teoria economica dell'impresa: incertezza, coordinamento organizzativo e diritti di proprietà

Questa tesi si basa sull’idea di teoria dell’impresa trattata da Mario Morroni > e sui modelli economici lavorati da Rosanna Nisticò >.L’impresa può essere definita come una funzione di produzione in cui l’output dipende dalla combinazione e dalla quantità impiegata degli input nel processo produttivo. L’incertezza è l’insieme dei fattori che influenzano il valore dello scambio tra le parti ma non è controllabile dalle parti. L’incertezza è una condizione di base che rende incompleti i contratti.L’incertezza è legata all’abilità individuale di prevedere eventi futuri e di rielaborare le informazioni. L’abilità di previsione è importante in tutti i casi in cui le conseguenze di una data scelta si verificano in un momento successivo a quello in cui la decisione e stata presa. I tre aspetti del coordinamento sono capabilities, transazioni e scala. Le capabilities dell’impresa consistono nell’abilità di produrre e vendere specifici beni e servizi che soddisfano la potenziale domanda.Le transazioni riguardano il passaggio di un diritto di proprietà o l’affitto di un bene o la fornitura di servizi.I costi di transazione includono tutti i costi legati allo scambio.Il terzo aspetto del coordinamento organizzativo consiste nel definire la dimensione dei vari processi, che permetta di sfruttare le economie di scala o di gamma. La dimensione di scala di un’impresa, un’unità produttiva o un impianto è costituita dal numero massimo di processi effettuati nell’unità di tempo. Lo sviluppo dell’impresa deriva dall’abilità del management di sfruttare al meglio i vantaggi provenienti dall’interazione dei tre aspetti del coordinamento organizzativo riguardanti lo sviluppo delle capabilities, l’organizzazione delle transazioni e il disegno della scala dei processi produttivi, limitando i fattori negativi causati essenzialmente dalla presenza di asimmetrie informative o dal prevalere degli interessi di singoli stakeholders. Poi presenta il modello di monopolio bilaterale e quello basato sulla teoria di proprietà

A generalized SIRVS model incorporating non-Markovian infection processes and waning immunity

The Markovian approach, which assumes constant transmission rates and thus leads to exponentially distributed inter-infection times, is dominant in epidemic modeling. However, this assumption is unrealistic as an individual's infectiousness depends on its viral load and varies over time. In this paper, we present a SIRVS epidemic model incorporating non-Markovian infection processes. The model can be easily adapted to accurately capture the generation time distributions of emerging infectious diseases, which is essential for accurate epidemic prediction. We observe noticeable variations in the transient behavior under different infectiousness profiles and the same basic reproduction number R0. The theoretical analyses show that only R0 and the mean immunity period of the vaccinated individuals have an impact on the critical vaccination rate needed to achieve herd immunity. A vaccination level at the critical vaccination rate can ensure a relatively low incidence among the population in case of future epidemics, regardless of the infectiousness profiles

Modeling Three-dimensional Invasive Solid Tumor Growth in Heterogeneous Microenvironment under Chemotherapy

A systematic understanding of the evolution and growth dynamics of invasive solid tumors in response to different chemotherapy strategies is crucial for the development of individually optimized oncotherapy. Here, we develop a hybrid three-dimensional (3D) computational model that integrates pharmacokinetic model, continuum diffusion-reaction model and discrete cell automaton model to investigate 3D invasive solid tumor growth in heterogeneous microenvironment under chemotherapy. Specifically, we consider the effects of heterogeneous environment on drug diffusion, tumor growth, invasion and the drug-tumor interaction on individual cell level. We employ the hybrid model to investigate the evolution and growth dynamics of avascular invasive solid tumors under different chemotherapy strategies. Our simulations reproduce the well-established observation that constant dosing is generally more effective in suppressing primary tumor growth than periodic dosing, due to the resulting continuous high drug concentration. In highly heterogeneous microenvironment, the malignancy of the tumor is significantly enhanced, leading to inefficiency of chemotherapies. The effects of geometrically-confined microenvironment and non-uniform drug dosing are also investigated. Our computational model, when supplemented with sufficient clinical data, could eventually lead to the development of efficient in silico tools for prognosis and treatment strategy optimization.Comment: 41 pages, 8 figure

