A method to design a fast chaotic oscillator using CCTA.

This article introduces a novel method for designing a fast chaotic oscillator using a CCTA (Current Conveyor Transconductance Amplifier) based on Chua's circuit. The proposed method uses innovative configurations and advanced simulation techniques to overcome challenges in high-speed operation, nonlinear dynamics, and Analog Building Block (ABB) selection. The design begins with nonlinear negative resistance, essential for Chua's diode characteristics, including two negative resistances, NR1 and NR2. The circuit integrates one CCTA block, two grounded capacitors, two fixed resistors, one inductor, and one potentiometer. It is simulated using PSPICE with IC (Integrated Circuit) macro-models and 180nm CMOS (Complementary Metal Oxide Semiconductor) technology. Various chaotic waveforms and attractors are produced, validating the theoretical and mathematical predictions. By varying the resistance values (1450Ω, 1650Ω, 1800Ω, 1950Ω), the circuit exhibits different chaotic behaviors, such as large limit cycles, double-scroll attractors, Rossler-type attractors, and I-periodic attractors. FFT (Fast Fourier Transform) analysis confirms the highest dominant operating frequency of 37.5MHz. A Monte Carlo simulation with 100 runs shows maximum voltage variations in the chaotic waveforms of 5.21 % and 4.61 % across the capacitors, demonstrating robustness and reliability. This design offers significant advancements in implementing high-frequency chaotic oscillators, with potential applications in various fields requiring chaotic signal generation.•A novel design of Chua's diode and Chua's chaotic oscillator using only one CCTA block is presented in this paper.•The proposed chaotic oscillator achieves the highest operating frequency of 37.5MHz.•The proposed circuit is simulated using commercially available ICs (MAX435 and AD844) and CMOS 180nm technology in PSPICE to confirm its workability.

Clinical and Microbiological Evaluation of the Chemomechanical Caries Removal Agents in Primary Molars.

BACKGROUND: Chemomechanical caries removal (CMCR) is a noninvasive procedure that uses a chemical substance to remove the diseased dentin. The natural dental architecture is also preserved using this technique, preventing patient discomfort and pulp irritation. This method of eliminating caries is based on disintegration. This method removes soft carious dentin using chemical agents and non-traumatic mechanical force. This study was carried out to evaluate the clinical and microbiological evaluation of the chemomechanical caries removal agents in primary molars. METHODS: For the elimination of caries, teeth in category I (polymer bur category) were treated with Smartburs II® (SS White Dental, Lakewood, New Jersey, United States) (n = 40). Teeth treated with the new Carisolv® technology (Mediteam, Sweden) to remove caries (n = 40). Teeth were treated for removal cavities in category III (conventional group) using excavators and carbon steel low-speed round burs from Dentsply Maillefer in Switzerland (n = 40). There was an evaluation of the efficacy of caries removal, the time required for caries removal, patient satisfaction, and microbial culture assessment. RESULTS: In comparison to the other two categories, the conventional category median caries detector dye values were considerably lower (p value<0.05). There was no substantial difference observed between study participants belonging to category I and category II with a p-value greater than 0.05. Time taken during the removal of caries was substantially greater in category I (455.46±73.7) as compared to category II (129.21±44.18) and the traditional category (113.26±37.7). The value of significance was less than 0.05. Contrarily, no discernible difference was observed between category I and category III (the p-value was greater than 0.05). In comparison to the other two categories, the conventional group's median facial expression scale scores were substantially higher (p = 0.02). In comparison, there was no discernible difference between categories I and II (p = 0.65). It was observed that there was no substantial variation in three categories regarding the number of colonies of bacteria prior to the eradication of caries (p-value greater than 0.05). After caries had been removed, the number of living bacterial colonies in category I was noticeably greater than those in the other two categories. However, there was no discernible variation between category II and category 1 (p-value greater than 0.05). CONCLUSION: The mechanical approach has the highest clinical efficacy for removing caries. Carisolv required the most time to remove cavities. Patient satisfaction levels were greater with Carisolv than with the mechanical approach. It was also observed that Carisolv as well as the mechanical technique had greater antibacterial effectiveness.

MODIS-Landsat fusion-based single-band algorithms for TSS and turbidity estimation in an urban-waste-dominated river reach.

