Dissipative Processes in the Early Universe: Bulk Viscosity

In this talk, we discuss one of the dissipative processes which likely take place in the Early Universe. We assume that the matter filling the isotropic and homogeneous background is to be described by a relativistic viscous fluid characterized by an ultra-relativistic equation of state and finite bulk viscosity deduced from recent lattice QCD calculations and heavy-ion collisions experiments. We concentrate our treatment to bulk viscosity as one of the essential dissipative processes in the rapidly expanding Early Universe and deduce the dependence of the scale factor and Hubble parameter on the comoving time $t$. We find that both scale factor and Hubble parameter are finite at $t=0$, revering to absence of singularity. We also find that their evolution apparently differs from the one resulting in when assuming that the background matter is an ideal and non-viscous fluid.Comment: 8 pages, 2 eps figure, Invited talk given at the 7th international conference on "Modern Problems of Nuclear Physics", 22-25 September 2009, Tashkent-Uzbekista

Risk Assessments are the Diagnosis not the Cure: How Using Algorithms as Diagnostic Tools Can Prevent the Bait-And-Switch of Unconstitutional Pretrial Practices

This Comment takes a closer look at the very timely debate revolving around the cash bail system. This Comment surveys the history of bail, addresses the problems caused by cash bail, evaluates the two main schools of thought on bail reform, and proposes a comprehensive solution to the identified problems

Analytical and stability studies on medical cosmetics

Two simple and sensitive spectrofluorimetric (method Ι) and spectrophotometric (method ΙΙ) methods have been developed for the determination of some chloride containing toothpastes and panthenol-containing cosmetic preparations respectively. Method Ι is based on quantitative fluorescence quenching of (terbium-salicylate-hexamine ternary complex) by fluoride which could be measured at λem/λex of 547nm/322nm. The ∆Fconcentration plot was rectilinear over the concentration range of 0.5-20 µg/ml. Method ΙΙ depends reaction of panthenol with nitrobenzoxadiazole chloride (NBD-Cl) and measuring the absorbance of the resultant product at 480nm. The absorbance- concentration plot was rectilinear over the concentration range of 2-20 µg/m

Using some Natural Minerals to Remove Cadmium from Polluted Water

تعد مشكلة ندرة المياه من اهم المشاكل التىتواجه الانسان فى مختلف المجالات المعيشية والاقتصادية مثل مجال الصناعة والزراعة و السياحة مما يدفع الانسان لاستخدام المياه منخفضة الجوده كمياه الصرف الصناعى. ويعتبر استخدام بعض المركبات الكيميائية فى التخلص من العناصر الثقيلة مثل الكادميوم هو نهج ضار بالبيئة. و من المعروف جيدًا أن عنصر الكادميوم يسبب مشاكل كبيرة عند وجوده في الماء ومن ثم يغزو التربة والنباتات والسلسلة الغذائية للإنسان. وبالتالى فان استخدام المواد الطبيعية بدلاً من المواد الكيميائية لإزالة الكادميوم من المياه الملوثة نهجصديقًا للبيئة. لذلك تم التركيز في هذا البحث على استخدام بعض المعادن الطبيعية مثلالمونتموريلونيتوالبنتونيتوالزيوليتلامتصاص عنصر الكادميوم من المياه الملوثة. و قد استخدمت تركيزات مختلفة من الكادميوم في المحاليل 10 و 30 و 50 جزء في المليون و تم معالجتها بثلاث نسب مختلفة لكل معدن (1 و 3 و 5٪ وزن الى حجم). وقد أثبتت النتائج التي تم الحصول عليها أن زيادة نسبة الاضافات إلى 5٪ تزيد من امتزاز الكادميوم من المحلول خاصة عند تركيز 50 جزء في المليون من الكادميوم. حصل الزيوليت على أعلى نسبة امتزاز (47.90 جزء في المليون) ، يليه مونتموريلونيت (44.99 جزء في المليون) وأقل نسبة كانت للبينتونيت (38.97 جزء في المليون). لذلك ، يمكن التوصية بأن إضافة الزيوليت هي المادة الأكثر ملاءمة لإزالة عنصر Cd من المياه الملوثة.Water scarcity is one of the most important problems facing humanity in various fields such as economics, industry, agriculture, and tourism. This may push people to use low-quality water like industrial-wastewater. The application of some chemical compounds to get rid of heavy metals such as cadmium is an environmentally harmful approach. It is well-known that heavy metals as cadmium may induce harmful problems when present in water and invade to soil, plants and food chain of a human being. In this case, man will be forced to use the low quality water in irrigation. Application of natural materials instead of chemicals to remove cadmium from polluted water is an environmental friendly approach. Attention was drawn in this research work to use some natural minerals as zeolite, bentonite and montmorillonite to adsorb cadmium element from polluted water. Various concentrations of cadmium in solutions 10, 30 and 50 ppm were treated with three different ratios of each mineral; 1, 3 and 5% (W/V). The obtained results proved that increasing the ratio of amendments to 5% increased Cd adsorption from solution particularly at 50ppm Cd. Zeolite obtained the highest ratio of adsorption (47.90 ppm), followed by montmorillonite (44.99 ppm) and the lowest was bentonite (38.97 ppm). Therefore, it can be recommended that addition of zeolite is the most favorable material to remove Cd element from polluted water

