Miracle-dz/UniFAR-FTUs huggingface dataset:
Technology Transfer Management Solutions for the Company’s Growth

In the first part of the Thesis, the importance of innovation and technology transfer is described, the analysis of innovation and technology transfer concepts is performed, and a review of technology transfer performance research is presented. The second part employs the logical model to group the factors and create the framework of empirical research. In the third part, the analysis, interpretation, and approbation of empirical data are performed. The results of the research show that innovations are essential for increasing the company's performance, innovations are facilitated by the acquisition of new knowledge and technologies, which in turn is facilitated by the internal research and development work.
Energy Status Recovery using Recurrent SVR Framework for Solar BLE Beacons

To address the short-lived battery lifetime of Bluetooth low energy (BLE) beacons, solar-powered designs were proposed, equipped with rechargeable energy storage such as a supercapacitor. However, energy status monitoring, which is essential for device maintenance, proved to be a major concern as the energy status of energy harvesting devices can change quickly due to charging and discharging behaviours. Existing energy status monitoring methods performed in a crowd-assisted manner or by demanding on-site data collections are accompanied by severe loss of energy status information. This paper presents an accurate energy status recovery framework with SVR to address this issue. The proposed framework leverages recurrent training of SVR with lost energy status information to capture features from discharge behaviour to achieve high accuracy while minimizing the training and prediction time. Multiple real-life BLE beacon energy level records are evaluated to demonstrate that our proposed framework can recover the energy information with at least 90% accuracy under a data loss rate of up to 99%.
High‐resolution and robust microseismic grouped imaging and grouping strategy analysis

As an advanced real‐time monitoring technique, microseismic source‐location imaging provides valuable information during hydraulic fracturing, for example, the development of fracture networks and the effective reservoir reconstruction volume. However, microseismic data always suffer from weak induced energy and susceptibility to noise interference. In the case of a low signal‐to‐noise ratio, it is extremely challenging to perform robust microseismic imaging. Here, we first introduce several state‐of‐the‐art imaging conditions and two hybrid imaging conditions, which are followed by a detailed analysis of the impact of different grouping strategies. Then, we briefly analyse the sensitivity of different imaging conditions to noise using a one‐dimensional signal. Next, several benchmark models, including two‐dimensional Marmousi‐II and three‐dimensional SEG Advanced Modeling, are used as numerical examples for testing the passive‐source imaging algorithms. Finally, three‐dimensional real microseismic data are used to further investigate the impact of the grouping strategy on the imaging. The numerical examples and field data demonstrate the effectiveness of the proposed grouping strategy for the grouped imaging conditions.
Effects on phased arrays radiation pattern due to phase error distribution in the phase shifter operation

A comprehensive study on the role of the phase errors distribution on the performances of the phased array systems has been produced. The analysis is brought out using a complete and behavioural model for radiation-pattern characteristics. The study has shown how the phase errors distribution actually affects the performances demonstrating that the rms phase error is a valuable figure of merit of phased array systems but it is not sufficient to completely describe the behaviour of a real system. The paper demonstrates that the antennas array beam shape is depending by the actual error phase distribution and that a good phase shifter has to have the phase errors as constant as possible and with a low rms value.
Sildenafil-Induced Revascularization of Rat Hindlimb Involves Arteriogenesis through PI3K/AKT and eNOS Activation

Hypoxia and inflammation play a major role in revascularization following ischemia. Sildenafil inhibits phosphodiesterase-5, increases intracellular cGMP and induces revascularization through a pathway which remains incompletely understood. Thus, we investigated the effect of sildenafil on post-ischemic revascularization. The left femoral artery was ligated in control and sildenafil-treated (25 mg/kg per day) rats. Vascular density was evaluated and expressed as the left/right leg (L/R) ratio. In control rats, L/R ratio was 33 ± 2% and 54 ± 9%, at 7- and 21-days post-ligation, respectively, and was significantly increased in sildenafil-treated rats to 47 ± 4% and 128 ± 11%, respectively. A neutralizing anti-VEGF antibody significantly decreased vascular density (by 0.48-fold) in control without effect in sildenafil-treated animals. Blood flow and arteriolar density followed the same pattern. In the ischemic leg, HIF-1α and VEGF expression levels increased in control, but not in sildenafil–treated rats, suggesting that sildenafil did not induce angiogenesis. PI3-kinase, Akt and eNOS increased after 7 days, with down-regulation after 21 days. Sildenafil induced outward remodeling or arteriogenesis in mesenteric resistance arteries in association with eNOS protein activation. We conclude that sildenafil treatment increased tissue blood flow and arteriogenesis independently of VEGF, but in association with PI3-kinase, Akt and eNOS activation.
Insightful learning to meet future work life challenges provided by LUAS Master School

