We used RUSI (rehabilitative ultrasound imaging) for measurement of abdominal and back muscle in different loading modes. Methods: We used diagnostic ultrasonography imaging for taking linear measurement of trunk muscles. We measured anterioposterior (AP) dimensions of lateral abdominal wall muscles- m. OE, m.OI, m.TrA and cross-section area (CSA) of lumbar m. multifidus. We compared two groups of sports-floorball players and sportsman using climbing and hanging (climbers, aerialists). We measured positions with compressive force (kneeling on all four with lifted knees) and with traction load (hang with upper limbs with flexion of lower limbs-with leg support and without). Study is made on 50 volunteers. Results: The pattern of thickness of abdominal muscles is same in all positions in both groups. The lowest is always AP thickness of m. TrA, wider is m. OE and the widest always m. OI. The resting thickness are in both groups almost in all cases the lowest. AP thickness in m. TrA in floorball players is only exception, there is lowest in hang without legs support. In all other case sis resting position always lowest. For m. OE are results same for both climbers and floorball players-the lowest thickness is in hang with legs support (floorball players 0,84 cm, climbers 0,87), greater activationis...

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In geotechnical engineering, the uncertainties such as the variability and uncertainty inherent in the geotechnical properties have caught more and more attentions from researchers and engineers. They have found that a single “Factor of Safety” calculated by traditional deterministic analyses methods can not represent the slope stability exactly. Recently in order to provide a more rational mathematical framework to incorporate different types of uncertainties in the slope stability estimation, reliability analyses and non-deterministic methods, which include probabilistic and non probabilistic (imprecise methods) methods, have been applied widely. In short, the slope non-deterministic analysis is to combine the probabilistic analysis or non probabilistic analysis with the deterministic slope stability analysis. It cannot be regarded as a completely new slope stability analysis method, but just an extension of the slope deterministic analysis. The slope failure probability calculated by slope non-deterministic analysis is a kind of complement of safety factor. Therefore, the accuracy of non deterministic analysis is not only depended on a suitable probabilistic or non probabilistic analysis method selected, but also on a more rigorous deterministic analysis method or geological model adopted. In this thesis, reliability concepts have been reviewed first, and some typical non-deterministic methods, including Monte Carlo Simulation (MCS), First Order Reliability Method (FORM), Point Estimate Method (PEM) and Random Set Theory (RSM), have been described and successfully applied to the slope stability analysis based on a numerical simulation method-Strength Reduction Method (SRM). All of the processes have been performed in a commercial finite difference code FLAC and a distinct element code UDEC. First of all, as the fundamental of slope reliability analysis, the deterministic numerical simulation method has been improved. This method has a higher accuracy than the conventional limit equilibrium methods, because of the reason that the constitutive relationship of soil is considered, and fewer assumptions on boundary conditions of slope model are necessary. However, the construction of slope numerical models, particularly for the large and complicated models has always been very difficult and it has become an obstacle for application of numerical simulation method. In this study, the excellent spatial analysis function of Geographic Information System (GIS) technique has been introduced to help numerical modeling of the slope. In the process of modeling, the topographic map of slope has been gridded using GIS software, and then the GIS data was transformed into FLAC smoothly through the program built-in language FISH. At last, the feasibility and high efficiency of this technique has been illustrated through a case study-Xuecheng slope, and both 2D and 3D models have been investigated. Subsequently, three most widely used probabilistic analyses methods, Monte Carlo Simulation, First Order Reliability Method and Point Estimate Method applied with Strength Reduction Method have been studied. Monte Carlo Simulation which needs to repeat thousands of deterministic analysis is the most accurate probabilistic method. However it is too time consuming for practical applications, especially when it is combined with numerical simulation method. For reducing the computation effort, a simplified Monte Carlo Simulation-Strength Reduction Method (MCS-SRM) has been developed in this study. This method has estimated the probable failure of slope and calculated the mean value of safety factor by means of soil parameters first, and then calculated the variance of safety factor and reliability