Ergodicity in the dynamics of holomorphic correspondences

This paper studies ergodic properties of certain measures arising in the dynamics of holomorphic correspondences. These measures, in general, are not invariant in the classical sense of ergodic theory. We define a notion of ergodicity, and prove a version of Birkhoff's ergodic theorem in this setting. In fact, we strengthen this classical result in the setting of rational maps on the Riemann sphere with the Lyubich measure. We also show the existence of ergodic measures when a holomorphic correspondence is defined on a compact complex manifold. Lastly, we give an explicit class of dynamically interesting measures that are ergodic as in our definition.Comment: 19 pages; see arXiv:2105.01012 for recurrence phenomena in a similar settin

Mozambique Civil War and Farida Karodia’s A Shattering of Silence

Mozambique civil war was fought between Mozambique’s ruling Marxist ‘Front for the Liberation of Mozambique’ (FRELIMO) and anti-communist insurgent forces of the ‘Mozambican National Resistance’ (RENAMO). Through the story of Faith, the novel reveals the reality of hundreds of thousands of children all over the world who are the victims of war, hunger, and political corruption. Being the sufferer of apartheid system, Farida Karodia has extensively written about the war, segregation during the apartheid, social and political situations in South Africa. ‘A Shattering of Silence’ deals with the history of colonialism and its brutal effects on the people of Africa

Data Mining and Machine Learning Approach for Air Quality Index Prediction

In recent years, Air Pollution has increased drastically and having worse effect of that on all the living beings. Majority of Countries in the world battling with increasing Air Pollution Levels. So, it has become a necessity to control and predict the Air Quality Index. In this research project, we will be implementing Data Mining and Machine Learning models to predict the AQI and Classify the AQI into buckets. For AQI prediction we have implemented five regression models Principal Component, Partial Least Square, Principal Component with Leave One Out CV, Partial Least Square with Leave One Out CV, Multiple regression AQI Data of Multiple Indian Cities. AQI Index further gets classified into 6 Different Categories called Buckets “Good, Satisfactory, Moderate, Poor, Very Poor and Severe” based on the value of the AQI. To predict the AQI bucket we have developed three classification models which are Multinomial Logistic Regression and K Nearest Neighbor and K Nearest Neighbors with repeat CV Classification algorithm. From the Air Quality Dataset of Different Indian Cities PLS model with Leave One Out Cross Validation was best at dimension reduction considering only the 5th component from all the models. In terms of accuracy PLS model was best with Lowest RMSE. From Station, Wise Data of Indian Cities KNN Model with Repeated CV and Tune Length 10 performed best in terms of accuracy and AUC

Assessing the feasibility and mechanism of destructive removal of per- and polyfluoroalkyl substances (PFAS) from water

One of the most pertinent challenges faced by the drinking water community is the widespread contamination of per- and polyfluoroalkyl substances (PFAS). These anthropogenic chemicals have been ubiquitously used in everyday products such as carpets, stain repellents, dyes, shampoos, non-stick cookware as well as in aqueous firefighting foams. PFAS are linked with adverse health effects in humans such as thyroid disease, obesity, immunological and reproductive disorders and linked to cancer in adults and low birth weight and developmental defects in infants. Conventional water treatment technologies have proven to be largely ineffective in PFAS remediation, due to their extreme stability and resistance to degradation. The overall goal of this dissertation is to assess the feasibility and performance of novel destructive technologies in treating PFAS. The specific objectives of this study are to: (i) investigate the impact of water quality and operating parameters on the treatment of a suite of PFAS using two destructive techniques, a) electron beam (e-beam) and b) electrochemical oxidation process (eAOP); (ii) elucidate the primary degradation mechanism of PFAS in these systems; (iii) differentiate the performance (energy requirements) of these systems in treating PFAS isomers; and (iv) develop a novel air-bubbling system to extract PFAS from contaminated soils to combine with destructive technologies via a treatment-train approach. The effect of chain length and functional group is observed while treating PFAS with e-beam technology with the short chain perfluorobutanoic acid and perfluorobutanesulfonic acid showing highest resistance to degradation. This chapter additionally highlights previously unknown degradation pathways of PFAS using a combination of fluorine mass balance and suspect screening. In eAOP system, the composition of supporting electrolyte and anodic voltage did not impact PFAS degradation. PFAS degradation strongly correlates with compound hydrophobicity and this study is the first to differentiate between degradation and loss in concentration due to the phenomena of electrochemical aerosolization of PFAS. Branched PFAS isomers preferentially degrade by e-beam treatment but show comparative/poorer removal in an eAOP system, compared to their linear forms. Soil washing is studied as a removal approach for PFAS-contaminated soils, that can be used as a standalone technique or along with destructive techniques in a treatment train system. A novel air-bubbling assisted soil washing system is used to investigate the removal of adsorbed PFAS from contaminated soils. The extraction efficiency from the soil is found to be inversely proportional to PFAS hydrophobicity, with poorest results observed for long chain perfluorodecanoic acid. Results from this dissertation (i) identify ideal conditions and energy requirements for the destructive removal of PFAS, (ii) highlight the challenges and knowledge gaps for the remediation of contaminated soils, and (iii) provide insight into the variable mechanisms of PFAS destruction and removal, impacted by the PFAS structure and operating parameters in both aqueous and soil matrices

