Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and optimization of ad hoc and sensor networks

Wireless ad hoc networks and sensor networks are emerging as new network classes that differ considerably from conventional wired or wireless networks. The distinguishing characteristic of ad hoc and sensor networks is the lack of deployed infrastructure, which poses the design challenge of distributing network management among the nodes. Nodes in ad hoc and sensor networks also have limited resources, so the distributed algorithms and protocols that manage network behavior must also enable fine-grained optimization at the resource-limited nodes. In addition, ad hoc and sensor networks have a wide range of potential applications, each of which requires custom performance guarantees, suggesting the need for a flexible methodology to optimize network behavior. This dissertation presents a flexible cross-layer framework for optimizing the performance of ad hoc and sensor networks that enables each node to greedily and locally optimize its behavior in order to achieve a global performance goal. The framework provides a customizable definition of node states and it enables the network designer to select the network mechanisms at any layer of the communication stack that require optimization. The framework's applicability and optimization potential are validated through three diverse case studies: (1) minimization of energy consumption in a sensor network test-bed using radio frequency signals; (2) maximization of throughput and fairness, and reduction of control overhead and link setup latency in an ultra wide band ad hoc network; and (3) maximization of battery lifetime and minimization of power consumption in an acoustic underwater sensor network. Each case study adopts a customized version of the framework by tailoring state definitions and by defining mechanisms that require optimization in accordance with the case study's performance requirements. In all cases, the framework yields significant improvement in network performance.
Computational Assessment of Botrytis cinerea Lipase for Biofuel Production

The demand for ecofriendly green catalysts for biofuel synthesis is greatly increasing with the effects of fossil fuel depletion. Fungal lipases are abundantly used as biocatalysts for the synthesis of biofuel. The use of Botrytis cinerea lipase is an excellent approach for the conversion of agroindustrial residues into biofuel. In this study, phylogenetic analyses were carried out and the physicochemical properties of B. cinerea lipase were assessed. Furthermore, the protein structure of B. cinerea lipase was predicted and refined. Putative energy-rich phytolipid compounds were explored as a substrate for the synthesis of biofuel, owing to B. cinerea lipase catalysis. Approximately 161 plant-based fatty acids were docked with B. cinerea lipase in order to evaluate their binding affinities and interactions. Among the docked fatty acids, the top ten triglycerides having the lowest number of binding affinities with B. cinerea lipase were selected, and their interactions were assessed. The top three triglycerides having the greatest number of hydrogen bonds and hydrophobic interactions were selected for simulations of 20 ns. The docking and simulations revealed that docosahexaenoic acid, dicranin, and hexadeca-7,10,13-trienoic acid had stable bonding with the B. cinerea lipase. Therefore, B. cinerea lipase has the potential to be used for the transesterification of fatty acids into biofuels, whereas docosahexaenoic acid, dicranin, and hexadeca-7,10,13-trienoic acid can be used as substrates of B. cinerea lipase for biofuel synthesis.
Edge-aware Multi-level Interactive Network for Salient Object Detection of Strip Steel Surface Defects

The performance of the salient object detection of strip surface defects has been promoted largely by deep learning based models. However, due to the complexity of strip surface defects, the existing models perform poorly in the challenging scenes such as noise disturbance, and low contrast between defect regions and background. Meanwhile, the detection results of existing models often suffer from coarse boundary details. Therefore, we propose a novel saliency model, namely an Edge-aware Multi-level Interactive Network, to detect the defects from the strip steel surface. Concretely, our model adopts the U-shape architecture where the two crucial points are the interactive feature integration and the edge-guided saliency fusion. Firstly, except the skip connection that combines the same stage of encoder and decoder, we deploy another connection, where the features from adjacent levels of encoder are transferred to the same stage of decoder. By this way, we are able to provide an effective fusion of multi-level deep features, yielding a well depiction for defects. Secondly, to give well-defined boundaries for prediction results, we add the edge extraction branch after each decoder block, where the progressive feature aggregation endows the edge with precise details and complete object cues. Meanwhile, together with the edge extraction branches, we deploy the saliency prediction branch at each decoder stage. After that, coupled with the fine edge information, we fuse all outputs of saliency prediction branches into the final saliency map, where the edge cue steers the saliency result to pay more attention to the boundary details. Following this way, we can provide a high-quality saliency map which can accurately locate and segment the defects. Extensive experiments are performed on the public dataset, and the results prove the effectiveness and robustness of our model which consistently outperforms the state-of-the-art models.
Development of a hand grip library intended for implementation in an ergonomic simulation tool - As a part of the IMMA research project at AB Volvo