of slope according to the assumed probability density function of safety factor. Case studies have confirmed that this method can reduce about 4/5 of time compared with traditional MCS-SRM, and maintain almost the same accuracy. First Order Reliability Method is an approximate method which is based on the Taylor\'s series expansion of performance function. The closed form solution of the partial derivatives of the performance function is necessary to calculate the mean and standard deviation of safety factor. However, there is no explicit performance function in numerical simulation method, so the derivative expressions have been replaced with equivalent difference quotients to solve the differential quotients approximately in this study. Point Estimate Method is also an approximate method involved even fewer calculations than FORM. In the present study, it has been integrated with Strength Reduction Method directly. Another important observation referred to the correlation between the soil parameters cohesion and friction angle. Some authors have found a negative correlation between cohesion and friction angle of soil on the basis of experimental data. However, few slope probabilistic studies are found to consider this negative correlation between soil parameters in literatures. In this thesis, the influence of this correlation on slope probability of failure has been investigated based on numerical simulation method. It was found that a negative correlation considered in the cohesion and friction angle of soil can reduce the variability of safety factor and failure probability of slope, thus increasing the reliability of results. Besides inter-correlation of soil parameters, these are always auto-correlated in space, which is described as spatial variability. For the reason that knowledge on this character is rather limited in literature, it is ignored in geotechnical engineering by most researchers and engineers. In this thesis, the random field method has been introduced in slope numerical simulation to simulate the spatial variability structure, and a numerical procedure for a probabilistic slope stability analysis based on Monte Carlo simulation was presented. The soil properties such as cohesion and friction angle were discretized to continuous random fields based on local averaging method. In the case study, both stationary and non-stationary random fields have been investigated, and the influence of spatial variability and averaging domain on the convergence of numerical simulation and probability of failure was studied. In rock medium, the structure faces have very important influence on the slope stability, and the rock material can be modeled as the combination of rigid or deformable blocks with joints in distinct element method. Therefore, much more input parameters like strength of joints are required to input the rock slope model, which increase the uncertainty of the results of numerical model. Furthermore, because of the limitations of the current laboratory and in-site testes, there is always lack of exact values of geotechnical parameters from rock material, even the probability distribution of these variables. Most of time, engineers can only estimate the interval of these variables from the limit testes or the expertise’s experience. In this study, to assess the reliability of the rock slope, a Random Set Distinct Element Method (RS-DEM) has been developed through coupling of Random Set Theory and Distinct Element Method, and applied in a rock slope in Sichuan province China.

第一章：諸論 一、樂府源流二、研究動機三、各章順序四、各節要旨 第二章張籍及其樂府詩 第一節：張籍小傳第二節：張籍樂府詩的命題第三節：張籍樂府詩的內容分析一、 暴露戰爭罪惡的樂府詩二、諷諭社會問題的樂府詩１．諷諭重稅２．諷諭徭役３． 諷諭官吏４．諷諭仙思三、代婦女訴怨的樂府詩第四節：張籍樂府詩的形式一、句 式二、平仄三、用韻四、語法第五節：張籍樂府詩的表現技巧一、比興二、設問三 、比擬四、警句五、結構第六節：小跋 第三章：王建及其樂府詩 第一節：王建小傳第二節：王建樂府詩的命題第三節：王建樂府詩的內容分析一、 厭惡戰爭的樂府詩二、諷諭社會的樂府詩三、議論生死的樂府詩四、為宮女訴怨的 宮詞樂府詩第四節：王建樂府詩的形式一、句式二、古體與近體絕句第五節：王建 樂府詩的表現技巧一、鑲嵌二、疊句三、疊字四、結構第六節：小跋 第四章：白居易及其樂府詩 第一節：白居易小傳第二節：白居易樂府詩的命題第三節：白居易樂府詩的內容分 析一、長恨砍與琵琶行二、體恤民困的樂府詩三、褒美節烈的樂府詩第四節：白居 易樂府詩的形式一、句式二、用韻三、對仗四、語法第五節：白居易樂府詩的表現 技巧一、諷諭二、疊句三、用典四、結構第六節：小跋 第五章：元稹及其樂詩的命題 第一節：元稹小傳第二節：元稹樂府詩的命題第三節：元稹樂府詩的內容分析一、 諷女禍亂國的樂府詩二、設苛徵厭戰的樂府詩三、諷仕途艱險樂的樂府詩第四節： 元稹樂府詩的形式一、句式二、對仗、三、語法第五節：元稹樂府詩的表現技巧一、 比興 二、諷諭三、結構第六節：小跋 第六章：劉禹錫及其樂府詩第一節：劉禹錫小傳第二節：劉禹錫樂府詩的命題第三節 ：劉禹錫樂府詩的內容分析一、寫巴蜀民歌的竹枝樂府詩二、歌武陸土俗呡風的樂府 詩三、感喟懷古的樂府詩第四節：劉禹錫樂府詩的形式一、句式二、詩體與內容的關 係第五節：劉禹錫樂府詩的表現技巧一、摹狀二、比擬三、結構第六節：小跋 第七章：韓愈及其樂府詩 第一節：韓愈小傳第二節：韓愈樂府詩的命題第三節：韓愈樂府詩的內容分析一、鬱 鬱不得行其志的樂府詩二、諷權奸亂政的樂府詩三、厭惡是非混淆的樂府詩第四節： 韓愈樂府詩的形式一、句式二、平仄三、用韻第五節：韓愈樂府詩的表現技巧一、怪 字與怪詞二、感喟句三、語助詞四、結構第六節：小跋 第八八章：孟郊及其樂府詩 第一節：孟郊小傳第二節：孟郊樂府詩的命題第三節：孟郊樂府詩的內容分析一、北 貧困無助的樂府詩二、設苛徵虐民的樂府詩三、諷征戍不休的欒府詩第四節：孟郊樂 府詩的形式一、句式二、平仄三、對仗第五節、孟郊樂府詩的表現技巧一、感嘆二、 比興三、結構第六節：小跋 第九章：李賀及其樂府詩 第一節：李賀小傳第二節：李賀樂府詩的命題第三節：李賀樂府詩的內容分析一、懷 才不遇的樂府詩二、感喟人生短促的樂府詩三、多病苦吟的樂府詩四、對歷史人事追 懷的樂府詩五、對鬼狐神仙遐思樂的樂府詩第四節：李賀樂府詩的形式一、句式二、 雜言體的用韻三、古體詩的對仗第五節：李賀樂府詩的表現技巧一、用字二、奇句三 、結構第六節：小跋 第十章：結論 一、中唐樂府是否入樂二、樂府詩集古題與新題歸類的矛盾三、無法確認的－－疑似 樂府詩四、中唐樂府詩人多喜新題樂府五、樂府與變文詩歌有關聯六、樂府詩衰微與 詞的興起七、中唐樂府詩的影響附：參考書目 /

