Per un\u2019architettura corale, contaminazione tra discipline e culture

La professione dell\u2019architetto oggi deve essere reinventata e fatta allontanare da quella immagine anacronistica dell\u2019architetto-artista capace di risolvere con \u201cil suo genio\u201d le complesse problematiche della societ\ue0 multietnica contemporanea che si manifesta anzitutto nelle nostre periferie. Ci sar\ue0 invece sempre pi\uf9 bisogno di progetti collettivi e non risposte individuali, in un positivo concorso non solo di professioni ma anche di discipline: architettura e design, con le altre arti plastiche, in primis la scultura. Certo l\u2019architettura non \ue8 un oggetto da osservare, ma un campo di energia e relazioni all\u2019interno del quale imparare a muoversi. Per questo il carattere privilegiato del progettista sar\ue0 la disponibilit\ue0 alla contaminazione con altre discipline e culture, in un mondo in cui si contraggono le distanze e tutto appare ad un passo

Minimization of Halftone Noise in FLAT Regions for Improved Print Quality

The work in this thesis proposes a novel algorithm for enhancing the quality of flat regions in printed color image documents. The algorithm is designed to identify the flat regions based on certain criteria and filter these regions to minimize the noise prior and post Halftoning so as to make the hard copy look visibly pleasing. Noise prior to halftone process is removed using a spatial Gaussian filter together with a Hamming window, concluded from results after implementing various filtering techniques. A clustered dithering is applied in each channel of the image as Halftoning process. Furthermore, to minimize the post halftone noise, the halftone structure of the image is manipulated according to the neighboring sub-cells in their respective channels. This is done to reduce the brightness variation (a cause for noise) between the neighboring subcells. Experimental results show that the proposed algorithm efficiently minimizes noise in flat regions of mirumal gradient change in color images

Speech identification and cortical potentials in individuals with auditory neuropathy

<p>Abstract</p> <p>Background</p> <p>Present study investigated the relationship between speech identification scores in quiet and parameters of cortical potentials (latency of P1, N1, and P2; and amplitude of N1/P2) in individuals with auditory neuropathy.</p> <p>Methods</p> <p>Ten individuals with auditory neuropathy (five males and five females) and ten individuals with normal hearing in the age range of 12 to 39 yr participated in the study. Speech identification ability was assessed for bi-syllabic words and cortical potentials were recorded for click stimuli.</p> <p>Results</p> <p>Results revealed that in individuals with auditory neuropathy, speech identification scores were significantly poorer than that of individuals with normal hearing. Individuals with auditory neuropathy were further classified into two groups, Good Performers and Poor Performers based on their speech identification scores. It was observed that the mean amplitude of N1/P2 of Poor Performers was significantly lower than that of Good Performers and those with normal hearing. There was no significant effect of group on the latency of the peaks. Speech identification scores showed a good correlation with the amplitude of cortical potentials (N1/P2 complex) but did not show a significant correlation with the latency of cortical potentials.</p> <p>Conclusion</p> <p>Results of the present study suggests that measuring the cortical potentials may offer a means for predicting perceptual skills in individuals with auditory neuropathy.</p

Enhancement of Power System Dynamic Performance by Coordinated Design of PSS and FACTS Damping Controllers

Due to environmental and economical constraints, it is difficult to build new power lines and to reinforce the existing ones. The continued growth in demand for electric power must therefore to a great extent be met by increased loading of available lines. A consequence of this is reduction of power system damping, leading to a risk of poorly damped power oscillations between generators. To suppress these oscillations and maintain power system dynamic performance, one of the conventional, economical and effective solutions is to install a power system stabilizer (PSS). However, in some cases PSS may not provide sufficient damping for the inter-area oscillations in a multi-machine power system. In this context, other possible solutions are needed to be exposed. With the evolution of power electronics, flexible AC transmission systems (FACTS) controllers turn out to be possible solution to alleviate such critical situations by controlling the power flow over the AC transmission line and improving power oscillations damping. However, coordination of conventional PSS with FACTS controllers in aiding of power system oscillations damping is still an open problem. Therefore, it is essential to study the coordinated design of PSS with FACTS controllers in a multi-machine power system. This thesis gives an overview of the modelling and operation of power system with conventional PSS. It gives the introduction to emerging FACTS controllers with emphasis on the TCSC, SVC and STATCOM controllers. The basic modelling and operating principles of the controllers are explained in this thesis, along with the power oscillations damping (POD) stabilizers. The coordination design of PSS and FACTS damping controllers over a wide range of operating conditions is formulated as an optimization problem. The objective function of this optimization problem is framed using system eigen values and it is solved using AAPSO and IWO algorithms. The optimal control parameters of coordinated controllers are obtained at the end of these optimization algorithms. A comprehensive approach to the hybrid coordinated design of PSS with series and shunt FACTS damping controllers is proposed to enhance the overall system dynamic performance. The robustness and effectiveness of proposed hybrid coordinated designs are demonstrated through the eigen value analysis and time-domain simulations. The proposed hybrid designs provide robust dynamic performance under wide range in load condition and providing significant improvement in damping power system oscillations under severe disturbance. The developed hybrid coordinated designs are tested in different multimachine power systems using AAPSO and IWO algorithms. The IWO based hybrid designs and AAPSO based hybrid designs are more effective than other control designs. In addition to this, the proposed designs are implemented and validated in real-time using Opal-RT hardware simulator. The real-time simulations of different test power systems with different proposed designs are carried out for a severe fault disturbance. Finally, the proposed controller simulation results are validated with real-time results

