The Dead Poet and the Non Artist: A Review of Notes on a Scandal

Fifty years ago a famous poet Mustafa Zaidi receives his lover and muse Shahnaz Gul in his apartment in Karachi. The next day he is found dead; she is unconscious. Gul is charged with murder and her trial becomes a main distraction from an impending war. Tooba Masood and Saba Imtiaz tried to piece together a social history of seventies Pakistan in what is now the country\u27s first true crime podcast

Identifying Semantically Duplicate Questions Using Data Science Approach: A Quora Case Study

Kaks küsimust on semantselt dubleeritud, arvestades, et täpselt sama vastus võib rahuldada mõlemaid küsimusi. Semantselt identsete küsimuste väljaselgitamine selliste sotsiaalmeedia platvormide kohta nagu Quora on erakordselt oluline, et tagada kasutajatele esitatud sisu kvaliteet ja kogus, lähtudes küsimuse kavatsusest ja nii rikastades üldist kasutajakogemust. Dubleerivate küsimuste avastamine on väljakutseks, sest looduskeel on väga väljendusrikas ning ainulaadset kavatsust saab edastada erinevate sõnade, fraaside ja lausekujunduse abil. Masinõppe ja sügava õppimise meetodid on teadaolevalt saavutanud paremaid tulemusi võrreldes traditsiooniliste loodusliku keeletöötlemise tehnikatega sarnaste tekstide väljaselgitamisel.Selles teoses, võttes Quora oma juhtumiuuringuks, uurisime ja kohaldasime erinevaid masinõppe- ja sügavõppetehnikaid ülesandel tuvastada Quora küsimuse paari andmestikul kahekordsed küsimused. Kasutades omaduste inseneritehnikat, eristavaid tähtsaid tehnikaid ning katsetades seitsme valitud masinõppe klassifikaatoriga, näitasime, et meie mudelid edestasid paari varasemat selle ülesandega seotud uuringut. Xgboost mudelil, mida söödetakse tähetaseme termilise sagedusega ja pöördsagedusega, saavutati teiste masinõppemudelite suhtes paremad tulemused ning edestati ka paari Deep learningi algmudelit.Meie kasutasime sügava õppimise tehnikat, et modelleerida neli erinevat sügavat neuralivõrgustikku, mis koosnevad Glove Embedding, Long Short Term Memory, Convolution, Max Pooling, Dense, Batch normaliseerimisest, aktuaalsetest funktsioonidest ja mudeli ühendamisest. Meie süvaõppemudelid saavutasid parema täpsuse kui masinõppemudelid. Kolm neljast väljapakutud arhitektuurist edestasid täpsust varasemast masinõppe- ja süvaõppetööst, kaks neljast mudelist edestasid täpsust varasemast sügava õppimise uuringust Quora küsitluspaari andmestik ning meie parim mudel saavutas täpsuse 85.82% mis on kunstilise seisundi Quora lähedane täpsus.Two questions are semantically duplicate, given that precisely the same answer can satisfy both the questions. Identifying semantically identical questions on, Question and Answering(QandA) social media platforms like Quora is exceptionally significant to ensure that the quality and the quantity of content are presented to users, based on the intent of the question and thus enriching overall user experience. Detecting duplicate questions is a challenging problem because natural language is very expressive, and a unique intent can be conveyed using different words, phrases, and sentence structuring. Machine learning and deep learning methods are known to have accomplished superior results over traditional natural language processing techniques in identifying similar texts.In this thesis, taking Quora for our case study, we explored and applied different machine learning and deep learning techniques on the task of identifying duplicate questions on Quora’s question pair dataset. By using feature engineering, feature importance techniques, and experimenting with seven selected machine learning classifiers, we demonstrated that our models outperformed a few of the previous studies on this task. Xgboost model, when fed with character level term frequency and inverse term frequency, achieved superior results to other machine learning models and also outperformed a few of the Deep learning baseline models.We applied deep learning techniques to model four different deep neural networks of multiple layers consisting of Glove embeddings, Long Short Term Memory, Convolution, Max pooling, Dense, Batch Normalization, Activation functions, and model merge. Our deep learning models achieved better accuracy than machine learning models. Three out of four proposed architectures outperformed the accuracy from previous machine learning and deep learning research work, two out of four models outperformed accuracy from previous deep learning study on Quora’s question pair dataset, and our best model achieved accuracy of 85.82% which is close to Quora state of the art accuracy