Riverine ecosystem management along an urban stretch mostly depends on high-frequent (daily-scale) monitoring of water quality at finer spatial resolutions. However, with the decrease in the number of in-situ monitoring stations owing to their expensive maintenance cost, there is a need to develop the next-generation remote sensing (RS) tools as an alternate approach with better synoptic coverage of river water quality assessment. This study advocates three novel model variants to estimate the total suspended solids (TSS) concentration at daily-scale using the public-domain MODIS and Landsat satellite datasets. The MODT model variant uses the 1-day×250 m MODIS public domain datasets, and the FUST model is based on the 1-day×30 m MODIS-Landsat fusion datasets, whereas the CFUST model integrates the Frank Copula with the FUST model. These hierarchical model variants are assessed in the urban-waste-dominated lower Ganges, namely the Hooghly River and the Brahmani River, in eastern India using the measured in-situ TSS datasets at multiple monitoring stations from 2016 to 2019. The results reveal that the CFUST is the best TSS estimation model variant that performs with the average coefficient of determination of 0.88-0.93, mean absolute error of 0.17-0.19, and normal root mean square error of 0.05-0.09. Conclusively, the proposed CFUST and CFUSTU stochastic models can be used as potential tools for TSS and turbidity assessment along the dynamic river systems, respectively.

Integrated remote sensing and machine learning tools for estimating ecological flow regimes in tropical river reaches.

With the gradual declining streamflow gauging stations in many world-rivers, emphasis is given nowadays to develop remote sensing (RS)-based approaches as the next-generation hydrometry for estimating riverine ecological flow regimes (EFR). For constructing EFR based on daily-streamflow data in scantily-gauged reaches, use of RS techniques in narrow flow-width tropical rain-fed rivers is constrained with the non-availability of finer spatial satellite data at daily scale. To address these limitations, this study proposes a novel framework that integrates the enhanced spatiotemporal adaptive reflectance fusion (FUS) of the 250 m × 1-day resolution Aqua-MODIS and 30 m × 1-day resolution Landsat satellite-based remote sensing images in the near-infrared region with the machine learning algorithms. These developed frameworks are named as Artificial Neural Network-based ANNFUS, Random Forest Regression-based RFRFUS, and Support Vector Regression-based SVRFUS models, which were tested for daily-scale streamflow estimation in a typical Brahmani River Basin, India. The results reveal that by addressing the linear and nonlinear dynamism between the streamflow and satellite signals, all the developed models could simulate the streamflow very well with the Nash-Sutcliffe efficiency>0.8, Kling-Gupta efficiency>0.8, relative root mean square error (rRMSE) of 0.051-0.12, and normalized RMSE of 0.23-0.36. However, for reproducing the high, median, and low streamflow regimes, the SVRFUS model was found to be the best with the NSE>0.85 and KGE>0.8. Conclusively, the proposed approach is found to have the potential to be replicated in other world-river basins to estimate ecological flow regimes at defunct gauging stations facilitating the basin-scale aquatic environmental management.

Robust sensor placement for sustainable leakage management in water distribution networks of developing economies: A hybrid decision support framework.

Urban water distribution networks (WDNs) in developing economies often refrain from investing in sensor-based leakage management technologies due to financial constraints and other techno-managerial issues. Thus, this study proposes a generalized decision support framework based on network sensitivity analysis (NSA) and multi-criteria decision-making (MCDM) to assess the prospect of effective leakage control through robust sensor placement in existing deficient WDNs. Four sensitivity parameters are formulated for NSA to ascertain the pressure response of the potential sensor positions for diverse hydraulic and leak scenarios. Subsequently, selecting the optimal number of sensors and their relative positions within the WDN is framed as an MCDM problem that entails the simultaneous maximization of Euclidean distances among the potential sensor positions and the leak-induced pressure residuals obtained at these sensors. The proposed methodology is developed on a numerical benchmark network assuming ideal conditions, and its applicability is verified on a sensor-equipped experimental network considering realistic system uncertainties. The outcome of this study aims to provide an insightful understanding of the system behavior that governs its leak localization potential and ascertain the practical challenges of sensor-based leakage monitoring in existing WDNs. Decision-makers of resource-strained utilities can beneficially utilize the proposed framework to assess the environmental and cost trade-offs of employing sensor-based technologies for leakage management and proactive decision-making before its actual implementation.

