Thematic analysis of big data in financial institutions using NLP techniques with a cloud computing perspective : a systematic literature review

This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keywordbased search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis

Cancer informatics analysis indicates high CHAC2 associated with unfavorable prognosis in breast cancer

Breast cancer remains the most commonly diagnosed cancer worldwide and exhibits a poor prognosis. The induction of genetic changes deregulates several genes that increase the disposal towards this life-threatening disease. CHAC2, a member of the glutathione degrading enzyme family has been shown to suppress gastric and colorectal cancer progression, however, the expression of CHAC2 in breast cancer has not been reported. We did an analysis of CHAC2 expression in breast cancer patients from various online tools like UALCAN, GEPIA2, GENT2, TIMER2, and bcGenExminer v4.8. Further, we used the Kaplan-Meier plotter to establish the significance of CHAC2 in BC patient survival and prognosis while TISIDB and TIMER databases were used to investigate the filtration of immune cells. The results showed that CHAC2 levels were high in breast cancer patients and elevated CHAC2 was associated with low overall survival. Taken together, the results of the present study show that like its paralog CHAC1, CHAC2 may also be an important biomarker and could have a potential therapeutic implication in breast cancer

A novel algorithm to integrate battery cyclic and calendar agings within a single framework

Cyclic and Calendar agings are the two primary sources of degradation in a battery. An accurate battery degradation model can only be achieved when both processes are considered. In this paper, a novel framework is proposed to integrate Cyclic and Calendar aging processes. The proposed framework is able to accommodate different individual Cyclic and Calendar aging models only with slight modifications. It also can work conveniently as a universal degradation framework in different applications, such as large-scale battery storage systems in microgrids (MGs) and electric vehicles (EVs)

Dual-activation protocol for tandem cross-aldol condensation: an easy and highly efficient synthesis of α,α′-bis(aryl/alkylmethylidene)ketones

Commercially available lithium hydroxide monohydrate (LiOH·H2O) was found to be a novel ‘dual activation’ catalyst for tandem cross-aldol condensation between cyclic/acyclic ketones and aromatic/heteroaromatic/styryl/alkyl aldehydes leading to an efficient and easy synthesis of α,α′-bis(aryl/alkylmethylidene)ketones at r.t. in short times. The reaction of aryl, heteroaryl, styryl and alkyl aldehydes with acyclic and five/six-membered cyclic ketones afforded excellent yields after 2 min to 1.25 h. The reaction conditions were compatible with various electron withdrawing and electron donating substituents, e.g. Cl, F, NO2, OMe and NMe2. The rate of the cross-aldol condensation was influenced by the nature of the ketone and electronic and steric factors associated with the aldehyde. The reaction took place at a faster rate for acyclic ketone (e.g., acetone) than that for cyclic ketone (e.g., cyclohexanone). In case of cycloalkanones, the rate of the reaction was dependent on the size of the ring of the cycloalkanone. The cross-aldol condensation of cyclopentanone was faster than that of cyclohexanone for a common aldehyde. In case of reactions involving aliphatic aldehyde having α-hydrogen atom no self-aldol condensation of the aldehyde took place

Multi-timescale power management for islanded microgrids including storage and demand response

Power management is an essential tool for microgrid (MG) safe and economic operation, particularly in the islanded operation mode. In this paper, a multi-timescale cost-effective power management algorithm (PMA) is proposed for islanded MG operation targeting generation, storage, and demand management. Comprehensive modeling, cost, and emission calculations of the MG components are developed in this paper to facilitate high accuracy management. While the MGs overall power management and operation is carried out every several minutes to hours, depending on the availability of the required data, simulation for highly dynamic devices, such as batteries and electric water heaters (EWHs) used for demand response (DR), are performed every minute. This structure allows accurate, scalable, and practical power management taking into consideration the intrainterval dynamics of battery and EWHs. Two different on/off strategies for EWH control are also proposed for DR application. Then, the PMA is implemented using the two different DR strategies and the results are compared with the no-DR case. Actual solar irradiation, ambient temperature, nonEWH load demand, and hot water consumption data are employed in the simulation studies. The simulation results for the MG studied show the effectiveness of the proposed algorithm to reduce both MGs cost and emission

A two-layer incentive-based controller for aggregating BTM storage devices based on transactive energy framework

In this paper, a two-layer controller is proposed to aggregate a fleet of behind-the-meter (BTM) energy storage devices based on the Transactive Energy (TE) concept. In the proposed model, aggregator offers an incentive to consumers to purchase power from and/or sell the excess power back to the grid. To do so, controller at the aggregator's side determines optimal incentive which has to be offered to consumers by maximizing its own profit. Then, local controller at the consumer's location optimizes battery operation by calculating purchased/sold power from/to the grid based on the local demand, PV generation, retail time-of-use (ToU) prices and demand charge, and the incentive offered by the aggregator to maximize its own profit. Different optimization problems are formulated in the two layers, and the profit of aggregator and consumers in the day-ahead energy market under perfect and imperfect prediction scenarios are compared