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The Indian way of life celebrates products made with the help of simple, indigenous tools by craftspeople with a strong fabric of tradition, aesthetic and artistry. The range of Indian handicrafts is as rich and varied as the country's cultural diversity.

Visual leopard identifications performed with camera traps using the capture–recapture method only consider areas of the skin that are visible to the equipment. The method presented here considered the spot or rosette formations of either the two flanks or the face, and the captured images were then compared and matched with available photographs. Leopards were classified as new individuals if no matches were found in the existing set of photos. It was previously assumed that an individual leopard’s spot or rosette pattern would not change. We established that the spot and rosette patterns change over time and that these changes are the result of injuries in certain cases. When compared to the original patterns, the number of spots may be lost or reduced, and some spots or patterns may change in terms of their prominence, shape, and size. We called these changes “obliterate changes” and “rejig changes”, respectively. The implementation of an earlier method resulted in a duplication of leopard counts, achieving an error rate of more than 15% in the population at Yala National Park. The same leopard could be misidentified and counted multiple times, causing overestimated populations. To address this issue, we created a new two-step methodology for identifying Sri Lankan leopards. The multi-point identification method requires the evaluation of at least 9–10 spot areas before a leopard can be identified. Moreover, the minimum leopard population at the YNP 1 comprises at least 77 leopards and has a density of 0.5461 leopards per km2.

Corex is an alternative ironmaking process and raceway is one of the important areas to maintain the stability of the furnace. The raceway parameters are well established for blast furnace operation. But for Corex process, it has not yet been established and optimized. Thus, a mathematical model was developed to determine various raceway parameters such as RAFT (raceway adiabatic flame temperature), tuyere gas velocity and kinetic energy. The model provides an idea about the raceway geometry, zone temperature and kinetic energy accumulated in tuyere gas. Besides, all the raceway parameters have been analyzed to find out their effects on the Corex process. It is found that RAFT influences the gasification reaction kinetics and higher RAFT generates more CO in reduction gas, which improves the metallisation degree of the DRI in shaft. It is also found that increased gas velocity and kinetic energy generate more fines and demand more coke to maintain char bed permeability. High coke rate increases the production cost and lowers the production of hot metal.

In the primary analysis of the pivotal JULIET trial of tisagenlecleucel, an autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy, the best overall response rate was 52% and the complete response rate was 40% in 93 evaluable adult patients with relapsed or refractory aggressive B-cell lymphomas. We aimed to do a long-term follow-up analysis of the clinical outcomes and correlative analyses of activity and safety in the full adult cohort. In this multicentre, open-label, single-arm, phase 2 trial (JULIET) done at 27 treatment sites in ten countries (Australia, Austria, Canada, France, Germany, Italy, Japan, the Netherlands, Norway, and the USA), adult patients (≥18 years) with histologically confirmed relapsed or refractory large B-cell lymphomas who were ineligible for, did not consent to, or had disease progression after autologous haematopoietic stem-cell transplantation, with an Eastern Cooperative Oncology Group performance status of 0–1 at screening, were enrolled. Patients received a single intravenous infusion of tisagenlecleucel (target dose 5 × 108 viable transduced CAR T cells). The primary endpoint was overall response rate (ie, the proportion of patients with a best overall disease response of a complete response or partial response using the Lugano classification, as assessed by an independent review committee) at any time post-infusion and was analysed in all patients who received tisagenlecleucel (the full analysis set). Safety was analysed in all patients who received tisagenlecleucel. JULIET is registered with ClinialTrials.gov, NCT02445248, and is ongoing. Between July 29, 2015, and Nov 2, 2017, 167 patients were enrolled. As of Feb 20, 2020, 115 patients had received tisagenlecleucel infusion and were included in the full analysis set. At a median follow-up of 40·3 months (IQR 37·8–43·8), the overall response rate was 53·0% (95% CI 43·5–62·4; 61 of 115 patients), with 45 (39%) patients having a complete response as their best overall response. The most common grade 3–4 adverse events were anaemia (45 [39%]), decreased neutrophil count (39 [34%]), decreased white blood cell count (37 [32%]), decreased platelet count (32 [28%]), cytokine release syndrome (26 [23%]), neutropenia (23 [20%]), febrile neutropenia (19 [17%]), hypophosphataemia (15 [13%]), and thrombocytopenia (14 [12%]). The most common treatment-related serious adverse events were cytokine release syndrome (31 [27%]), febrile neutropenia (seven [6%]), pyrexia (six [5%]), pancytopenia (three [3%]), and pneumonia (three [3%]). No treatment-related deaths were reported. Tisagenlecleucel shows durable activity and manageable safety profiles in adult patients with relapsed or refractory aggressive B-cell lymphomas. For patients with large B-cell lymphomas that are refractory to chemoimmunotherapy or relapsing after second-line therapies, tisagenlecleucel compares favourably with respect to risk–benefit relative to conventional therapeutic approaches (eg, salvage chemotherapy). Novartis Pharmaceuticals.