Scaling up the Digitalization of the Public Distribution System: A COVID-19 Pandemic Impetus.

Food grain is an essential commodity that requires proper procurement, transportation, storage, and distribution with easy access to needy people. Therefore, The Indian government has established a Public Distribution System (PDS) to distribute the food grain across the country. This study emphasizes the need for digitalization of PDS and how the COVID-19 accelerates the digitalization of PDS. We proposed a three-layer conceptual framework: food grain supply chain network, digital processes automation and digital technologies. We collaborate the three-layer perspectives to ensure food security during the COVID-19 pandemic and reduce the food grain leakage in PDS. This study also supports the United Nations Sustainable Development Goals, and National Food Security Act aims to achieve the Zero Hunger goal. The proposed research will assist policymakers, institutions, and the government in implementing the digitalization of PDS for greater efficiency and transparency during a pandemic. Furthermore, this work may help to achieve the ambitious "Digital India" programme, which aims to improve India's online infrastructure and electronic services.

A review on environmental occurrence, toxicity and microbial degradation of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs).

In recent years, Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) have surfaced as a novel class of pollutants due to their incomplete degradation in wastewater treatment plants and their inherent ability to promote physiological predicaments in humans even at low doses. The occurrence of the most common NSAIDs (diclofenac, ibuprofen, naproxen, and ketoprofen) in river water, groundwater, finished water samples, WWTPs, and hospital wastewater effluents along with their toxicity effects were reviewed. The typical concentrations of NSAIDs in natural waters were mostly below 1 µg/L, the rivers receiving untreated wastewater discharge have often showed higher concentrations, highlighting the importance of effective wastewater treatment. The critical analysis of potential, pathways and mechanisms of microbial degradation of NSAIDs were also done. Although studies on algal and fungal strains were limited, several bacterial strains were known to degrade NSAIDs. This microbial ability is attributed to hydroxylation by cytochrome P450 because of the decrease in drug concentrations in fungal cultures of Phanerochaete sordida YK-624 on incubation with 1-aminobenzotriazole. Moreover, processes like decarboxylation, dehydrogenation, dechlorination, subsequent oxidation, demethylation, etc. also constitute the degradation pathways. A wide array of enzymes like dehydrogenase, oxidoreductase, dioxygenase, monooxygenase, decarboxylase, and many more are upregulated during the degradation process, which indicates the possibility of their involvement in microbial degradation. Specific hindrances in upscaling the process along with analytical research needs were also identified, and novel investigative approaches for future monitoring studies are proposed.

Significance of different mixing conditions on performance and microbial communities in anaerobic digester amended with granular and powdered activated carbon.

Conductive materials amendment in anaerobic digestion (AD) is a promising strategy for boosting the methanogenesis process. Despite mixing is a critical parameter, the behavior of digesters amended with conductive additives upon different mixing conditions has rarely been investigated. This study investigated continuous mixing, intermittent mixing (10 min in every 12 h), and non-mixing conditions for digesters amended with granular activated carbon (GAC) and powdered activated carbon (PAC). The non-mixed GAC digester provided the highest methane yield (318 ± 28 mL/g COD) from synthetic blackwater, while intermittently mixed GAC and control exhibited similar methane yields (290-294 mL/g COD). For non-mixed systems, microbial richness and diversity increased with GAC and PAC amendment. In contrast, continuous and intermittent mixing increased microbial diversity and richness in control reactors while reduced the same in GAC and PAC amended reactors. Overall, various mixing conditions distinctly changed the degree of enrichment/retention of microbes and consequently influenced methane recovery.

Hydraulic performance benchmarking for effective management of water distribution networks: An innovative composite index-based approach.