The future of jobs will differ substantially from today’s work and the disruption will happen rapidly. New work categories will emerge as old ones will seize to exist. This all will require a lot of learning and therefore the quality of learning will play such an important role in the future. The main objective of the paper is to make sure LUAS master school students will be considered as attractive recruitments by the employers’ of the future working environment. The benefit of this, to the degree students, is to guarantee their future employment in the changing working environment, and also to provide them capabilities to succeed in their careers. The literature review consists of the future of jobs, engaging learning experience –model and issues related to universities of applied sciences. The empiric research is conducted around factors currently advancing and preventing students’ insightful learning in LUAS. The empirical data is collected from both students and teachers of LUAS. The end result of the research is a recommendations list for future actions for LUAS teachers. By implementing the actions, the level of insightful learning in LUAS is bound to improve. Besides teachers’ actions, the school itself has a major role in enhancing the students’ insightful learning and ways in succeeding in that, are also suggested in the paper. Insights and insightful learning are mentioned in both LUAS’s vision and values, and hence redeeming those promises need to be present in LUAS’s teachers’ practices at school every day. In order to ensure the teachers’ implementation of the recommendations, they need to be committed to insightful learning practices. The results of the study reveal that most of the actions expected of teachers in enhancing insightful learning are very practical and simple and should be implemented, naturally, by means of insightful learning.
The Automated System for Selection of Optimal Solutions for the Process of Iron Ore Mining Based on Artificial Intelligence Methodology

This paper about solution in iron ore mining industry which based on automated system for selection of reflection of iron ores by genetic algorithm and economic-mathematical modeling BDW (Blasting and drilling work). The combination of these works is not possible without the introduction of artificial intelligence because the choice of the optimal combination of models is based on the selection of one in a million.
Towards Privacy Preservation using Clustering based Anonymization: Recent Advances and Future Research Outlook

With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user’s privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms.
Outage Performance of Cooperative MISO-NOMA Based Satellite-Terrestrial Networks

This paper studies the outage performance of a cooperative multiple-input single-output satellite-terrestrial net-work in which the satellite equipped with multiple antennas simultaneously serves two single antenna terrestrial users in a non-orthogonal manner, called MISO-NOMA. Specifically, the satellite utilizes the maximal-ratio transmission technique while the user with strong channel gain (called the strong user) assists to the user with weak channel gain (called the weak user) by acting as a decode-and-forward relay with prior knowledge of weak user. We assume that channels are subjected to Shadowed-Rician and Nakagami-m fading for the satellite and terrestrial links, respectively. In order to characterize the performance behavior of both users, outage probability expressions are derived. Theoretical results validated by simulations demonstrate that user-cooperation has more impact on the performance improvement of the weak user under heavy shadowing condition which is the most crucial channel effect in satellite-terrestrial environment. In addition, with the increasing number of antennas, the weak and strong users receive the best performance improvement in non-cooperation and user-cooperation cases, respectively.
DPRL: Task Offloading Strategy Based on Differential Privacy and Reinforcement Learning in Edge Computing

Mobile edge computing has been widely used in various IoT devices due to its excellent computing power and good interaction speed. Task offloading is the core of mobile edge computing. However, most of the existing task offloading strategies only focus on improving the unilateral performance of MEC, such as security, delay, and overhead. Therefore, focus on the security, delay and overhead of MEC, we propose a task offloading strategy based on differential privacy and reinforcement learning. This strategy optimizes the overhead required for the task offloading process while protecting user privacy. Specifically, before task offloading, differential privacy is used to interfere with the user’s location information to avoid malicious edge servers from stealing user privacy. Then, on the basis of ensuring user privacy and security, combined with the resource environment of the MEC network, reinforcement learning is used to select appropriate edge servers for task offloading. Simulation results show that our scheme improves the performance of MEC in many aspects, especially in security and resource consumption. Compared with the typical privacy protection scheme, the security is improved by 7%, and the resource consumption is reduced by 9% compared with the typical task offloading strategy.
Experimental Evaluation of Downlink Scheduling Algorithms using OpenAirInterface

