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ObjectivesTo compare the incidence of anti tuberculosis drug-induced hepatotoxicity (ATDH) with those on old vs. revised WHO doses in human immunodeficiency virus (HIV) negative children. The secondary objective was to determine the overall incidence of hepatitis in children on Anti tubercular treatment (ATT) and isoniazid prophylactic therapy (IPT).MethodsChildren attending pediatric outpatient / admitted in wards, on ATT/ IPT between January 2007 and December 2017 (11 y) were included. Children were divided into Group 1 (treated based on old doses, from January 2007 to December 2011) and Group 2 (treated based on revised doses from January 2012 to December 2017). Children with multi drug resistant tuberculosis (MDRTB) and pre-existing liver disease were excluded.ResultsA total of 515 children were enrolled. Twelve children developed ATDH with an overall incidence of 2.3%. Five out of 260 (1.9%) developed hepatitis with old doses vs. 7 of the 255 (2.7%) with revised doses; this difference was not statistically significant. When calculated only for active TB (excluding children on IPT), overall incidence of hepatitis was 2.7%. Comparison between group 1 (2.04%) and group 2 (3.5%) was again not statistically significant. Ten out of 12 children who developed hepatitis were restarted on ATT without recurrence. No child on IPT developed hepatitis. There was no mortality.ConclusionsRevised WHO dosing does not increase incidence of hepatitis compared to old dosing in HIV negative children. Overall incidence was 2.3%. Hepatitis did not occur with IPT.

We reported here a boy aged 5 years who presented for the evaluation of recurrent croup since infancy. On chest examination, breath sounds were reduced throughout the right lung field with a shifting of the trachea and cardiac apex to the right side. The chest radiograph showed a small right lung with decreased vascularity, hyperinflated left lung and mediastinum shifted towards the right side. Flexible bronchoscopy revealed tracheomalacia with left bronchomalacia due to external pulsatile compression. In CT angiogram, the right pulmonary artery (PA) was absent with dilated left PA. Echocardiography did not show any features of pulmonary arterial hypertension (PAH). Since the child was growing well, and there was no limitation of activity and evidence of PAH, he was managed conservatively and kept on follow-up. Though unilateral absent PA is a rare condition, it should be suspected in children with unilateral hypoplastic lung.

Existing techniques for Yoga pose recognition build classifiers based on sophisticated handcrafted features computed from the raw inputs captured in a controlled environment. These techniques often fail in complex real-world situations and thus, pose limitations on the practical applicability of existing Yoga pose recognition systems. This paper presents an alternative computationally efficient approach for Yoga pose recognition in complex real-world environments using deep learning. To this end, a Yoga pose dataset was created with the participation of 27 individual (8 males and 19 females), which consists of ten Yoga poses, namely Malasana, Ananda Balasana, Janu Sirsasana, Anjaneyasana, Tadasana, Kumbhakasana, Hasta Uttanasana, Paschimottanasana, Uttanasana, and Dandasana. To capture the videos, we used smartphone cameras having 4 K resolution and 30 fps frame rate. For the recognition of Yoga poses in real time, a three-dimensional convolutional neural network (3D CNN) architecture is designed and implemented. The designed architecture is a modified version of the C3D architecture initially introduced for the recognition of human actions. In the proposed modified C3D architecture, the computationally intensive fully connected layers are pruned, and supplementary layers such as the batch normalization and average pooling were introduced for computational efficiency. To the best of our knowledge, this is among the first studies, which utilized the inherent spatial–temporal relationship among Yoga poses for their recognition. The designed 3D CNN architecture achieved test recognition accuracy of 91.15% on the in-house prepared Yoga pose dataset consisting of ten Yoga poses. Furthermore, on the publicly available dataset, the designed architecture achieved competitive test recognition accuracy of 99.39%, along with multifold improvement in the execution speed compared to the existing state-of-the-art technique. To promote further study, we will make the in-house created Yoga pose dataset publicly available to the research community.

Accurate spatiotemporal demand forecasting in distributed environments is challenging because of data heterogeneity, non-stationarity across clients, and privacy constraints. Traditional federated learning approaches often suffer from poor performance when global model updates do not align with local data distribution. This study proposes a novel VMD-structured LSTM–DSTGCRN with GAT framework with a Client-Side Validation (CSV) mechanism to address these challenges. Variational Mode Decomposition (VMD) is applied locally to decompose raw broadband demand signals into Intrinsic Mode Functions (IMFs), isolating characteristic frequency components and reducing cross-frequency interference. The decomposed signals are processed using an LSTM—MultiHead Attention—AGCRN backbone to jointly capture temporal dependencies and adaptive spatial correlations with Graph Attention Networks (GATs). In the federated setting, the CSV enables the selective integration of aggregated global parameters at the module level, allowing clients to retain locally optimal parameters while adopting beneficial global updates. Experimental results on multimodal transport demand datasets demonstrate that implemented approach achieves higher prediction accuracy, faster convergence, and improved robustness compared with baseline federated graph learning models. The proposed framework provides an effective, privacy-preserving solution for nonstationary heterogeneous spatiotemporal forecasting tasks. The simulation results demonstrate that the proposed model significantly improves the accuracy compared to the baseline model by reducing the MAE by 28% in centralized models. Furthermore, in federated learning setup, the MAE decreases by 40.6% and RMSE by 20.1%.

