Development of the Readout Chamber of the ALICE Transition Radiation Detector and Evaluation of its Physics Performance in the Quarkonium Sector

In the central barrel of the ALICE detector, resonances are proved, among other channels,through their electronic decays. Doing so, the abundantly produced pions make the electrons identification complicated. Therefore the adoption of an electron-pion separator, like the Transition Radiation Detector (TRD), is of particular importance. This thesis went along with the first real dimension TRD prototype from the first theoretical considerations of its readout chamber over testing it for its mechanical and electrostatical stability up to determining its pion rejection capability. It was shown that the prototype is stabile, both mechanically and electrostatically. With 90% of the electrons identifide, the TRD allows to reject 98% of the pions for particles with momenta at 2~GeV/c. The gained data were implemented in a fast simulations package, which includes three of the ALICE central detectors: ITS, TPC, and TRD. With 3.8 x 10^{-3} of the central events recorded in one ALICE year, the detector physics performance was studied in the Quarkonium sector. The pion rejection was considered with and without a TRD contribution. It was found that only with the TRD it will be possible to detect the Quarkonium resonances significantly. The significance of the recorded signal is at the level of 95% of an ideal situation where the pions are perfectly identified

Search with the CMS detector for heavy resonances decaying into an electron pair

This note presents the CMS experiment potential to discover heavy resonances decaying into an electron-positron pair, such as Kaluza-Klein excitations of a Z or graviton boson predicted in extra dimension models (the TeV^-1 model and the Randall-Sundrum model), or as neutral heavy Z' boson predicted by Grand Unified Theories. Full and fast simulation and reconstruction are used to investigate these productions, with the pileup condition corresponding to a luminosity equal to 2 times 10^ 33; mathrm cm ^ -2 mathrm s ^ -1 . For an integrated luminosity of 60 fbinv, a 5 sigma discovery limit has been obtained for a mass of 5.9 tevct in the case of Kaluza-Klein excitation Z boson production. For the Randall-Sundrum graviton production, the limit is found for graviton masses of 1.8 tevct with a coupling parameter constant c=0.01 and 4.1 tevct for c=0.1 . For the six Z' models considered here, the 5 sigma discovery limit ranges for masses from 3.6 tevct (rm Z_psi) to 4.6 tevct (rm Z_ALRM)

SOA enabled ELTA: approach in designing business intelligence solutions in Era of Big Data

The current work presents a new approach for designing business intelligence solutions. In the Era of Big Data, former and robust analytical concepts and utilities need to adapt themselves to the changed market circumstances. The main focus of this work is to address the acceleration of building process of a “data-centric” Business Intelligence (BI) solution besides preparing BI solutions for Big Data utilization. This research addresses the following goals: reducing the time spent during business intelligence solution’s design phase; achieving flexibility of BI solution by adding new data sources; and preparing BI solution for utilizing Big Data concepts. This research proposes an extension of the existing Extract, Load and Transform (ELT) approach to the new one Extract, Load, Transform and Analyze (ELTA) supported by service-orientation concept. Additionally, the proposed model incorporates Service-Oriented Architecture concept as a mediator for the transformation phase. On one side, such incorporation brings flexibility to the BI solution and on the other side; it reduces the complexity of the whole system by moving some responsibilities to external authorities

An unusual association of pyoderma gangrenosum with ulcerative colitis and thyrotoxicosis successfully treated with infliximab: a case report

Pyoderma Gangrenosum (PG) is a rare chronic immune-mediated inflammatory dermatosis manifested as painful skin ulceration, commonly affecting the lower limbs. The pathogenesis of the disease is complex. Abnormalities in neutrophil function, dysregulation of the innate immune system, and Tumor Necrosis Factor (TNF) were postulated. An underlying associated systemic disease was reported in more than 50% of PG patients, including inflammatory bowel disease, rheumatoid arthritis, and malignancies, with few cases reported an associated thyroid disease. Authors report a case of extensive PG associated with both ulcerative colitis and thyrotoxicosis co-morbidities not mentioned before in one patient. The patient was successfully treated with the anti-TNF alpha infliximab