Role of Zinc in patients with Nephrotic syndrome

Introduction:  Nephrotic syndrome(NS) is one of the most common cause of chronicmorbidity in developing countries. This study is aimed to assess the effect of zincsupplementation in patients with NS and to evaluate its association with serum albuminlevel, relapse rate and infection frequency. Materials & method:  In this randomized, double blind, placebo-controlled trial study, 60patients with NS, both with the first episode and first relapse, age between2-10 years were included. Among the 60 patients, 30 patients of NS getting zinc were inzinc group and 30 patients of nephrotic syndrome getting placebo were in placebogroup. Zinc status was assessed before and after giving zinc or placebo..Results; Serum zinc level has been found significantly lower during relapse (0.54±0.18and 0.56±0.22), it has increased during remission, which is (0.85±0.42) normal in zincgroup and remained low (0.69±0.14) in placebo group. The mean serum albumin levelduring relapse were low in both groups,14 days later it was increased but still low.The difference of mean percentage of increase of height after 6 months was notstatistically significant (3.3±1.2 % vs. 3.3±1.9 %) in two groups.19 patients (63.3%) in zinc group developed relapse compared to 15 patients (50%) in placebo group, the difference was not statistically significant. Infection had occurred 73.3% after zincsupplementation as compared with 63.3% in placebo group. Conclusion: When zinc was given in RDA for short duration doesn’t reduce relapsein NS and doesn’t significant increase zinc level compared to placebo

Mini-batch k-Means versus k-Means to Cluster English Tafseer Text: View of Al-Baqarah Chapter

Al-Quran is the primary text of Muslims' religion and practise. Millions of Muslims around the world use al-Quran as their reference guide, and so knowledge can be obtained from it by Muslims and Islamic scholars in general. Al-Quran has been reinterpreted to various languages in the world, for example, English and has been written by several translators. Each translator has ideas, comments and statements to translate the verses from which he has obtained (Tafseer). Therefore, this paper tries to cluster the translation of the Tafseer using text clustering. Text clustering is the text mining method that needs to be clustered in the same section of related documents. The study adapted (mini-batch k-means and k-means) algorithms of clustering techniques to explain and to define the link between keywords known as features or concepts for Al-Baqarah chapter of 286 verses. For this dataset, data preprocessing and extraction of features using TF-IDF (Term Frequency-Inverse Document Frequency), and PCA (Principal Component Analysis) applied. Results show two/three-dimensional clustering plotting assigning seven cluster categories (k=7) for the Tafseer. The implementation time of the mini-batch k-means algorithm (0.05485s) outperforms the time of the k-means algorithm (0.23334s). Finally, the features 'god', 'people', and 'believe' was the most frequent features

K-means variations analysis for translation of English Tafseer Al-Quran text

Text mining is a powerful modern technique used to obtain interesting information from huge datasets. Text clustering is used to distinguish between documents that have the same themes or topics. The absence of the datasets ground truth enforces the use of clustering (unsupervised learning) rather than others, such as classification (supervised learning). The “no free lunch” (NFL) theorem supposed that no algorithm outperformed the other in a variety of conditions (several datasets). This study aims to analyze the k-means cluster algorithm variations (three algorithms (k-means, mini-batch k-means, and k-medoids) at the clustering process stage. Six datasets were used/analyzed in chapter Al-Baqarah English translation (text) of 286 verses at the preprocessing stage. Moreover, feature selection used the term frequency–inverse document frequency (TF-IDF) to get the weighting term. At the final stage, five internal cluster validations metrics were implemented silhouette coefficient (SC), Calinski-Harabasz index (CHI), C-index (CI), Dunn’s indices (DI) and Davies Bouldin index (DBI) and regarding execution time (ET). The experiments proved that k-medoids outperformed the other two algorithms in terms of ET only. In contrast, no algorithm is superior to the other in terms of the clustering process for the six datasets, which confirms the NFL theorem assumption

Co-optimization of Operational Unit Commitment and Reserve Power Scheduling for Modern Grid

Modern power grids combine conventional generators with distributed energy resource (DER) generators in response to concerns over climate change and long-term energy security. Due to the intermittent nature of DERs, different types of energy storage devices (ESDs) must be installed to minimize unit commitment problems and accommodate spinning reserve power. ESDs have operational and resource constraints, such as charge and discharge rates or maximum and minimum state of charge (SoC). This paper proposes a linear programming (LP) optimization framework to maximize the unit-committed power for a specific optimum spinning reserve power for a particular power grid. Using this optimization framework, we also determine the total dispatchable power, non-dispatchable power, spinning reserve power, and arbitrage power using DER and ESD resource constraints. To describe the ESD and DER constraints, this paper evaluates several factors: availability, dispatchability, non-dispatchability, spinning reserve, and arbitrage factor. These factors are used as constraints in this LP optimization to determine the total optimal reserve power from the existing DERs. The proposed optimization framework maximizes the ratio of dispatchable to non-dispatchable power to minimize unit commitment problems within a specific range of spinning reserve power set to each DER. This optimization framework is implemented in the modified IEEE 34-bus distribution system, adding ten DERs in ten different buses to verify its efficacy