Intelligent Moving Manikin (IMMA) is an ergonomic simulation and analysis tool, created by a research group consisting of industry and academic partners. This master thesis is commissioned by AB Volvo as a part of the IMMA research project with purpose of creating a hand grip library for the manikin to use in different ergonomic simulations and analysis. This function is needed for the software to be more useful and the task is to identify a set of typical grips that are used in different assembly situations and implement these in a library in the IMMA demonstrator, and also to develop a framework for how to use these by identifying variables regarding the hand ergonomics, this will then be a base for a possible future automatic grip recognition function in IMMA. Two plants have been visited to investigate what grip types are being used while assembly and a large data gathering from theory, research and interviews have been made to be able to strengthen the choices and also to find the variables for the framework. The results of the tasks are integrated in a graphical user interface which is to show possible ways of how the implementations could be presented in the software for the end user. Eight grip types are implemented in the grip library and three variables suggested for the framework. These variables are to distinguish different grip types from one another and for each categorise values to be able to, in a particular context, identify the most suitable one for that specific task.
Data-driven Support of Coaches in Professional Cycling using Race Performance Prediction

: In individual sports, the judgment of which training activity will lead to the best performance is mostly based on the expert knowledge of the coach. Recent advances in data collection and data science have opened up new possibilities for performing a data-driven analysis to support the coach in improving the training programs of the athletes. In this paper, we investigate several methods to do such analysis for professional cyclists. We provide the coach with a framework to predict the Maximum Mean Powers (MMPs) of a cyclist in an upcoming race based on the recently performed training sessions. This way the coach can experiment with several planned alternatives to ﬁgure out the best way for preparing the athlete for a race. We conduct multiple prediction models through an extensive analysis of a real dataset collected recently about the performance of professional riders with varying physiologies and temporal performance peaks. We show that the application of the hybrid model using XGBoost and CatBoost has clear advantages. Additionally, we show that the accuracy of our general model can be further increased by ﬁltering according to the mountain stages. We have additionally validated the proposed framework using an openly available real dataset and the results were consistent with those of the collected data. We offer an open source implementation of our proposed framework.
Responsible research and innovation training programs: implementation and evaluation of the HEIRRI project

Abstract Responsible research and innovation, or RRI, is a concept that aims to bring together society and science for a better future. There are six key elements of RRI: public engagement, gender equality, science education, open access, ethics and governance. Higher Education Institutions and Responsible Research and Innovation (HEIRRI) project aimed to bring the concept of RRI into the educational system. Using state-of-the-art review of good practices, HEIRRI team developed 10 training programs on RRI for different higher education institution educational levels, including a summer school and a massive open online course (MOOC). We conducted pilot of the trainings and evaluated participants’ experiences. Satisfaction with HEIRRI training programs on responsible research and innovation was high, both for participants and for the trainers, and trainings raised awareness of RRI. Participants’ feedback was used to identify areas that need improvement and provided for recommendations for final versions of the HEIRRI training programs. In order to equip researchers with skills to recognize and apply RRI values, RRI should be included in their education. HEIRRI training is suitable for a range of different disciplines, including forensic science, and is free to use and adjust for specific contexts (available from: https://rri-tools.eu/heirri-training-programmes). Supplemental data for this article is available online at https://doi.org/10.1080/20961790.2021. 1970319 .
Effective mesoscale, short-range ensemble forecasting

Abstract : This study developed and evaluated a short-range ensemble forecasting (SREF) system with the goal of producing useful forecast probability (FP). Real-time, 0 to 48-h forecasts from four different SREF systems were compared for 129 forecast cases over the Pacific Northwest. Eight analyses from different operational forecast centers were used as initial conditions (ICs) for running the Fifth-Generation Pennsylvania State University-National Center of Atmospheric Research Mesoscale Model (MM5). Additional ICs were generated through linear combinations of the original 8 analyses, but this did not result in an increase in FP skill commensurate with the increase in ensemble size. It was also found that an ensemble made up of unequally likely members can be skillful as long as all members at least occasionally perform well. Model error is a large source of forecast uncertainty and must be accounted for to maximize SREF utility, particularly for mesoscale, sensible weather phenomena. Inclusion of model perturbations in a SREF increased dispersion toward statistical consistency, but low dispersion remained problematic. Additionally, model perturbations notably improved FP skill (both reliability and resolution), revealing the significant influence of model uncertainty. Systematic model errors (i.e., biases) should always be removed from a SREF since they are a large part of forecast error but do not contribute to forecast uncertainty. A grid-based, 2-week, running-mean bias correction was shown to improve FP skill through: 1) better reliability by adjusting the ensemble mean toward the verification's mean, and 2) better resolution by reducing unrealistic ensemble variance.
Development and characterisation of a novel myotube-motoneuron 3D co-culture system

