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Background: Severe acute respiratory virus coronavirus-2 disease (COVID-19) vaccination among antenatal mothers plays a vital role in preventing maternal and neonatal morbidity and mortality. We attempted to find out the COVID-19 vaccine uptake rate and to determine the factors associated with COVID-19 vaccine uptake. Methods: Community-based analytical cross-sectional study was conducted for 6 months amongst antenatal mothers who are residing in the field practice area of primary healthcare (PHC). The participants were selected based on simple random sampling. Face-to-face interviews were conducted in their house using a pre-designed structured questionnaire after obtaining consent. Results: Amongst 246 antenatal mothers, 82.5% were in the 20-29 years of age group. Only 17.9% of the mothers were illiterates, 88.2% of the participants were homemakers. Majority (97%) belonged to the Hindu religion and more than half of them lived in nuclear families. One hundred and fifty (61%) had taken at least one dose of the vaccine. The logistic regression model was statistically significant (P < 0.05) and had explained 14.2% of the variance in acceptance of the COVID-19 vaccine and correctly classified 67.9% of cases. Fear concerning the safety of the vaccine and its side effects was the most commonly reported reason for not taking the vaccine. Conclusion: Targeted interventions to maximise COVID-19 vaccine uptake among pregnant women from ethnic minority communities and those living in deprived areas should be explored.

This paper considers a Markovian retrial queueing system with an optional service, unreliable server, balking and feedback. An arriving customer can avail of immediate service if the server is free. If the potential customer encounters a busy server, it may either join the orbit or balk the system. The customers may retry their request for service from the orbit after a random amount of time. Each customer gets the First Essential Service (FES). After the completion of FES, the customers may seek the Second Optional Service (SOS) or leave the system. In the event of unforeseen circumstances, the server may encounter a breakdown, at which point an immediate repair process will be initiated. After the service completion, the customer may leave the system or re-join the orbit if not satisfied and demand regular service as feedback. In this investigation, the stationary queue size distributions are framed using a recursive approach. Various system performance measures are derived. The effects induced by the system parameters on the performance metrics are numerically and graphically analysed.

Transparent conducting metal oxides (TCOs) are well known for their application in optoelectronic devices. However, the nonlinear optical (NLO) study of TCOs helps to understand the characteristic features which give insight into their practical utilization as well as device application in each optical spectrum. In this work, we studied the structural, electrical, optical, and nonlinear optical properties of Sn-doped CdO. The structural, electrical, optical, and nonlinear optical properties of Sn-doped CdO were studied by using an X-ray diffractometer (XRD), Hall measurement system, UV–vis spectrometer, and Z-scan measurement. The XRD analysis shows that Sn-doped CdO is a polycrystalline material with a preferential peak of a plane (111) shifting slightly toward the (200) plane as Sn doping suddenly increases. Sn-doped CdO proved good electrical properties with conductivity increase from 0.192 × 102 Ω−1 cm−1 to 2.03 × 102 Ω−1 cm−1 and decrease in resistivity from 52.1 × 10−3 Ωcm to 4.92 × 10−3 Ωcm as the molar dopant concentration increase from 0.0001 M to 0.0005 M. The obtained bandgap for undoped and 0.0005 M, 0.0004 M, 0.0003 M, 0.0002 M, 0.0001 M Sn-doped CdO are 2.37, 1.63, 1.70, 1.81, 1.86, and 2.09 respectively. The NLO absorption coefficient (β) for 0.0003 M, 0.0004 M and 0.0005 M are 6.31 × 10−3 m/W, 11.47 × 10−3 m/W and 10.6 × 10−3 m/W respectively.

Plastics are a synthetic or semisynthetic material that is extensively used in our routine life because of its remarkable characteristics such as thermal, mechanical properties, durability, and stability, so they have widespread application. This massive use of plastics has caused various issues to human health and the environment. Nearly 700 species, including endangered species,have been affected by plastics. Enzyme - mediated and microbial degradation of plastic waste is a potential technique in which the process of biodeterioration, thermal decomposition, and integration actually took place and is ultimately transformed into gases and a microbial community, where the process leaves few residual effects. Recent studies have shown that bacteria and bacterial consortia, biofilm formation and fungi may biodegrade many polymers. Depending on the organisms, the molecular weight of the plastics, the kind of polymer, and the climate, waste plastic can be converted into methane, CO2, biomass, water and inorganic chemicals. Plastics induce oxidative stress, enhanced and inflammatory reactions, translocation or absorption, in people, animals and plants. Numerous studies have shown that people are susceptible to metabolic disorders, neurodegeneration, and malignancy.We have also talked about how we are working on using the metabolic processes of bacteria to minimize soil and water pollution by microplastics. More study is required to discover the crucial ecological characteristics and elements that affect plastic degradation in order to predict the fate of plastics in various contexts and to enable the development of solutions for minimising plastic waste.

