Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of protein antigens in immunisation against infection caused by Streptococcus pneumoniae

Streptococcus pneumoniae (the pneumococcus) is a major human pathogen which causes pneumonia, septicaemia and otitis media infections. The pneumococcus asymptomatically colonises the upper respiratory tract, which may act as a reservoir for subsequent infection of the lower respiratory tract. Current vaccines available include a polysaccharide vaccine (Pneumovax), and a conjugate vaccine (Prevenar). However, problems with both vaccines exist; Pneumovax does not elicit protection in children under the age of 2 years, the elderly or people who are immunocompromised, and Prevenar is an expensive vaccine that has limited serotype coverage. Serotype replacement has also become a growing problem. Therefore, new vaccine targets which can elicit broad cross-serotype protection are required. Pneumolysin and neuraminidase A are proteins that are highly conserved, in all serotypes of pneumococci. Throughout the research presented in this thesis, MF1 outbred mice and Balb/c inbred mice were immunised with PdB or neuraminidase A protein. Following I.P. or subcutaneous immunisation the mice were challenged using a variety of doses of virulent passaged pneumococci by the intraperitoneal, intravenous or intranasal routes. Immunisation with neuraminidase A elicited specific anti-neuraminidase antibodies. However, these antibodies were unable to protect the mice from acute pneumococcal challenge. Immunisation with PdB elicited a high titre of anti-PdB antibodies which were able to inhibit PLY activity in vitro. However, the antibodies were unable to protect the mice from acute pneumococcal challenge, or invasive pneumococcal challenge. Following colonisation challenge a significant drop in pneumococci recovered from nasopharynx of PdB immunised mice was seen. This significant drop in bacterial numbers correlated with a significant increase in the titre of specific IgG antibodies, as well as a significant increase in the number of specific IgG and IgA producing B cells, present in the cervical lymph nodes of PdB immunised mice, in comparison to that of the controls. Immunisation with neuraminidase A was unable to protect the mice from intraperitoneal and intranasal pneumococcal challenge, therefore, may not be necessary to include in future protein vaccines developed. Immunisation with PdB was able to significantly reduce nasopharyngeal colonisation, but was unable to clear colonisation. Therefore, PdB should be considered, along with other protective pneumococcal proteins, for inclusion in any future protein vaccine developed against pneumococcal colonisation.
Hybrid sensing method in mobile ad-hoc networks (MANET)

The article presents the results of the efficiency analysis of sensing for the OFDM (Orthogonal Frequency Division Multiplexing) signals. The metrics used to assess the efficiency of sensing methods are defined in the article. The paper also describes a model of the system and presents the simulation results. To conduct the research a hybrid detector was used, combining a fast detection method with a method ensuring high accuracy. Thanks to such a combination an increase in detection speed was obtained while maintaining the required reliability and sensitivity. The tests were carried out for the model of WiMAX signal and for the registered military radio signal.
Real-Time Object Coordinate Detection and Manipulator Control Using Rigidly Trained Convolutional Neural Networks

Objects embedded in the environment, such as switches, control buttons, sockets, et al., are devices that need frequent operations. To devise manipulators to operate such devices automatically, we propose a visual-position control scheme that directly converts the visual coordinate detections to motor commands. We train ConvNets with rigid 3D coordinate information, which is obtained from a single basis image of the target object. Our proposed training data preparation frameworks automatically generate and organize the required structure of the training images for the network. The ConvNet’s superior image recognition capability results in high success rate in object detection and high precision in coordinate estimation. In our static experiments, in-range plane coordinate detection achieves an average success rate of 91% from various view-point directions; the depth coordinate detection achieves an average success rate of 86% based on an extended success range. In our dynamic experiments, a low-precision manipulator was used to press a down elevator call button and achieved an overall success rate of 98%. A high-precision manipulator was used for an object localization task and achieved a precision of ± 0.3 mm using a low-resolution camera.
A Method for Self-Service Rehabilitation Training of Human Lower Limbs

Recently, rehabilitation robot technologies have been paid more attention by the researchers in the fields of rehabilitation medicine engineering and robotics. To assist active rehabilitation of patients with unilateral lower extremity injury, we propose a new self-service rehabilitation training method in which the injured lower limbs are controlled by using the contralateral healthy upper ones. First, the movement data of upper and lower limbs of a healthy person in normal walk are acquired by gait measurement experiments. Second, the eigenvectors of upper and lower limb movements in a cycle are extracted in turn. Third, the linear relationship between the movement of upper and lower limbs is identified using the least squares method. Finally, the results of simulation experiments show that the established linear mapping can achieve good accuracy and adaptability, and the self-service rehabilitation training method is effective.
The performance of an uplink Large Scale MIMO system with MMSE-SIC detector