SOLUBILITY ENHANCEMENT OF LURASIDONE HYDROCHLORIDE BY PREPARING SMEDDS

Objective: The objective of the study was to enhance solubility of Lurasidone HCl, an atypical antipsychotic drug, by formulating self-micro emulsifying drug delivery system (SMEDDS) and its characterization.Methods: Solubility study of Lurasidone hydrochloride (LH) was carried out in various surfactants, co surfactants and oils. Pseudo ternary phase diagrams were constructed to identify the self-micro emulsification region. Screening was done so as to determine the proper combination of components. Based on this, LH SMEDDS were prepared using Cremophor RH40 (surfactant), Soluphor P (co-surfactant) and Capmul MCM (oil). The preconcentrate SMEDDS were evaluated for clarity(visual), precipitation, % transmittance, robustness to dilution, freeze thawing, particle size distribution and zeta potential and adsorbed SMEDDS were evaluated for drug content, flow properties, in-vitro dissolution and ex-vivo diffusion studies.Results: The optimized LH SMEDDS composed of 14% Cremophor RH40, 68% Soluphor P, 18% Capmul MCM with a particle size of 3.95 µm and zeta potential of more than 50 mV showing 80% dissolution in 60 min.Conclusion: The results of this study prove that SMEDDS help in improving the solubility, dissolution and bioavailability of lurasidone hydrochloride.Â

Modelling Compressive Strength of Recycled Aggregate Concrete Using Neural Networks and Regression

Recycled aggregates are used in concrete and is called as Recycled Aggregate Concrete (RAC). This paper aims at predicting the 28 day compressive strength of RAC using two techniques namely Artificial Neural Network (ANN) and Non-linear regression (NLR). Five ANN and NLR models were developed with input parameters as  per cubic proportions of cement, sand, natural coarse aggregate, recycled coarse aggregate, water , admixtures  used in the mix designs and non-dimensional parameters sand aggregate ratio, water to total materials ratio and the replacement ratio of recycled coarse aggregates to natural aggregates(by volume) in concrete. The effects of each parameter on networks in both techniques were studied. Comparing the techniques shows that ANN performs better than NLR equations. With limited amount of input parameters also, ANN1 predicted the strength of RAC better as compared to NLR1. The performance of ANN models and NLR models improved with the use of non-dimensional parameters

Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach

Background: Decision makers use the process of determining the best course of action by processing, analysing & interpreting the data to gain insights, known as Business Intelligence. Some decision support systems use sales figures to predict future expansion, but few consider the effect of customer data. Objectives: The main objective of this study is to build a model that will give a forecast based on fine-tuned sales numbers using some customer-centric features. Methods/Approach: We first use the RFM model to segment the customers into distinct segments based on customer buying characteristics and then discard the segments that are irrelevant to the business. Then we use the ARIMA model to do the sales forecasting for the remainder of the data. Results: Using this model, we were able to achieve a better fitment of the data for the prediction model and achieved a better accuracy when used after RFM analysis. Conclusions: We tried to merge two different concepts to do a cross-functional analysis for better decision-making. We were able to present the RFM-ARIMA model as a better metric or approach to fine-tune the sales analysis