Software reliability growth models (SRGMs) are universally admitted and employed for reliability assessment. The process of software reliability analysis is separated into two components. The first component is model construction, and the second is parameter estimation. This study concentrates on the second segment parameter estimation. The past few decades of literature observance say that the parameter estimation was typically done by either maximum likelihood estimation (MLE) or least squares estimation (LSE). Increasing attention has been noted in stochastic optimization methods in the previous couple of decades. There are various limitations in the traditional optimization criteria; to overcome these obstacles metaheuristic optimization algorithms are used. Therefore, it requires a method of search space and local optima avoidance. To analyze the applicability of various developed meta-heuristic algorithms in SRGMs parameter estimation. The proposed approach compares the meta-heuristic methods for parameter estimation by various criteria. For parameter estimation, this study uses four meta-heuristics algorithms: Grey-Wolf Optimizer (GWO), Regenerative Genetic Algorithm (RGA), Sine-Cosine Algorithm (SCA), and Gravitational Search Algorithm (GSA). Four popular SRGMs did the comparative analysis of the parameter estimation power of these four algorithms on three actual-failure datasets. The estimated value of parameters through meta-heuristic algorithms are approximately near the LSE method values. The results show that RGA and GWO are better on a variety of real-world failure data, and they have excellent parameter estimation potential. Based on the convergence and R 2 distribution criteria, this study suggests that RGA and GWO are more appropriate for the parameter estimation of SRGMs. RGA could locate the optimal solution more correctly and faster than GWO and other optimization techniques.

Placental trophoblast cells are potentially at risk from circulating endocrine-disrupting chemicals, such as bisphenol A (BPA). To understand how BPA and the reputedly more inert bisphenol S (BPS) affect the placenta, C57BL6J mouse dams were fed 200 μg/kg body weight BPA or BPS daily for 2 wk and then bred. They continued to receive these chemicals until embryonic day 12.5, whereupon placental samples were collected and compared with unexposed controls. BPA and BPS altered the expression of an identical set of 13 genes. Both exposures led to a decrease in the area occupied by spongiotrophoblast relative to trophoblast giant cells (GCs) within the junctional zone, markedly reduced placental serotonin (5-HT) concentrations, and lowered 5-HT GC immunoreactivity. Concentrations of dopamine and 5-hydroxyindoleacetic acid, the main metabolite of serotonin, were increased. GC dopamine immunoreactivity was increased in BPA- and BPS-exposed placentas. A strong positive correlation between 5-HT⁺ GCs and reductions in spongiotrophoblast to GC area suggests that this neurotransmitter is essential for maintaining cells within the junctional zone. In contrast, a negative correlation existed between dopamine⁺ GCs and reductions in spongiotrophoblast to GC area ratio. These outcomes lead to the following conclusions. First, BPS exposure causes almost identical placental effects as BPA. Second, a major target of BPA/BPS is either spongiotrophoblast or GCs within the junctional zone. Third, imbalances in neurotransmitter-positive GCs and an observed decrease in docosahexaenoic acid and estradiol, also occurring in response to BPA/BPS exposure, likely affect the placental–brain axis of the developing mouse fetus.

Recent advancements in signal processing and computational power have revolutionized computer vision applications in diverse industries such as agriculture, food processing, biomedical, and the military. These developments are propelling efforts to automate processes and enhance efficiency. Notably, computational techniques are replacing labor-intensive manual methods for assessing the maturity indices of fruits and vegetables during critical growth stages. This review paper focuses on recent advancements in computer vision techniques specifically applied to determine the maturity indices of fruits and vegetables within the food processing sector. It highlights successful applications of Nuclear Magnetic Resonance (NMR), Near-Infrared Spectroscopy (NIR), thermal imaging, and image scanning. By examining these techniques, their underlying principles, and practical feasibility, it offers valuable insights into their effectiveness and potential widespread adoption. Additionally, integrating biosensors and AI techniques further improves accuracy and efficiency in maturity index determination. In summary, this review underscores the significant role of computational techniques in advancing maturity index assessment and provides insights into their principles and effective utilization. Looking ahead, the future of computer vision techniques holds immense potential. Collaborative efforts among experts from various fields will be crucial to address challenges, ensure standardization, and safeguard data privacy. Embracing these advancements can lead to sustainable practices, optimized resource management, and progress across industries. Graphical Abstract Highlights 1. Recent advancements in signal processing and computation drive interest in computer vision across industries. 2. The review focuses on non-destructive methods in fruits and vegetables. 3. Computational techniques replace manual methods for maturity index determination. 4. The principles of techniques are highlighted, along with their successful applications. 5. The potential of computation techniques in destructive, non-destructive methods, biosensors, and AI summarized.