'EUPIGCLASS' project group. EC working document.EEC Commission. (1994). Commission Regulation (EC) No 3127/94 of 20 December 1994 amending Regulation (EC) No 2967/85 laying Dowd detailed rules for the application of the Community scale for grading pig carcases. Official Journal of the European Communities, 43-44.EEC Commission. (2006). Commission Regulation (EC) No 1197/2006 of 7 August 2006 amending regulation (EEC) No 2967/85 laying down detailed rules for the application of the Community scale for grading pig carcasses. Official Journal of the European Union, 49, L 217, 8/8/2006, 6-7.EEC Commission. (2008). Commission Regulation (EC) No. 1249/2008 (2008, 12.16). of 10 December 2008 laying down detailed rules on the implementation of the community scales for the classification of beef, pig and sheep carcases and the reporting of prices thereof. Official Journal of the European Union, L337.EEC Commission. (2013). Regulation (EU) No. 1308/2013 (2013, 12.20). of the European Parliament and of the Council of 17 December 2013 establishing a common organisation of the markets in agricultural products and repealing Council Regulations (EEC) No 922/72, (EEC) No 234/79, (EC) No 1037/2001 and (EC) No 1234/2007. Official Journal of the European Union, L347.EEC Commission. (2017). Commission Implementng Regulaton (EU) 2017/1184 of 20 April 2017 laying down rules for the applicaton of Regulaton (EU) No 1308/2013 of the European Parliament and of the Council as regards the Union scales for the classifcaton of beef, pig and sheep carcasses and as regards the reportng of market prices of certain categories of carcasses and live animals. Official Journal of the European Union. htps://eur-lex.europa.eu/legal-content/EN/TXT/?uri= CELEX%3A32017R1184.Font-i-Furnols, M., Čandek-Potokar, M., Daumas, G., Gispert, M., Judas, M. & Seynaeve, M. (2016). Comparison of national ZP equations for lean meat percentage assessment in SEUROP pig classification. Meat science, 113, 1-8.Gangsei, L. E., Kongsro, J., Olsen, E. V., Røe, M., Alvseike, O. & Sæbø, S. (2016). Prediction precision for lean meat percentage in Norwegian pig carcasses using 'Hennessy grading probe 7': Evaluation of methods emphasized at exploiting additional information from computed tomography. Acta Agriculturae Scandinavica, Section A — Animal Science, 66(1), 17-24.Kušec, G., Kralik, G., Djurkin, I., Margeta, V., Maltar, Z. & Petričević, A. (2006). Comparison of different methods for lean percentage evaluation in pig carcasses. Acta Agraria Kaposváriensis, 10(2), 57-62.Kušec, G., Đurkin, I., Petričević, A., Kralik, G., Maltar, Z., Margeta, V. & Hanžek, D. (2007). Equations for lean share estimation in swine carcasses in Croatia. Poljoprivreda, 13 (1), 70-73.Kušec, G., Đurkin, I., Petričević, A., Kralik, G., Maltar, Z. & Margeta, V. (2009). Carcass leanness of pigs in Croatia estimated by EU referent method. Italian Journal of Animal Science, 8(3), 249-251.Kušec, G., Đurkin, I., Lukić, B., Radišić, Ž., Petričević, A. & Maltar, Z. (2011). The Equation for Prediction of Lean Meat Percentage by Hennessy Grading Probe in Croatia. Agriculturae Conspectus Scientificus, 76 (4), 329-331.NN 71/2018 Pravilnik o razvrstavanju i označivanju goveđih, svinjskih i ovčjih trupova te označivanju mesa koje potječe od goveda starih manje od 12 mjeseci.Sack, E. (1983). Using instruments to grade pork sides. Fleischwirtschaft, 63(3), 372–379.Vester-Christensen, M., Erbou, S.G.H., Hansen, M.F., Olsen, E.V., Christensen, L.B., Hviid, M., Ersboll, B.K. & Larsen, R. (2009). Virtual dissection of pig carcasses. Meat Science, 81, 699–704Walstra, P. & Merkus, G. S. M. (1996). Procedure for assessment of the lean meat percentage as a consequence of the new EU reference dissection method in pig carcass classification: based on discussion in the EU Management Committee on Pig Meat and based on discussions with dissection experts during a meeting on May 18-19, 1994 at Zeist, NL (No. 96.014). ID-DLO.

