Miracle-dz/UniFAR-FTUs huggingface dataset:
GraphLib: a Graph-Mining Library based on the Pregel Model

Big data analytics form a new area of focus for many research studies. Analysed data are most commonly represented in graphs leading to the need of exploring and advancing graph mining techniques and algorithms. Pregel a programming Bulk Synchronous Parallel model for processing large graphs is gaining popularity for such analytics. Its flexible programming model and scalable architecture are two main aspects that make its spread rapid. Several Pregel-based systems and platforms have been developed, however with limited implementations of algorithms built on top of Pregel. In this thesis project, we propose and present the development of a library with graph-mining algorithms based on the Pregel model. We distinguish Apache Giraph, a popular open-source Pregel-based implementation that tends to become prominent among other Pregel-based systems. The library is built on top of Giraph and will be soon contributed to the open-source community. Apart from the design and implementation, we present the banchmarking performed for evaluating Pregel’s properties. We show that the algorithms implemented following the Pregel model can scale and be executed in an efficient way. We finally show that Pregel model is a promising model and can lead graph-mining analytics to be conducted following this direction. Categories and
Metabolic traits specific for lipid-overproducing strain of Mucor circinelloides WJ11 identified by genome-scale modeling approach

The genome-scale metabolic model of a lipid-overproducing strain of Mucor circinelloides WJ11 was developed. The model (iNI1159) contained 1,159 genes, 648 EC numbers, 1,537 metabolites, and 1,355 metabolic reactions, which were localized in different compartments of the cell. Using flux balance analysis (FBA), the iNI1159 model was validated by predicting the specific growth rate. The metabolic traits investigated by phenotypic phase plane analysis (PhPP) showed a relationship between the nutrient uptake rate, cell growth, and the triacylglycerol production rate, demonstrating the strength of the model. A putative set of metabolic reactions affecting the lipid-accumulation process was identified when the metabolic flux distributions under nitrogen-limited conditions were altered by performing fast flux variability analysis (fastFVA) and relative flux change. Comparative analysis of the metabolic models of the lipid-overproducing strain WJ11 (iNI1159) and the reference strain CBS277.49 (iWV1213) using both fastFVA and coordinate hit-and-run with rounding (CHRR) showed that the flux distributions between these two models were significantly different. Notably, a higher flux distribution through lipid metabolisms such as lanosterol, zymosterol, glycerolipid and fatty acids biosynthesis in iNI1159 was observed, leading to an increased lipid production when compared to iWV1213. In contrast, iWV1213 exhibited a higher flux distribution across carbohydrate and amino acid metabolisms and thus generated a high flux for biomass production. This study demonstrated that iNI1159 is an effective predictive tool for the pathway engineering of oleaginous strains for the production of diversified oleochemicals with industrial relevance.
A Study on Botnet Detection Techniques

A botnet is a network of compromised computers, termed bots that are used for malicious purposes. When a computer becomes compromised typically through a drive-by download, that has embedded malicious software, that computer becomes a part of a botnet. A bot typically runs hidden and uses a covert channel to communicate with its command and control server. Botnets are controlled through protocols such as IRC and HTTP and in protocol-conforming manners. This makes the detection of botnet command and control a challenging problem. In this paper we discuss some of the botnet detection techniques and compare their advantages, disadvantages and features used in each technique.
Influence of ApoE on LRP1 Function and Amyloid Transport Across the Blood-Brain Barrier

Alzheimer's disease is a progressive incurable neurological disorder and is the leading cause of dementia in the elderly. Common symptoms include failure of memory, language and mood disturbances confusion and depression. The clinical duration of Alzheimer's disease is approximately 8-10 years postdiagnosis and patients require increasing assistance throughout its progression. The currently available treatments are palliative, focussing on improving cognition and only result in mild improvements in small subsets of patients for a relatively short period of time. It is therefore clear that new approaches are needed if a therapy is to be developed that has the potential to be disease modifying and slows the progression of Alzheimer's disease. One of the main pathological hallmarks of Alzheimer's disease is the accumulation of beta-amyloid (Aβ) peptides in the brain. In particular the soluble Ap species has been linked to neurotoxicity, reductions in long term potentiation, and can induce cognitive deficits in animal models of AD after intracerebral injection. In addition, increased accumulation of Ap in the brain is also associated with the APOE4 allele, which is the strongest genetic factor for developing Alzheimer's disease. APOE exists as three alleles with the two copies of the APOE4 allele conferring a 15-fold increase in the risk of developing AD when compared to APOE3 homozygous individuals. Recently it has been demonstrated that this accumulation occurs as a result of defective clearance mechanisms. One of the main routes of clearance of Aβ is through the blood-brain barrier (BBB) where specialised transporters facilitate its clearance. The Low Density Lipoprotein receptor-related protein (LRP1) plays a prominent role in the BBB clearance of Aβ and its surface expression is at least in part regulated by ectodomain cleavage by a variety of 'sheddases'. In particular, this thesis focuses on the previously identified LRP1 sheddases ADAM10 and MMP9. The shedding of LRP1 produces a soluble LRPl fragment, which is unable to endocytose and subsequently transcytose bound ligands. In this thesis, I examined the role of apoE in the regulation of LRP1 and the transcytosis and clearance of Aβ through the BBB. I demonstrate that the shedding of LRP1 is modulated by the different apoE isoforms and that inhibition of LRP1 sheddases increases the clearance of Aβ through the BBB. MMP9 is the primary candidate as the main driver of this mechanism due to its apoE isoform dependent activity and increased expression in AD. In summary, I identified a novel target for treatment strategies that facilitates the clearance of Aβ though the BBB. This approach may be particularly effect in individuals with the APOE4 allele, who are in desperate need of viable therapeutics to combat AD.
A cooperative two-tier framework for efficient routing in MANET

