Miracle-dz/UniFAR-FTUs huggingface dataset:
Conserving the biodiversity of Kuwait through DNA barcoding the flora

ii Acknowledgements.iii Chapter 1 Introduction 1.1 Aims of the thesis and chapter overview . 1 1.2 Geography, Topography and Geology, and Soils of Kuwait . 3 1.2.1 Geography . 3 1.2.2 Topography and Geology . 5 1.2.3 Soils . 7 1.3 Climate . 10 1.4 Phytogeography . 11 1.5 Vegetation of Kuwait . 13 1.5. 1 Distribution of the vegetation . 14 1.5. 2 Present vegetation . 15 1.5. 2.1 Changes to plant communities . 17 1.6 Conservation and threats . 18 1.6. 2 Natural regeneration of vegetation . 19 1.6. 3 Active restoration of vegetation . 20 1.6. 4 Biological soil crusts . 21 1.6. 5 Conservation through protected areas . 22 1.7 Natural resources and land management . 25 Chapter 2 Checklist of the flora of Kuwait 2.1 Current knowledge of the flora of Kuwait . 29 2.1.2 Botanical Exploration of Kuwait . 31 2.2 Methods . 33 2.3 Results . 36 2.3. 1 Floristic analyses of the flora of Kuwait . 48 2.3. 2 Native and naturalised vascular plants of Kuwait . 52
BACKWARD PROJECTION ALGORITHM AND

The aim of this study is to highlight the fully three-dimensional flow, the three-dimensional quantitative measurement advantages and the accuracy of the backward projection algorithm for a flow around an obstacle. The calibration method presented is based on an analytical relation between the real co-ordinate and the image co-ordinate for each camera. The calibration step consists in determining the geometric parameters of the stereoscopic system without preliminary knowledge of the experimental geometry of the device. In order to minimize the errors during the reconstruction stage, a backward projection method is used with the help of the analytical relations. The analysis of errors related to the calibration and to the determination of the three velocity components is carried out. The accuracy of the stereoscopic particle image velocimetry (PIV) system is assessed by taking measurements of flow around a square section cylinder in angular displacement configuration with prisms to minimize the optical distortions. With the use of this 3D measurement technique, the flow topology around a square cylinder is investigated and a quantitative study of instantaneous velocity fields is explained. A comparison between different measurements systems, 2D PIV and 3D LDV, is exposed and the accuracy of our stereo PIV algorithm is highlighted.
Common and rare variants in HFE are not associated with Parkinson's disease in Europeans

A recent study suggested that the p.H63D variant in HFE, a gene involved in iron homeostasis, may modify alpha-synuclein pathology, the pathological hallmark of Parkinson's disease (PD). If indeed this gene and specific variant are involved in PD, we expect to find differential distribution of HFE variants when comparing PD patients and controls. We analyzed genome-wide association study (GWAS) data from 14,671 PD patients and 17,667 controls and full sequencing data from additional 1,647 PD patients and 1,050 controls, using logistic regression models, and burden and Kernel tests. The HFE p.H63D variant was not associated with PD, nor did all the other common variants in the HFE locus. We did not find association of rare HFE variants with PD as well in all types of burden and Kernel tests. Our results do not support a role for HFE in PD.
Nucleotide Excision Repair in Yeast

Nucleotide Excision Repair (NER) is a conserved DNA repair pathway capable of removing a broad spectrum of DNA damage. In human cells a defect in NER leads to the disorder Xeroderma pigmentosum (XP). The yeast Saccharomyces cerevisiae is an excellent model organism to study the mechanism of NER. The yeast proteins Rad4 and Rad23 are important in NER and involved both Transcription-Coupled and Global Genome NER sub-pathways. Chapter 2 describes a novel mechanism of generegulation by the GG-NER E3 ligase. This protein complex can regulate dNTP synthesis via UV-induced Rad4 ubiquitination. In Chapter 3 we describe the effect of this histone on TC-NER at the rDNA. We find that Rad34 is only required when Histone H1 is present. In Chapter 4 we analyze the phenotype of a yeast strain containing a RAD4 mutation. The mutation affects a conserved residue that when altered in the human homolog XPC, leads to the cancer prone disorder XP. Chapter 5 is dedicated to the Rad4-Rad23 interaction that we describe in more detail. We uncovered a novel N-terminal interaction of Rad23 with Rad4 that is important for TC-NER. Finally, in Chapter 6 we reveal an unexpected UV phenotype for the commonly used wild-type yeast strain WCG4A. In this strain we identified the mutation to reside in the RAD4 gene
Athletes' Substance Abuse and Mental Health

