Prospective of study of gestational diabetes mellitus risk in relation to maternal recreation physical activity before and after pregnancy

Background: Gestational diabetes mellitus is common complications of pregnancy. Physical activity is associated with a lower risk of type 2 diabetes mellitus. The present study aimed to know association between physical activity and gestational diabetes mellitus in the first 20 weeks of their pregnancy.Methods: In the current case-control study, 50 pregnant females with gestational diabetes mellitus as the case group and 50 pregnant females as control group were selected. To diagnose gestational diabetes mellitus using diagnostic criteria. Females with abnormal oral glucose challenge test (>140mg/dL) were asked to perform the three-hour 100 g oral glucose tolerance test. The details of physical activity were collected by pregnancy physical activity questionnaire. Anthropometric and other data were recorded for all of the participants.Results: Females with low total physical activity at early pregnancy were at a significantly higher risk of developing gestational diabetes mellitus compared to the ones with higher levels of physical activity. After adjusting for age, body mass index (BMI), gravidity and a family history of diabetes, females with low physical activity during 20 weeks of pregnancy were at a significantly higher risk of developing gestational diabetes mellitus. Females with the low intensity of sedentary, light and moderate physical activity are at a higher risk of developing gestational diabetes mellitus compared to females with a higher intensity of sedentary, light and moderate physical activity.Conclusions: Females should be encouraged to do regular daily physical activity during pregnancy, if there is no specific contraindication to it.

Multi-objective Optimisation of Multi-robot Task Allocation with Precedence Constraints

Efficacy of the multi-robot systems depends on proper sequencing and optimal allocation of robots to the tasks. Focuses on deciding the optimal allocation of set-of-robots to a set-of-tasks with precedence constraints considering multiple objectives. Taguchi’s design of experiments based parameter tuned genetic algorithm (GA) is developed for generalised task allocation of single-task robots to multi-robot tasks. The developed methodology is tested for 16 scenarios by varying the number of robots and number of tasks. The scenarios were tested in a simulated environment with a maximum of 20 robots and 40 multi-robot foraging tasks. The tradeoff between performance measures for the allocations obtained through GA for different task levels was used to decide the optimal number of robots. It is evident that the tradeoffs occur at 20 per cent of performance measures and the optimal number of robot varies between 10 and 15 for almost all the task levels. This method shows good convergence and found that the precedence constraints affect the optimal number of robots required for a particular task level

Designing Overlapping Networks for Publish-Subscribe Systems

From the publish-subscribe systems of the early days of the Internet to the recent emergence of Web 3.0 and IoT (Internet of Things), new problems arise in the design of networks centered at producers and consumers of constantly evolving information. In a typical problem, each terminal is a source or sink of information and builds a physical network in the form of a tree or an overlay network in the form of a star rooted at itself. Every pair of pub-sub terminals that need to be coordinated (e.g. the source and sink of an important piece of control information) define an edge in a bipartite demand graph; the solution must ensure that the corresponding networks rooted at the endpoints of each demand edge overlap at some node. This simple overlap constraint, and the requirement that each network is a tree or a star, leads to a variety of new questions on the design of overlapping networks. In this paper, for the general demand case of the problem, we show that a natural LP formulation has a non-constant integrality gap; on the positive side, we present a logarithmic approximation for the general demand case. When the demand graph is complete, however, we design approximation algorithms with small constant performance ratios, irrespective of whether the pub networks and sub networks are required to be trees or stars

An Approach to Improve Multi objective Path Planning for Mobile Robot Navigation using the Novel Quadrant Selection Method

Currently, automated and semi-automated industries need multiple objective path planning algorithms for mobile robot applications. The multi-objective optimisation algorithm takes more computational effort to provide optimal solutions. The proposed grid-based multi-objective global path planning algorithm [Quadrant selection algorithm (QSA)] plans the path by considering the direction of movements from starting position to the target position with minimum computational effort. Primarily, in this algorithm, the direction of movements is classified into quadrants. Based on the selection of the quadrant, the optimal paths are identified. In obstacle avoidance, the generated feasible paths are evaluated by the cumulative path distance travelled, and the cumulative angle turned to attain an optimal path. Finally, to ease the robot’s navigation, the obtained optimal path is further smoothed to avoid sharp turns and reduce the distance. The proposed QSA in total reduces the unnecessary search for paths in other quadrants. The developed algorithm is tested in different environments and compared with the existing algorithms based on the number of cells examined to obtain the optimal path. Unlike other algorithms, the proposed QSA provides an optimal path by dramatically reducing the number of cells examined. The experimental verification of the proposed QSA shows that the solution is practically implementable

