Cointegration growth, poverty and inequality in Sudan

This analytical review explores the links between growth, poverty and inequality in Sudan for the period 1956-2003. This paper build upon different models to investigate empirically the relationship between economic growth – as measured by GDP per capita growth- and inequality as measured by Gini coefficient (the growth, inequality and poverty triangle hypotheses), using data from the national and international sources. The paper tries to answer the following questions: i) whether growth, inequality and poverty are cointegrated, ii( whether growth Granger causes inequality, iii) and whether inequality Granger causes poverty. Finally, a VAR is constructed and impulse response functions (IRFs) are employed to investigate the effects of macroeconomic shocks. The results suggest that growth; poverty and inequality are cointegrated when poverty and inequality are the dependent variable, but are not cointegrated when growth is the dependent variable. In the long- run the causality runs from inequality, poverty to growth, to poverty. In the short-run causal effects, runs from poverty to growth. Thus, there is unidirectional relationship, running from growth to poverty, both in the long- run and short rungrowth; poverty; inequality; Sudan

Social protection and economic growth in the Sudan: Trends, perspectives, cointegration and causality

This paper takes into account the recent role of social protection on economic growth as a socio-economic-political stabilizer. Social protection outcome in Sudan is influenced by limited targeting actions with very low interventions between results in economic growth and accesses to basic social services. These may affects the social protection contributes to the process of development in the Sudan during the period under consideration. The results show that more social spending increase output which enhances GDP per capita growth by 0.5% with 3.1% towards convergence equilibrium in the long run. Moreover, universal approach and expanded cover to social protection services which aim at building a social protection as a productive factor may have contributed to enhancing income security, education and health outcomes, reducing the poverty, income inequality, socio-political stability, encouraged poor productive activities and enhancing economic growth lead to sustainable development.Social Protection, Growth, Cointegration, causality, Sudan

Register Transfer Level Implementation Of Pooling - Based Feature Extraction For Finger Vein Identification

Recently, finger vein biometric identification methods have had more attention among the researchers due to its various advantages such as: uniqueness to individuals, immunity to ages and invisibility to human eye (hard to duplicate). Many improvements methods were utilized to increase the speed and accuracy of the identification. Feature extraction techniques based on global feature extraction such as Principle Component Analysis (PCA) were implemented. However, the results did not show robustness to occlusions and misalignments on the finger vein images. Therefore, local feature extraction techniques were used to overcome these issues. A pooling based feature extraction technique for finger vein identification was implemented in this research. The proposed algorithm extracted the local feature information of the finger vein pattern (patches), and used these patches to improve the robustness of the identification. The algorithm was mainly inspired by spatial pyramid pooling in generic image classification combined with PCA. With patch size = 4, four pyramid levels = [1x1, 2x2, 3x3, 4x4] and ~38 % dimension reduction on the extracted features vector (10 PCA coefficient), the accuracy of the identification was 88.69 % which was higher than PCA by 10.10%. The proposed algorithm was implemented on hardware using Verilog-HDL, and targeting Field Programmable Gate Array (FPGA) applications. The result showed an outstanding speed improvement compared to software implementation. The time consumed by the hardware for extracting the features of one image was 310X time faster than the consumed time for software implementation. With those improvements in accuracy and the speed, the proposed algorithm contributes to the advancement of finger vein biometric system

Design, Construction and Performance of an Ohmic Fruit Juice Evaporator

A fruit juice ohmic evaporator (FlOE) was designed and constructed. The design was done in accordance with the basic principle of ohmic heating to provide heat for evaporation instead of steam or conventional direct heating, to overcome problems arising through these methods of heating. The FlOE was constructed mainly from stainless-steel. A cylinderical heating vessel of internal diameter 20.5 cm and length of 3 1 cm was constructed to enclose electrodes supplying the heat for evaporation. The heating vessel was coated internally with epoxy resin to isolate the wall of the vessel from electric current passing through the fluid. Three sets of electrodes connected to the three-phase alternating current supply were used. Each set of electrodes composed of a three parallel stainless-steel plates. A vacuum pump was used to lower the boiling point of the juice below 65°C and as low as 45°C to prevent the nutrient material from damage. Salt-water solution and pineapple juice were used to study the performance of the FlOE. Three types of tests were done. Preliminary tests were conducted to ensure that the FJOE operates within the design limits and to check for fluid and electric leakage. Performance of the FlOE was computed by testing the system in both batch and continuous operation using salt-water solution and pineapple juice. Electric conductivity of the dilute pineapple juice was first measured to find the maximum allowable level of the juice inside the FlOE to prevent current overload or high temperatures during evaporations. Four tests using salt-water solution, two of them in batch mode and two in a continuous mode were conducted. Another four test using pineapple juice of initial concentration of 10% were conducted to achieve a final concentration of 40%, two of them were batch tests and the others in continuous mode operation. Results of all tests were tabulated and illustrated in graphs. Electric current and the total area of electrodes used was found to be the controlling factors during evaporation using the FlOE. Increasing the total contact area between electrodes and the fluid was found to increase the average apparent current and hence the power consumption. Energy cost using the FJOE was found to be relatively cheap and of low cost and the evaporation economy was found to be 0.7. The FlOE was found to be a suitable evaporation equipment for concentrating fruit juices and other food materials in a small scale industries without any need of steam boilers and of low energy cost

