Design of a Multidimensional Model Using Object Oriented Features in UML

A data warehouse is a single repository of data which includes data generated from various operational systems. Conceptual modeling is an important concept in the successful design of a data warehouse. The Unified Modeling Language (UML) has become a standard for object modeling during analysis and design steps of software system development. The paper proposes an object oriented approach to model the process of data warehouse design. The hierarchies of each data element can be explicitly defined, thus highlighting the data granularity. We propose a UML multidimensional model using various data sources based on UML schemas. We present a conceptual-level integration framework on diverse UML data sources on which OLAP operations can be performed. Our integration framework takes into account the benefits of UML (its concepts, relationships and extended features) which is more close to the real world and can model even the complex problems easily and accurately. Two steps are involved in our integration framework. The first one is to convert UML schemas into UML class diagrams. The second is to build a multidimensional model from the UML class diagrams. The white-paper focuses on the transformations used in the second step. We describe how to represent a multidimensional model using a UML star or snowflake diagram with the help of a case study. To the best of our knowledge, we are the first people to represent a UML snowflake diagram that integrates heterogeneous UML data sources

Training on an Appetitive (Delay)-Conditioning Task Enhances Oscillatory Waves During Sleep in the Cortical and Amygdalar Network

Oscillating waves during sleep play an essential role in memory consolidation. The cortical slow wave activity (SWA) and sigma waves during NREM sleep and theta waves during REM sleep increase after a variety of memory tasks including declarative, procedural and associative learning tasks. These oscillatory waves during sleep help to promote neural dialog between circuitries, which possibly plays a causal role in memory consolidation. However, the role of sleep-associated oscillating waves in a complex appetitive-conditioning paradigm is not clear. The parietal cortex and amygdala are involved in the cognitive evaluation of the environmental stimuli, and appetitive conditioning. Here, we have studied the changes in sleep architecture and oscillatory waves during NREM and REM sleep in the parietal cortices and amygdalar-local field potential (A-LFP) after appetitive-conditioning in the rat. We observed that REM sleep increased significantly after appetitive conditioning, which significantly positively correlated with performance on the appetitive-conditioning task. Further, the cortical SWA (0.1–4.5 Hz), and sigma (12–14.25 Hz) waves during NREM sleep, theta (6–9 Hz) waves during REM sleep, the amygdalar SWA (0.1–3.75 Hz) during NREM sleep and theta (6–8.25 Hz) waves during REM sleep significantly increased after appetitive conditioning. Interestingly, the augmented oscillatory waves significantly positively correlated with the performances on the appetitive-conditioning task. Our results suggest that the augmented REM sleep after conditioning may be required for the consolidation of appetitive-conditioned memory. Further, a significant correlation between augmented power in oscillatory waves during sleep and performance suggesting that these waves may be playing a crucial role in the consolidation of appetitive-conditioned memory

Socio-demographic and Clinico-pathological Profile of Cervical Cancer Patients at a Tertiary Care Centre in New Delhi: A Five-Year Retrospective analysis

Background: Cervical cancer remains a major public health challenge in low and middle-income countries including India. However, if detected early, it is preventable and curable. Objective: The present study aimed to ascertain the sociodemographic and clinical profile of cervical cancer patients visiting a tertiary cancer center. Methodology: A retrospective study was carried out at the Delhi State Cancer Institute, New Delhi. The study population included 136 women who were diagnosed with cervical cancer. A pretested data extraction sheet was used as the study tool for collecting information from the inpatient records. Descriptive analysis and chi-square test were performed and the level of significance was set at p<0.05. Results: A total of 136 cervical cancer patients with mean age of 46 ± 9.85 and mean BMI of 23.78 ± 5.03, were studied retrospectively. About 36.8% of patients were aged between 40-49 years and 57.4% were illiterate. While 40.4% of the patients belonged to FIGO stage II, 27.2% had FIGO stage III cancer. Majority (63.2%) of patients were diagnosed with squamous cell carcinoma (SCC), while the rest were adenocarcinoma (25%) and adenosquamous (11.8%). Clinical stage of cancer was found to be significantly associated with educational status (p=0.03) and dietary practices (p=.007). Conclusion: Our study found higher percentage of women with stage II and III cervical lesions and reaffirms the importance of education and healthy diet in early detection and prevention of cervical cancer. Therefore, it is suggested that accelerated population awareness and screening, incorporating digital innovations including vaccination programs are mandatory

Fuzzy Logic in Surveillance Big Video Data Analysis: Comprehensive Review, Challenges, and Research Directions

CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term “Big Video Data” (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this paper, we draw researchers’ attention towards the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook towards future research directions derived from our critical assessment of the efforts invested so far in this exciting field

A Comparative Study on the Debittering of Kinnow (<i>Citrus reticulate</i> L.) Peels: Microbial, Chemical, and Ultrasound-Assisted Microbial Treatment

Kinnow mandarin (Citrus reticulate L.) peels are a storehouse of well-known bioactive compounds, viz., polyphenols, flavonoids, carotenoids, limonoids, and tocopherol, which exhibit an effective antioxidant capacity. However, naringin is the most predominant bitter flavanone compound found in Kinnow peels that causes their bitterness. It prohibits the effective utilization of peels in food-based products. In the present study, a novel approach for the debittering of Kinnow peels has been established to tackle this problem. A comparative evaluation of the different debittering methods (chemical, microbial, and ultrasound-assisted microbial treatments) used on Kinnow peel naringin and bioactive compounds was conducted. Among the chemical and microbial method; solid-state fermentation with A. niger led to greater extraction of naringin content (7.08 mg/g) from kinnow peels. Moreover, the numerical process optimization of ultrasound-assisted microbial debittering was performed by the Box–Behnken design (BBD) of a response surface methodology to maximize naringin hydrolysis. Among all three debittering methods, ultrasound-assisted microbial debittering led to a greater hydrolysis of naringin content and reduced processing time. The optimum conditions were ultrasound temperature (40 °C), time (30 min), and A. niger koji extract (1.45%) for the maximum extraction rate of naringin (11.91 mg/g). These debittered Kinnow peels can be utilized as raw material to develop therapeutic food products having a high phytochemical composition without any off-flavors or bitterness

Recruitment and retention of US South Asians for an epidemiologic cohort: Experience from the MASALA study.

IntroductionThere are few longitudinal studies about South Asians (SAs) and little information about recruitment and retention approaches for this ethnic group.MethodsWe followed 906 SAs enrolled in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort for 5 years. Surviving participants were invited for a second clinical exam from 2015 to 2018. A new wave of participants was recruited during 2017-2018. We assessed the yields from different methods of recruitment and retention.ResultsA total of 759 (83%) completed the second clinical exam, and 258 new participants were enrolled. Providing a nearby community hospital location for the study exam, offering cab/shared ride reimbursement, and conducting home visits were the most effective methods for enhancing retention. New participant recruitment targeted women and individuals with lower socioeconomic status, and we found that participant referrals and active community engagement were most effective. Mailing invitational letters to those identified by electronic health records had very low yield.ConclusionRecruitment and retention strategies that address transportation barriers and increase community engagement will help increase the representation of SAs in health research