The quality of the information that reaches the destination is being affected by different disturbing factors encountered in the environment through which it is transmitted. The main purpose is to retrieve the information at the reception as accurately as possible, therefore, the idea behind each technology implemented in nowadays wireless systems is to improve the experience of users by finding the right solution for their needs. In order to support the affirmations made above, in this paper we propose an uplink Massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system when its resources are used to fulfill the requests of multiple users which are active in the same time. At the reception is employed a minimum mean-square-error (MMSE) detector, followed by successive interference cancellation (SIC). Taking into account the proposed architecture, the transmitter, receiver and communication channel will be implemented in Matlab. Its performance will be analyzed in terms of Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR) when the number of base station (BS) antennas varies between 10 to 50.
iMACE: In-Memory Acceleration of Classic McEliece Encoder

Asymmetric code-based crypto-systems have been developed in the last decade due to rapid evolution of quantum computing that can potentially compromise RSA and ECC based crypto-systems. The McEliece crypto-system based on the general decoding problem is one of the front runner candidates for post-quantum cryptography but the energy-efficiency is limited by the heavy data traffic between the processing elements and the memory. In memory-computing (IMC) architectures can remove the energy-efficiency barriers posed by Von-Neumann computing due to movement of data between the processor and the memory. Emerging non-volatile memories (NVM) such as, Resistive RAM (ReRAM) implemented in a crossbar array are promising substrates to realize IMC due to excellent High Resistance State (HRS) to Low Resistance State (LRS) ratios and high-densities. Therefore, McEliece can be benefited substantially by in-memory acceleration. We propose, iMACE, a high performance and area-efficient hardware implementation of the core encoding function of McEliece by exploiting ReRAM-based IMC. Simulation results show 18.8X-94X better throughput and 46%-97% reduction in energy consumption compared to the FPGA-based implementation.
Evaluation of MySAP Sales and Distribution Module Using Technology Acceptance Model (a case study approach at oil and gas company in Indonesia)

The purpose of this study is to empirically examine the factors that can affect the acceptance and use of mySAP system Sales and Distribution Module. This research uses Rajan & Baral’s Technology Acceptance Model approach. As for the object of research is public oil and gas company in Indonesia (XYZ). The respondents in this study were employees of XYZ marketing operation division in Jakarta, West Java, and Banten area who intensively used mySAP system Sales and Distribution Module. The sampling technique used was random sampling with a limit of 34 people. Primary data obtained through interviews and questionnaires. Method of data analysis used was Partial Least Square Method with the help of software SmartPLS. The results showed that the factors that succeeded in explaining the related acceptance and use of mySAP system Sales and Distribution Module at XYZ company were complexity, compatibility, training, and perceived ease of use factors, which indirectly affected intention to use, usage, panoptic empowerment and individual performance.
Constructing Passive Self-Located and Collision-Free Identification System by Leveraging DMD-Based Visible Light Communication

Massive objects identification and position tracking are key issues for Internet of Things (IoT) system such as large-scale factories, warehouses, and supply chain management. In this paper, a novel self-located and collision-free identification system by leveraging Digital Micromirror Device (DMD)-based visible light communication is proposed. By utilizing a series of image pattern broadcast and identification tags collection from objects, the proposed system can gather multiple objects’ (up to 1024) position and attribute information at a time. The prototype experiment showed that the system can achieve location accuracy with less than 10 cm where the distance between transponder and tags is 2.5 m with 4-kbps communication rate.
Electricity Power Load Forecast via Long Short-Term Memory Recurrent Neural Networks

This paper focuses on electricity load forecasting of large-scale electrical grid. The smart grid is developing rapidly in the half-decade; it becomes the new goal of electricity industrial construction. Stability, reliable, flexible and self-cure are the main electricity characters, and accuracy forecasting power load play an important role in this process of smart construction. In this paper, we take Estonia country power load as a case, convert the load data to supervised learning data, and then use long short-term memory (LSTM) recurrent network do training model and forecasting. The experiment demonstrates the method is efficient, and compared with support vector regressive (SVR), LSTM could extract the feature of power load better accuracy and obtain the better performance of forecasting. The result can help national grid planning.
Continuous Variable Entanglement over Different Degree of Freedom for Entanglement Multiplexing

Continuous variable entanglement is usually set between pairs of optical modes sharing the same geometrical property where distinguishability is demanded to polarization and/or frequency. Thus, the inherent non-local correlation shows-up in quantum quadratures relative to two distinct e.m. modes. In this contribution we will show how the polarization d.o. f. of a pair of entangled modes is coupled to the optical orbital angular momentum giving, at the end, a pair of entangled modes that have orthogonal OAM and polarization. We also show how this experimental scheme can be extended to give more than a pair of entangled modes paving the way to CV entanglement multiplexing.
Real-time Validation of Digital Models for Manufacturing Systems: a Novel Signal-processing-based Approach