Programmability and softwarization advocate the emerging era of open-source platforms, which embraced by both industry and academia is foreseen as a vital pillar in the construction of next generation mobile networks. Such a valuable open-source project is OpenAirInterface (OAI), which provides a standard compliant mobile network infrastructure, merely based on general purpose hardware computers. While OAI is nowadays widely used by industry and research institutes in proof-of-concept or commercial wireless testbeds, an analysis of the complex functions within the platform is yet to be performed in a large scale. We believe that further research is required to demystify the capabilities of existing tools and present guidelines that alleviate the enhancement and development of additional features. In this context, in this work we shed light on one of the crucial components of any mobile system, namely resource scheduling, while providing an analysis of the available code and instructions to ease the development of new scheduling algorithms based on OAI. Moreover, we demonstrate a performance evaluation of up to 10 UEs for existing and newly implemented scheduling algorithms. Results show, that the development of additional algorithms in OAI is achievable and the experimental behavior follows the theory. Our implementation and observations can serve as a basis for research in the field, and foster the elaboration of theoretical concepts and emerging 5G solutions in practical testbeds.
Application of articial neural networks for predicting core parameters for the SAFARI-1 nuclear research reactor

The in-core fuel management optimisation (ICFMO) problem is a nonlinear assignment problem in which an optimal fuel reload conguration for a nuclear reactor core is sought. Function evaluations for the problem are performed by a reactor core calculation code and are deemed computationally expensive. This computational cost severely hinders eorts toward the investigation of appropriate techniques for solving the ICFMO problem. It is possible, however, to reduce this cost by replacing the reactor code with a computationally cheaper surrogate model. In this paper, articial neural network (ANN) surrogate models are constructed for the prediction of core parameters for the SAFARI-1 nuclear research reactor. The parameters correspond to possible ICFMO objectives and constraints. The Neural Network Toolbox within the Matlab software suite is utilised for the construction. A description of the neural networks is given, along with details regarding the training process. The accuracy of the neural networks is veried on dierent sets of data points by comparing the predicted values to their actual values. The results indicate that the ANNs may be employed to signicantly reduce the computational time required within an investigation of appropriate techniques for solving SAFARI-1 ICFMO problems. Furthermore, possibilities exist for using neural networks (provided that sucient generalisation can be attained) in areas other than ICFMO e.g. within reactor codes.
Estimation of Time-Frequency Muscle Synergy in Wrist Movements

Muscle synergy analysis is a kind of modularized decomposition of muscles during exercise controlled by the central nervous system (CNS). It can not only extract the synergistic muscles in exercise, but also obtain the activation states of muscles to reflect the coordination and control relationship between muscles. However, previous studies have mainly focused on the time-domain synergy without considering the frequency-specific characteristics within synergy structures. Therefore, this study proposes a novel method, named time-frequency non-negative matrix factorization (TF-NMF), to explore the time-varying regularity of muscle synergy characteristics of multi-channel surface electromyogram (sEMG) signals at different frequency bands. In this method, the wavelet packet transform (WPT) is used to transform the time-scale signals into time-frequency dimension. Then, the NMF method is calculated in each time-frequency window to extract the synergy modules. Finally, this method is used to analyze the sEMG signals recorded from 8 muscles during the conversion between wrist flexion (WF stage) and wrist extension (WE stage) movements in 12 healthy people. The experimental results show that the number of synergy modules in wrist flexion transmission to wrist extension (Motion Conversion, MC stage) is more than that in the WF stage and WE stage. Furthermore, the number of flexor and extensor muscle synergies in the frequency band of 0–125 Hz during the MC stage is more than that in the frequency band of 125–250 Hz. Further analysis shows that the flexion muscle synergies mostly exist in the frequency band of 140.625–156.25 Hz during the WF stage, and the extension muscle synergies appear in the frequency band of 125–156.25 Hz during the WE stage. These results can help to better understand the time-frequency features of muscle synergy, and expand study perspective related to motor control in nervous system.
MINIMALLY INVASIVE SURGICAL DEVICES WITH MICRO FORCE SENSORS: A SMART SCALPEL AND DISSECTOR TOOL

