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Abstract Over the years, prediction techniques for the highly variable angular velocity of the Earth represented by Earth's rotation (UT1-UTC) and length-of-day (LOD) have been continuously improved. This is because many applications like navigation, astronomy, space exploration, climate studies, timekeeping, disaster monitoring, and geodynamic studies, all rely on predictions of these Earth rotation parameters. They provide early warning of changes in the Earth's rotation, allowing various industries and scientific fields to operate more precisely and efficiently. Thus, in our study, we focused on short-term prediction for UT1-UTC (dUT1) and LOD. Our prediction approach is to combine machine learning (ML) technique with efficient evolutionary computation (EC) algorithms to achieve reliable and improved predictions. Gaussian process regression (GPR) is used as the ML technique with genetic algorithm (GA) as the EC algorithm. GA is used for hyperparameter optimization of GPR model as selecting appropriate values for hyperparameter are essential to ensure that the prediction model can accurately capture the underlying patterns in the data. We conducted some experiments with our prediction approach to thoroughly test its capabilities. Moreover, two forecasting strategies were used to assess the performance in both hindcast and operational settings. In most of the experiments, the data used are the multi-technique combinations (C04) generated by International Earth Rotation and Reference Systems Service (IERS). In one of the experiments, we also investigated the performance of our prediction model on dUT1 and LOD from four different products obtained from IERS EOP 20 C04, DTRF20, JTRF20 and USNO. The prediction products are evaluated with real estimates of the EOP product with which the model is trained. The combined excitations of the atmosphere, oceans, hydrology, and sea level (AAM + OAM + HAM + SLAM) are used as predictors because they are highly correlated to the input data. The results depict the highest performance of 0.412 ms in dUT1 and 0.092 ms/day in LOD, on day 10 of predictions. It is worth noting that the later predictions were obtained by incorporating the uncertainty of the input data as weights in the prediction model, which was a novel approach tested in this study. Graphical Abstract

Abstract Growing interest in Earth Orientation Parameters (EOP) resulted in various approaches to the EOP prediction algorithms, as well as in the exploitation of distinct input data, including the observed EOP values from various operational data centers and modeled effective angular momentum functions. Considering these developments and recently emerged new methodologies, the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) was pursued in 2021–2022. The campaign was led by Centrum Badań Kosmicznych Polskiej Akademii Nauk in cooperation with Deutsches GeoForschungsZentrum and under the auspices of the International Earth Rotation and Reference Systems Service. This paper provides the analysis and evaluation of the polar motion predictions submitted during the 2nd EOP PCC with the prediction horizons between 10 and 30 days. Our analysis shows that predictions are highly reliable with only a few occasional discrepancies identified in the submitted files. We demonstrate the accuracy of EOP predictions by (a) calculating the mean absolute error relative to polar motion observations from September 2021 through December 2022 and (b) assessing the stability of the predictions in time. The analysis shows unequal results for the x and y components of polar motion (PMx and PMy, respectively). Predictions of PMy are usually more accurate and have a smaller spread across all submitted files when compared to PMx. We present an analysis of similarity between the participants to indicate what methods and input data give comparable output. We also prepared the ranking of prediction methods for polar motion summarizing the achievements of the campaign.

Abstract In 2021, the International Earth Rotation and Reference Systems Service (IERS) established a working group tasked with conducting the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) to assess the current accuracy of EOP forecasts. From September 2021 to December 2022, EOP predictions submitted by participants from various institutes worldwide were systematically collected and evaluated. This article summarizes the campaign's outcomes, concentrating on the forecasts of the dX, dY, and dψ, dε components of celestial pole offsets (CPO). After detailing the campaign participants and the methodologies employed, we conduct an in-depth analysis of the collected forecasts. We examine the discrepancies between observed and predicted CPO values and analyze their statistical characteristics such as mean, standard deviation, and range. To evaluate CPO forecasts, we computed the mean absolute error (MAE) using the IERS EOP 14 C04 solution as the reference dataset. We then compared the results obtained with forecasts provided by the IERS. The main goal of this study was to show the influence of different methods used on predictions accuracy. Depending on the evaluated prediction approach, the MAE values computed for day 10 of forecast were between 0.03 and 0.16 mas for dX, between 0.03 and 0.12 mas for dY, between 0.07 and 0.91 mas for dψ, and between 0.04 and 0.41 mas for dε. For day 30 of prediction, the corresponding MAE values ranged between 0.03 and 0.12 for dX, and between 0.03 and 0.14 mas for dY. This research shows that machine learning algorithms are the most promising approach in CPO forecasting and provide the highest prediction accuracy (0.06 mas for dX and 0.08 mas for dY for day 10 of prediction). Graphical abstract