The task of forecasting future values of the time series based on its previous values is the basis for planning in the economy, trade, energy and technical fields. Forecasting of a radio-electronic situation in the conditions of shortage of radio-frequency resource is a very important component of modern high-tech military conflicts, the transport basis of which are multi-antenna radio-emitting means. For this purpose, an analysis of the known methods of forecasting the radio-electronic situation is carried out in that article. It is established that nowadays there are many models of time series prediction, namely: regression and autoregressive models, neural network models, exponential smoothing models, Markov-based models, classification models, etc. Based on the above analysis, it is found that the most appropriate for use in the prediction problems of the electronic environment of multi-antenna communication systems are time series prediction methods, which are based on autoregressive models. The article proposes a technique for predicting the condition of the radio-electronic environment, which allows to increase the noise immunity of communication systems in the conditions of deliberate interference and the unsteady nature of the predicted process, in order to ensure electromagnetic compatibility and increase the efficiency of use of radio frequency resource by complexes. To solve the scientific problem, we use the general scientific methods of analysis and synthesis of complex technical systems, the theory of noise immunity of radio engineering systems and methods of mathematical modeling. It is advisable to use this technique while assessing the electronic environment and identifying measures to enhance the security of communications systems. The calculations show that the use of this method allows to reduce the error of the forecast by an average of 20%. It is advisable to practically implement the proposed methodology while developing software for programmable radio stations. ; Задача прогнозирования будущих значений временного ряда на основе его предыдущих значений является основой для планирования в экономике, торговле, энергетике и технических областях. Прогнозирование радиоэлектронной обстановки в условиях дефицита радиочастного ресурса является очень важной составляющей современных высокотехнологичных военных конфликтов, транспортной основой которых выступают многоантенные радиоизлучающие средства. С этой целью в указанной статье проведен анализ известных методов прогнозирования радиоэлектронной обстановки. Установлено, что на сегодняшний день существует множество моделей прогнозирования временных рядов, а именно: регрессивные и авторегрессионные модели, нейросетевые модели, модели экспоненциального сглаживания, модели на базе цепей Маркова, классификационные модели и др. На основе указанного анализа установлено, что наиболее целесообразным для использования в задачах прогнозирования радиоэлектронной обстановки многоантенных систем радиосвязи являются методы прогнозирования временных рядов на основе авторегрессионных моделей. В статье предложена методика прогнозирования состояния радиоэлектронной обстановки, которая позволяет повысить помехозащищенность систем связи в условиях воздействия преднамеренных помех и нестационарном характере прогнозируемого процесса с целью обеспечения электромагнитной совместимости и повышения эффективности при использовании радиочастотного ресурса комплексами связи. Для решения научной задачи использованы общенаучные методы анализа и синтеза сложных технических систем, теории помехозащищенности радиотехнических систем и методы математического моделирования. Указанную методику целесообразно использовать при оценке радиоэлектронной обстановки и определения мер, направленных на повышение помех защищенности систем связи. Расчеты показывают, что использование указанной методики позволяет уменьшить погрешность прогноза в среднем на 20%. Практически реализовать предложенную методику целесообразно при разработке программного обеспечения программируемых радиостанций. ; Завдання прогнозування майбутніх значень часового ряду на основі його попередніх значень є основою для планування в економіці, торгівлі, енергетиці та технічних галузях. Прогнозування радіоелектронної обстановки в умовах дефіциту радіочастного ресурсу є дуже важливою складовою сучасних високотехнологічних воєнних конфліктів, транспортною основою яких виступають багатоантенні радіовипромінюючі засоби. З цією метою в зазначеній статті проведено аналіз відомих методів прогнозування радіоелектронної обстановки. Встановлено, що на сьогоднішній день існує безліч моделей прогнозування часових рядів, а саме: регресивні і авторегресійні моделі, нейромережеві моделі, моделі експоненціального згладжування, моделі на базі ланцюгів Маркова, класифікаційні моделі та інш. На основі зазначеного аналізу встановлено, що найбільш доцільним для використання в задачах прогнозування радіоелектронної обстановки багатоантенних систем радіозв'язку є методи прогнозування часових рядів на основі авторегресійних моделей. У статті запропонована методика прогнозування стану радіоелектронної обстановки, що дозволяє підвищити завадозахищеність систем зв'язку в умовах впливу навмисних завад та нестаціонарному характері процесу, що прогнозується, з метою забезпечення електромагнітної сумісності та підвищення ефективності використанні радіочастотного ресурсу комплексами зв'язку. Для вирішення наукового завдання використані загальнонаукові методи аналізу та синтезу складних технічних систем, теорії завадозахищеності радіотехнічних систем та методи математичного моделювання. Зазначену методику доцільно використовувати при оцінці радіоелектронної обстановки та визначення заходів, що спрямовані на підвищення завадозахищеності систем зв'язку. Розрахунки показують, що використання зазначеної методики дозволяє зменшити похибку прогнозу в середньому на 20%. Практично реалізувати запропоновану методику доцільно при розробці програмного забезпечення програмованих радіостанцій.