The objective of this work is to create or enhance a medical device by providing information on the forces acting on it using sensors that were built with MEMS fabrication techniques. It is important that the device serve a functional purpose as described by the customer or in this case a surgeon that would be using the tool. Many medical tools are quite effective at the task in which they were designed for, but when they are transformed to perform minimally invasive, or robotic surgeries certain tactile elements are lost. This is where it is important to create a way in which these elements can be sensed and translated in such a way so that the physician can seamlessly move from open surgeries to minimally invasive surgeries (MIS) with out losing quality of skills. The goal of this project is to develop force sensors for minimally invasive surgical products. As a proof of concept, a force sensor was developed for a scalpel using silicon as the structure and sensing mechanism. After successful completion of the proof of concept and subsequent feedback from a surgeon, a force sensor was developed for a minimally invasive dissector tool. In this work, force sensors were integrated on a 25 μm thick polyimide film, and rolled onto the exposed surface of a dissector tool. The structure provided a simple, but yet effective way to recognize and characterize forces applied to the tool. This new method shows great possibilities for new low cost, flexible, polymer, tactile sensing systems which could open the door for tactile guided minimally invasive tools.
Removal of Reactive Blue 19 from Aqueous Solution and Batik Wastewater Using Iron Modified Activated Carbon

In this research, iron modified activated carbon (FeAC) was prepared through chemical activation method to enhance the adsorption potential of activated carbon (AC) towards the removal of Reactive Blue 19 (RB 19) dye in batik wastewater and aqueous solution. The adsorbents were characterized by various characterization techniques while the industrial wastewater and aqueous solution were characterized using UV-Vis spectroscopy and chemical oxygen demand (COD) analysis. The effects of various parameters such as adsorbent dosage, amount of H2O2, contact time, initial RB 19 dye concentration, pH and the reusability of the adsorbent in the presence and absence of 30 % w/w H2O2 were investigated. In the presence of H2O2, FeAC exhibited the highest removal efficiencies ( > 90.0 %) for RB 19 dye in both industrial wastewater and aqueous solution using 0.2 g adsorbent, 10 mL of 30 % w/w H2O2 and at ambient pH within 480 minutes compared to the AC and FeAC alone.
A Heuristic Approach to Maximize Workload Smoothness for an Apparel Production Line with Work Sharing and Worker Movement.

KUMAR KODAKKAT, NIKIL. A Heuristic Approach to Maximize Workload Smoothness for an Apparel Production Line with Work Sharing and Worker Movement. (Under the direction of Dr. Jeff A. Joines). A variation of the Simple Assembly Line Balancing Problem (SALBP) is examined. We consider an apparel production line where workers serve as the main constraint, with each worker potentially cross trained to do one or more tasks with a predefined efficiency. The primary objective is to obtain the optimal assignment of workers to tasks to maximize workload smoothness for the line which is measured by Mean Absolute Deviation (MAD) of workload and satisfy demand/production for a given time period. We provide a comparative assessment of scenarios where work sharing is used and not used. We also study the effects of allowing workers to be idle in these scenarios (i.e., workers would be able to perform other duties that are not directly related to production of the product). Smaller instances of the problem can be solved directly using CPLEX whereas larger versions cannot be solved optimally within reasonable computation times. Therefore, a heuristic utilizing a Genetic Algorithm is developed to find good solutions. A real industrial case is also solved as an example. The worker to task assignment generated by solving these static models is then used to build simulation models in order to account for stochastic processing times. The results of the deterministic system are compared to the stochastic systems with ±10%, ±20% and ±30% variation in the Standard Operation Time based on a PERT distribution. We also determine when a worker is required to move to another station based on the number waiting in queue (termed as buffer size) at each station. This is done by comparing the effect of two types of policies – no buffers and a common buffer size for all stations. We measure three responses MAD, Actual Production and Total Worker Movement, to determine the “optimal” buffer size. © Copyright 2013 Nikil Kumar Kodakkat
THESIC : Una plataforma flexible para la validation funcional de circuitos integrados

This work aims at describing the main features of a system, the so-called THESIC tester, devoted to the functional validation of integrated circuits. This system, initially designed to set-up radiation test experiments of digital architectures, is composed of two main blocs: a motherboard for the interface with the user and the control of the circuit under test operation and a daughter board in which is implemented a suitable architecture around the circuit to be tested. The THESIC version presented in this paper was enhanced in order to significantly simplify the daughter board developments, whose architecture is implemented through an FPGA programming