The effect of building information modelling implementation on sustainable project performance in the construction industry: a mediating impact of circular economy practices

This research is present about the effect of building information modelling implementation on sustainable project performance in construction industry: a mediating impact of circular economy practices in Malaysia by using a Natural-Resource-Based View (NRBV) framework. The objectives of this study is to investigate the relationship between BIM implementation and circular economy practices, to investigate the relationship between BIM implementation and sustainable project performance, to investigate the relationship between circular economy practices and sustainable project performance and to investigate the mediating effect of circular economy practices on relationship between BIM implementation and sustainable project performance. This study was conducted using stratified method sampling and send the questionnaire via the company’s emails. From these findings, the BIM implementation in Malaysian construction company are currently in developing progress as construction companies to have sustainable project performance. The implication of this study is the theoretical implication would be benefits to the other research to reach the gaps on adding more extensive studies on circular economy in Malaysian construction industry while the practitioner’s industry can apply circular economy effectively in reducing the material consumption in construction industry in Malaysia

Volatility spillovers and frequency dependence between oil price shocks and green stock markets

This study uses wavelet coherence and frequency connectedness techniques to examine the time-frequency dependence and risk connectivity between oil shocks and green stocks. The results show that on mid-term and long-term scales, the dependence relationships between the oil and green stock markets are tighter while lead-lag patterns are mixed and time-varying. Total risk spillovers between the oil and green stock markets are mostly conveyed over time. Risk spillovers from the oil market are substantially larger in the green stock market. Furthermore, global crises such as the Great Recession, the oil price collapse, and the COVID-19 pandemic have substantially amplified the magnitude of risk spillovers. Overall, the green stock market has not yet developed enough potential for a larger independence from the conventional energy market. Hence, for participants in the energy and financial markets who have different time horizons for asset allocation and risk management and for committed investors in particular, the examination of time-frequency dependence and risk spillovers can be quite beneficial.info:eu-repo/semantics/publishedVersio

Finite Element Analysis of Different Pin Diameter of External Fixator in Treating Tibia Fracture

Biomechanical perspective of external fixator is one of the biggest elements that should be considered in treating fracture bone. This is due to the mechanical behavior of the structure could be analyzed and optimized in order to avoid failure, increase bone fracture healing rate and prevents preterm screw loosening. There are three significant factors that affect the stability of external fixator and those are the placement of pin at the bone, configuration and components of external fixator. All these factors contribute to a question, what is the optimum pin diameter which exerts good stress distribution? To date, the research on the above-mentioned factors are limited in the literature. Therefore, this study was conducted to evaluate the unilateral external fixator with different pin sizes in treating tibia shaft fracture via the finite element method. First and foremost, the development of the tibia shaft fracture was conducted using Mimics software. The computed tomography (CT) data image was utilized to develop three-dimensional tibia bone followed by crafting fracture on the bone. Meanwhile, the unilateral external fixator was developed using SolidWorks software. In this study, five pin diameters (4.5, 5.0, 5.5, 6.0 and 6.5 mm) were developed and analyzed. Both tibia bone and external fixator were meshed in 3-matic software. Simulation of this configuration took place in a finite element software, Marc.Mentat. From the findings, it is shown that the larger diameter of pin demonstrated the lowest stress distribution. The size of the 5.5mm pin shows optimum diameter in terms of stress distribution with the value of 21.50 MPa in bone and 143.33 MPa in fixator. Meanwhile the displacement value of 1.42mm in bone and 1.20mm in fixator. In conclusion, it is suggested that the pin diameter of 5.5 mm is the most favorable option in treating tibia shaft fracture in terms of mechanical perspective

Reserve Allocation in Active Distribution Systems for Tertiary Frequency Regulation: A Coalitional Game Theory-based Approach

This paper proposes a coalitional game theory-based approach for reserve optimization to enable DERs participate in tertiary frequency regulation. A two-stage approach is proposed to effectively and precisely allocate spinning reserve requirements from each DER in distribution systems. In the first stage, two types of characteristic functions: worthiness index (WI) and power loss reduction (PLR) of each coalition are computed. In the second stage, the equivalent Shapley values are computed based on the characteristic functions, which are used to determine distribution factors for reserve allocation among DERs