The task of forecasting future values of the time series based on its previous values is the basis for planning in the economy, trade, energy and technical fields. Forecasting of a radio-electronic situation in the conditions of shortage of radio-frequency resource is a very important component of modern high-tech military conflicts, the transport basis of which are multi-antenna radio-emitting means. For this purpose, an analysis of the known methods of forecasting the radio-electronic situation is carried out in that article. It is established that nowadays there are many models of time series prediction, namely: regression and autoregressive models, neural network models, exponential smoothing models, Markov-based models, classification models, etc. Based on the above analysis, it is found that the most appropriate for use in the prediction problems of the electronic environment of multi-antenna communication systems are time series prediction methods, which are based on autoregressive models. The article proposes a technique for predicting the condition of the radio-electronic environment, which allows to increase the noise immunity of communication systems in the conditions of deliberate interference and the unsteady nature of the predicted process, in order to ensure electromagnetic compatibility and increase the efficiency of use of radio frequency resource by complexes. To solve the scientific problem, we use the general scientific methods of analysis and synthesis of complex technical systems, the theory of noise immunity of radio engineering systems and methods of mathematical modeling. It is advisable to use this technique while assessing the electronic environment and identifying measures to enhance the security of communications systems. The calculations show that the use of this method allows to reduce the error of the forecast by an average of 20%. It is advisable to practically implement the proposed methodology while developing software for programmable radio stations. ; Задача прогнозирования будущих значений временного ряда на основе его предыдущих значений является основой для планирования в экономике, торговле, энергетике и технических областях. Прогнозирование радиоэлектронной обстановки в условиях дефицита радиочастного ресурса является очень важной составляющей современных высокотехнологичных военных конфликтов, транспортной основой которых выступают многоантенные радиоизлучающие средства. С этой целью в указанной статье проведен анализ известных методов прогнозирования радиоэлектронной обстановки. Установлено, что на сегодняшний день существует множество моделей прогнозирования временных рядов, а именно: регрессивные и авторегрессионные модели, нейросетевые модели, модели экспоненциального сглаживания, модели на базе цепей Маркова, классификационные модели и др. На основе указанного анализа установлено, что наиболее целесообразным для использования в задачах прогнозирования радиоэлектронной обстановки многоантенных систем радиосвязи являются методы прогнозирования временных рядов на основе авторегрессионных моделей. В статье предложена методика прогнозирования состояния радиоэлектронной обстановки, которая позволяет повысить помехозащищенность систем связи в условиях воздействия преднамеренных помех и нестационарном характере прогнозируемого процесса с целью обеспечения электромагнитной совместимости и повышения эффективности при использовании радиочастотного ресурса комплексами связи. Для решения научной задачи использованы общенаучные методы анализа и синтеза сложных технических систем, теории помехозащищенности радиотехнических систем и методы математического моделирования. Указанную методику целесообразно использовать при оценке радиоэлектронной обстановки и определения мер, направленных на повышение помех защищенности систем связи. Расчеты показывают, что использование указанной методики позволяет уменьшить погрешность прогноза в среднем на 20%. Практически реализовать предложенную методику целесообразно при разработке программного обеспечения программируемых радиостанций. ; Завдання прогнозування майбутніх значень часового ряду на основі його попередніх значень є основою для планування в економіці, торгівлі, енергетиці та технічних галузях. Прогнозування радіоелектронної обстановки в умовах дефіциту радіочастного ресурсу є дуже важливою складовою сучасних високотехнологічних воєнних конфліктів, транспортною основою яких виступають багатоантенні радіовипромінюючі засоби. З цією метою в зазначеній статті проведено аналіз відомих методів прогнозування радіоелектронної обстановки. Встановлено, що на сьогоднішній день існує безліч моделей прогнозування часових рядів, а