Th e physical and emotional pressures experienced by competitive athletes are challenging. Research shows that 52% of professional football players have admitted to opioid use (Cottler et al., 2011), while up to 89% of college athletes have reported using alcohol as a response to injuries or feelings of pressure (Anderson et al., 1991). In addition, about 33.2% of National Collegiate Athletic Association (NCAA) athletes reported having experienced episodes of depression (Debonis, 2019). Th is paper will examine the reasons why so many athletes’ careers result in drug abuse and how the mental health of these athletes plays a role in substance abuse within the athletic community
A MATCH-MOVING METHOD COMBINING AI AND SFM ALGORITHMS IN HISTORICAL FILM FOOTAGE

Abstract. Searching for suitable material for photogrammetry is a key part in the documentation of Cultural Heritage. Photogrammetry can be used to produce a metrically certified 3D model. Material contained in historical film footage archives is especially useful for documentation when the heritage has been lost. In this research an innovative match-moving method is proposed that aims to exploit Artificial Intelligence and SfM algorithms to identify the frames extracted from a film footage in which the lost monument appears and that are suitable to be processed with photogrammetry for its 3D reconstruction. First of all the identification and tracking of the heritage in the videos was performed training an object detection Neural Network. Then the frames detected were automatically extracted with the coordinates of the bounding boxes that contain the monument. The camera motions were identified by selecting only the shots taken from multiple points of view of the same scene and analysing the evolution of the bounding boxes position over time. A further check of the material was necessary to select only sequences and to eliminate single frames and images from different historic periods. After this process, only the correct frames were automatically selected and processed with photogrammetry and the quality of the obtained 3D model was assessed. The method experimented in this research represents a powerful tool in the field of Cultural Heritage because it makes the selection of suitable material for photogrammetry automatic. Moreover it offers important insights that could be extended to other sectors.
OPTIMIZATION OF MECHANICAL PROPERTIES OF BASALT WOVEN / APA-6 COMPOSITE PARTS BY MEANS OF VELOCITY CONTROL

Nowadays the use of liquid composite molding (LCM) processes for the development of composite parts for both structural and non-structural applications has become of increasing interest. Carbon fiber has been traditionally used as reinforcement for its outstanding properties (chemically inert and very stiff), but it has the disadvantage of its high cost. Glass fiber is susceptible to undergo surface damage and to be sensitive to alkaline environment. Additionally, the current environmental requirements promote the use of natural fibers as reinforcement, in order to achieve more sustainable GreenComposites. In this context, basalt fibers are an alternative to current reinforcements. They are obtained directly from the molten single material, thus being biologically inactive, non-toxic, good electric insulator, and resistant to high temperature. Moreover they exhibit mechanical properties comparable with those of Eglass [1].
FORMULATION AND IN-VITRO EVALUATION OF MOUTH DISSOLVING TABLETS OF VALSARTAN

Mouth dissolving tablets of Valsartan drug were prepared by using three different superdisintegrants like Cross carmellose sodium, Sodium starch glycolate and Cros-povidone. Each superdisintegrant was used in three different concentrations like 2.5 % w/w, 5 % w/w and 7.5 % w/w. Total 9 formulations were developed and optimized for nature and concentration of superdisintegrants in tablets with respective to disintegration time, wetting time, water absorption ratio and in-vitro drug release. In the present study, it was proved that the formulations containing Sodium starch glycolate have shown good in-vitro results compared to other formulations. However the formulations containing 7.5 % w/w concentration of any superdisintegrants have shown better optimum results, hence selected as best formulations in this study.
Validasi Basa Nukleotida Gen 16S rRNA sebagai Dasar Pengelolaan Sumberdaya Mimi (Carcinoscorpius rotundicauda Lattreille, 1802) dari Perairan Pulau Jawa dan Madura.