The aim of this Thesis was to characterise the behaviour and interaction of primary muscle derived cells (MDCs) and motoneurons within a collagen-based 3D in vitro culture system. Cells cultured under uniaxial tension within 3D collagen matrices are known to selforientate along the lines of principle strain. In the case of skeletal muscle cells, this leads to the formation of aligned myotubes, thereby generating cultures which more closely recapitulate the architecture of in vivo muscle. Since maturation of muscle in vivo is dependent on functional innervation, integration of this model with a physiologically correct neural input would further improve both the accuracy and complexity of the in vitro construct. Furthermore, reliable neuromuscular junction formation in 3D culture could have substantial benefits for the study of neuromuscular disease and the testing of novel therapeutic agents. The behaviour of primary rat MDCs within an established collagen-based 3D culture system was optimised and subsequently characterised. A comparison of this model to conventional 2D cell culture techniques was carried out using immunohistochemical and PCR analysis. Investigation of myogenin expression levels over a three week culture period in both 2D and 3D found no significant differences between the two systems, indicating a conserved ability for MDC differentiation in both models. Immunohistochemical data illustrated the alignment of uniaxial myotubes in 3D compared with randomly orientated and branched myotubes in conventional culture, demonstrating the improved biomimicity of myotubes developed in 3D and under directional tension. The presence of motoneurons within the 3D co-culture was found to promote maturation of the MDCs as indicated by levels of macroscopic construct contraction and by quantitative PCR analysis. Co-localisation of pre- and post- synaptic markers in culture indicated the presence of putative synaptic contacts within the model. The model presented in this Thesis represents a step forward in the development of physiologically accurate in vitro models of skeletal muscle, which may help in future investigations of skeletal muscle development, physiology and pathology.
End of Life Care for People with an Intellectual Disability a Qualitative Exploration of Staff Carers’ Experiences

Background: Although people with intellectual disability are increasingly living longer and experiencing high levels of multiple co-morbidity, palliative care has received limited attention among this population. There is some evidence that their needs are unattended and that more could be done to improve care services.Methods: 28 carers who cared for older people with intellectual disability at end of life were interviewed. Data were analysed using thematic analysis. Results: One major theme emerged relating to the practical management s of end of life care in formal institutionalised settings. Seven subthemes also emerged: pain, learned acceptance, transitions, decisions, conversations about dying, GP care, rapid decline and challenges with interventions.Conclusion: Gaps emerged in the care of the person with intellectual disability. Pain assessment and pain management are particular challenges. End of life care was not always effectively planned, and earlier intervention, including end of life conversations are needed. More needs to be done in terms of education for carers, health care workers, and especially those in the acute care setting and palliative care services who may be unfamiliar with the needs of this cohort.
Morphology-Governed Performance of Multi-Dimensional Photocatalysts for Hydrogen Generation

In the past few decades, extensive studies have been performed to utilize the solar energy for photocatalytic water splitting; however, up to the present, the overall efficiencies reported in the literature are still unsatisfactory for commercialization. The crucial element of this challenging concept is the proper selection and design of photocatalytic material to enable significant extension of practical application perspectives. One of the important features in describing photocatalysts, although underestimated, is particle morphology. Accordingly, this review presents the advances achieved in the design of photocatalysts that are dedicated to hydrogen generation, with an emphasis on the particle morphology and its potential correlation with the overall reaction performance. The novel concept of this work—with the content presented in a clear and logical way—is based on the division into five parts according to dimensional arrangement groups of 0D, 1D, 2D, 3D, and combined systems. In this regard, it has been shown that the consideration of the discussed aspects, focusing on different types of particle morphology and their correlation with the system’s efficiency, could be a promising route for accelerating the development of photocatalytic materials oriented for solar-driven hydrogen generation. Finally, concluding remarks (additionally including the problems connected with experiments) and potential future directions of particle morphology-based design of photocatalysts for hydrogen production systems have been presented.
DEVELOPMENT OF AN ANDROID APP TO CONTROL AND MANAGE COGNITIVE NETWORKS