In geotechnical engineering, the uncertainties such as the variability and uncertainty inherent in the geotechnical properties have caught more and more attentions from researchers and engineers. They have found that a single “Factor of Safety” calculated by traditional deterministic analyses methods can not represent the slope stability exactly. Recently in order to provide a more rational mathematical framework to incorporate different types of uncertainties in the slope stability estimation, reliability analyses and non-deterministic methods, which include probabilistic and non probabilistic (imprecise methods) methods, have been applied widely. In short, the slope non-deterministic analysis is to combine the probabilistic analysis or non probabilistic analysis with the deterministic slope stability analysis. It cannot be regarded as a completely new slope stability analysis method, but just an extension of the slope deterministic analysis. The slope failure probability calculated by slope non-deterministic analysis is a kind of complement of safety factor. Therefore, the accuracy of non deterministic analysis is not only depended on a suitable probabilistic or non probabilistic analysis method selected, but also on a more rigorous deterministic analysis method or geological model adopted. In this thesis, reliability concepts have been reviewed first, and some typical non-deterministic methods, including Monte Carlo Simulation (MCS), First Order Reliability Method (FORM), Point Estimate Method (PEM) and Random Set Theory (RSM), have been described and successfully applied to the slope stability analysis based on a numerical simulation method-Strength Reduction Method (SRM). All of the processes have been performed in a commercial finite difference code FLAC and a distinct element code UDEC. First of all, as the fundamental of slope reliability analysis, the deterministic numerical simulation method has been improved. This method has a higher accuracy than the conventional limit equilibrium methods, because of the reason that the constitutive relationship of soil is considered, and fewer assumptions on boundary conditions of slope model are necessary. However, the construction of slope numerical models, particularly for the large and complicated models has always been very difficult and it has become an obstacle for application of numerical simulation method. In this study, the excellent spatial analysis function of Geographic Information System (GIS) technique has been introduced to help numerical modeling of the slope. In the process of modeling, the topographic map of slope has been gridded using GIS software, and then the GIS data was transformed into FLAC smoothly through the program built-in language FISH. At last, the feasibility and high efficiency of this technique has been illustrated through a case study-Xuecheng slope, and both 2D and 3D models have been investigated. Subsequently, three most widely used probabilistic analyses methods, Monte Carlo Simulation, First Order Reliability Method and Point Estimate Method applied with Strength Reduction Method have been studied. Monte Carlo Simulation which needs to repeat thousands of deterministic analysis is the most accurate probabilistic method. However it is too time consuming for practical applications, especially when it is combined with numerical simulation method. For reducing the computation effort, a simplified Monte Carlo Simulation-Strength Reduction Method (MCS-SRM) has been developed in this study. This method has estimated the probable failure of slope and calculated the mean value of safety factor by means of soil parameters first, and then calculated the variance of safety factor and reliability of slope according to the assumed probability density function of safety factor. Case studies have confirmed that this method can reduce about 4/5 of time compared with traditional MCS-SRM, and maintain almost the same accuracy. First Order Reliability Method is an approximate method which is based on the Taylor\'s series expansion of performance function. The closed form solution of the partial derivatives of the performance function is necessary to calculate the mean and standard deviation of safety factor. However, there is no explicit performance function in numerical simulation method, so the derivative expressions have been replaced with equivalent difference quotients to solve the differential quotients approximately in this study. Point Estimate Method is also an approximate method involved even fewer calculations than FORM. In the present study, it has been integrated with Strength Reduction Method directly. Another important observation referred to the correlation between the soil parameters cohesion and friction angle. Some authors have found a negative correlation between cohesion and friction angle of soil on the basis of experimental data. However, few slope probabilistic studies are found to consider this negative correlation between soil parameters in literatures. In this thesis, the influence of this correlation on slope probability of failure has been investigated based on numerical simulation method. It was found that a negative correlation considered in the cohesion and friction angle of