Abstract VLBI simulation studies are carried out to investigate the impact of any proposed station or strategy on the geodetic parameters, such as Earth Orientation Parameters (EOP) and Terrestrial Reference Frame (TRF). In general, such studies are performed for making decisions on any new development in the existing VLBI network. Thus, for selecting the favorable locations for establishment of a VLBI antenna in India, simulation studies are performed on 42 potential locations to cover the whole Indian subcontinent. Furthermore, the simulation setup is divided into four scenarios that consider the current and future situations of the global VLBI network. Extensive simulation strategy is applied with optimized scheduling for each network geometry, Monte-Carlo simulations and analysis in the VieSched++ software. Since only the simulation results are thought to be insufficient for a thorough evaluation of the realistic performance of locations, environmental factors are also investigated in the current study. The environmental factors affecting the operation and vulnerability of the VLBI technique at the potential locations are also incorporated in the present study. For this, a weighted scoring model is developed with the scores and weights based on the probable impact and occurrence frequency of disrupting environmental events, respectively. This approach will avoid the possibility of new VLBI station ending up in an unfavorable location in India and, underperforming substantially in terms of the achieved improvement of geodetic parameters as determined from the simulation study. The VLBI Global Observing System (VGOS) network is being established at a global level to create a uniformly distributed network of the next generation VLBI system to meet the goals of the Global Geodetic Observing Systems (GGOS). India is planning to establish its first VGOS antenna, and therefore, this study helps to mark the high-performance favorable locations for VGOS. The improvements in geodetic parameters of favorable locations identified in the simulation study are 6.7–11.2% in the first scenario, 12.8–46.8% in second scenario, 9–20.5% in third scenario and 2.9–6.1% in fourth scenario. The favorable locations outperform other Indian locations by a factor of 1.1–5.8. In addition to that, the Indian locations having environmental factors that might affect the VGOS adversely are not portrayed as the favorable choice. Graphical Abstract

Accurate Earth orientation parameter (EOP) predictions are needed for many applications, e.g., for the tracking and navigation of interplanetary spacecraft missions. One of the most difficult parameters to forecast is the length of day (LOD), which represents the variation in the Earth’s rotation rate since it is primarily affected by the torques associated with changes in atmospheric circulation. In this study, a new-generation time-series prediction algorithm is developed. The one-dimensional convolutional neural network (1D CNN), which is one of the deep learning methods, is introduced to model and predict the LOD using the IERS EOP 14 C04 and axial Z component of the atmospheric angular momentum (AAM), which was taken from the German Research Centre for Geosciences (GFZ) since it is strongly correlated with the LOD changes. The prediction procedure operates as follows: first, we detrend the LOD and Z-component series using the LS method, then, we obtain the residual series of each one to be used in the 1D CNN prediction algorithm. Finally, we analyze the results before and after introducing the AAM function. The results prove the potential of the proposed method as an optimal algorithm to successfully reconstruct and predict the LOD for up to 7 days.

Background: Tuberculosis (TB), as a communicable disease, is an ongoing global epidemic that accounts for a high burden of global mortality and morbidity. Drug-resistant TB strains are one of the hurdles in controlling TB problem. TB is the most common opportunistic infection in people living with human immunodeficiency viruses (PLHIV). To accelerate progress toward the goal of ending TB by 2035, it is imperative to outline the incidence and mortality trends of TB in India. Aims and Objectives: This study aims to provide deep insights into the recent trends of TB incidence and mortality in India from 2018 to 2023. Materials and Methods: This is a cross-sectional type of observational study done, over a period of 6 years from 2018 to 2023. It included 21,223 suspected samples tested for TB and rifampicin (rif) resistance with GeneXpert CBNAAT (cepheid) for infection of Mycobacterium tuberculosis (MTB). Results: Out of total 21,223 samples tested, 17.97% of samples were positive for MTB. From the year 2018 to 2023, 9.5% decline was seen in overall TB cases. However, the diagnosis of rif resistance is in similar range for all the study years. New patients show slight decline, but previous patients show higher rif resistance. There is decreased TB incidence in PLHIV. Conclusion: Overall, the incidence of TB has decreased in the past 6 years. The acquired drug resistance was seen in 32.6% cases. The study found an overall decrease in the incidence of TB in PLHIV.