YUNITA NUGRAHENI. Validation Nucleotide Based of Genes 16S rRNA as The Basic for Resources Management Horseshoe Crabs (Carcinoscorpius rotundicauda Lattreille, 1802) from Coastal Java and Madura Island. Supervised by NURLISA ALIAS BUTET and YUSLI WARDIATNO. Horseshoe crab (Carcinoscorpius rotundicauda) is a living fossil animal inhabiting estuary and mangrove areas and diversifying since the time of paleogene (65-23 Mya). The existence of this species belongs to the category of data deficient (2017) declared by IUCN. Therefore, it is necessary to identify horseshoe crab (C. rotundicauda) through a biomolecular approach to find out the molecular identity information of the horseshoe crab. The purpose of this research was to identify the sequence of nucleotide bases specific to 16S rRNA gene and to discover the relationship of horseshoe crab (C. rotundicauda) from coastal Java and Madura Island. The method used is DNA barcoding. The use of a 16S rRNA gene marker resulted in a 451 bp nucleotide length. The construction of phylogeny trees shows a clear distinction between Carcinoscorpius and Tachypleus there were 28 specific nucleotide sites as species identifiers.
A family of temporal logics on finite trees

Year: 2010 Vol.: 77 Fasc. : 3-4 Title: A family of temporal logics on ¯nite trees Author(s): Zolt¶an ¶Esik and Szabolcs Iv¶an We associate a temporal logic XTL(L) with each class L of (regular) tree languages and provide both an algebraic and a game-theoretic characterization of the expressive power of the logic XTL(L). Address: Zolt¶an ¶Esik Department of Computer Science University of Szeged Hungary Address: Szabolcs Iv¶an Department of Computer Science University
Current Trends in Ancient Grains-Based Foodstuffs: Insights into Nutritional Aspects and Technological Applications.

For centuries, ancient grains fed populations, but due to their low yield, they were abandoned and replaced by high-yielding species. However, currently, there is a renewed interest in ancient wheat and pseudocereal grains from consumers, farmers, and manufacturers. Ancient wheat such as einkorn, emmer, spelt, and Kamut®, are being reintegrated because of their low fertilizer input, high adaptability and important genetic diversity. New trends in pseudocereal products are also emerging, and they are mostly appreciated for their nutritional outcomes, particularly by the gluten-free market. Toward healthier lifestyle, ancient grains-based foodstuffs are a growing business and their industrialization is taking 2 pathways, either as a raw ingredient or a functional ingredient. This paper deals with these grain characteristics by focusing on the compositional profile and the technological potential.
Mechanics of Advanced Composite Structures

2D Electro-Elastic FGPM Cylinders Piezoelectric Shear Deformation Theory First-Order Electric Potential Theory In this study, an analytical solution is presented based on the voltage feedback control method for the two-dimensional electro-elastic static response of functionally graded piezoelectric material (FGPM) cylinders. Using first-order shear deformation theory as well as first-order electric potential theory and applying the energy method, a differential equations system is extracted, which is solved as a classical eigenvalue problem. The results show the significant impact of heterogeneity on the electromechanical behavior of the cylinders. Furthermore, control gain affects the electric potential and electromechanical behavior of the head where the voltage is applied. The present research also introduces an analytical solution with no limitation to specific conditions in cylinder heads and without any need for convergence check. Moreover, the results show that any changes in cylinder head conditions affect the behavior of FGPM cylinders. The results were compared with those from the finite element method (FEM), leading us to a reasonable agreement.
CHARACTERISING THE INFLUENCE OF FIBRE ANGLE ON THE BENDING OF UNCURED CARBON FIBRE PREPREG