Mobile Ad hoc Networks (MANET) is a collection of randomly moving wireless devices within a particular area. Unlike in cellular networks, there are no fixed base-stations to support routing and mobility management. Further more, many resources such as power energy and bandwidth are very limited in MANET. Concentrating on these problems, we present a cooperative two-tier framework. The framework contains two algorithms, SSCA and DSRU. SSCA is mainly responsible for topology management, and DSRU is responsible for the routing update. SSCA is a GPS based clustering mechanism. By predicting the next location of mobile node with its historic trajectory, it adjusts the node's transmit power in advance, and controls all nodes in suitable size clusters. Based on the clusters, DSRU is proposed in order to control the routing overhead while gets relatively exact global topology information. Experiment on the GloMoSim simulator shows that the framework proposed in this paper results in a notable reduction both in energy consumption and in routing overhead, and a notable improvement in network throughput, especially for the networks composed of high-speed mobile hosts.
Different Chemical Compound Profiles of Indonesian Coffee Beans as Studied Chromatography/Mass Spectrometry

Coffee is a favourite beverage worldwide, with all of its benefits. The taste and flavour of coffee depending on the chemical components of the coffee. And the presence of chemicals in the coffee ground depends on the original tree as well as on processing. In this report, some volatiles from ground and brewed coffee beans of Robusta, Arabica, and peaberry coffee beans were extracted to be described after chemical analysis. Mainly gas chromatography/mass spectrometry methods were the main spectroscopy utilized to obtain chemistry information that was taken from hot brewed coffees. The result showed that peaberry coffee beans were very rich in components compared to others. Arabica coffee beans also had stronger in taste and flavour compared to robusta as seen from the profile compositions of chloroform and n-hexane extracts. Better processing would also be suggested for each type of coffee beans.
Assessment of lipid profile levels of chickens (Gallus gallus domestica) exposed to factory sites in Nnewi, Anambra State, South Eastern Nigeria

This study investigated the lipid profile levels of the factory chickens in Nnewi, Anambra State, Nigeria. A total of twenty-nine chicks aged between four and five months were grown to adult birds (chickens) for the study. They were allowed to feed from the surrounding homeland until they were due for the experiment. The chickens were sacrificed to obtain the blood for analyses. Thereafter, 5ml of blood sample was collected from the heart into plain containers for estimation of biochemical parameters: triacylglycerol (TAG), total cholesterol (TC), low density lipoprotein (LDL), high density lipoprotein (HDL), and very low density lipoprotein (VLDL). Biochemical parameters were assayed using standard laboratory methods. Thereafter, the data obtained were subjected to statistical analysis by Students t-test using Statistical package for social sciences (SPSS) (Version 16) software and Statistical significance was tested at P0.05). These findings may be attributed to the short duration of this study. Keywords: Industrialization, Industrial effluent, Lipid profile, Factory chickens (Gallus gallus domestica), Nnewi.
S1P1 Agonist SEW2871 Inhibits human Th17 cell generation and functions

S1P1 is a G-protein coupled receptor expressed by lymphocytes and is required for their egress out of secondary lymphoid organs. Fingolimod, an agonist of four S1P receptors including S1P1, is used in multiple sclerosis (MS) treatment in humans; however, less is known regarding the impact of S1P1 agonists on the egress-independent biology and functions of human Th17 and Treg cells. In this study, we explored the effects of S1P1 agonists SEW2871 and FTY720 on the ex vivo generation and functions of human Th17 and Treg cells. We report that a selective S1P1 agonist SEW2871 inhibited human Th17 development ex vivo, from naïve T cells, associated with reduced IL-17 and IL-22 production, demonstrated by ELISA and intracellular cytokine staining. On the other hand, Treg generation was augmented in SEW2871+ cultures. Additionally, SEW2871-exposed monocytes and dendritic cells produced less IL-23, a cytokine required for Th17 cell-expansion and pathogenicity. Th17 differentiation in the presence or absence of antigen presenting cells revealed that SEW2871 could inhibit Th17 generation either by blocking APC-derived IL-23 or directly affecting T cells. Our data provide novel information as to egress-independent effects of S1P1 agonists on Th17 cells.
Highly Effective Cell Equalization in a Lithium-Ion Battery Management System