The Hungarian community in Mamluk Egypt

The Hungarian community had no noticeable activity in the era of Mamluk period 1250–1382, and that’s because of their small numbers which no one can observe their roles in that period. In addition to that, the Islamic sources do not name any strange population by its names or nationality but reported them as the Franks. Even foreign sources and travelers books in the same period did not mention the names or groups that had a prominent role in the Egyptian society in the Mamluk era in that period. So the researcher cannot link the term Franks and any groups or individuals who had a significant role in the Egyptian society during the period under study. By the end of the first half of the 14th Century, the Eighth Century AH, the Ottoman Empire appeared as a new force in the area to increase the conflict between the East and the West. The ambitions of the Ottoman Empire exceeded the maritime boundary between it and Europe, so the military clash resulted a large number of Hungarian prisoners, and with many wars and victories the palaces of the Sultans had been filled by them. The Ottoman Sultans sent large numbers of Hungarian prisoners as gifts to the Mamluk Sultan in Cairo, as an expression to their victory, and the great role they played to support the Islam. As a result of the large number of those prisoners, the Ottoman Empire slave markets were filled with them, so they had been sent to other markets, and Egypt was the first destination for releasing this quantity of prisoners. Genoa played a great role in transferring these prisoners for sale in slave markets in Egypt. By the time, in the 15th Century AD, the Ninth AH, the Hungarian community ranked first level among all Mamluks coming from the European lands. They entered into the sultan’s entourage, or came to the service of princes, and some of them joined the military teams. The writings of foreign travelers who visited Egypt during this century confirmed that Hungarians represented the largest number of the other Mamluks who came from European continent. Despite Hungarians involvement in the Egyptian society in the Mamluk era, and despite their respect for the saying “peoples on the religion of their kings”, they have often expressed their culture of traditions and customs. Some of them convinced by Islam, some of whom embraced it to escape tribute or imprisonment. That latter category in the late Mamluk period, brought actions contrary to Islamic law and Egyptian society traditions, the matter forming diseases that contributed significantly to the collapse of country power. The researcher relied on many Arab sources, writing of foreign travelers and some Turkish sources; trying to compare and link between what came in these sources, to reach the nature of the life of that community within the Egyptian society in the Mamluk era

NETWORK ACTIVITY AUDITING USING LINUX RUNNING OFF A LOOPBACK ROOT FILESYSTEM

Areas where network traffic auditing helps network administrators is to identify sources of network activities with regards to the quality of data transmission (such as packet losses and latency) and quantity of data transmitted (such as absolute number of bytes and packets, as well as their rate of transmission per second), which are used to take the next step to remedy problems raised by them. In this project, we utilized a Debian GNU/Linux operating system running off a Loopback Root Filesystem as a network traffic auditing system. The project covers the design of the Linux system, use of Argus (Audit Record Generation and Utilization System-a network traffic auditing suite of tools), and the interpretation of the data gathered. The focus is to evaluate the designed system, analyze the data gathered and propose the next steps to improve the network traffic auditing system and the network it has audited

Explainable methods for knowledge graph refinement and exploration via symbolic reasoning