The metal ion sensing characteristics of a novel Schiff-based ratiometric UV-visible chemosensor (E)-N’-(1-(2,5-dihydroxy phenyl)ethylidene) isonicotinohydrazide) (R1) has been explored. In EtOH:H2O (7:3, v/v), it has high sensitivity and selectivity for Zn2+ among a series of metal ions. With the addition of Zn2+ ions solution, R1 displayed discriminating spectral activity. The other metal ions did not affect R1 in any way. Furthermore, the addition of Zn2+ ions to R1 and LMCT action caused the shifting of the peak to a longer wavelength of 406 nm. The interaction of Zn2+ ions with R1 was further investigated using Density Functional Theory (DFT) investigations. Zn2+-R1 combination has a lower energy (2.2667 kcal.mol-1 to 0.9339 kcal.mol-1) than R1, indicating a strong connection with excellent stability. The Zn2+-R1 complex’s association constant (Ka) was discovered to be 6795M-1 and 6836M-1 using Benesi-Hildebrand and Scatchard plots respectively. The detection limit was determined to be 276 nM.

Natural disasters are utmost incidents inside the earth''''s system that lead to sudden demise or bruise to humans, and destruction of precious materials, involving buildings, conveyance systems, farming land, forest and natural environment. Occurrences of economic losses due to natural disasters have resulted owing to the escalated susceptibility of the society globally and also due to weather-related disasters. Satellite image sensing remains the hypothetical instrument for disaster management as it provides information spanning wide-reaching areas and also at short time period. In this work we plan to develop a method called, Enhanced Frost Filter and Tanimoto Similarity Classification (EFF-TSC) for efficient disaster management using satellite images is proposed. The EFF-TSC method for disaster management is split into three steps. They are pre-processing, segmentation and classification. With the input image collected from satellite image database, first preprocessing is performed to preserve important features at the edges and remove the noisy pixel by means of an Enhanced Frost Filter Preprocessing model. Second, to the pre-processed satellite image, Threshold Pixel Segmentation is applied to partition into multiple segments. Finally, to the partitioned images, Tanimoto Similarity Classification is applied to classify the segmented image into two types, namely disastrous image and non-disastrous image. With this, an efficient disaster management is carried out with better accuracy and minimal time consumption. The application of the study is demonstrated using the Disaster image data set collected from Kaggle during the 2017. The results show the capability of the proposed EFFTSC method for disaster management across time and space from different images with considerable accuracy by also reducing peak signal to noise ratio with considerable time. The findings also suggest that the potential for forensic analysis of disasters using pixel segmentation and classification based on collected images can be utilized to several locations affected by disasters.

It is anticipated that the millimetrewave, often known as mm-wave, technology that will be used in 5G networks will greatly enhance network capacity. The mm-wave signals, on the other hand, are prone to obstructions than the ones at lower bands; this demonstrates the impact that route loss has on the network coverage. Because of the fractal nature of cellular coverage and the different path loss exponents that apply to different directions, it has been suggested that a route loss model in a multi-directional manner for 5G UDN networks. This is due to the fact that different directions have path loss exponents. In addition, the proposed loss model is applied to the 5G ultra-dense network in order to calculate the coverage probability, association probability, and handoff probability (UDN). According to the numerical findings of this research, in 5G UDN, the influence of anisotropic path loss increases the association probability with long link distance. It has also come to light that the performance of the handoff suffers tremendously as a consequence of the anisotropic propagation environment. A new difficulty has arisen for 5G UDN as a consequence of the substantial handoff overhead that has been produced.

Undoped CdO films and three different molarities of cadmium oxide (CdO) films were doped with fluorine on glass slides via a chemical spray pyrolysis method. The samples were analyzed using X-ray diffractometry (XRD), scanning electron microscopy (SEM), UV–VIS-NIR spectroscopy, electrical measurements, and Z-scan measurements. XRD measurement revealed better crystallinity with preferred orientation at [111] plane. Nanospheres of varying sizes were obtained from the surface micrographs. The direct band gap energy decreased as the fluorine concentration increased. The film resistivity recorded a decline with increased carrier mobility as the flourine concentration increased. The nonlinear optical absorption coefficient (β) gotten for 0.0003 M, 0.0004 M, and 0.0005 M at 632.8 nm wavelength are 3.83×10–3 m/W, 5.7 × 10–3 m/W and 5.48×10–3 m/W respectively. The deposited films find potential application in solar cell and optoelectronic devices.