Recently, the connection between manufacturing systems and their digital counterparts has become of great significance for planning and control activities in a short-term scope. However, the alignment of a digital model with a very dynamic system is not always guaranteed, and traditional validation techniques cannot be used since they are designed for off-line simulators and rely on the availability of a large amount of data. This work develops a novel validation procedure inspired by signal-processing theory and a novel approach called quasi Trace Driven Simulation. The procedure is coherent with a Real-Time Simulation framework since it does not require large datasets to provide a good solution. The approach has been tried on test cases which demonstrated its applicability to a manufacturing environment.
An Evacuation Model Based on the Improved Particle Swarm Optimization

In recent years, the unexpected factors leading to frequent accidents which causing heavy casualties and economic losses. Therefore, how to make use of computer technology to simulate the evacuation process has an important practical significance. In this paper, we propose a new model for evacuation. Based on the traditional particle swarm algorithm, we revise the inertia weight of the algorithm to balance the global and local search ability according to the aggregation degree of the population. Taking the interaction between individual and others, individual and obstacles or wall into account, we introduce the social force model to simulate this phenomenon and use it as a collision resolution strategy. Then, we combine the improved PSO-based model with modified Social Force Model to form the velocity updating rule. In the end, through simulation and analyzation, we testify the modified model is very effective.
An Asymmetric Switched Capacitor Multilevel Inverter With Component Reduction

This paper proposes a new switched capacitor (SC-Type) inverter for asymmetrical multilevel inverters (MLIs) with fewer components. In order to balance the voltages of the capacitors, the proposed topology uses a special method to charge/discharge the capacitors. In the proposed inverter, the number of switches, the number of dc voltage sources, the amount of blocked voltage in the switches, and the power losses are reduced. Fewer components result in lower size, complexity, and cost of MLI. Cascade connection of the proposed topology is used to achieve a higher number of output voltage levels. The MATLAB simulations and experimental results of a 25-level MLI verify the good performance of the proposed inverter.
Self Timed SRAM Array with Enhanced low Voltage Read and Write Capability

The scaling of technology resulted in shrinking of device sizes, however suffers from threshold variation. These variations impact the performance and stability of static random access memory (SRAM). The proposed work takes the advantage of the conventional Replica-Bit-Line (RBL) technique to design a self-timed SRAM control circuit which keeps track of timing variations and generated control signals. A variable wordline scheme is proposed to ensure successful read and write operation of SRAM at low voltages. The 2 Kb (2048 bits) SRAM designed in 65 nm UMC technology, operates with clock frequency reaching upto 1 GHz at the nominal supply voltage of 1.2 V and at 50 MHz with the supply voltage of 0.58 V. The variations in the Sense Amplifier Enable (SAE) generation are reduced upto 92% compared to the conventional RBL technique.
Construction of Maximal Entanglement EAQECCs of Distance 4 from Caps in Projective Space PG(r,4)

The entanglement-assisted (EA) formalism is a generalization of the standard stabilizer formalism, and it can transform arbitrary quaternary classical linear codes into entanglement-assisted quantum error correcting codes (EAQECCs) by using of shared entanglement between the sender and the receiver. Using a decomposition of the 126-cap in PG(5,4), we firstly constructed LCD n-cap with 6The entanglement-assisted (EA) formalism is a generalization of the standard stabilizer formalism, and it can transform arbitrary quaternary classical linear codes into entanglement-assisted quantum error correcting codes (EAQECCs) by using of shared entanglement between the sender and the receiver. Using a decomposition of the 126-cap in PG(5,4), we firstly constructed LCD n-cap with 6≤n≤120. From these LCD caps, we then derived the related maximal entanglement [[n, n-6,4;6]] EAQECCs. Finally, using LCD LCD subcaps of 126-cap obtained, we constructed maximal entanglement EAQECCs with parameters [[n, n-;k,4;k]] for 6≤k≤11.
An inquiry into the predictability of failure events in chillers and boilers

This paper explores the predictability of failure events in large boilers and chillers by using data inherent in sensor and actuator data from four boilers and five chillers of a central heating and cooling plant (CHCP) in Ottawa, Canada are extracted. The plant operators are interviewed to understand how they handle failure events, and their logbooks are reviewed to extract the date and time of the recorded failure events. The sensor and actuator data up to two weeks prior to each of these failure events are used to develop regression tree models that predict the remaining time-to failure. The results indicate that about half of the modelled failure events could be accurately predicted by looking at the data available in the distributed control system. Further, rules that define operational conditions leading to failure events were derived by using the regression tree models. It is argued that this regression tree modelling and rule extraction method can guide controls technicians to reconFigure the setpoints and control loop parameters. Lastly, future work recommendations are developed to study prognostics in CHCP equipment by using more comprehensive datasets and to demonstrate the use of prognostics in operational decision-making.
Review of Battery Cell Balancing Methodologies for Optimizing Battery Pack Performance in Electric Vehicles

