Public health nurse perspectives on predicting nonattendance for cervical cancer screening through classification, ensemble, and deep learning models.

OBJECTIVES: Women's attendance to cervical cancer screening (CCS) is a major concern for healthcare providers in community. This study aims to use the various algorithms that can accurately predict the most barriers of women for nonattendance to CS. DESIGN: The real-time data were collected from women presented at OPD of primary health centers (PHCs). About 1046 women's data regarding attendance and nonattendance to CCS were included. In this study, we have used three models, classification, ensemble, and deep learning models, to compare the specific accuracy and AU-ROC for predicting non-attenders for CC. RESULTS: The current model employs 22 predictors, with soft voting in ensemble models showing slightly higher specificity (96%) and sensitivity (93%) than weighted averaging. Bagging excels with the highest accuracy (98.49%), specificity (97.3%), and ideal sensitivity (100%) with an AUC of 0.99. Classification models reveal Naive Bayes with higher specificity (97%) but lower sensitivity (91%) than Logistic Regression. Random Forest and Neural Network achieve the highest accuracy (98.49%), with an AUC of 0.98. In deep learning, LSTM has an accuracy of 95.68%, higher specificity (97.60%), and lower sensitivity (93.42%) compared to other models. MLP and NN showed the highest AUC values of 0.99. CONCLUSION: Employing ensemble and deep learning models proved most effective in predicting barriers to nonattendance in cervical screening.

Phytostabilization of coal mine overburden waste, exploiting the phytoremedial efficacy of lemongrass under varying level of cow dung manure.

A pot study was performed to assess the phytoremedial potential of Cymbopogon citratus (D.C.) Staf. for reclamation of coal mine overburden dump wastes, emphasizing the outcome of amendment practices using cow dung manure (CM) and garden soil mixtures on the revegetation of over-burden wastes (OB). Wastes amendment with cow dung manure and garden soil resulted in a significant increase in soil health and nutrient status along with an increment in the phytoavailability of Zn and Cu which are usually considered as micronutrients, essential for plant growth. A significant increment in the total biomass of lemongrass by 38.6% under CM20 (OB: CM 80:20) was observed along with improved growth parameters under amended treatments as compared to OB (100% waste). Furthermore, the proportionate increases in the assimilative rate, water use efficiency, and chlorophyll fluorescence have been observed with the manure application rates. Lemongrass emerged out to be an efficient metal-tolerant herb species owing to its high metal-tolerance index (>100%). Additionally, lemongrass efficiently phytostablized Pb and Ni in the roots. Based on the strong plant performances, the present study highly encourages the cultivation of lemongrass in coal mining dumpsites for phytostabilization coupled with cow-dung manure application (20% w/w).

Barriers to Minimisation of agri-products wastage through Optimizing logistics in India: An ISM modelling approach.

The research work identifies and priorities the factors affecting agri-logistics causing wastage of the agricultural products during its transit from farm to the point of consumption so that logistics mechanism for agriculture sector in India can be optimised by removing the barriers leading to hindrances in safe, timely, economical and good condition delivery of the agri consignment. The field of agri-logistics remains at the crucial nexus of the agricultural and logistics industries and has the potential to improve the nation's system for distributing food. The post-harvest wastage in India has been massive due to inefficiencies agri-logistics management and faulty food distribution mechanism. It is an exploratory study that along the factors (barriers) identified and synthesised from literature review of the concerned area. The identified barriers were reduced and finalised in consultation with the experts using Delphi technique. With the help of ISM questionnaire, a model has been developed reflecting the drivers and dependents out of the barriers considered for the study. The result is further validated through MICMAC analysis. The result of the study has come up with the interpretive structure model depicting hierarchy of the barriers pushing from down to top causing agriculture wastage. The paper holds originality in the sense that it comes up with fresh perspectives on the factors causing hindrances in the efficient logistics operation that certainly helps to minimise wastage of the agri-products in the post-harvest stages. The identification of the barriers and their detriments to the other factors will help to take essential steps on how to overcome the issues and optimize the agri-logistics that would minimise the agri-wastage in India and prove to be a game changer to the agri-trade sector.

A Fully-Passive Wireless Microsystem for Recording of Neuropotentials using RF Backscattering Methods.

The ability to safely monitor neuropotentials is essential in establishing methods to study the brain. Current research focuses on the wireless telemetry aspect of implantable sensors in order to make these devices ubiquitous and safe. Chronic implants necessitate superior reliability and durability of the integrated electronics. The power consumption of implanted electronics must also be limited to within several milliwatts to microwatts to minimize heat trauma in the human body. In order to address these severe requirements, we developed an entirely passive and wireless microsystem for recording neuropotentials. An external interrogator supplies a fundamental microwave carrier to the microsystem. The microsystem comprises varactors that perform nonlinear mixing of neuropotential and fundamental carrier signals. The varactors generate third-order mixing products that are wirelessly backscattered to the external interrogator where the original neuropotential signals are recovered. Performance of the neuro-recording microsystem was demonstrated by wireless recording of emulated and in vivo neuropotentials. The obtained results were wireless recovery of neuropotentials as low as approximately 500 microvolts peak-to-peak (µV(pp)) with a bandwidth of 10 Hz to 3 kHz (for emulated signals) and with 128 epoch signal averaging of repetitive signals (for in vivo signals).

Tree responses to foliar dust deposition and gradient of air pollution around opencast coal mines of Jharia coalfield, India: gas exchange, antioxidative potential and tolerance level.

Atmospheric pollution by opencast mining activities affects tree species around the mining area. The present study evaluated the responses of five native tree species to air pollution in Jharia coalfield. Sites were selected as closest to farthest from the mining area. Foliar dust deposition and foliar sulphate content affected stomatal conductance, superoxide dismutase activity and ascorbic acid and, thus, increased the susceptibility of sensitive species. Ficus benghalensis and Butea monosperma showed maximum dust deposition, while Adina cordifolia showed minimum deposition. Maximum dust deposition in Ficus benghalensis lowered stomatal conductance and, thus, checked the flux of other acidic gaseous pollutants which led to minimum variation in leaf extract pH. Higher stomatal conductance in Adina cordifolia and Aegle marmelos, on the other hand, facilitated the entry of acidic pollutants and disrupted many biological functions by altering photosynthesis and inducing membrane damage. Low variations in Ficus religiosa, Ficus benghalensis and Butea monosperma with sites and seasons suggest better physiological and morphological adaptations towards pollution load near coal mining areas. Tree species with better adaptation resisted variation in leaf extract pH by effectively metabolising sulphate and, thus, had higher chlorophyll content and relative water content.