soil can reduce the variability of safety factor and failure probability of slope, thus increasing the reliability of results. Besides inter-correlation of soil parameters, these are always auto-correlated in space, which is described as spatial variability. For the reason that knowledge on this character is rather limited in literature, it is ignored in geotechnical engineering by most researchers and engineers. In this thesis, the random field method has been introduced in slope numerical simulation to simulate the spatial variability structure, and a numerical procedure for a probabilistic slope stability analysis based on Monte Carlo simulation was presented. The soil properties such as cohesion and friction angle were discretized to continuous random fields based on local averaging method. In the case study, both stationary and non-stationary random fields have been investigated, and the influence of spatial variability and averaging domain on the convergence of numerical simulation and probability of failure was studied. In rock medium, the structure faces have very important influence on the slope stability, and the rock material can be modeled as the combination of rigid or deformable blocks with joints in distinct element method. Therefore, much more input parameters like strength of joints are required to input the rock slope model, which increase the uncertainty of the results of numerical model. Furthermore, because of the limitations of the current laboratory and in-site testes, there is always lack of exact values of geotechnical parameters from rock material, even the probability distribution of these variables. Most of time, engineers can only estimate the interval of these variables from the limit testes or the expertise’s experience. In this study, to assess the reliability of the rock slope, a Random Set Distinct Element Method (RS-DEM) has been developed through coupling of Random Set Theory and Distinct Element Method, and applied in a rock slope in Sichuan province China.

Le Nazioni Unite hanno espresso la necessità che aziende e consumatori si orientino ad una maggiore sostenibilità. Questa tesi parte da tale constatazione e analizza l’impatto che la sostenibilità ha sulle strategie aziendali, sull’orientamento all’innovazione di prodotto e processo e, infine, sulle relazioni che si sviluppano nella filiera produttiva. Inoltre, viene analizzata la relazione tra le strategie di marketing e le scelte di consumo sostenibili. Vengono analizzate, innanzitutto, due aziende di moda. L’analisi qualitativa mira a definire cosa sia per loro la sostenibilità, in che modo questa le metta alla prova e cosa possa garantire il successo duraturo di una strategia sostenibile. Viene successivamente analizzato un campione di 1104 intervistati per comprendere come le aziende di moda dovrebbero comunicare il loro impegno nella sostenibilità, attraverso mezzi di comunicazione specifici e ben definiti, soprattutto a quei consumatori che sono propensi - ma non del tutto convinti - ad acquistare abbigliamento sostenibile. Infine, viene analizzato un campione di 99 aziende vinicole a cui è stato sottoposto un questionario per capire se esiste una relazione tra la collaborazione tra i partner lungo la filiera produttiva e la performance sostenibile dell’impresa. I risultati evidenziano i fattori che orientano le strategie sostenibili delle aziende vinicole e della moda, e il comportamento dei consumatori. Vengono evidenziate soluzioni pratiche che possano accompagnare le aziende verso un crescente orientamento alla sostenibilità. / This doctoral thesis aims at shedding light on the United Nations’ call-to-action for producers and consumers who, jointly, have to contribute to sustainable development. The aim is to investigate the impact of sustainability on companies’ strategies by exploring their orientation towards process and product innovation, and the consequent business relationships that develop along the supply chain. Furthermore, the thesis seeks to investigate the role of some marketing strategies and the consumers’ sustainable attitudes. Two fashion companies are analyzed to explore what sustainability is for them and how they are challenged by sustainability concerns. Propositions are formulated that can help companies define long-term and effective sustainability-oriented strategies. A sample of 1104 respondents is then analyzed to understand how sustainability-oriented fashion companies should communicate their commitment, through specific and well defined communication tools, to those consumers who are positively disposed towards sustainable clothing but are still afraid of these “new” products. Lastly, a sample of 99 wine companies is analyzed. They were asked to answer a questionnaire whose purpose is to understand if there is a relationship between collaboration among partners along the supply chain and sustainable performance. Results allow for a better understanding of the factors driving sustainable strategies of fashion and wine companies, and consumer behavior. Practical solutions are highlighted.