Class Representative Projection for Text-based Zero-Shot Learning

Title from PDF of title page viewed November 5, 2020Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 44-48)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2020There have been significant advances in supervised machine learning and enormous benefits from deep learning for a range of diverse applications. Despite the success of deep learning, in reality, very few works have shown progress in text classification. Transfer learning, known as the zero-shot learning (ZSL) or generalized zero-shot learning (G-ZSL), is receiving much attention due to its ability to transfer knowledge learned from a known (seen) domain to unknown (unseen) domains. But most of the ZSL works are relying on large training corpus and external semantic knowledge. Thus, there are very few studies that have investigated the improvement of text classification performance in sorely text-based ZSL/G-ZSL. In this thesis, a class representative framework was proposed for text-based ZSL by designing the novel projection method, learned from the seen classes, and applying it to transfer the knowledge to the unseen classes effectively. We designed a three-step approach, which consists of (1) sentence-based embeddings, (2) deep neural networks, and (3) class-based representative classifiers. Experimental results show that the proposed projection framework achieves the best classification results in text-based ZSL/G-ZSL compared with the state-of-the-art approaches investigated with three benchmark datasets including large newsgroup post of 20 classes called 20 Newsgroup Dataset and DBpedia dataset on various topics.Introduction -- Background and Related Work -- Literature on Document Embeddings -- Proposed Framework -- Conclusion and future wor

AI-Driven Decision Support Systems in Management: Enhancing Strategic Planning and Execution

Artificial intelligence (AI) is transforming strategic decision-making processes across various industries. Organizations increasingly rely on AI-driven decision support systems that leverage massive amounts of data and real-time analytics to enable more informed planning and predictive capabilities. However, less focused research has explored the integration and impact of such tools specifically within managerial strategy and execution contexts. This study conducts qualitative and quantitative analysis on the deployment of machine learning-based recommendation systems aimed at enhancing the strategic capabilities of management teams. Results indicate that AI decision tools led to improved analytic capacities, competitive response times, and reimagined vision planning, yet also posed transparency and trust challenges around advanced automation techniques. Findings provide novel implications into AI’s emerging role in augmenting and extending higher-level organizational strategy design and enactment by key decision-makers and leaders. Future directions are discussed related to addressing responsible development issues as adoption continues accelerating

ASSESSMENT, EVALUATION AND ANALYSIS OF THE MEDICATION ERRORS OF THE PATIENTS ADMITTED AT THE EMERGENCY DEPARTMENT OF A TERTIARY CARE TEACHING HOSPITAL OF A SOUTH INDIAN CITY

OBJECTIVE: The study was to assess, evaluate and analyze the medication errors of the patients admitted at the Emergency Department of a tertiary care teaching hospital.METHODS: The study was conducted for 6 months. Data was collected from the patients  admitted in the Emergency Department. The collected data was analyzed to identify medication errors and prescription errors in emergency unit in hospital by using drug information tools like Micromedex, Drug interaction checker, Stockley drug interaction text, BNF, Journals with good impact factor etc.RESULTS: A total of 200 patients were enrolled in the study according to the inclusion criteria and exclusion criteria in which 108 were males and 92 were females. 340 medication errors were obtained in 122 patients and 78 patients did not have any error. Medication errors were more commonly in the age group of 61-70 years (49%). In 340 medication errors, DDIs were the most (63.3%), followed by drug duplication (13.53%) and drugs given without indication (8.5%). In DDIs moderate interactions were the mostly seen error. On prescription analysis, drugs prescribed without strength (67.6%), omission error (16.4%), drugs prescribed without frequency (16%) was the most commonly seen. The most common pharmacological classification of drugs associated with medication errors were Antibiotics (25.6%), Anti-hypertensive drugs (13.65%) and Anti-platelet drugs (12.9%).CONCLUSION: Incidence of medication errors was mainly due to the use of Antibiotics. Due to the fast paced nature and overcrowding in ED, more number of prescription errors were obtained.   Â