Batteries are gaining entry into every home and office for they are widely used because of their variant benefits. However, these batteries are prone to failure caused by charge imbalance in the batteries connected in either series or parallel, which can sometimes be catastrophic and hence they require to be properly monitored in a real-time manner. There exist many battery balancing schemes which are broadly grouped into either passive or active schemes. All these schemes have their own advantages and disadvantages, and hence it is upon the user to decide on which scheme will best work for them. However, research has proven that the hybrid scheme will be the best as it couples the benefits of all schemes. This study will review the various battery cell balancing methodologies and evaluate their relationship with battery performance. At present there are a few studies tackling the mechanical vibration of battery balancing performance. This study shows that battery balancing performance during long-term should be evaluated from various temperature and vibration frequencies.
A Machine Learning Approach for Enhancing Defence Against Global Terrorism

The objective of this work is to predict the region and country of a terrorist attack using machine learning approaches. The work has been carried out upon the Global Terrorism Database (GTD), which is an open database containing list of terrorist activities from 1970 to 2017. Six machine learning algorithms have been applied on a selected set of features from the dataset to achieve an accuracy of up to 82%. The results suggest that it is possible to train machine learning models in order to predict the region and country of terrorist attack if certain parameters are known. It is postulated that the work can be used for enhancing security against terrorist attacks in the world.
30 GHz Monolithically Integrated Frequency Multiplier Based on GaN Planar Schottky Barrier Diode

A 30 GHz monolithically integrated frequency multiplier based on the planar GaN Schottky barrier diode are successfully fabricated. The impact of material parameters and anode dimension on the frequency multiplier are investigated. The use of the monolithic integrated approach significantly improves the performance of the GaN frequency multiplier. The frequency multiplier gains a more than 3 dBm output power with frequency range from 21.5 to 39.5 GHz. The maximum output power is 9.68 dBm at 28.5 GHz with efficiency up to 16.1%. At the central frequency of 30 GHz, the output power is 9.64 dBm with 15.0% efficiency.
Reinforcement Learning for Adaptive Resource Allocation in Fog RAN for IoT With Heterogeneous Latency Requirements

In light of the quick proliferation of Internet of things (IoT) devices and applications, fog radio access network (Fog-RAN) has been recently proposed for fifth generation (5G) wireless communications to assure the requirements of ultra-reliable low-latency communication (URLLC) for the IoT applications which cannot accommodate large delays. To this end, fog nodes (FNs) are equipped with computing, signal processing and storage capabilities to extend the inherent operations and services of the cloud to the edge. We consider the problem of sequentially allocating the FN’s limited resources to IoT applications of heterogeneous latency requirements. For each access request from an IoT user, the FN needs to decide whether to serve it locally at the edge utilizing its own resources or to refer it to the cloud to conserve its valuable resources for future users of potentially higher utility to the system (i.e., lower latency requirement). We formulate the Fog-RAN resource allocation problem in the form of a Markov decision process (MDP), and employ several reinforcement learning (RL) methods, namely Q-learning, SARSA, Expected SARSA, and Monte Carlo, for solving the MDP problem by learning the optimum decision-making policies. We verify the performance and adaptivity of the RL methods and compare it with the performance of the network slicing approach with various slicing thresholds. Extensive simulation results considering 19 IoT environments of heterogeneous latency requirements corroborate that RL methods always achieve the best possible performance regardless of the IoT environment.
Finite Response and Confluence of State-based Supervisory Controllers

The development of logic controllers for cyber-physical systems is a difficult and error-prone process. Supervisor synthesis can ease the development process, by synthesizing a correct-by-construction supervisor from a model of the plant and a model of the control requirements. Subsequently, this synthesized supervisor can be used to automatically generate the controller code. However, for a supervisor to be implemented as a (logic) controller, additional properties need to be satisfied which are not guaranteed by supervisor synthesis. These properties are finite response and confluence. In the literature, conditions are provided for a supervisor, modeled as a set of finite-state automata, to have finite response and to be confluent. Algorithms are defined to check these conditions. Recently, extended finite-state automata, i.e, automata augmented with variables, are being used increasingly often in the context of supervisory control. In this paper, sufficient conditions and algorithms are provided that can be used to verify if a supervisor, modeled as a set of extended finite-state automata, has finite response and is confluent. The algorithms are applied to three case studies from the literature to demonstrate their applicability to models used for supervisor synthesis.