Deep and handcrafted feature supported diabetic retinopathy detection: A study

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes; (i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database

Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores

Best Practices for the Ocean Moored Observatories

Real-time spatio-temporal meteorological and oceanographic data, from the Ocean moored observatories, are essential for the precise forecast of the ocean state, climate variability studies and reliable weather prediction. Precise spatio-temporal measurement of subsurface parameters such as temperature, salinity and current are essential to understand the intra-seasonal and inter-annual evolution of monsoons and tropical cyclones. To cater to this time-critical information, moored observatories have to continuously be operational in the harsh marine environment to measure these essential ocean variables. However, bio-fouling and corrosion limits the life time and accuracy of the highly precise measuring instruments. Thus, best practices in these moored observations are essential for long term accurate and cost-effective ocean observation. The Indian moored buoy network which has been operational since 1997, has been providing quality data over the past decade. This paper describes the best operational practices and quality control processes followed in the Indian moored buoy system design, sensor calibration, testing, integration, deployment, retrieval, and data quality control over the past two decades, which has helped to achieve an average meteorological data return of 90%

Experimental and Theoretical Investigations of Different Diketopyrrolopyrrole-Based Polymers

Diketopyrrolopyrrole (DPP)-based polymers are often considered las the most promising donor moiety in traditional bulk heterojunction solar cell devices. In this paper, we report the synthesis, characterization of various DPP-based copolymers with different molecular weights, l and polydisper sity where other aromatic repeating units (phenyl or thiophene based) are connected by alternate double bonds or triple bonds. Some of the copolymers were used for device fabrication and the crucial parameters such as fill factor (FF) and open circuit voltage (V-oc) were calculated. The density functional theory was used to optimize the geometries and deduce highest occupied molecular orbital lowest unoccupied molecular orbital gaps of all the polymers and'theoretically predict their optical and electronic properties. Optical properties of all the polymers, electrochemical properties and band gaps were also obtained experimentally and compared with the theoretically predicted values

Acquired rifampicin resistance in thrice-weekly antituberculosis therapy: impact of HIV and antiretroviral therapy

Risk factors for acquired rifampicin resistance (ARR) among tuberculosis patients on thrice-weekly antituberculosis therapy were baseline isoniazid resistance and HIV. Among HIV-infected patients, higher mycobacterial burden and lower CD4 count, but not highly active antiretroviral therapy, were significantly associated with ARR. Background: Risk factors for acquired rifampicin resistance (ARR) in human immunodeficiency virus (HIV)/tuberculosis coinfection, in the highly active antiretroviral therapy (HAART) era, needs evaluation. We studied the impact of HIV and HAART on ARR among patients taking thrice-weekly antituberculosis therapy. Methods: This cross-protocol analysis included patients with newly diagnosed, rifampicin-susceptible pulmonary tuberculosis, with and without HIV, enrolled in clinical trials (who took >80% of medication) at the National Institute for Research in Tuberculosis between 1999 and 2013. All patients received rifampicin and isoniazid for 6 months reinforced with pyrazinamide and ethambutol in the first 2 months, given thrice-weekly throughout the study along with HAART in one of the groups. Outcomes were categorized and multivariate logistic regression analysis performed to identify risk factors for ARR. Results: The per-protocol results included patients with tuberculosis: 246 HIV-uninfected patients (HIV–TB+), 212 HIV patients not on HAART (non-HAART), and 116 HIV-infected patients on HAART. Median CD4 counts of the latter 2 groups were 150 and 93 cells/μL, respectively, and the median viral loads were 147 000 and 266 000 copies/mL, respectively. Compared with HIV–TB+, the relative risks (RRs) for an unfavorable response in the coinfected, non-HAART and HAART groups were 2.1 (95% confidence interval [CI], 1.7–14.8; P<.0001) and 2.1 (95% CI, .9–5.2; P=.3), whereas for ARR, the RRs were 21.1 (95% CI, 2.6–184; P<.001) and 8.2 (95% CI, .6–104; P=.07), respectively. Conclusions: HIV-infected patients with tuberculosis treated with a thrice-weekly antituberculosis regimen are at a higher risk of ARR, compared with HIV-uninfected patients, in the presence of baseline isoniazid resistance. HAART reduces but does not eliminate the risk of ARR

Application of a Household-Based Molecular Xenomonitoring Strategy to Evaluate the Lymphatic Filariasis Elimination Program in Tamil Nadu, India