Saliency Based Opportunitstic Search for Object Part Extraction and Labeling

We study the task of object part extraction and labeling, which seeks to understand objects beyond simply identifiying their bounding boxes. We start from bottom-up segmentation of images and search for correspondences between object parts in a few shape models and segments in images. Segments comprising different object parts in the image are usually not equally salient due to uneven contrast, illumination conditions, clutter, occlusion and pose changes. Moreover, object parts may have different scales and some parts are only distinctive and recognizable in a large scale. Therefore, we utilize a multi-scale shape representation of objects and their parts, figural contextual information of the whole object and semantic contextual information for parts. Instead of searching over a large segmentation space, we present a saliency based opportunistic search framework to explore bottom-up segmentation by gradually expanding and bounding the search domain.We tested our approach on a challenging statue face dataset and 3 human face datasets. Results show that our approach significantly outperforms Active Shape Models using far fewer exemplars. Our framework can be applied to other object categories

Bis[hexa­amminecobalt(III)] penta­chloride nitrate

The title compound, [Co(NH3)6]2Cl5(NO3), was obtained under hydro­thermal conditions. The asymmetric unit contains three Co3+ ions, one lying on an inversion center and the other two located at 2/m positions. All Co3+ ions are six-coordinated by NH3 mol­ecules, forming [Co(NH3)6]3+ octahedra, with Co—N distances in the range 1.945 (4)–1.967 (3) Å. The nitrate N atom and one of the O atoms lie at a mirror plane. Among the Cl− anions, one lies in a general position, one on a twofold axis and two on a mirror plane. N—H⋯O and N—H⋯Cl hydrogen bonds link the cations and anions into a three-dimensional network

A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation

Due to the abundant neurophysiological information in the electroencephalogram (EEG) signal, EEG signals integrated with deep learning methods have gained substantial traction across numerous real-world tasks. However, the development of supervised learning methods based on EEG signals has been hindered by the high cost and significant label discrepancies to manually label large-scale EEG datasets. Self-supervised frameworks are adopted in vision and language fields to solve this issue, but the lack of EEG-specific theoretical foundations hampers their applicability across various tasks. To solve these challenges, this paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2), which integrates neurological theory to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. Sequentially, inter-view and cross-view contrastive learning pipelines in combination with various augmentation methods are applied to capture neural features from different views. By modeling prior neural knowledge based on homologous neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations. Experimental results on different downstream tasks demonstrate that our method outperforms state-of-the-art methods, highlighting the superior generalization of neural knowledge-supported EEG representations across various brain tasks.Comment: 14pages,7 figure

Decoupling the Amplitude and Wavelength of Anisotropic Topography and the Influence on Osteogenic Differentiation of Mesenchymal Stem Cells Using a High-Throughput Screening Approach

High-throughput screening (HTS) methods based on anisotropically topography gradients have been broadly used to investigate the interactions between cells and biomaterials. However, few studies focus on the optimum parameters of topography for osteogenic differentiation because the structures of topography are complex with multiple combinations of parameters. In this study, we developed polydimethylsiloxane (PDMS)-based wrinkled topography gradients (amplitudes between 144 and 2854 nm and wavelengths between 0.91 and 13.62 mu m) and decoupled the wavelength and amplitude via imprinting lithography and shielded plasma oxidation. The PDMS wrinkle gradient was then integrated with the bottomless 96-well plate to constitute the wrinkled HTS platform, which consists of 70 different wrinkle parameters. From the in vitro culture of bone marrow stem cells, it was observed that aligned topography has an important influence on the macroscopic cell behavior (i.e., cell area, elongation, and nucleus area). Furthermore, the optimum wrinkle parameter (wavelength: 1.91 mu m; amplitude: 360 nm) for osteogenic differentiation of stem cells was determined via this screening plate approach. This screening platform is not only beneficial for a better understanding of the interactions between topography and biomaterials but also advances the development of bone tissue engineering developments