This paper proposes a highly effective voltage cell equalization method for lithium-ion (Li-ion) battery management systems (BMSs) for several applications, such as nearly zero energy buildings, microgrids, and electric vehicles. The concept of the suggested method is the proper control of the charging current of each cell of a battery stack through the suitable regulation of the gate source of each cell's parallel-connected mosfet. Thus, the mosfet can act as a controllable current source for providing the proper equalization current according to the specific needs of each battery cell. The correct voltage level of the gate-source of each mosfet is determined by a cell equalization controller through the comparison of the performance of each cell of a battery stack with the healthier cell, which is detected by an aging estimation algorithm based on the evaluation of the internal resistor and the capacity fade of each cell. Therefore, smoother and faster cell equalization is achieved compared to the conventional dissipative method. The practicality and the high effectiveness of the proposed cell equalization technique have been experimentally validated. Several experimental results are presented to demonstrate the operational improvements of the proposed cell equalization technique against the conventional one.
Research on Content Distribution Mechanism of MP2P Networks

In consideration of the current MP2P network disturbance from frequent location changes of the mobile nodes, the limited computing power and storage capacity, this paper proposes a flexible strategy of partition management by assigning different functions to nodes according to some standards, regarding a subnet as a unit and using super-nodes to manage subnets. In order to speed up the data transmission and replacement efficiency, data is processed with partial network coding technology. Additionally, a trans-subnet query algorithm is proposed to link subnets, conduct information exchange between subnets and resource location. Results of the experiment manifested that this strategy can efficiently increase the resource download success rate and hit rate, reduce the average delay, improve the content distribution efficiency of MP2P networks, and simultaneously enhance system robustness.
An Approach to Corpus-Based Interpreting Studies: Developing EPIC (European Parliament Interpreting Corpus)

Empirical research on simultaneous interpreting is hampered by the problem of collecting sufficient material (recordings of source speeches and interpreted target speeches) for the testing of hypotheses and validation of existing theories. In other words, corpora have long been awaited in the field of Interpreting Studies. In January 2004 a research group on corpus-based interpreting studies was set up in the Department of Interdiscip linary Studies in Translation, Languages and Cultures (SITLeC) of the University of Bologna at F orli, in order to create an electronic parallel corpus with source and target speeches in Italian, English and Spanish. The main research interest of the group is the study of interpreters’ strategi es across different language directions (directionality) and language-pair related difficul ties. In this paper, the authors illustrate the various stages of development of the EPIC corpus, h ighlighting both research and pedagogical applications of this “multidimensional” tool compri sing video, audio and written materials, which can be retrieved, selected and analyzed by using co rpus linguistics techniques. 2
Physical Layer Security in Wireless Communications

Physical layer security has recently become an emerging technique to complement and significantly improve the communication security of wireless networks. Compared to cryptographic approaches, physical layer security is a fundamentally different paradigm where secrecy is achieved by exploiting the physical layer properties of the communication system, such as thermal noise, interference, and the time-varying nature of fading channels.Written by pioneering researchers, Physical Layer Security in Wireless Communications supplies a systematic overview of the basic concepts, recent advancements, and open issues in providing communication security at the physical layer. It introduces the key concepts, design issues, and solutions to physical layer security in single-user and multi-user communication systems, as well as large-scale wireless networks.The book starts with a brief introduction to physical layer security. The rest of the book is organized into four parts based on the different approaches used for the design and analysis of physical layer security techniques: Information Theoretic Approaches: introduces capacity-achieving methods and coding schemes for secure communication, as well as secret key generation and agreement over wireless channels Signal Processing Approaches: covers recent progress in applying signal processing techniques to design physical layer security enhancements Game Theoretic Approaches: discusses the applications of game theory to analyze and design wireless networks with physical layer security considerations Graph Theoretic Approaches: presents the use of tools from graph theory and stochastic geometry to analyze and design large-scale wireless networks with physical layer security constraints Presenting high-level discussions along with specific examples, illustrations, and references to conference and journal articles, this is an ideal reference for postgraduate students, researchers, and engineers that need to obtain a macro-level understanding of physical layer security and its role in future wireless communication systems.
Conditional Guide RNAs: Programmable Conditional Regulation of CRISPR/Cas Function in Bacterial and Mammalian Cells via Dynamic RNA Nanotechnology