ENHANCED BI SYSTEMS WITH ON-DEMAND DATA BASED ON SEMANTIC-ENABLED ENTERPRISE SOA

Since the 1990s, companies have been investing into IT infrastructure initiatives such as Enterprise Resource Planning (ERP) systems, Supply Chain Management (SCM) systems, and Customer Relationship Management (CRM) systems in order to increase efficiency, effectiveness, and internal process integration, among other goals. The current value of Business Intelligence (BI) for companies could be summarized by two main achievements: improvement of management of processes and improvement of operational processes. This paper will identify current requirements of BI and present a linkage to service-oriented architectures including added-values. Semantic-enabled Enterprise Service-Oriented Architecture (SESOA) is an enterprise solution that links businesses to external systems based on Web Services and SOA concept. It represents a lightweight web application that annotates Web Services that are coming from different service providers with semantics so that the indexing and discovery of these services can be more comprehensive. BI applications can be considered as service consumers in SESOA and can discover, select and invoke the services supplied by the external systems (service providers). In this way, SESOA forms the bridge between SOA and BI concepts to deliver in real time the ?on-demand? data as services and this opens the BI market to include SMEs as main resources of these services

The Impact of Intellectual Capital on the Financial Performance in Insurance Firms Listed in Amman Stock Exchange: Using the (VAIC) Model

Purpose: The aim of this study is to examine the impact of intellectual capital (human capital, structural capital, and employed capital) on the financial performance of listed insurance companies in the Amman Stock Exchange   Theoretical framework: Intellectual capital has become increasingly important in generating value for companies, and many researchers have linked it to corporate financial performance and strategic competitive advantage.   Design/methodology/approach: The study population consist of 21 insurance companies listed on the Amman Stock Exchange in Jordan during the period of 2011-2020. Intellectual capital was measured using the value added intellectual coefficient model (Pulic, 2000), and its impact on financial performance was analyzed using published financial statements of the insurance companies.   Findings:  The results of the study found a statistically significant positive effect of human and employed capital on financial performance as measured by the rate of return on assets and return on equity. Furthermore, the study revealed a significant positive effect of intellectual capital, specifically human capital, on financial performance measured by market value (Tobin's Q).   Research, Practical & Social implications: The study suggests that insurance companies should treat intellectual capital as a strategic resource and monitor and invest in it periodically for continuous development. The study suggests building a positive organizational culture that supports intellectual capital is recommended   Originality/value:  This study contributes to the understanding of the relationship between intellectual capital and financial performance for the first time in the insurance industry in Amman Stock Exchange. The findings highlight the importance of managing and investing in intellectual capital as a strategic resource to enhance financial performance

Universities’ Role in Developing Vocational Education in Jordan

This study examined the role of universities in developing vocational education and transforming TVET in developing workforce towards globalization. To achieve the objectives of the study, a scale of the role of universities in developing vocational education was developed. The study population consisted of all faculty members at Al-Balqa Applied University who were (1400) faculty members. The study sample consisted of (50) members of the teaching staff who teach vocational courses who were selected randomly. The results of the study showed that the role of universities in developing vocational education came to a medium degree. Considering these results, the study recommended universities to continuously update the vocational education equipment and workshops

Anforderungspriorisierung und Designempfehlungen für Betriebliche Umweltinformationssysteme der nächsten Generation – Ergebnisse einer explorativen Studie