Accurate and effective mapping of soil properties is regarded as a critical task in environmental and agricultural management. The evaluation of properties of soil is a daunting task while monitoring and sensing the environment. Existing sampling methods is a time-consuming and laborious job and they are limited based on the regions. However, the need of soil analysis and its properties is essential at landscape level. In this paper, we use Recurrent Convolution Neural Network (RCNN) to assess the soil properties via its classification task. The model in turn is compared with conventional geostatistical spatial interpolation methods. The utilization of Recurrent Neural Network (RNN) aims at studying the spatial and temporal variability of the properties of soil that adopts Kriging interpolation technique. The simulation is conducted to study the efficacy of the model under different soil conditions and the efficacy of RCNN is reported. The results of simulation shows that the proposed method achieves higher rate of classification accuracy than other models.

Several MANET designs looked into renewable resource efficient protocols. The amount of energy dissipated by MANET nodes is an important coordinating component. In monitoring applications, power usage is ensured, and battery depletions are reduced to avoid frequent replacement. MANET's primary goal is to relay data by utilising energy-efficient routing techniques and extending the network's lifespan. In this research, we provide an Energy Efficient Protocol on Remaining Energy, a new paradigm for MANET multicasting. Our idea is based on a tone system that integrates solutions across the Medium Access Control (MAC) layer to deliver more efficiency and better performance. To provide increased stability and lower energy consumption, the protocol includes a new construction mechanism for mobile nodes that uses a clustering strategy based on distance and remaining energy. We also propose modifying the conventional multicast flow by introducing unicast links between clusters. In addition, we describe a strategy based on a busy tone system to eliminate excessive control overhead produced by control packets in error recovery in our model. The simulation results are displayed in NS2, and they indicate improvements in reliability, packet delivery ratio, energy usage, and throughput.

Background and Objectives: The purpose of the study was to evaluate the prevalence of periodontal disease status and oral hygiene practices in urban and rural population of Kancheepuram District, Tamil Nadu, India. Methods: This epidemiological survey was carried out on 1650 participants taken from both urban and rural areas of Kancheepuram District. The study groups will belong to rural and urban areas in the ratio of 1:2, respectively. Every individual was assessed with the oral hygiene index-simplified, community periodontal index of treatment needs and periodontal disease index. In addition, the oral hygiene practices were also studied and recorded in a specially designed pro forma. Data were subjected to statistical analysis using SPSS 19.0 software. Results: The study showed that 50% and 36% of the study participants have gingivitis and periodontitis, respectively, while only 14% of the study participants did not present with any form of periodontal disease. The study also showed that 16.63% of the study subjects among urban and 7.63% of them among rural do not have any type of periodontal disease. About 57.09% of the study participants among urban and 36.54% of the study participants among rural areas have gingivitis. The remaining 26.3% of the study participants in urban areas and 55.8% of the study participants in rural areas have periodontitis. It was observed that majority of participants brush once a day using Medium bristle tooth brush and toothpaste as dentifrice. Conclusion: Periodontal disease is widely spread among population of Kancheepuram District, with greater prevalence in rural population than in urban population. This could be mainly due to the lack of awareness and limited availability of resources. These estimates are vital for the future planning of dental services in Kancheepuram District, Tamil Nadu, India.

Background and Aim: The periodontal microbiome being complex, this study was aimed to detect and quantify the prevalence of Filifactor alocis in various stages of periodontitis and to evaluate its prospect as a diagnostic marker for periodontal disease. Settings and Design: Sixty subjects were selected (20 healthy controls, 20 with chronic periodontitis, and 20 with aggressive periodontitis) for the study. Materials and Methods: Clinical parameters probing depth and the level of clinical attachment was recorded, subgingival plaque samples were collected. The F. alocis 16srDNA was cloned, sequenced, and used as the standard for real-time quantification of bacterial load using SYBR green chemistry. Statistical Analysis: Clinical, microbiological, and quantitative polymerase chain reaction (PCR) data were analyzed using ANOVA and Pearson's coefficient correlation. Results: (a) Real-time PCR analysis showed the highest average F. alocis count in chronic periodontitis subjects (32,409.85), which was followed by count in healthy controls (3046.15) and the least count in aggressive periodontitis subjects (939.84). The bacterial count was statistically significant at P = 0.005. (b) An intra-group comparison reveals that there was a statistically significant increase in the bacterial count with age and mean probing pocket depth at P = 0.0005. Conclusion: F. alocis population in aggressive periodontitis was lower compared to chronic periodontitis and healthy controls. The F. alocis population surge in healthy controls may be due to geographical variations and the ethnicity of the subjects. A higher population of F. alocis in chronic periodontitis proves its high pathogenic potential to invade the host tissues to aid in further periodontal destruction.