Lymphatic filariasis (LF) is one of the world’s foremost debilitating infectious diseases with nearly 800 million people at risk of infection. Given that LF is a mosquito-borne disease, the use of molecular xenomonitoring (MX) to detect parasite DNA/RNA in mosquitoes can serve as a valuable tool for LF monitoring and evaluation, particularly in Culexvector areas. We investigated using MX in a low-level prevalence district of Tamil Nadu, India by applying a household-based sampling strategy to determine trap location sites. Two independent mosquito samples were collected in each of a higher human infection hotspot area (sites with community microfilaria prevalence �1%) and across a larger evaluation area that also encompassed the hotspots. Pooled results showed mostly reproducible outcomes in both settings and a significant higher pool positivity in the hotspot area. A follow-up survey conducted two years later reconfirmed these findings while also showing a reduction in pool positivity and estimated prevalence of infection in mosquitoes in both settings. The utilization of a household-based sampling strategy for MX proved effective and should be further validated in wider epidemiological settings

Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases; (i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods

Assessment of CI Engine Performance and Exhaust Air Quality Outfitted with Real-Time Emulsion Fuel Injection System

The main target of the current research work is effectively eliminating fossil fuel dependency and improving the exhaust air quality of conventional Compression Ignition (CI) engines. This research paper demonstrates for the first time that a nanofluid (water without surfactant) stored in separate tanks can be quantified, collected, and immediately emulsified by a high shear mixer before transfer into the combustion chamber of a diesel engine. The experiment was carried out under different load states (25%, 50%, 75% and 100%) with a constant speed of 1500 rpm. Biofuel was extracted from citronella leaves using an energy-intensive process. The 5% water share was used for preparing the biofuel emulsion and nano-biofuel emulsion. A cobalt chromate nanoadditive was used to make the nanofluid. An experimental investigation was performed with prepared test fuels, namely, ultra-low sulphur diesel (ULSD), 100% Citronella (B100), surfactant-free Diesel emulsion (SDE), surfactant-free bioemulsion (SBE), and Surfactant free nano-bioemulsion (SNBE), in a test engine. The properties of the sample test fuels was ensured according to EN and ASTM standards. The observation performance results show that the SNBE blend exhibited lower BTE (by 0.5%) and higher SFC (by 3.4%) than ULSD at peak load. The emission results show that the SNBE blend exhibited lower HC, CO, NOx, and smoke emissions by 23.86%, 31.81%, 2.94%, and 24.63%, respectively, compared to USD at peak load. The CP and HRR results for SNBE were closer to ULSD fuel. Overall, the novel concept of an RTEFI (Real-time emulsion fuel injection) system was proved to be workable and to maintain its benefits of better fuel economy and greener emissions

Experimental and Theoretical Investigations of Different Diketopyrrolopyrrole-Based Polymers

Diketopyrrolopyrrole (DPP)-based polymers are often considered as the most promising donor moiety in traditional bulk heterojunction solar cell devices. In this paper, we report the synthesis, characterization of various DPP-based copolymers with different molecular weights, and polydispersity where other aromatic repeating units (phenyl or thiophene based) are connected by alternate double bonds or triple bonds. Some of the copolymers were used for device fabrication and the crucial parameters such as fill factor (FF) and open circuit voltage (Voc) were calculated. The density functional theory was used to optimize the geometries and deduce highest occupied molecular orbital–lowest unoccupied molecular orbital gaps of all the polymers and theoretically predict their optical and electronic properties. Optical properties of all the polymers, electrochemical properties, and band gaps were also obtained experimentally and compared with the theoretically predicted values

Challenges and opportunities of Low Viscous Biofuel- a prospective review

Under the roof of solid industrialization and accelerated intensification of multiple ranges of mobilization, a huge rise in precious fuel consumption and pollution was observed. Based on the recent hardships of fossil fuels, experts are undoubtedly eager in carrying out their research in renewable environment-friendly fuels. There have been many reviews of works considering the parameters and standards of biodiesel, which is only from various vegetable and seed oils. But very little review work was carried out on only plant-based biofuel. Plant-based fuel has a lower viscosity and higher volatility properties. The target of this review was to make a bridge to overcome these research gaps. This review extensively studies the biological background, production outcome, properties, and reliability of plant-based biofuel and also deeply investigates the feasibility of usage in a diesel engine. From deep investigation, it was identified that most of the low viscous fuel had higher brake thermal efficiency (BTE) (2% to 4%) and NOx emission (5% to 10%) than high viscous biodiesel. The formation of hydrocarbon (HC), CO, and smoke emission was similar to high viscous biodiesel. Overall, the low viscous fuel effectively improves the engine behaviors