Database query optimisation based on measures of regret

The query optimiser in a database management system (DBMS) is responsible for �nding a good order in which to execute the operators in a given query. However, in practice the query optimiser does not usually guarantee to �nd the best plan. This is often due to the non-availability of precise statistical data or inaccurate assumptions made by the optimiser. In this thesis we propose a robust approach to logical query optimisation that takes into account the unreliability in database statistics during the optimisation process. In particular, we study the ordering problem for selection operators and for join operators, where selectivities are modelled as intervals rather than exact values. As a measure of optimality, we use a concept from decision theory called minmax regret optimisation (MRO). When using interval selectivities, the decision problem for selection operator ordering turns out to be NP-hard. After investigating properties of the problem and identifying special cases which can be solved in polynomial time, we develop a novel heuristic for solving the general selection ordering problem in polynomial time. Experimental evaluation of the heuristic using synthetic data, the Star Schema Benchmark and real-world data sets shows that it outperforms other heuristics (which take an optimistic, pessimistic or midpoint approach) and also produces plans whose regret is on average very close to optimal. The general join ordering problem is known to be NP-hard, even for exact selectivities. So, for interval selectivities, we restrict our investigation to sets of join operators which form a chain and to plans that correspond to left-deep join trees. We investigate properties of the problem and use these, along with ideas from the selection ordering heuristic and other algorithms in the literature, to develop a polynomial-time heuristic tailored for the join ordering problem. Experimental evaluation of the heuristic shows that, once again, it performs better than the optimistic, pessimistic and midpoint heuristics. In addition, the results show that the heuristic produces plans whose regret is on average even closer to the optimal than for selection ordering

The Impact of the COVID-19 Pandemic on Students’ Mental Health and Sleep in Saudi Arabia

BACKGROUND: Mental health problems are prevalent among university students in Saudi Arabia. This study aimed to investigate the impact of the COVID-19 pandemic on university students’ mental health and sleep in Saudi Arabia. Method: A total of 582 undergraduate students from Saudi Arabia aged between 18 and 45 years old (M = 20.91, SD = 3.17) completed a cross-sectional online questionnaire measuring depression, anxiety, stress, resilience, and insomnia during the COVID-19 pandemic (2020). Analysis included an independent samples t-test, one-way ANOVA, and Hierarchical regression analysis. RESULTS: Undergraduate students reported high levels of depression, anxiety, and perceived stress and low levels of resilience (p < 0.001) during the pandemic. In addition, students reported experiencing insomnia. A hierarchical regression analysis indicated that lower resilience, high levels of insomnia, having a pre-existing mental health condition, and learning difficulties (such as dyslexia, dyspraxia, or dyscalculia) were significantly associated with high levels of depression and stress. In addition, lower resilience, a high level of insomnia, and pre-existing mental health conditions were significantly associated with high levels of anxiety. Finally, a lower level of psychological resilience and a high level of insomnia were significantly associated with increased levels of depression, anxiety and stress within university students. CONCLUSION: This study has provided evidence that a lower level of psychological resilience and insomnia were associated with mental health problems among undergraduate students in Saudi Arabia, thus enhancing psychological resilience and interventions to support sleep and mental health are vital to support student well-being outcomes throughout the pandemic

Detecting Arabic Offensive Language in Microblogs Using Domain-Specific Word Embeddings and Deep Learning

In recent years, social media networks are emerging as a key player by providing platforms for opinions expression, communication, and content distribution. However, users often take advantage of perceived anonymity on social media platforms to share offensive or hateful content. Thus, offensive language has grown as a significant issue with the increase in online communication and the popularity of social media platforms. This problem has attracted significant attention for devising methods for detecting offensive content and preventing its spread on online social networks. Therefore, this paper aims to develop an effective Arabic offensive language detection model by employing deep learning and semantic and contextual features. This paper proposes a deep learning approach that utilizes the bidirectional long short-term memory (BiLSTM) model and domain-specific word embeddings extracted from an Arabic offensive dataset. The detection approach was evaluated on an Arabic dataset collected from Twitter. The results showed the highest performance accuracy of 0.93% with the BiLSTM model trained using a combination of domain-specific and agnostic-domain word embeddings

Database query optimisation based on measures of regret

The query optimiser in a database management system (DBMS) is responsible for �nding a good order in which to execute the operators in a given query. However, in practice the query optimiser does not usually guarantee to �nd the best plan. This is often due to the non-availability of precise statistical data or inaccurate assumptions made by the optimiser. In this thesis we propose a robust approach to logical query optimisation that takes into account the unreliability in database statistics during the optimisation process. In particular, we study the ordering problem for selection operators and for join operators, where selectivities are modelled as intervals rather than exact values. As a measure of optimality, we use a concept from decision theory called minmax regret optimisation (MRO). When using interval selectivities, the decision problem for selection operator ordering turns out to be NP-hard. After investigating properties of the problem and identifying special cases which can be solved in polynomial time, we develop a novel heuristic for solving the general selection ordering problem in polynomial time. Experimental evaluation of the heuristic using synthetic data, the Star Schema Benchmark and real-world data sets shows that it outperforms other heuristics (which take an optimistic, pessimistic or midpoint approach) and also produces plans whose regret is on average very close to optimal. The general join ordering problem is known to be NP-hard, even for exact selectivities. So, for interval selectivities, we restrict our investigation to sets of join operators which form a chain and to plans that correspond to left-deep join trees. We investigate properties of the problem and use these, along with ideas from the selection ordering heuristic and other algorithms in the literature, to develop a polynomial-time heuristic tailored for the join ordering problem. Experimental evaluation of the heuristic shows that, once again, it performs better than the optimistic, pessimistic and midpoint heuristics. In addition, the results show that the heuristic produces plans whose regret is on average even closer to the optimal than for selection ordering