Designing a mobile health smokeless tobacco cessation intervention in Odisha, India: User and provider perspectives

OBJECTIVE: There is limited evidence on the development of mobile health (mHealth) interventions for smokeless tobacco (SLT) cessation, despite its widespread use in South Asia. This formative qualitative study explored the perceptions of tobacco users and healthcare providers (HCPs) regarding developing a mHealth intervention for SLT cessation. METHODS: This was a qualitative study using in-depth interviews (IDIs) with tobacco users (n = 26) and primary care physicians (PCPs) (n = 5) and focus group discussions (FGDs) with counsellors (n = 2) in four urban primary health centres (UPHCs) in Berhampur, Odisha from February to March 2020. The data were coded and analysed by two researchers using a framework analysis method. The discussion guides and initial codes were developed based on the Transtheoretical Model (TTM) of behaviour change. RESULTS: The results were elaborated under four themes: (1) Current scenario of SLT use; (2) Barriers and facilitators for quitting SLT; (3) Barriers and facilitators for mHealth counselling; and (4) Design and delivery of the proposed intervention. SLT use was prevalent in the community regardless of sociodemographic factors. Peer factors accounted for both tobacco consumption as well as considering cessation. Participants considered mobile message counselling helpful and acceptable. Not having a mobile phone and illiteracy were identified as barriers while ease of access and rising popularity of social media applications were considered facilitators to the use of mHealth for quitting tobacco. Participants preferred messages that were pictorial, short and simple, in the local language, and tailored to individual's needs. CONCLUSIONS: This is the first study that provides evidence within the Indian context that the text messaging platform may be used for delivering an SLT cessation intervention. The integration of a theoretical basis and research findings from target users can guide future intervention development

Ice Shapes on a Tail Rotor

Testing of a thermally-protected helicopter rotor in the Icing Research Tunnel (IRT) was completed. Data included inter-cycle and cold blade ice shapes. Accreted ice shapes were thoroughly documented, including tracing, scanning and photographing. This was the first time this scanning capability was used outside of NASA. This type of data has never been obtained for a rotorcraft before. This data will now be used to validate the latest generation of icing analysis tools

Parent Satisfaction with Outpatient Pediatric Endoscopy Procedures at University of New Mexico Children\u27s Hospital

As a part of endoscopy quality improvement (EQI) project, we decided to measure parent satisfaction about pediatric endoscopy service at University of New Mexico Children\u27s Hospital

Automatic face mask detection system in public transportation in smart cities using IoT and deep learning

The World Health Organization (WHO) has stated that the spread of the coronavirus (COVID-19) is on a global scale and that wearing a face mask at work is the only effective way to avoid becoming infected with the virus. The pandemic made governments worldwide stay under lock-downs to prevent virus transmissions. Reports show that wearing face masks would reduce the risk of transmission. With the rise in population in cities, there is a greater need for efficient city management in today’s world for reducing the impact of COVID-19 disease. For smart cities to prosper, significant improvements to occur in public transportation, roads, businesses, houses, city streets, and other facets of city life will have to be developed. The current public bus transportation system, such as it is, should be expanded with artificial intelligence. The autonomous mask detection and alert system are needed to find whether the person is wearing a face mask or not. This article presents a novel IoT-based face mask detection system in public transportation, especially buses. This system would collect real-time data via facial recognition. The main objective of the paper is to detect the presence of face masks in real-time video stream by utilizing deep learning, machine learning, and image processing techniques. To achieve this objective, a hybrid deep and machine learning model was designed and implemented. The model was evaluated using a new dataset in addition to public datasets. The results showed that the transformation of Convolution Neural Network (CNN) classifier has better performance over the Deep Neural Network (DNN) classifier; it has almost complete face-identification capabilities with respect to people’s presence in the case where they are wearing masks, with an error rate of only 1.1%. Overall, compared with the standard models, AlexNet, Mobinet, and You Only Look Once (YOLO), the proposed model showed a better performance. Moreover, the experiments showed that the proposed model can detect faces and masks accurately with low inference time and memory, thus meeting the IoT limited resources