Detection of trend changes in time series using Bayesian inference

Change points in time series are perceived as isolated singularities where two regular trends of a given signal do not match. The detection of such transitions is of fundamental interest for the understanding of the system's internal dynamics. In practice observational noise makes it difficult to detect such change points in time series. In this work we elaborate a Bayesian method to estimate the location of the singularities and to produce some confidence intervals. We validate the ability and sensitivity of our inference method by estimating change points of synthetic data sets. As an application we use our algorithm to analyze the annual flow volume of the Nile River at Aswan from 1871 to 1970, where we confirm a well-established significant transition point within the time series.Comment: 9 pages, 12 figures, submitte

Statistical Mechanics of Learning: A Variational Approach for Real Data

Using a variational technique, we generalize the statistical physics approach of learning from random examples to make it applicable to real data. We demonstrate the validity and relevance of our method by computing approximate estimators for generalization errors that are based on training data alone.Comment: 4 pages, 2 figure

A Hybrid Continual Machine Learning Model for Efficient Hierarchical Classification of Domain-Specific Text in The Presence of Class Overlap (Case Study: IT Support Tickets)

In today’s world, support ticketing systems are employed by a wide range of businesses. The ticketing system facilitates the interaction between customers and the support teams when the customer faces an issue with a product or a service. For large-scale IT companies with a large number of clients and a great volume of communications, the task of automating the classification of incoming tickets is key to guaranteeing long-term clients and ensuring business growth. Although the problem of text classification has been widely studied in the literature, the majority of the proposed approaches revolve around state-of-the-art deep learning models. This thesis addresses the following research questions: What are the reasons behind employing black box models (i.e., deep learning models) for text classification tasks? What is the level of polysemy (i.e., the coexistence of many possible meanings for a word or phrase) in a technical (i.e., specialized) text? How do static word embeddings like Word2vec fare against traditional TFIDF vectorization? How do dynamic word embeddings (e.g., PLMs) compare against a linear classifier such as Support Vector Machine (SVM) for classifying a domain-specific text? This integrated article thesis aims to investigate the aforementioned issues through five empirical studies that were conducted over the past four years. The observation of our studies is an emerging theory that demonstrates why traditional ML models offer a more efficient solution to domain-specific text classification compared to state-of-the-art DL language models (i.e., PLMs). Based on extensive experiments on a real-world dataset, we propose a novel Hybrid Online Offline Model (HOOM) that can efficiently classify IT Support Tickets in a real-time (i.e., dynamic) environment. Our classification model is anticipated to build trust and confidence when deployed into production as the model is interpretable, efficient, and can detect concept drifts in the data