The financial time series data is a highly nonlinear signal and hence difficult to predict precisely. The prediction accuracy can be improved by linearizing the signal. In this paper, the nonlinear data sample is linearized by decomposing it into several Intrinsic Mode Functions (IMFs). A hybrid multi-layer decomposition technique is developed. The decomposition method proposed in this paper is composed of both Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) methods individually. As a new contribution to the previous literature in this study, the VMD is used to decompose further the higher frequency signals obtained from the EMD-based decomposed signal. The result analysis shows that the double decomposition technique improves prediction accuracy. This is a new introduction to the field of stock market prediction. The prediction accuracy of the proposed model is verified by applying it to three different stock market data. Historical data (closing price) is implemented to obtain one day ahead predicted closing price. Comparative analysis of other previously implemented methods like Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Learning Machine (ELM), along with the proposed method, is performed. Firefly algorithm is implemented for optimizing the kernel factors. It is observed that the proposed hybrid model outperformed other methods discussed in this study.

Commonly used for Parkinson’s disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the large number of possible stimulation settings (i.e., programming) requires numerous clinic visits. Here, we examine whether functional magnetic resonance imaging (fMRI) can be used to predict optimal stimulation settings for individual patients. We analyze 3 T fMRI data prospectively acquired as part of an observational trial in 67 PD patients using optimal and non-optimal stimulation settings. Clinically optimal stimulation produces a characteristic fMRI brain response pattern marked by preferential engagement of the motor circuit. Then, we build a machine learning model predicting optimal vs. non-optimal settings using the fMRI patterns of 39 PD patients with a priori clinically optimized DBS (88% accuracy). The model predicts optimal stimulation settings in unseen datasets: a priori clinically optimized and stimulation-naïve PD patients. We propose that fMRI brain responses to DBS stimulation in PD patients could represent an objective biomarker of clinical response. Upon further validation with additional studies, these findings may open the door to functional imaging-assisted DBS programming. Deep brain stimulation programming for Parkinson’s disease entails the assessment of a large number of possible simulation settings, requiring numerous clinic visits after surgery. Here, the authors show that patterns of functional MRI can predict the optimal stimulation settings.

We present a formally exact and simulation-free approach for the normalization of X-ray Thomson scattering (XRTS) spectra based on the f-sum rule of the imaginary-time correlation function (ITCF). Our method works for any degree of collectivity, over a broad range of temperatures, and is applicable even in nonequilibrium situations. In addition to giving us model-free access to electronic correlations, this new approach opens up the intriguing possibility to extract a plethora of physical properties from the ITCF based on XRTS experiments.

The study investigates the impact of Phase Change Material (PCM) and nano Phase Change Materials (NPCM) on solar still performance. PCM and a blend of NPCM are placed within 12 copper tubes submerged in 1 mm of water to enhance productivity. Thermal performance is assessed across four major scenarios with a fixed water level of 1 mm in the basin. These scenarios include the conventional still, equipped with 12 empty copper rods and 142 g of PCM in each tube, as well as stills with NPCM Samples 1 and 2. Sample 1 contains 0.75% nanoparticle concentration plus 142 g of PCM in the first 6 tubes, while Sample 2 features 2% nanoparticle concentration plus 142 g of PCM in the subsequent 6 tubes. Aluminum oxide (Al2O3) nanoparticles ranging in size from 20 to 30 nm are utilized, with paraffin wax (PW) serving as the latent heat storage (LHS) medium due to its 62 °C melting temperature. The experiments are conducted under the local weather conditions of Vaddeswaram, Vijayawada, India (Latitude-80.6480 °E, Longitude-16.5062 °N). A differential scanning calorimeter (DSC) is utilized to examine the thermal properties, including the melting point and latent heat fusion, of the NPCM compositions. Results demonstrate that the addition of nanoparticles enhances both the specific heat capacity and latent heat of fusion (LHF) in PCM through several mechanisms, including facilitating nucleation, improving energy absorption during phase change, and modifying crystallization behavior within the phase change material. Productivity and efficiency measurements reveal significant improvements: case 1 achieves 2.66 units of daily production and 46.23% efficiency, while cases 2, 3, and 4 yield 3.17, 3.58, and 4.27 units of daily production, respectively. Notably, the utilization of NPCM results in a 60.37% increase overall productivity and a 68.29% improvement in overall efficiency.