Knowledge Graphs (KGs) have applications in many domains such as Finance, Manufacturing, and Healthcare. While recent efforts have created large KGs, their content is far from complete and sometimes includes invalid statements. Therefore, it is crucial to refine the constructed KGs to enhance their coverage and accuracy via KG completion and KG validation. It is also vital to provide human-comprehensible explanations for such refinements, so that humans have trust in the KG quality. Enabling KG exploration, by search and browsing, is also essential for users to understand the KG value and limitations towards down-stream applications. However, the large size of KGs makes KG exploration very challenging. While the type taxonomy of KGs is a useful asset along these lines, it remains insufficient for deep exploration. In this dissertation we tackle the aforementioned challenges of KG refinement and KG exploration by combining logical reasoning over the KG with other techniques such as KG embedding models and text mining. Through such combination, we introduce methods that provide human-understandable output. Concretely, we introduce methods to tackle KG incompleteness by learning exception-aware rules over the existing KG. Learned rules are then used in inferring missing links in the KG accurately. Furthermore, we propose a framework for constructing human-comprehensible explanations for candidate facts from both KG and text. Extracted explanations are used to insure the validity of KG facts. Finally, to facilitate KG exploration, we introduce a method that combines KG embeddings with rule mining to compute informative entity clusters with explanations.Wissensgraphen haben viele Anwendungen in verschiedenen Bereichen, beispielsweise im Finanz- und Gesundheitswesen. Wissensgraphen sind jedoch unvollständig und enthalten auch ungültige Daten. Hohe Abdeckung und Korrektheit erfordern neue Methoden zur Wissensgraph-Erweiterung und Wissensgraph-Validierung. Beide Aufgaben zusammen werden als Wissensgraph-Verfeinerung bezeichnet. Ein wichtiger Aspekt dabei ist die Erklärbarkeit und Verständlichkeit von Wissensgraphinhalten für Nutzer. In Anwendungen ist darüber hinaus die nutzerseitige Exploration von Wissensgraphen von besonderer Bedeutung. Suchen und Navigieren im Graph hilft dem Anwender, die Wissensinhalte und ihre Limitationen besser zu verstehen. Aufgrund der riesigen Menge an vorhandenen Entitäten und Fakten ist die Wissensgraphen-Exploration eine Herausforderung. Taxonomische Typsystem helfen dabei, sind jedoch für tiefergehende Exploration nicht ausreichend. Diese Dissertation adressiert die Herausforderungen der Wissensgraph-Verfeinerung und der Wissensgraph-Exploration durch algorithmische Inferenz über dem Wissensgraph. Sie erweitert logisches Schlussfolgern und kombiniert es mit anderen Methoden, insbesondere mit neuronalen Wissensgraph-Einbettungen und mit Text-Mining. Diese neuen Methoden liefern Ausgaben mit Erklärungen für Nutzer. Die Dissertation umfasst folgende Beiträge: Insbesondere leistet die Dissertation folgende Beiträge: • Zur Wissensgraph-Erweiterung präsentieren wir ExRuL, eine Methode zur Revision von Horn-Regeln durch Hinzufügen von Ausnahmebedingungen zum Rumpf der Regeln. Die erweiterten Regeln können neue Fakten inferieren und somit Lücken im Wissensgraphen schließen. Experimente mit großen Wissensgraphen zeigen, dass diese Methode Fehler in abgeleiteten Fakten erheblich reduziert und nutzerfreundliche Erklärungen liefert. • Mit RuLES stellen wir eine Methode zum Lernen von Regeln vor, die auf probabilistischen Repräsentationen für fehlende Fakten basiert. Das Verfahren erweitert iterativ die aus einem Wissensgraphen induzierten Regeln, indem es neuronale Wissensgraph-Einbettungen mit Informationen aus Textkorpora kombiniert. Bei der Regelgenerierung werden neue Metriken für die Regelqualität verwendet. Experimente zeigen, dass RuLES die Qualität der gelernten Regeln und ihrer Vorhersagen erheblich verbessert. • Zur Unterstützung der Wissensgraph-Validierung wird ExFaKT vorgestellt, ein Framework zur Konstruktion von Erklärungen für Faktkandidaten. Die Methode transformiert Kandidaten mit Hilfe von Regeln in eine Menge von Aussagen, die leichter zu finden und zu validieren oder widerlegen sind. Die Ausgabe von ExFaKT ist eine Menge semantischer Evidenzen für Faktkandidaten, die aus Textkorpora und dem Wissensgraph extrahiert werden. Experimente zeigen, dass die Transformationen die Ausbeute und Qualität der entdeckten Erklärungen deutlich verbessert. Die generierten unterstützen Erklärungen unterstütze sowohl die manuelle Wissensgraph- Validierung durch Kuratoren als auch die automatische Validierung. • Zur Unterstützung der Wissensgraph-Exploration wird ExCut vorgestellt, eine Methode zur Erzeugung von informativen Entitäts-Clustern mit Erklärungen unter Verwendung von Wissensgraph-Einbettungen und automatisch induzierten Regeln. Eine Cluster-Erklärung besteht aus einer Kombination von Relationen zwischen den Entitäten, die den Cluster identifizieren. ExCut verbessert gleichzeitig die Cluster- Qualität und die Cluster-Erklärbarkeit durch iteratives Verschränken des Lernens von Einbettungen und Regeln. Experimente zeigen, dass ExCut Cluster von hoher Qualität berechnet und dass die Cluster-Erklärungen für Nutzer informativ sind