As the interest in Mobile Ad Hoc networks is constantly growing for tactical, disaster-response situation, academic research and civilian contexts, some new protocol must be implemented to let those network scale. Cognitive networking may solve the problem thanks to its network-wide performances goal. The goal of this project is to develop a working testbed that will be exploited in the study and the analysis of cognitive networking mechanism and algorithms. This tool is developed in Android
Directional selection at gene expression level contributes to the speciation of Asian rice cultivars

Differences in expression levels play important roles in phenotypic variation across species, especially those closely related species with limited genomic differences. Therefore, studying gene evolution at expression level is important for illustrating phenotypic differentiation between species, such as the two Asian rice cultivars, Oryza sativa L. ssp. indica and Oryza sativa L. ssp. japonica. In this study, we evaluated the gene expression variation at inter-subspecies and intra-subspecies level using transcriptome data from seedlings of three indica and japonica rice and defined four groups of genes under different natural selections. We found a substantial of genes (about 79%) that are under stabilizing selection at the expression level in both subspecies, while about 16% of genes are under directional selection. Genes under directional selection have higher expression level and lower expression variation than those under stabilizing selection, which suggest a potential explanation to subspecies adaptation to different environments and interspecific phenotypic differences. Subsequent functional enrichment analysis of genes under directional selection shows that indica rice have experienced the adaptation to environmental stresses, and also show differences in biosynthesis and metabolism pathways. Our study provide an avenue of investigating indica-japonica differentiation through gene expression variation, which may guide to rice breeding and yield improvement.
An E-Plane-Focused Triple-Layer Multibeam Luneburg Lens Antenna for 5G Millimeter-Wave Applications

A triple-layer multibeam Luneburg lens antenna (MLLA) at the millimeter-wave band is proposed. The proposed lens features a triple-layer all-dielectric cylindrical structure. Each layer features the same configuration, which is realized by hole drilling technology. Seven feed antennas are positioned on the periphery of the middle layer to perform multibeam radiation. The feed antennas are horizontally polarized, hence the MLLA is E-plane-focused. Except for focusing in the middle layer, the wavefront coming from the feed antennas can be also focused in the top and bottom layers. Therefore, the sidelobe level (SLL) in the H-plane of the triple-layer MLLA is reduced drastically when compared with a single-layer MLLA. The triple-layer MLLA is fabricated and tested, with a measured −10 dB impedance bandwidth of 24–34 GHz. The measured peak gain reaches 18.2 dBi at 28 GHz, while the beam scanning coverage is up to ±55°. The antenna is well developed for 5G millimeter-wave applications, with the advantages of low cost, light weight (only 32 g), enhanced gain, and multibeam function.
Investigations On Flow And Flow-Induced Vibration Of Candu Fuel Bundles

Excitations induced by three-dimensional unsteady flows of ordinary water coolant through a string of CANDU fuel bundles in a fuel channel are investigated in this thesis. Several comprehensive computational fluid dynamics (CFD) models are developed and solved by means of large eddy simulation (LES), high performance computers and parallel processing scheme. The 12-bundle flow model is the first ever developed concerning flow in a very complex CANDU fuel channel. The lateral fluid flow and flow-induced excitations on every fuel bundle are obtained and analyzed for various combinations of bundle angular positions. The coherent nature of the flow through the multiple bundles inside the fuel channel exhibiting fluid excitations of frequencies spreaded over a wide band in the power spectra is a source of bundle lateral vibration. The flow features of different bundle regime are correlated both in time and frequency domain and they are sensitive to the bundle-to-bundle angular position. This finding directs that, to study the flow and flow-induced excitations and vibrations of a bundle string, it is necessary to include all bundles for fluid-structure interactions. Results from the computational model reveal that the misaligned interface changes the flow pattern in the fuel channel. The mean lateral fluid forces increase by an order of magnitude and their RMS values raise about 3 to 4 times at some configurations compared to fully aligned situation. Experiments are also performed using the simulated CANDU bundles in an out-reactor setup to verify the computational results. An analysis of a complete fuel channel of a nuclear reactor using LES is at the forefront of current research worldwide and this study is a major step forward towards understanding and unfolding the fuel bundle vibration phenomenon.