саме: регресивні і авторегресійні моделі, нейромережеві моделі, моделі експоненціального згладжування, моделі на базі ланцюгів Маркова, класифікаційні моделі та інш. На основі зазначеного аналізу встановлено, що найбільш доцільним для використання в задачах прогнозування радіоелектронної обстановки багатоантенних систем радіозв'язку є методи прогнозування часових рядів на основі авторегресійних моделей. У статті запропонована методика прогнозування стану радіоелектронної обстановки, що дозволяє підвищити завадозахищеність систем зв'язку в умовах впливу навмисних завад та нестаціонарному характері процесу, що прогнозується, з метою забезпечення електромагнітної сумісності та підвищення ефективності використанні радіочастотного ресурсу комплексами зв'язку. Для вирішення наукового завдання використані загальнонаукові методи аналізу та синтезу складних технічних систем, теорії завадозахищеності радіотехнічних систем та методи математичного моделювання. Зазначену методику доцільно використовувати при оцінці радіоелектронної обстановки та визначення заходів, що спрямовані на підвищення завадозахищеності систем зв'язку. Розрахунки показують, що використання зазначеної методики дозволяє зменшити похибку прогнозу в середньому на 20%. Практично реалізувати запропоновану методику доцільно при розробці програмного забезпечення програмованих радіостанцій.

The task of forecasting future values of the time series based on its previous values is the basis for planning in the economy, trade, energy and technical fields. Forecasting of a radio-electronic situation in the conditions of shortage of radio-frequency resource is a very important component of modern high-tech military conflicts, the transport basis of which are multi-antenna radio-emitting means. For this purpose, an analysis of the known methods of forecasting the radio-electronic situation is carried out in that article. It is established that nowadays there are many models of time series prediction, namely: regression and autoregressive models, neural network models, exponential smoothing models, Markov-based models, classification models, etc. Based on the above analysis, it is found that the most appropriate for use in the prediction problems of the electronic environment of multi-antenna communication systems are time series prediction methods, which are based on autoregressive models. The article proposes a technique for predicting the condition of the radio-electronic environment, which allows to increase the noise immunity of communication systems in the conditions of deliberate interference and the unsteady nature of the predicted process, in order to ensure electromagnetic compatibility and increase the efficiency of use of radio frequency resource by complexes. To solve the scientific problem, we use the general scientific methods of analysis and synthesis of complex technical systems, the theory of noise immunity of radio engineering systems and methods of mathematical modeling. It is advisable to use this technique while assessing the electronic environment and identifying measures to enhance the security of communications systems. The calculations show that the use of this method allows to reduce the error of the forecast by an average of 20%. It is advisable to practically implement the proposed methodology while developing software for programmable radio stations. ; Задача прогнозирования будущих значений временного ряда на основе его предыдущих значений является основой для планирования в экономике, торговле, энергетике и технических областях. Прогнозирование радиоэлектронной обстановки в условиях дефицита радиочастного ресурса является очень важной составляющей современных высокотехнологичных военных конфликтов, транспортной основой которых выступают многоантенные радиоизлучающие средства. С этой целью в указанной статье проведен анализ известных методов прогнозирования радиоэлектронной обстановки. Установлено, что на сегодняшний день существует множество моделей прогнозирования временных рядов, а именно: регрессивные и авторегрессионные модели, нейросетевые модели, модели экспоненциального сглаживания, модели на базе цепей Маркова, классификационные модели и др. На основе указанного анализа установлено, что наиболее целесообразным для использования в задачах прогнозирования радиоэлектронной обстановки многоантенных систем радиосвязи являются методы прогнозирования временных рядов на основе авторегрессионных моделей. В статье предложена методика прогнозирования состояния радиоэлектронной обстановки, которая позволяет повысить помехозащищенность систем связи в условиях воздействия преднамеренных помех и нестационарном характере