Evaluation of groundwater quality and its suitability for drinking and agricultural use in Thanjavur city, Tamil Nadu, India

As groundwater is a vital source of water for domestic and agricultural activities in Thanjavur city due to lack of surface water resources, groundwater quality and its suitability for drinking and agricultural usage were evaluated. In this study, 102 groundwater samples were collected from dug wells and bore wells during March 2008 and analyzed for pH, electrical conductivity, temperature, major ions, and nitrate. Results suggest that, in 90% of groundwater samples, sodium and chloride are predominant cation and anion, respectively, and NaCl and CaMgCl are major water types in the study area. The groundwater quality in the study site is impaired by surface contamination sources, mineral dissolution, ion exchange, and evaporation. Nitrate, chloride, and sulfate concentrations strongly express the impact of surface contamination sources such as agricultural and domestic activities, on groundwater quality, and 13% of samples have elevated nitrate content (>45 mg/l as NO3). PHREEQC code and Gibbs plots were employed to evaluate the contribution of mineral dissolution and suggest that mineral dissolution, especially carbonate minerals, regulates water chemistry.Groundwater suitability for drinking usage was evaluated by the World Health Organization and Indian standards and suggests that 34% of samples are not suitable for drinking. Integrated groundwater suitability map for drinking purposes was created using drinking water standards based on a concept that if the groundwater sample exceeds any one of the standards, it is not suitable for drinking. This map illustrates that wells in zones 1, 2, 3, and 4 are not fit for drinking purpose. Likewise, irrigational suitability of groundwater in the study region was evaluated, and results suggest that 20% samples are not fit for irrigation. Groundwater suitability map for irrigation was also produced based on salinity and sodium hazards and denotes that wells mostly situated in zones 2 and 3 are not suitable for irrigation. Both integrated suitability maps for drinking and irrigation usage provide overall scenario about the groundwater quality in the study area. Finally, the study concluded that groundwater quality is impaired by man-made activities, and proper management plan is necessary to protect valuable groundwater resources inThanjavur city

Confirmation of Sentinel Lymph Node Identity by Analysis of Fine-Needle Biopsy Samples Using Inductively Coupled Plasma–Mass Spectrometry

Background: The sentinel lymph node (SLN) biopsy technique is a reliable means of determining the tumor-harboring status of regional lymph nodes in melanoma patients. When technetium 99 m-labeled antimony trisulfide colloid (99 mTc-Sb2S3) particles are used to perform preoperative lymphoscintigraphy for SLN identification, they are retained in the SLN but are absent or present in only tiny amounts in non-SLNs. The present study investigated the potential for a novel means of assessing the accuracy of surgical identification of SLNs. This involved the use of inductively coupled plasma-mass spectrometry (ICP-MS) to analyze antimony concentrations in fine-needle biopsy (FNB) samples from surgically procured lymph nodes. Methods: A total of 47 FNB samples from surgically excised lymph nodes (32 SLNs and 15 non-SLNs) were collected. The SLNs were localized by preoperative lymphoscintigraphy that used 99 mTc-Sb2S3, blue dye, and gamma probe techniques. The concentrations of antimony were measured in the FNB samples by ICP-MS. Results: The mean and median antimony concentrations (in parts per billion) were .898 and .451 in the SLNs, and .015 and .068 in the non-SLNs, the differences being highly statistically significant (P < .00005). Conclusions: Our results show that ICP-MS analysis of antimony concentrations in FNB specimens from lymph nodes can accurately confirm the identity of SLNs. Used in conjunction with techniques such as proton magnetic resonance spectroscopy for the nonsurgical evaluation of SLNs, ICP-MS analysis of antimony concentrations in FNB samples could potentially serve as a minimally invasive alternative to surgery and histopathologic evaluation to objectively classify a given node as sentinel or nonsentinel and determine its tumor-harboring status. © 2007 The Author(s)