Understanding the bending behaviour of uncured carbon fibre prepreg is vital for process models investigating the formation of defects during the manufacturing of carbon fibre components, however existing characterisation techniques are not readily applied to such a complex material. Standard Dynamic Mechanical Analysis packages typically calculate results according to Engineer’s Bending Theory, assuming infinite shear stiffness and that plane sections remain plane. This renders the technique unsuitable for materials in which any significant shear deformation occurs, such as uncured carbon fibre prepreg. This work presents a technique by which results for these materials can be post-processed with the use of a Timoshenko shear modifier term re-derived to consider viscous effects, allowing behaviour in bending to be analysed via this powerful and efficient technique. Values compare well to similar shear mechanics previously investigated, giving confidence in the validity of the approach.
The Benefits of Deploying Smart Contracts on Trusted Third Parties

The hype about Bitcoin has overrated the potential of smart contracts deployed on-blockchains (on-chains) and underrated the potential of smart contracts deployed on-Trusted Third Parties (on-TTPs). As a result, current research and development in this field is focused mainly on smart contract applications that use on-chain smart contracts. We argue that there is a large class of smart contract applications where on-TTP smart contracts are a better alternative. The problem with on-chain smart contracts is that the fully decentralised model and indelible append-only data model followed by blockchains introduces several engineering problems that are hard to solve. In these situations, the inclusion of a TTP (assuming that the application can tolerate its inconveniences) instead of a blockchain to host the smart contract simplifies the problems and offers pragmatic solutions. The intention and contribution of this paper is to shed some light on this issue. We use a hypothetical use case of a car insurance application to illustrate technical problems that are easier to solve with on-TTP smart contracts than with on-chain smart contracts.
CaM-Gen: Causally Aware Metric-Guided Text Generation

Content is created for a well-defined purpose, often described by a metric or signal represented in the form of structured information. The relationship between the goal (metrics) of target content and the content itself is non-trivial. While large-scale language models show promising text generation capabilities, guiding the generated text with external metrics is challenging.These metrics and content tend to have inherent relationships and not all of them may be of consequence. We introduce CaM-Gen: Causally aware Generative Networks guided by user-defined target metrics incorporating the causal relationships between the metric and content features. We leverage causal inference techniques to identify causally significant aspects of a text that lead to the target metric and then explicitly guide generative models towards these by a feedback mechanism. We propose this mechanism for variational autoencoder and Transformer-based generative models. The proposed models beat baselines in terms of the target metric control while maintaining fluency and language quality of the generated text. To the best of our knowledge, this is one of the early attempts at controlled generation incorporating a metric guide using causal inference.
A Conformal Transformation-based Approach for Analysis and Synthesis of Cylindrical Metasurfaces With Non-circular Cross Sections

We present methods for analyzing and designing cylindrical metasurfaces with non-circular cross sections based on conformal transformations. It can be difficult to treat irregularly shaped surfaces since they generally do not admit straightforward solutions to the Helmholtz wave equation. We address this issue by employing conformal transformations to map the physical space into a computational space in which the surface coincides with a circular cylinder. The electromagnetic boundary conditions on the surface remain intact under the transformations due to their angle-preserving nature. However, they are much more easily enforced. As a result, modal solutions for the scattered fields are readily obtainable, which facilitate the derivation of closed-form analysis and synthesis equations for general non-circular cylindrical metasurfaces. Notably, within the proposed framework, it is easy to guarantee the passivity and losslessness, and hence enhance the practical implementability of the generated designs.
A Comprehensive Survey on Curriculum Learning

Curriculum learning (CL) is a training strategy that trains a machine learning model from easier data to harder data, which imitates the meaningful learning order in human curricula. As an easy-to-use plug-in tool, the CL strategy has demonstrated its power in improving the generalization capacity and convergence rate of various models in a wide range of scenarios such as computer vision and natural language processing, etc. In this survey article, we comprehensively review CL from various aspects including motivations, definitions, theories, and applications. We discuss works on curriculum learning within a general CL framework, elaborating on how to design a manually predefined curriculum or an automatic curriculum. In particular, we summarize existing CL designs based on the general framework of Difficulty Measurer + Training Scheduler and further categorize the methodologies for automatic CL into four groups, i.e., Self-paced Learning, Transfer Teacher, RL Teacher, and Other Automatic CL. Finally, we present brief discussions on the relationships between CL and other methods, and point out potential future research directions deserving further investigations.
A Benchmark Corpus and Neural Approach for Sanskrit Derivative Nouns Analysis