A guide RNA (gRNA) directs the function of a CRISPR protein effector to a target gene of choice, providing a versatile programmable platform for engineering diverse modes of synthetic regulation (edit, silence, induce, bind). However, the fact that gRNAs are constitutively active places limitations on the ability to confine gRNA activity to a desired location and time. To achieve programmable control over the scope of gRNA activity, here we apply principles from dynamic RNA nanotechnology to engineer conditional guide RNAs (cgRNAs) whose activity is dependent on the presence or absence of an RNA trigger. These cgRNAs are programmable at two levels, with the trigger-binding sequence controlling the scope of the effector activity and the target-binding sequence determining the subject of the effector activity. We demonstrate molecular mechanisms for both constitutively active cgRNAs that are conditionally inactivated by an RNA trigger (ON → OFF logic) and constitutively inactive cgRNAs that are conditionally activated by an RNA trigger (OFF → ON logic). For each mechanism, automated sequence design is performed using the reaction pathway designer within NUPACK to design an orthogonal library of three cgRNAs that respond to different RNA triggers. In E. coli expressing cgRNAs, triggers, and silencing dCas9 as the protein effector, we observe a median conditional response of ≈4-fold for an ON → OFF “terminator switch” mechanism, ≈15-fold for an ON → OFF “splinted switch” mechanism, and ≈3-fold for an OFF → ON “toehold switch” mechanism; the median crosstalk within each cgRNA/trigger library is <2%, ≈2%, and ≈20% for the three mechanisms. To test the portability of cgRNA mechanisms prototyped in bacteria to mammalian cells, as well as to test generalizability to different effector functions, we implemented the terminator switch in HEK 293T cells expressing inducing dCas9 as the protein effector, observing a median ON → OFF conditional response of ≈4-fold with median crosstalk of ≈30% for three orthogonal cgRNA/trigger pairs. By providing programmable control over both the scope and target of protein effector function, cgRNA regulators offer a promising platform for synthetic biology.
Multi‐objective synergistic planning of EV fast‐charging stations in the distribution system coupled with the transportation network

The adoption of environmentally friendly electric vehicles (EVs) is increasing over the years due to the increased awareness of energy and environmental challenges. However, the current growth is slow because of lack of proper charging infrastructures. This study proposes a multi-objective synergistic planning model of an EV charging station considering the interaction between the distribution system and transportation networks as the fast charging of EVs affects the operation of both networks. The developed model minimises the power losses and voltage deviation of the distribution system and maximises the EV flow served by the fast charging station (FCS) simultaneously taking into account permissible waiting time and service radius of FCS. Multi-objective grey wolf optimiser (MOGWO) algorithm is used to obtain the non-dominated solutions and fuzzy satisfaction-based decision-making method is employed to reach final planning scheme. The effectiveness of the proposed model is investigated on the IEEE 123-bus distribution system coupled with a 25-node transportation network. The influence of different objectives, service radius and waiting time on the planning of FCS is also explored. Results reveal that the developed method can provide rational siting and sizing of FCS and it is also found that proper service radius and waiting time provide more convenience to the customer.
A Learning Architecture to Support Autonomous Resource Scheduling and Allocation in the Cloud

The advent of on-demand computing facilitated by computational clouds, provides an almost unlimited resource supply to support the execution of applications and processes. Through a process known as virtualisation large server machines are divided up into smaller units known as virtual machines. These virtual machines can then be procured on demand to support application deployments, workflow executions and service delivery via the cloud. However, optimally allocating these virtual resources to support a given application deployment or workflow execution on a cloud platform presents a number of significant research challenges. Virtualisation is enabled through a domain level hypervisor which controls access to the shared hardware amongst the competing virtual machines. Switching between domains and attempting to distribute access to these shared mediums is non-trivial and causes performance interference effects amongst the virtual machines. This presents a challenge when attempting to plan a resource allocation to support a given application or workflow running in these environments. Removing these interference effects entirely, is a very difficult problem and is one of the principle challenges facing virtualisation research in the coming years. However from a resource planning perspective it is possible to reason over these variabilities to achieve a near optimal resource allocation which satisfies the defined objective criteria. Markov Decision Processes provide a decision theoretic framework which facilitates planning and scheduling under uncertainty. By modeling the allocation of resources under this framework and solving using techniques such as reinforcement learning and dynamic programming this thesis provides a learning architecture to allocate/schedule resources adhering to defined optimisation criteria. Using data from real cloud deployments we empirically evaluate our proposed solutions with respect to two different application types. The first is a workflow application deployment where the requirement is to schedule tasks to resources to ensure that both cost and makespan constraints are achieved. The second is an application scaling problem where the goal is to optimise application response time at a minimum cost for varying numbers of user requests. For both of these problems the underlying resource is variable and changes accordingly. We present a number of novel advancements from both a learning and optimisation perspective.