Estimating cost is a very wearisome activity in all aspect. A person with broad scope and good thinking for the future makes more precise decisions. It helps in governing and planning the software risks which are admirably correct and precise. In 1960 regression analysis and mathematical formulae were practiced to determine cost. We need to think more than simply putting numbers into a formula and accept the results to attaining the accuracy of software cost estimation. The changing methods of estimating software cost have made the researchers to think diversely. Barry Bohem birthed COCOMO model for software cost estimation in 1981 which is considered to be more efficient as compared to previous models. Thereafter number of researchers has been trying to improve the efficiency by keeping the base of COCOMO model. The paper drafts a novel variable reduction technique called feed-forward neural network with PCA to measure the estimation model accuracy. This is based on a COCOMO sample data set which collects and maintains a large software project data repository. PCA is a kind of classification method which can reduces number of factors into a few absolute factors.

Internet of Things (IoT) is a ubiquitous network that assists the system to monitor and organize the world by processing, collection, and analysis of produced data by IoT nodes. The IoT is susceptible to numerous attacks, wherein the sinkhole attack is the most destructive one, which affects the communication amongst network devices. The security of the IoT is affected due to the lack of dynamic network topology, centralized control, and network communications, so that the trust-based routing is important. The conventional trust-based routing strategies are ineffective to offer protection against attack and thus, trust management remains a major challenge in routing. This paper proposes a novel technique namely Atom Search Sunflower Optimization (ASSFO) for providing trust-based routing in IoT network. The proposed ASSFO is designed by incorporating Sunflower Optimization (SFO) in Atom Search Optimization (ASO). The method utilizes several trust factors for determining and isolating attacks while optimizing the performance of the network. The trust factors considered in the method include indirect trust, recent trust, integrity factor, direct trust, and availability factor. Moreover, the trusted nodes are selected using the trust factor and further used for secure routing using the proposed ASSFO algorithm. The proposed ASSFO outperformed other methods with maximal energy of 0.908, maximal trust of 0.845, maximal PDR of 0.907, and maximal throughput of 0.909.

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There are few studies on type 2 diabetes complications from Indian metropolitan and non-metropolitan cities. Macrovascular and microvascular complications during the second year of LANDMARC, a 3-year nationwide prospective observational study (CTRI/2017/05/008452) , were evaluated in participants from metropolitan versus non-metropolitan cities. LANDMARC included participants with T2D who were on ≥2 antihyperglycemic medications. Of the total 6234 participants, 2376 and 3858 were from metropolitan and non-metropolitan cities, respectively. Age, T2D duration, and baseline A1C were similar across groups. Microvascular complications were significantly higher in participants from non-metropolitan than metropolitan cities (12.93% vs. 4.65%; p<0.0001) during the 2 years. Neuropathy was the most common microvascular complication in both metropolitan and non-metropolitan cities. Among macrovascular complications, a greater number of participants reported heart failure (p=0.0160) and CV deaths in non-metropolitan cities than metropolitan cities (Table) . The present data from India demonstrates that participants from non-metropolitan cities may have higher complications, particularly microvascular, and may need to be better understood through future studies. The LANDMARC results elicit a pattern of disease progression among participants with T2D. Disclosure S. Kalra: Speaker's Bureau; Boehringer Ingelheim International GmbH, Novo Nordisk A/S, Sanofi. A.K. Das: Advisory Panel; Boehringer Ingelheim International GmbH, Novo Nordisk, Roche Diagnostics, Sanofi. Speaker's Bureau; Dr. Reddy’s Laboratories Ltd., Lupin Pharmaceuticals, Inc., USV Private Limited. S. Joshi: Advisory Panel; Abbott, Boehringer Ingelheim International GmbH, Dr. Reddy's Laboratories Ltd., Eli Lilly and Company, Novo Nordisk, Roche Diabetes Care. Consultant; Biocon, Glenmark Pharmaceuticals, Sanofi, USV Private Limited. A. Mithal: Advisory Panel; Eris Lifesciences Ltd. Consultant; Glenmark Pharmaceuticals, Lupin Pharmaceuticals, Inc., USV Private Limited. Speaker's Bureau; Abbott Diabetes, AstraZeneca, Biocon, Boehringer Ingelheim International GmbH, Dr. Reddy’s Laboratories Ltd., Novartis AG, Novo Nordisk, Sanofi. K. Kumar: None. A. Unnikrishnan: Advisory Panel; Intas Pharmaceuticals Ltd. Speaker's Bureau; Abbott, AstraZeneca, Boehringer-Ingelheim, Sanofi. Other Relationship; Novo Nordisk, Serdia Pharmaceuticals (India) Pvt. Ltd., Torrent Pharmaceuticals Ltd. H. Thacker: None. B. Sethi: None. S. Chowdhury: None. A. Nair: None. S. Mohanasundaram: Employee; Sanofi. V. Salvi: Employee; Sanofi. D. Chodankar: Employee; Sanofi. C. Trivedi: None. S. Wangnoo: None. A.H. Zargar: Advisory Panel; Sanofi. Speaker's Bureau; AstraZeneca, Biocon, Boehringer Ingelheim International GmbH, Intas Pharmaceuticals Ltd., Janssen Pharmaceuticals, Inc., Lupin Pharmaceuticals, Inc., Novo Nordisk, USV Private Limited. N. Rais: None. Funding Sanofi, India