This paper presents first benchmark corpus of Sanskrit Pratyaya (suffix) and inflectional words (padas) formed due to suffixes along with neural network based approaches to process the formation and splitting of inflectional words. Inflectional words spans the primary and secondary derivative nouns as the scope of current work. Pratyayas are an important dimension of morphological analysis of Sanskrit texts. There have been Sanskrit Computational Linguistics tools for processing and analyzing Sanskrit texts. Unfortunately there has not been any work to standardize & validate these tools specifically for derivative nouns analysis. In this work, we prepared a Sanskrit suffix benchmark corpus called Pratyaya-Kosh to evaluate the performance of tools. We also present our own neural approach for derivative nouns analysis while evaluating the same on most prominent Sanskrit Morphological Analysis tools. This benchmark will be freely dedicated and available to researchers worldwide and we hope it will motivate all to improve morphological analysis in Sanskrit Language.
A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs

Neural embedding approaches have become a staple in the fields of computer vision, natural language processing, and more recently, graph analytics. Given the pervasive nature of these algorithms, the natural question becomes how to exploit the embedding spaces to map, or align, embeddings of different data sources. To this end, we survey the current research landscape on word, sentence and knowledge graph embedding algorithms. We provide a classification of the relevant alignment techniques and discuss benchmark datasets used in this field of research. By gathering these diverse approaches into a singular survey, we hope to further motivate research into alignment of embedding spaces of varied data types and sources.
The Sample Complexity of Level Set Approximation

We study the problem of approximating the level set of an unknown function by sequentially querying its values. We introduce a family of algorithms called Bisect and Approximate through which we reduce the level set approximation problem to a local function approximation problem. We then show how this approach leads to rate-optimal sample complexity guarantees for H{\"o}lder functions, and we investigate how such rates improve when additional smoothness or other structural assumptions hold true.
Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning

Model-based reinforcement learning is a framework in which an agent learns an environment model, makes planning and decision-making in this model, and finally interacts with the real environment. Model-based reinforcement learning has high sample efficiency compared with model-free reinforcement learning, and shows great potential in the real-world application. However, model-based reinforcement learning has been plagued by dynamics bottleneck. Dynamics bottleneck is the phenomenon that when the timestep to interact with the environment increases, the reward of the agent falls into the local optimum instead of increasing. In this paper, we analyze and explain how the coupling relationship between model and policy causes the dynamics bottleneck and shows improving the exploration ability of the agent can alleviate this issue. We then propose a new planning algorithm called Maximum Entropy Cross-Entropy Method (MECEM). MECEM can improve the agent's exploration ability by maximizing the distribution of action entropy in the planning process. We conduct experiments on fourteen well-recognized benchmark environments such as HalfCheetah, Ant and Swimmer. The results verify that our approach obtains the state-of-the-art performance on eleven benchmark environments and can effectively alleviate dynamics bottleneck on HalfCheetah, Ant and Walker2D.
Scheduling with Complete Multipartite Incompatibility Graph on Parallel Machines

In this paper we consider a problem of job scheduling on parallel machines with a presence of incompatibilities between jobs. The incompatibility relation can be modeled as a complete multipartite graph in which each edge denotes a pair of jobs that cannot be scheduled on the same machine. We provide several results concerning schedules, optimal or approximate with respect to the two most popular criteria of optimality: Cmax (makespan) and ∑Cj (total completion time). We consider a variety of machine types in our paper: identical, uniform, and unrelated. Our results consist of delimitation of the easy (polynomial) and NP-hard problems within these constraints. We also provide algorithms, either polynomial exact algorithms for the easier problems, or algorithms with a guaranteed constant worst-case approximation ratio. In particular, we fill the gap on research for the problem of finding a schedule with the smallest ∑Cj on uniform machines. We address this problem by developing a linear programming relaxation technique with an appropriate rounding, which to our knowledge is a novelty for this criterion in the considered setting.