Hydraulic performance assessment and benchmarking of water distribution networks (WDNs) impose a major challenge to water utilities worldwide. Presently, benchmarking strategies for WDNs are not fully developed, especially for analyzing intermittent systems commonly encountered in non-developed nations. To overcome these limitations, this paper proposes an index-based benchmarking strategy for WDNs, comparing their actual hydraulic performance and expected serviceability. A robust Hydraulic Performance Index (HPI) is developed as a global metric to account for the combined impact of multiple hydraulic outputs, concerning their benchmark values. The applicability of this index is verified on a numerical benchmark network, and its usefulness is demonstrated on a real-world intermittent WDN located in Kolkata (India) by coupling the HPI-based framework with hydraulic models using the EPANET-MATLAB programmer's toolkit. A scenario-based analysis is conducted using extended-period simulation to obtain the HPI for diverse service levels and leakage conditions of the WDN models. The HPI is designed to effectively capture the localized pressure reduction during peak flow, prioritize hydraulic outputs based on regional constraints, and penalize systems with unsustainably high hydraulic output. The developed strategy is also effective in performance benchmarking of WDNs of different nations with diverse serviceability and threshold parameters on a common platform. Finally, the practical efficacy and generalizability of the HPI-based results in the context of case-specific performance management of WDNs, along with limitations, recommendations and future perspectives are elucidated upon.

High-rate blackwater anaerobic digestion under septic tank conditions with the amendment of biosolids-derived biochar synthesized at different temperatures.

Septic tanks have been widely used for blackwater treatment in developing countries, while high-rate septic tanks with improved methane recovery are yet to be achieved. This study investigated biosolids-derived biochar (synthesized at 300℃, 425℃, and 550℃) as an additive for developing high-rate septic tanks. The experiments were conducted with anaerobic bioreactors operated with synthetic blackwater under septic tank conditions. All biochar amended reactors demonstrated a steady increase in daily methane production for increasing OLR from 0.08 to 3 g COD/L/d. The control reactor showed significant process disturbances at OLRs ≥ 2 g COD/L/d with an accumulation of volatile fatty acids followed by pH drop. At OLR of 3 g COD/L/d, the daily methane production from biochar amended reactors was ~ 4.3 times higher than the control (300 vs. 70 mL per day). Biochar addition established a robust microbiome consisted of a higher abundance of hydrogenotrophic and acetoclastic methanogens and hydrogen-producing fermentative bacteria.

Identifying Myocardial Infarction Using Hierarchical Template Matching-Based Myocardial Strain: Algorithm Development and Usability Study.

BACKGROUND: Myocardial infarction (MI; location and extent of infarction) can be determined by late enhancement cardiac magnetic resonance (CMR) imaging, which requires the injection of a potentially harmful gadolinium-based contrast agent (GBCA). Alternatively, emerging research in the area of myocardial strain has shown potential to identify MI using strain values. OBJECTIVE: This study aims to identify the location of MI by developing an applied algorithmic method of circumferential strain (CS) values, which are derived through a novel hierarchical template matching (HTM) method. METHODS: HTM-based CS H-spread from end-diastole to end-systole was used to develop an applied method. Grid-tagging magnetic resonance imaging was used to calculate strain values in the left ventricular (LV) myocardium, followed by the 16-segment American Heart Association model. The data set was used with k-fold cross-validation to estimate the percentage reduction of H-spread among infarcted and noninfarcted LV segments. A total of 43 participants (38 MI and 5 healthy) who underwent CMR imaging were retrospectively selected. Infarcted segments detected by using this method were validated by comparison with late enhancement CMR, and the diagnostic performance of the applied algorithmic method was evaluated with a receiver operating characteristic curve test. RESULTS: The H-spread of the CS was reduced in infarcted segments compared with noninfarcted segments of the LV. The reductions were 30% in basal segments, 30% in midventricular segments, and 20% in apical LV segments. The diagnostic accuracy of detection, using the reported method, was represented by area under the curve values, which were 0.85, 0.82, and 0.87 for basal, midventricular, and apical slices, respectively, demonstrating good agreement with the late-gadolinium enhancement-based detections. CONCLUSIONS: The proposed applied algorithmic method has the potential to accurately identify the location of infarcted LV segments without the administration of late-gadolinium enhancement. Such an approach adds the potential to safely identify MI, potentially reduce patient scanning time, and extend the utility of CMR in patients who are contraindicated for the use of GBCA.

Morphological and elemental mapping of gallstones using synchrotron microtomography and synchrotron X-ray fluorescence spectroscopy.