Macrovascular and microvascular complications were evaluated during the first 2 years of LANDMARC, a 3-year prospective observational study (CTRI/2017/05/008452) that included participants with T2D on ≥2 antihyperglycemic medications. Out of 6234 evaluable participants (mean baseline values - age: 52.1 years, T2D duration: 8.59 years and A1C: 8.05%) , 5318 participants completed the 2-year follow-up. The mean A1C decreased by 0.58% (baseline: 8.05%) in 2 years. The microvascular complications were frequent in 17.6% participants (1096/6234) ; while the incidence of macrovascular complications was 1.1% participants (66/6234) . Neuropathy was the most commonly reported complication (baseline: 11.8% and 2-years: 14.4%) . Overall, complications were more common in participants with BMI ≥23 kg/m2, A1C ≥7% or having CV risk factors (Table) . A total of 41 deaths were reported; of which 30 deaths were attributed to CV causes (sudden death [n=19], myocardial infarction [n=9], stroke [n=1], and coronary artery procedure [n=1]) . The 2-year results indicate more complications among those who were overweight, or with suboptimal glycemic control or having CV risk factors. Neuropathy was the predominant T2D complication. These results offer insights into disease progression and suggest the need for controlling risk factors and timely treatment adjustment in participants with T2D. Disclosure N. Rais: None. A.K. Das: Advisory Panel; Boehringer Ingelheim International GmbH, Novo Nordisk, Roche Diagnostics, Sanofi. Speaker's Bureau; Dr. Reddy’s Laboratories Ltd., Lupin Pharmaceuticals, Inc., USV Private Limited. S. Joshi: Advisory Panel; Abbott, Boehringer Ingelheim International GmbH, Dr. Reddy’s Laboratories Ltd., Eli Lilly and Company, Novo Nordisk, Roche Diabetes Care. Consultant; Biocon, Glenmark Pharmaceuticals, Sanofi, USV Private Limited. A. Mithal: Advisory Panel; Eris Lifesciences Ltd. Consultant; Glenmark Pharmaceuticals, Lupin Pharmaceuticals, Inc., USV Private Limited. Speaker's Bureau; Abbott Diabetes, AstraZeneca, Biocon, Boehringer Ingelheim International GmbH, Dr. Reddy’s Laboratories Ltd., Novartis AG, Novo Nordisk, Sanofi. S. Kalra: Speaker's Bureau; Boehringer Ingelheim International GmbH, Novo Nordisk A/S, Sanofi. A. Unnikrishnan: Advisory Panel; Intas Pharmaceuticals Ltd. Speaker's Bureau; Abbott, AstraZeneca, Boehringer-Ingelheim, Sanofi. Other Relationship; Novo Nordisk, Serdia Pharmaceuticals (India) Pvt. Ltd., Torrent Pharmaceuticals Ltd. H. Thacker: None. B. Sethi: None. S. Chowdhury: None. A. Nair: None. S. Mohanasundaram: Employee; Sanofi. V. Salvi: Employee; Sanofi. D. Chodankar: Employee; Sanofi. C. Trivedi: None. S. Wangnoo: None. A.H. Zargar: Advisory Panel; Sanofi. Speaker's Bureau; AstraZeneca, Biocon, Boehringer Ingelheim International GmbH, Intas Pharmaceuticals Ltd., Janssen Pharmaceuticals, Inc., Lupin Pharmaceuticals, Inc., Novo Nordisk, USV Private Limited. K. Kumar: None. Funding Sanofi, India

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Introduction A single‐flap approach (SFA) is the elevation of a periodontal flap to access the defect only from one side. Several studies have reported that the SFA is at least as clinically effective as the elevation of a flap at both buccal and palatal/lingual aspects. However, studies regarding the SFA have reported only 6 to 10 months follow‐up clinical outcomes. The purpose of this case series was to investigate the outcomes of the SFA for periodontal regeneration with a collagen membrane and bone grafts in regard to linear bone defect fill and clinical parameters such as gingival recession (GR), pocket depth (PD), and clinical attachment level (CAL) for the 15‐month follow‐up. Case Series Based on the deepest pocket depth site, the flap retraction side for each case, either buccal or palatal/lingual, was determined. After retraction of a one‐side full thickness flap, complete removal of granulation tissue and thorough scaling and root planing were performed. Demineralized ground cortical bone grafts were gently packed into the defect areas and an absorbable collagen membrane was placed over the grafts in 13 cases from 11 patients. Conclusion This case series demonstrated that the SFA for periodontal regeneration with a collagen membrane and bone grafts resulted in decreased PD with minimum GR, gain in CAL, and bone fill at defect sites for the 15‐month follow‐up.

The coronavirus outbreak has affected the whole world critically. Amongst all other things, wearing a mask nowadays is mandatory to avoid the spread of the virus according to the World Health Organization. All the people in the country prefer to live a salubrious life by wearing a mask in public gatherings to avoid contracting the deadly virus. Recognizing faces wearing a mask is often a tedious job as there are no substantial datasets available comprising of masked as well as unmasked images. In this paper, we propose a stacked Conv2D model that is highly efficient for the detection of facial masks. Such convolutional neural networks work effectively as they can deduce even minute pixels of the images. The proposed model is a stack of 2-D convolutional layers with relu activations as well as Max Pooling and we implemented this model by using Gradient Descent for training and binary cross-entropy as a loss function. We trained our model on an amalgam of two datasets that are RMFD (Real World Masked Face Dataset) and Kaggle Datasets. Overall, we achieved a validation/testing accuracy of 95% and a training accuracy of 97%. In addition to this, we also developed an email notification system that sends an email whenever a person is entering without a mask and it will also prompt the user to wear the mask before entering into the system. Such a system is beneficial to large multinational companies and can be deployed there as the spread of viruses there is high because employees are from different regions.