The growing need for access to safer food items is increasing, and hence, there is a need for a better supply chain management system in the food industry is increasing. The increased complexity of the existing systems tends to introduce more issues to the stakeholders, and also, the cost of product traceability is quite high. Hence, the industry is looking for effective solutions in relation to drug traceability, and the application of Blockchain technology enables the stakeholders in the food and beverage (F&B) sector to track the movement of goods, supported in gathering the required details so that the contaminated products can be identified and recalled without much delay and lesser recall costs to protect the lives of the individuals. The tampered food items are increasing and are impacting the supply chain process, brand name of the companies, and claim assurance. They create an adverse impact on the health of the individuals and cause higher economic loss to the health-care industry. The existing studies tend to focus on laying emphasis of the need for an enhanced, effective, and end tracking systems in the industry. The emergence of Blockchain technology enables centralized tracking of information support in enhancing the data privacy and increasing transparency and support in eradicating the tampered food products in the supply chain system. These approaches leverage the usage of smart contracts and decentralize the storage of information in a secure manner for enhanced product traceability in the F&B industry. The implementation of smart contracts generates better data governance, which tends to meet the needs and requirements of the stakeholders, and applies effective measures of food traceability. The primary objective of the study is to perform an analysis of Blockchain in enhancing drug traceability in the food sector. The researcher uses quantitative analysis for the study as it helps in understanding the critical determinants influencing drug traceability in food effectively, the survey method is used to gather the information, and past reviews are also used to possess a better understanding of the subject area effectively.

“The golden highways of the technological route serve as the ultimate metaphor for the society of artificial intelligence,” explains the author of this present excursion. Unsupervised predictor is a technique that is used in all upbringings and algometry computations, and it is the foundation of all of them. The accuracy-based artificial intelligence and genetic algorithm-based prediction are becoming more important in business intelligence, with the crossover revealing the highest percentage of interest-based profit in the food industry. Our suggested approach aims to determine the utmost amplification of the food business profit with the help of food sale prediction. Hence, owners of outlet sale, supermarket sale, grocery store, and so on can maintain the stock. Therefore, our study strives to elucidate the relationship between food business intelligence and artificial intelligence in the context of soft computing. The study combines the previous data with the record of the current data and compares the existing development of business management in order to create the topic of the TOR method, which will be the artificial intelligence buzzword in the town-based food business intelligence. This paper uses artificial intelligence, business intelligence, and neural networks to forecast the food sale prediction by evaluating the data of each individual client. Consider the situation in which this technology is applied in every grocery store in the country. Our suggested model has achieved low mean squared error and low variance. Food mart owners will have a far better grasp of their clientele and will be able to categorise them in this situation, enabling them to proceed with future sales forecasting.

Introduction: Knowledge of the gingival biotype or phenotype is of fundamental importance to an oral clinician. There is a direct correlation between gingival biotype and susceptibility to gingival recession following surgical and restorative procedures. The thick and slightly scalloped marginal gingiva with short and wide teeth on the one hand and the thin and highly scalloped marginal gingiva with slender teeth on the other may be the reason for different periodontal entities or so-called “gingival biotypes.” Aim: The present study is to analyze gingival biotype based on dentopapillary complex, taking transparency of the periodontal probe through the gingival margin as a method to differentiate thin from thick gingiva in different age groups. Materials and Methods: Sixty periodontally healthy individuals with all anterior teeth in both the jaws were participated in this study. Results: Thick biotype had area of anterior crowns as 490.17 mm2. Thin biotype had area of anterior crowns as 420.14 mm2 with P = 0.00 having high clinical significance. Conclusion: Teeth with large dentopapillary area had thick gingival biotype. Taking into consideration that the shape, size, form of the tooth and the surrounding gingiva is of paramount importance for the causation and progression of disease.