BACKGROUND AND AIM: Regional differences in gallstone (GS) composition are well documented in the Indian subcontinent. The reasons for the same are unknown. Etiopathogenesis of GS remains elusive despite advances in instrumentation. This was an in-depth analysis of the chemical, structural, and elemental composition of GS with special reference to synchroton studies. METHODS: We used high-end sensitive analytical complementary microscopic and spectroscopic methods techniques, such as X-ray diffraction, scanning electron microscopy, Fourier transform infrared, synchrotron X-ray fluorescence spectroscopy (SR-XRF), and 2D and 3D synchrotron microtomography (SR-µCT), to study the ultra structure and trace element composition of three major types of GS (cholesterol, mixed, and pigment). SR-XRF quantified the trace elements in GS. RESULTS: The cholesterol GS (monohydrate and anhydrate) were crystalline, with high calcium content. The pigment GS were amorphous, featureless, black, and fragile, with high calcium bilirubinate and carbonate salts. They had the highest concentration of iron (average 31.50 ppm) and copper (average 92.73 ppm), with bacterial inclusion. The mixed stones had features of both cholesterol and pigment GS with intermediate levels of copper (average 20.8 ppm) and iron (average 17.78 ppm). CONCLUSION: SR-µCT has, for the first time, provided cross-sectional computed imaging delineating the framework of GS and mineral distribution. It provided excellent mapping of cholesterol GS. SR-XRF confirmed that pigment GS had high concentrations of copper and iron with bacterial inclusions, the latter possibly serving as a nidus to the formation of these stones.

Hierarchical Template Matching for 3D Myocardial Tracking and Cardiac Strain Estimation.

Myocardial tracking and strain estimation can non-invasively assess cardiac functioning using subject-specific MRI. As the left-ventricle does not have a uniform shape and functioning from base to apex, the development of 3D MRI has provided opportunities for simultaneous 3D tracking, and 3D strain estimation. We have extended a Local Weighted Mean (LWM) transformation function for 3D, and incorporated in a Hierarchical Template Matching model to solve 3D myocardial tracking and strain estimation problem. The LWM does not need to solve a large system of equations, provides smooth displacement of myocardial points, and adapt local geometric differences in images. Hence, 3D myocardial tracking can be performed with 1.49 mm median error, and without large error outliers. The maximum error of tracking is up to 24% reduced compared to benchmark methods. Moreover, the estimated strain can be insightful to improve 3D imaging protocols, and the computer code of LWM could also be useful for geo-spatial and manufacturing image analysis researchers.

Mine sludge waste recycling as bio-stimulant for applications in anaerobic wastewater treatment.

This study examined the applicability of two mine sludge wastes, mine tailing sludge (MTS) and acid mine drainage sludge (AMDS) as iron-rich bio-stimulant for enhancing organic matter degradation in anaerobic process. Batch treatment of domestic sewage having 343 ± 10 mg/L chemical oxygen demand (COD) using MTS and AMDS as additives mixed with septic tank sludge as anaerobic inoculum produced lower start-up time, higher efficiency of COD removal, enhanced biomass retention, and higher acidogenic and methanogenic activity after stabilization. Biostimulation induced by mine sludge waste additives in anaerobic system were observed to have correlation with percentage of iron content in the additives, as well as difference in surface charge between biomass and the additives. Treatment efficiency induced by the two mine sludge waste based additives were similar at 90% confidence limit, however, was found to be higher than lower iron containing additive laterite soil, while lower than higher iron containing synthetic zero valent nano iron as additives used for comparison. The study was supported by scanning electron microscope, atomic force microscope and optical microscope images of sludge granule sand surface charge measurement.

Markov random field segmentation for industrial computed tomography with metal artefacts.

X-ray Computed Tomography (XCT) has become an important tool for industrial measurement and quality control through its ability to measure internal structures and volumetric defects. Segmentation of constituent materials in the volume acquired through XCT is one of the most critical factors that influence its robustness and repeatability. Highly attenuating materials such as steel can introduce artefacts in CT images that adversely affect the segmentation process, and results in large errors during quantification. This paper presents a Markov Random Field (MRF) segmentation method as a suitable approach for industrial samples with metal artefacts. The advantages of employing the MRF segmentation method are shown in comparison with Otsu thresholding on CT data from two industrial objects.

A Novel Hierarchical Template Matching Model for Cardiac Motion Estimation.