прогнозируемого процесса с целью обеспечения электромагнитной совместимости и повышения эффективности при использовании радиочастотного ресурса комплексами связи. Для решения научной задачи использованы общенаучные методы анализа и синтеза сложных технических систем, теории помехозащищенности радиотехнических систем и методы математического моделирования. Указанную методику целесообразно использовать при оценке радиоэлектронной обстановки и определения мер, направленных на повышение помех защищенности систем связи. Расчеты показывают, что использование указанной методики позволяет уменьшить погрешность прогноза в среднем на 20%. Практически реализовать предложенную методику целесообразно при разработке программного обеспечения программируемых радиостанций. ; Завдання прогнозування майбутніх значень часового ряду на основі його попередніх значень є основою для планування в економіці, торгівлі, енергетиці та технічних галузях. Прогнозування радіоелектронної обстановки в умовах дефіциту радіочастного ресурсу є дуже важливою складовою сучасних високотехнологічних воєнних конфліктів, транспортною основою яких виступають багатоантенні радіовипромінюючі засоби. З цією метою в зазначеній статті проведено аналіз відомих методів прогнозування радіоелектронної обстановки. Встановлено, що на сьогоднішній день існує безліч моделей прогнозування часових рядів, а саме: регресивні і авторегресійні моделі, нейромережеві моделі, моделі експоненціального згладжування, моделі на базі ланцюгів Маркова, класифікаційні моделі та інш. На основі зазначеного аналізу встановлено, що найбільш доцільним для використання в задачах прогнозування радіоелектронної обстановки багатоантенних систем радіозв'язку є методи прогнозування часових рядів на основі авторегресійних моделей. У статті запропонована методика прогнозування стану радіоелектронної обстановки, що дозволяє підвищити завадозахищеність систем зв'язку в умовах впливу навмисних завад та нестаціонарному характері процесу, що прогнозується, з метою забезпечення електромагнітної сумісності та підвищення ефективності використанні радіочастотного ресурсу комплексами зв'язку. Для вирішення наукового завдання використані загальнонаукові методи аналізу та синтезу складних технічних систем, теорії завадозахищеності радіотехнічних систем та методи математичного моделювання. Зазначену методику доцільно використовувати при оцінці радіоелектронної обстановки та визначення заходів, що спрямовані на підвищення завадозахищеності систем зв'язку. Розрахунки показують, що використання зазначеної методики дозволяє зменшити похибку прогнозу в середньому на 20%. Практично реалізувати запропоновану методику доцільно при розробці програмного забезпечення програмованих радіостанцій.

An objective Build-Operate-Transfer (BOT) contract evaluation at the conceptual stage, in countries facing budget constraints, will lead to undertaking projects which are anticipated to be viable in the future. An objective analysis of various risk variables and their impact on a BOT project's future outcome requires study and integration of many likely scenarios into the contract terms, which is complicated and time-consuming. If the process of examining the financial parameters and uncertainties of a BOT project could be automated, this would be a milestone in objective decision-making from various stakeholders' points of view. A soft computing model would let the user analyze many probable scenarios more accurately. In this study two soft computing methods, artificial neural network (ANN) and gene expression programming (GEP) are applied onto two distinct BOT case studies to illustrate automation of their assessment processes. First a case study of BOT model on dormitory projects in Cyprus is analyzed. An ANN model with correlation coefficient of 0.9064 is developed to model the relationship between important project parameters and risk variables. Significant factors, used in ANN model development, were extracted from sensitivity analysis and Monte Carlo simulation results obtained from conventional spreadsheet data. The resulting consensus based on this model would yield to fair contractual agreements for both the government and the concession company. iv Second financial viability of undertaking a BOT contract for sewer and water projects in California, USA is analyzed. Furthermore by aid of sensitivity analysis, risk parameters are identified. Sensitivity analysis results demonstrated that project construction cost factor determines the financial viability of undertaking a BOT contract. Therefore, reliable construction cost prediction, based on limited information, at early stages of the project planning phase