Případová studie fyzioterapeutické léčby pacienta s artrózou po totální výměně kolene

Abstraktní Název: Případová studie fyzioterapeutické léčby pacienta s artrózou po totální výměně kolene. Cíl práce: Cílem této práce je zhodnotit rehabilitaci pacienta po totální náhradě kolene v důsledku osteoartrózy. Je rozdělena na dvě části, teoretickou část, přehled a popisuje anatomickou strukturu kolene, kineziologii, biomechaniku a vývoj a onemocnění. Praktická část si klade za cíl popsat vyšetřovací postupy, implementaci terapie a závěr pacienta ve vztahu k dané diagnóze. Klinické nálezy: Tato případová studie shrnuje stav 50letého pacienta po celkové výměně kolene. Pacient měl omezenou pohyblivost a omezené klouby kolem operované strany. Tam pravé koleno má otok, změnu barvy kůže, teplotu a jizvu. Metody: Všechny použité postupy vycházely z literatury dané Univerzity Karlovy v Praze, Fakulty tělesné výchovy a sportu. Výsledek: pacient byl zavázán k terapeutickým sezením, případ pacienta se vyvíjel pozitivně s bolestí, rozsahem pohybu a svalovou nerovnováhou kolenních kloubů. Závěr: Aplikované terapie měly pozitivní účinek na případ pacienta. Klíčová slova: Osteoartritida, kolenní kloub, Varus deformita, bolest kolena, ztuhlost kotníku, náhrada kolene.Title: A Case study of physiotherapy treatment of arthrosis patient after total knee replacement. Thesis aim: The aim of this thesis is to review the rehabilitation of a patient after total knee replacement due to osteoarthritis. It is divided into two parts theoretical part review and describe the anatomical structure of the knee, kinesiology, biomechanics and development and disease. The practical part aims to describe the examination procedures, therapy implementations and conclusion for the patient in relation to the given diagnose. Clinical findings: This case study reviews the condtion of a 50 years old patient after a total knee replacement. The patient had limited mobility and restricted joints around the operated side. There right knee has a swelling, change of skin colour, temperature and a scar. Methods: All the used procedures were based on the literature given thought by the Charles University in Prague, Faculty of Physical Education and Sports. Result: patient was committed to the therapeutic sessions , the patient case progressed positively with the pain, range of motion and muscle imbalance for the knee joints. Conclusion: The applied therapies had a positive effective for patient case. Keywords: Osteoarthritis, Knee joint, Varus deformity, knee pain, Ankle stiffness , knee replacement.FyzioterapieFakulta tělesné výchovy a sportuFaculty of Physical Education and Spor

Stock market prediction using machine learning classifiers and social media, news

Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors’ behavior. In this paper, we use algorithms on social media and financial news data to discover the impact of this data on stock market prediction accuracy for ten subsequent days. For improving performance and quality of predictions, feature selection and spam tweets reduction are performed on the data sets. Moreover, we perform experiments to find such stock markets that are difficult to predict and those that are more influenced by social media and financial news. We compare results of different algorithms to find a consistent classifier. Finally, for achieving maximum prediction accuracy, deep learning is used and some classifiers are ensembled. Our experimental results show that highest prediction accuracies of 80.53% and 75.16% are achieved using social media and financial news, respectively. We also show that New York and Red Hat stock markets are hard to predict, New York and IBM stocks are more influenced by social media, while London and Microsoft stocks by financial news. Random forest classifier is found to be consistent and highest accuracy of 83.22% is achieved by its ensemble

2-Amino-4-(4-bromo­phen­yl)-6-ferro­cenyl­pyridine-3-carbonitrile

The title compound, [Fe(C5H5)(C17H11BrN3)], was synthesized by the reaction of 4-bromo­benzaldehyde, acetyl­ferrocene and ammonium acetate in an aqueous medium. The crystal packing is stabilized by inter­molecular N—H⋯N hydrogen bonds. The dihedral angles between the phenyl ring and the pyridine and cyclopentadienyl rings are 51.67 (13) and 12.12 (21)°, respectively

2-Amino-4-(4-chloro­phen­yl)-6-ferro­cenylpyridine-3-carbonitrile

In the mol­ecule of the title compound, [Fe(C5H5)(C17H11ClN3)], the dihedral angles between the two five–membered rings and between the two six-membered rings are 3.28 (4) and 51.33 (4)°, respectively. In the crystal structure, inter­molecular N—H⋯N hydrogen bonds link the mol­ecules into centrosymmetric dimers

Photo-activity and low resistivity in N/Nb Co-doped TiO2 thin films by combinatorial AACVD

A combinatorial aerosol assisted chemical vapour deposition (cAACVD) cation–anion co-doping study has been undertaken for the first time, which investigates the interplay of nitrogen and niobium co-dopants and the resultant functional properties within TiO2 thin films. This study advantageously creates a single doped TiO2 thin film which incorporates many compositions that transition from nitrogen doped TiO2 to niobium doped TiO2 across the film's width, in a single deposition. The film was split into a grid and the physical properties of each grid position characterised by X-Ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), Scanning Electron Microscopy (SEM) and UV-visible transmission spectroscopy (UV/Vis). Functional properties such as photo-catalytic activity, water contact angles and resistivity were also characterised. The study was successful in creating and identifying the optimum dopant concentration at which these TiO2 films exhibited both a high rate of photo-activity and favourable transparent conducting oxide (TCO) properties. Whilst most co-doping studies report relatively homogenous film, the inhomogeneity of these films allows both functional properties to exist in conjunction. To the authors knowledge this is the first instance cation and anion co-doping has been explored in the combinatorial regime