Zur Förderung von Nachhaltigkeit in Unternehmen muss eine Vielzahl von heterogenen Daten in umweltrelevante Informationen konvertiert und durch Betriebliche Umweltinformationssysteme (BUIS) bereitgehalten werden. Da diese derzeit noch nicht im wünschenswerten Umfang strategische Informationen und Entscheidungshilfen bieten, sind die gegenwärtigen Systeme den Anforderungen aus der Diskussion um Nachhaltigkeit nicht gewachsen. Wir entwerfen daher ein BUIS der Zukunft (BUIS 2.0), welches den Anforderungen strategischer Nachhaltigkeit gerecht wird. Die Ergebnisse einer Erhebung, die die Anforderungen an BUIS2.0 früherer Befragungen, Workshops und Experteninterviews priorisiert, werden vorgestellt. Als ein unmittelbares Ergebnis der Erhebung werden erste Ansätze von architektonischen Konzepten für BUIS 2.0 aufgezeigt, die in dem Projekt „IT-For-Green“ umgesetzt werden

A novel and reliable framework of patient deterioration prediction in Intensive Care Unit based on long short-term memory-recurrent neural network

The clinical investigation explored that early recognition and intervention are crucial for preventing clinical deterioration in patients in Intensive Care units (ICUs). Deterioration of patients is predictable and can be preventable if early risk factors are recognized and developed in the clinical setting. Timely detection of deterioration in ICU patients may also lead to better health management. In this paper, a new model was proposed based on Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) to predict deterioration of ICU patients. An optimisation model based on a modified genetic algorithm (GA) has also been proposed in this study to optimize the observation window, prediction window, and the number of neurons in hidden layers to increase accuracy, AUROC, and minimize test loss. The experimental results demonstrate that the prediction model proposed in this study acquired a significantly better classification performance compared with many other studies that used deep learning models in their works. Our proposed model was evaluated for two tasks: mortality and sudden transfer of patients to ICU. Our results show that the proposed model could predict deterioration before one hour of onset and outperforms other models. In this study, the proposed predictive model is implemented using the state-of-the-art graphical processing unit (GPU) virtual machine provided by Google Colaboratory. Moreover, the study uses a novel time-series approach, which is minute-by-minute. This novel approach enables the proposed model to obtain highly accurate results (i.e., an AUROC of 0.933 and an accuracy of 0.921). This study utilizes the individual and combined effectiveness of different types of variables (i.e., vital signs, laboratory measurements, GCS, and demographic data). In this study, data was extracted from MIMIC-III database. The ad-hoc frameworks proposed by previous studies can be improved by the novel and reliable prediction framework proposed in this research, which will result in predictions of more accurate performance. The proposed predictive model could reduce the required observation window (i.e., a reduction of 83%) for the prediction task while improving the performance. In fact, the proposed significant small size of observation window could obtain higher results which outperformed all previous works that utilize different sizes of observation window (i.e., 48 hours and 24 hours). Moreover, this research demonstrates the ability of the proposed predictive model to achieve accurate results (>80%) on 'raw' data in an experimental work. This shows that the rule-based pre-processing of clinical features is unnecessary for deep learning predictive models

Applying Machine Learning of Erythrocytes Dynamic Antigens Store in Medicine

Erythrocytes Dynamic Antigens Store (EDAS) is a new discovery. EDAS consists of self-antigens and foreign (non-self) antigens. In patients with infectious diseases or malignancies, antigens of infection microorganism or malignant tumor exist in EDAS. Storing EDAS of normal individuals and patients in a database has, at least, two benefits. First, EDAS can be mined to determine biomarkers representing diseases which can enable researchers to develop a new line of laboratory diagnostic tests and vaccines. Second, EDAS can be queried, directly, to reach a precise diagnosis without the need to do many laboratory tests. The target is to find the minimum set of proteins that can be used as biomarkers for a particular disease. A hypothetical EDAS is created. Hundred-thousand records are randomly generated. The mathematical model of hypothetical EDAS together with the proposed techniques for biomarker discovery and direct diagnosis are described. The different possibilities that may occur in reality are experimented. Biomarkers' proteins are identified for pathogens and malignancies, which can be used to diagnose conditions that are difficult to diagnose. The presented tool can be used in clinical laboratories to diagnose disease disorders