is crucial for development of an objective BOT agreement. This study utilized gene expression programming (GEP) which is a derivative of genetic algorithm (GA) and genetic programming (GP), and developed a prediction model with correlation coefficient of 0.8467 for estimating the construction cost of water and sewer rehabilitation/replacement projects. Contribution of this thesis to knowledge is by exploiting ANN model's capability to incorporate many scenarios, we developed an automated tool to define concession terms considering potential risks; and by utilizing GEP model 's ability to create an explicit equation, we developed a formula for a project construction cost prediction to help improve objective financial appraisal of a BOT project. Author keywords: Public-Private-Partnership; Build-Operate-Transfer; Monte Carlo simulation; Contracts; Cost Estimation; Artificial Neural Network; Gene Expression programming; Dormitory Projects; Water and Sewer Replacement/Rehabilitation Projects. ; Bütçe kısıtlamalarıyla karşı karşıya ülkelerde objektif Yap-İşlet-Devret (YİD) sözleşmelerinin kavramsal aşamada değerlendirilmesi, gelecekde positive degerli projelerin uygulamasina yol açacaktır. Çeşitli risk değişkenleri ve YİD projenin gelecek, sonuclarin üzerindeki etkileri objektif bir analiz yapmak karmaşık ve zaman alıcıdır; çünkü sözleşme şartları içine birçok muhtemel senaryolar entegrasyonunu gerektirir. YİD projenin mali parametreleri ve çeşitli belirsizliklerin incelenme süreci otomatik olursa, bu yaklaşım birçok paydaşların objektif belirleme açısından bir dönüm noktası olabilir. Soft Computing modelleri kullanıcıya daha çok senaryoları analiz etmesine izin verdiyi icin, objective karar vermesine yol vermekdedir. Bu çalışmada iki Soft Computing yöntemleri, Yapay Sinir Ağları (ANN) ve Gen tabir programlama (GEP), projelerin etkili parametrelerini belirlemek için, uygulanmiştir. İlk Kıbrıs'ta yurt projelerinde YİD modelinin bir vaka çalışması analiz edildi. 0.9064 korelasyon katsayısı ile bir YİD modeli önemli proje parametrelerinin ve risk değişkenler arasındaki ilişkiyi modellemek için geliştirildi. YİD modelinde kullanılan önemli faktörler, Hassasiyet analizi ve Monte Carlo simülasyonun konvansiyonel elektronik tablo verilerinin uzerine yapilan sonuçlara dayanarak geliştirilmiştir. Bu modele dayalı ortaya çıkan uzlaşma, hükümet ve imtiyaz şirketine adil sözleşme ortami doğuracaktir. Bu araştimada, bir de Kaliforniya ABD kanalizasyon ve su projeleri için YİD sözleşmesinin finansal kapasitesi analiz edildi. Ayrıca hassasiyet analizi yardımıyla, risk parametreleri belirlenmiştir. Hassasiyet analizi sonuçları YİD projenin inşaat vi maliyetinin mali geliri belirleyen factor oldugunu göstermiştir. Bu nedenle, proje planlama aşamasının sınırlı bilgiye dayalı, güvenilir inşaat maliyet tahmini, objektif bir YİD sözleşmesi gelişimi için çok önemlidir. Bu çalışmada kullanılan Gen tabir programlama (GEP) model sonucunda su ve kanalizasyon rehabilitasyonu / değiştirme projelerinin inşaat maliyetini tahmin etmek için 0.8467 korelasyon katsayısı ile tahmin modeli geliştirmiştir. Bu tezin bilgiye katkisi, birçok senaryolari dahil etmekle, ANN modelin yeteneğini kullanarak, potansiyel riskleri göz önüne alarak, sözleşme terimleri tanımlamak için otomatik bir araç geliştirdi; ve basit bir denklem oluşturmakla GEP modelin yeteneğini kullanarak, bir YİD projenin mali değerlendirmeye yardımcı olmak üzere inşaat maliyet tahmini için bir formül geliştirdi. Anahtar Kelimeler: Kamu-Özel-Ortaklığı; Yap-İşlet-Devret (YİD); Monte Carlo simülasyonu; Sözleşmeler; Maliyet Tahmini; Yapay Sinir Ağları (ANN); Gen tabir programlama (GEP); Yurt Projeleri; Su ve Kanalizasyon Yedek / Rehabilitasyon Projeleri. ; Doctor of Philosophy in Civil Engineering. Thesis (Ph.D.)--Eastern Mediterranean University, Faculty of Engineering, Dept. of Civil Engineering, 2015. Supervisor: Prof. Dr. Tahir Çelik.

Abstract

Objectives
This study aimed to construct, validate, and calibrate an exposure matrix that would be used to estimate personal airborne exposures to total dust, manganese, nickel, chromium, and aluminum for welders in the WHAT-ME cohort. The Workers' Health in Apprenticeship Trades: metal and electrical (WHAT-ME) study established a cohort of women and men welders to investigate pregnancy and other birth outcomes along with health issues related to welding. To construct the matrix, data were extracted and assembled from the literature and analyzed to produce exposure models. Final models derived in this first step were then compared with external data gathered under controlled conditions and later combined to form calibrated models.