Cardiovascular disease diagnosis and prognosis can be improved by measuring patient-specific in-vivo local myocardial strain using Magnetic Resonance Imaging. Local myocardial strain can be determined by tracking the movement of sample muscles points during cardiac cycle using cardiac motion estimation model. The tracking accuracy of the benchmark Free Form Deformation (FFD) model is greatly affected due to its dependency on tunable parameters and regularisation function. Therefore, Hierarchical Template Matching (HTM) model, which is independent of tunable parameters, regularisation function, and image-specific features, is proposed in this article. HTM has dense and uniform points correspondence that provides HTM with the ability to estimate local muscular deformation with a promising accuracy of less than half a millimetre of cardiac wall muscle. As a result, the muscles tracking accuracy has been significantly (p < 0.001) improved (30%) compared to the benchmark model. Such merits of HTM provide reliably calculated clinical measures which can be incorporated into the decision-making process of cardiac disease diagnosis and prognosis.

An IAEA multi-technique X-ray spectrometry endstation at Elettra Sincrotrone Trieste: benchmarking results and interdisciplinary applications.

The International Atomic Energy Agency (IAEA) jointly with the Elettra Sincrotrone Trieste (EST) operates a multipurpose X-ray spectrometry endstation at the X-ray Fluorescence beamline (10.1L). The facility has been available to external users since the beginning of 2015 through the peer-review process of EST. Using this collaboration framework, the IAEA supports and promotes synchrotron-radiation-based research and training activities for various research groups from the IAEA Member States, especially those who have limited previous experience and resources to access a synchrotron radiation facility. This paper aims to provide a broad overview about various analytical capabilities, intrinsic features and performance figures of the IAEA X-ray spectrometry endstation through the measured results. The IAEA-EST endstation works with monochromatic X-rays in the energy range 3.7-14 keV for the Elettra storage ring operating at 2.0 or 2.4 GeV electron energy. It offers a combination of different advanced analytical probes, e.g. X-ray reflectivity, X-ray absorption fine-structure measurements, grazing-incidence X-ray fluorescence measurements, using different excitation and detection geometries, and thereby supports a comprehensive characterization for different kinds of nanostructured and bulk materials.

Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang.

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction.

High pre-diagnosis attrition among patients with presumptive MDR-TB: an operational research from Bhopal district, India.

BACKGROUND: Pre-diagnosis attrition needs to be addressed urgently if we are to make progress in improving MDR-TB case detection and achieve universal access to MDR-TB care. We report the pre-diagnosis attrition, along with factors associated, and turnaround times related to the diagnostic pathway among patient with presumptive MDR-TB in Bhopal district, central India (2014). METHODS: Study was conducted under the Revised National Tuberculosis Control Programme setting. It was a retrospective cohort study involving record review of all registered TB cases in Bhopal district that met the presumptive MDR-TB criteria (eligible for DST) in 2014. In quarter 1, Line Probe Assay (LPA) was used if sample was smear/culture positive. Quarter 2 onwards, LPA and Cartridge-based Nucleic Acid Amplification Test (CbNAAT) was used for smear positive and smear negative samples respectively. Pre-diagnosis attrition was defined as failure to undergo DST among patients with presumptive MDR-TB (as defined by the programme). RESULTS: Of 770 patients eligible for DST, 311 underwent DST and 20 patients were diagnosed as having MDR-TB. Pre-diagnosis attrition was 60% (459/770). Among those with pre-diagnosis attrition, 91% (417/459) were not identified as 'presumptive MDR-TB' by the programme. TAT [median (IQR)] to undergo DST after eligibility was 4 (0, 10) days. Attrition was more than 40% across all subgroups. Age more than 64 years; those from a medical college; those eligible in quarter 1; patients with presumptive criteria 'previously treated - recurrent TB', 'treatment after loss-to-follow-up' and 'previously treated-others'; and patients with extra-pulmonary TB were independent risk factors for not undergoing DST. CONCLUSION: High pre-diagnosis attrition was contributed by failure to identify and refer patients. Attrition reduced modestly with time and one factor that might have contributed to this was introduction of CbNAAT in quarter 2 of 2014. General health system strengthening which includes improvement in identification/referral and patient tracking with focus on those with higher risk for not undergoing DST is urgently required.