Background Since 2005, India has experienced an impressive 77% reduction in maternal mortality compared to the global average of 43%. What explains this impressive performance in terms of reduction in maternal mortality and improvement in maternal health outcomes? This paper evaluates the effect of household wealth status on maternal mortality in India, and also separates out the performance of the Empowered Action Group (EAG) states and the Southern states of India. The results are discussed in the light of various pro-poor programmes and policies designed to reduce maternal mortality and the existing supply side gaps in the healthcare system of India. Using multiple sources of data, this study aims to understand the trends in maternal mortality (1997–2017) between EAG and non EAG states in India and explore various household, economic and policy factors that may explain reduction in maternal mortality and improvement in maternal health outcomes in India. Methods This study triangulates data from different rounds of Sample Registration Systems to assess the trend in maternal mortality in India. It further analysed the National Family Health Surveys (NFHS). NFHS-4, 2015–16 has gathered information on maternal mortality and pregnancy-related deaths from 601,509 households. Using logistic regression, we estimate the association of various socio-economic variables on maternal deaths in the various states of India. Results On an average, wealth status of the households did not have a statistically significant association with maternal mortality in India. However, our disaggregate analysis reveals, the gains in terms of maternal mortality have been unevenly distributed. Although the rich-poor gap in maternal mortality has reduced in EAG states such as Bihar, Odisha, Assam, Rajasthan, the maternal mortality has remained above the national average for many of these states. The EAG states also experience supply side shortfalls in terms of availability of PHC and PHC doctors; and availability of specialist doctors. Conclusions The novel contribution of the present paper is that the association of household wealth status and place of residence with maternal mortality is statistically not significant implying financial barriers to access maternal health services have been minimised. This result, and India’s impressive performance with respect to maternal health outcomes, can be attributed to the various pro-poor policies and cash incentive schemes successfully launched in recent years. Community-level involvement with pivotal role played by community health workers has been one of the major reasons for the success of many ongoing policies. Policy makers need to prioritise the underperforming states and socio-economic groups within the states by addressing both demand-side and supply-side measures simultaneously mediated by contextual factors.

A fundamental property of higher eukaryotes that underpins their evolutionary success is stable cell-cell cohesion. Yet, how intrinsic cell rheology and stiffness contributes to junction stabilization and maturation is poorly understood. We demonstrate that localized modulation of cell rheology governs the transition of a slack, undulated cell-cell contact (weak adhesion) to a mature, straight junction (optimal adhesion). Cell pairs confined on different geometries have heterogeneous elasticity maps and control their own intrinsic rheology co-ordinately. More compliant cell pairs grown on circles have slack contacts, while stiffer triangular cell pairs favour straight junctions with flanking contractile thin bundles. Counter-intuitively, straighter cell-cell contacts have reduced receptor density and less dynamic junctional actin, suggesting an unusual adaptive mechano-response to stabilize cell-cell adhesion. Our modelling informs that slack junctions arise from failure of circular cell pairs to increase their own intrinsic stiffness and resist the pressures from the neighbouring cell. The inability to form a straight junction can be reversed by increasing mechanical stress artificially on stiffer substrates. Our data inform on the minimal intrinsic rheology to generate a mature junction and provide a springboard towards understanding elements governing tissue-level mechanics. How intrinsic cell properties such as stiffness contribute to cell-cell junction stabilization is not well described. Here they show that higher levels of intrinsic cell mechanics at the cortex, cytoskeleton and nucleus of neighboring cells promote junctional maturation.