Background: Tuberculosis (TB) remains a significant public health challenge in developing countries, including India, where children under 15 years account for 12% of all TB cases. The National Tuberculosis Elimination Programme (NTEP) recommends the use of GeneXpert/Cartridge-Based Nucleic Acid Amplification Test (CBNAAT) for TB diagnosis, followed by Line Probe Assay (LPA) for detecting drug resistance. This study aimed to determine the prevalence of TB and rifampicin resistance in pediatric patients and to evaluate the utility of CBNAAT in comparison with LPA and Ziehl-Neelsen staining. Methods: This retrospective study was conducted over one year, from 1st January 2022 to 31st December 2022, at a tertiary care hospital in western Maharashtra. A total of 528 samples from pediatric patients (aged

Diphtheria is an acute infectious toxin mediated disease caused by Corynebacterium diphtheriae. Effective vaccination has decreased the incidence of disease globally. However, despite of a full-fledged Universal Immunization Programme, the cases are still seen in children and adolescents in many states in India. Here, we report three recent cases of diphtheria from Solapur district of Maharashtra to flag the awareness of the disease and importance of vaccination. Keywords: Corynebacterium diphtheria, immunization, Diphtheria antitoxin

Tuberculosis (TB) is an underreported epidemic. Musculoskeletal TB is a relatively rare extrapulmonary complication of Mycobacterium tuberculosis involving just 1%–3% of patients. It commonly affects the spine and osteoarticular joints such as hip and knee, and it is mostly monoarticular. Oligo and polyarticular involvement, also called tuberculous rheumatism or Poncet's disease (PD), is an extremely rare occurrence due to a vigorous immune response to mycobacteria which contributes to the arthritic process. Here, we report a case of PD in a 3-year-old girl, who presented with complaints of insidious onset polyarthritis, mimicking juvenile arthritis. Cervical lymph node aspirate isolated acid-fast bacilli. The child was diagnosed as a case of PD, and after starting antituberculous therapy, her symptoms dramatically resolved. This patient illustrates the need to consider the possibility of TB in high-risk pediatric populations in countries like India as one of the causes of nonresolving chronic arthritis.

Background: Despite endorsement of the line probe assay (LPA) for the diagnosis of drug-resistant pulmonary tuberculosis patients, there is limited data available on the performance of LPAs in India, especially from high burden states like Maharashtra, for the early diagnosis and detection of drug resistance, in order to initiate timely and appropriate treatment. Objective: To evaluate the utility of the line probe assay (LPA) for the early diagnosis of drug-resistant pulmonary tuberculosis as compared to the ′Gold standard′ 1% proportion method (PM). Materials and Methods: A total of 687 patients suspected of pulmonary tuberculosis were screened. One hundred samples (95 sputum and 5 BAL), positive for Acid Fast Bacilli (AFB) by Ziehl Neelson (ZN) smears, were included in the study. Digested and decontaminated specimens were subjected directly to the LPA (Genotype MTBDR@ plus assay) and were processed in parallel using the conventional culture on the Lowenstein-Jensen (LJ) medium followed by drug susceptibility testing (DST) using the PM. Results: All the 100 samples gave interpretable results on LPA with a turnaround time of 24-48 hours as opposed to six to eight weeks taken by the 1% proportion method. Sensitivity for the detection of rifampicin, isoniazid, and multidrug resistance (MDR) was 98.1, 92.1, and 95%, respectively, with a specificity of 97.8% for rifampicin and 98.33% for MDR detection. It also had the additional advantage of allowing a study of mutation patterns. Conclusions: High performance characteristics and a short turnaround time makes LPA an excellent diagnostic tool, for an early and accurate diagnosis, in a high MDR- TB-prevalent region, as reflected from our data.

Background: Drug resistance is a major problem in the treatment of tuberculosis (TB). An estimate of drug resistance is extremely important in the epidemiology and control of TB. However, an assessment of the magnitude of drug resistance in TB is not very well described globally and data remains scantier for India. In view of this, we reviewed our data over last five years. Materials and Methods: Six hundred and seventy-three Mycobacterium tuberculosis isolates were subjected to drug susceptibility against primary anti-tuberculosis drugs by economic variant proportion method. All isolates resistant to isoniazid and rifampicin were taken as multi-drug resistant (MDR). Results: Out of the 673 strains tested, 95 (14.11%) showed monoresistance, 365 (54.23%) strains were found to be resistant to more than one drug. A total of 118 (17.53%) strains were found to be resistant to all the four drugs tested. MDR was seen with 320 (47.54%) isolates. This study observed maximum resistance with rifampicin (74.4%) followed by streptomycin (70.0%), isoniazid (53.2%), and ethambutol (21.7%). Conclusion: While this information may not reflect true prevalence of drug resistance in the region, this may help in further planning long term surveillance studies to know the trend of drug resistance in this area.