Methods
A systematic literature search was conducted to identify and extract all relevant data from published journal articles appearing in selected databases. Summary data were extracted that represented airborne personal exposures to total, inhalable and respirable dusts along with metal concentrations for manganese, nickel, chromium, and aluminum. Mathematical exposure models were derived and a validation of the models undertaken in the second part of this study. The most common welding combinations of welding process, base metal, and consumable (welding scenarios) for welders taking part in the WHAT-ME study were identified through detailed welding questionnaires completed by WHAT-ME participants. These were replicated under controlled conditions with a welder equipped with a personal air sampling pump to gather samples. A gravimetric analysis was performed to determine total dust exposures followed by a metals analysis using ICP-MS. Predictions were made for these welding scenarios replicated in the laboratory, using the exposure models derived in the literature and the predictions correlated against the results from the welding laboratory replications.


Results
The systematic review yielded 92 published articles from which 737 summary statistics were extracted representing 4620 personal samples of total dust, 4762 of manganese, 4679 of nickel, 3972 of chromium, and 676 of aluminum. The highest total dust exposures were for flux-core arc welding (FCAW) while the highest manganese producing base metal was mild steel. For nickel, the highest emissions were from high alloyed steel using gas metal arc welding while chromium emissions were most abundant in manual metal arc welding on stainless steel. Aluminum exposures were highest in FCAW welding and on aluminum as a base metal. The replication of 21 scenarios covered more than 90% of the scenarios in the WHAT-ME study. Sixty-one laboratory welding sessions took place with a minimum of two replications per scenario. Spearman rank correlations between predicted exposures and mean measured exposures yielded a rho of 0.93 (P &lt; 0.001) for total dust, 0.87 (P &lt; 0.001) for manganese, 0.54 (P &lt; 0.024) for nickel, 0.43 (P = 0.055) for chromium, and 0.29 (P = 0.210) for aluminum.


Conclusions
This study produced the first welding exposure matrix composed of process, base metal, and consumable. This model was able to predict exposures observed under controlled conditions and could be used by any researcher to estimate welding exposures in a wide range of occupational contexts.

Mathematical optimization methods are the basic mathematical tools of all artificial intelligence theory. In the field of machine learning and deep learning the examples with which algorithms learn (training data) are used by sophisticated cost functions which can have solutions in closed form or through approximations. The interpretability of the models used and the relative transparency, opposed to the opacity of the black-boxes, is related to how the algorithm learns and this occurs through the optimization and minimization of the errors that the machine makes in the learning process. In particular in the present work is introduced a new method for the determination of the weights in an ensemble model, supervised and unsupervised, based on the well known Analytic Hierarchy Process method (AHP). This method is based on the concept that behind the choice of different and possible algorithms to be used in a machine learning problem, there is an expert who controls the decisionmaking process. The expert assigns a complexity score to each algorithm (based on the concept of complexity-interpretability trade-off) through which the weight with which each model contributes to the training and prediction phase is determined. In addition, different methods are presented to evaluate the performance of these algorithms and explain how each feature in the model contributes to the prediction of the outputs. The interpretability techniques used in machine learning are also combined with the method introduced based on AHP in the context of clinical decision support systems in order to make the algorithms (black-box) and the results interpretable and explainable, so that clinical-decision-makers can take controlled decisions together with the concept of "right to explanation" introduced by the legislator, because the decision-makers have a civil and legal responsibility of their choices in the clinical field based on systems that make use of artificial intelligence. No less, the central point is the interaction between the expert who controls the algorithm construction process and the domain expert, in this case the clinical one. Three applications on real data are implemented with the methods known in the literature and with those proposed in this work: one application concerns cervical cancer, another the problem related to diabetes and the last one focuses on a specific pathology developed by HIV-infected individuals. All applications are supported by plots, tables and explanations of the results, implemented through Python libraries. The main case study of this thesis regarding HIV-infected individuals concerns an unsupervised ensemble-type problem, in which a series of clustering algorithms are used on a set of features and which in turn produce an output used again as a set of meta-features to provide a set of labels for each given cluster. The meta-features and labels obtained by choosing the best algorithm are used to train a Logistic regression meta-learner, which in turn is used through some explainability methods to provide the value of the contribution that each algorithm has had in the training phase. The use of Logistic regression as a meta-learner classifier is motivated by the fact that it provides appreciable results and also because of the easy explainability of the estimated coefficients.