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\"Both process planning and scheduling are very important functions of manufacturing, which affects together the cost to manufacture a product and the time to deliver it. This book contains various approaches proposed by researchers, to integrate the process planning and scheduling functions of manufacturing under varying configurations of shops. It is useful for both beginners and advanced researchers to understand and formulate the Integration Process Planning and Scheduling (IPPS) problem effectively\"-- Provided by publisher.

Our paper examines the key determinants of COVID-19 vaccination coverage in India and presents an analytical framework to probe whether vaccine hesitancy, socioeconomic factors and multi-dimensional deprivations (MPI) play a role in determining COVID-19 vaccination uptake. Our exploratory analysis reveals that COVID-19 vaccine hesitancy has a negative and statistically significant impact on COVID-19 vaccination coverage. A percentage increase in vaccine hesitancy can lead to a decline in vaccination coverage by 30 percent. Similarly, an increase in the proportion of people living in multi-dimensional poverty reduces the COVID-19 vaccination coverage. A unit increase in MPI or proportion of people living in acute poverty leads to a mean decline in vaccination coverage by 50 percent. It implies that an increase in socioeconomic deprivation negatively impacts health outcomes, including vaccination coverage. We additionally demonstrated that gender plays a significant role in determining how access to digital technologies such as the internet impacts vaccine coverage and hesitancy. We found that, as males’ access to the internet increases, vaccination coverage also increases. This may be attributed to India’s reliance on digital tools (COWIN, AAROGYA SETU, Imphal, India) to allocate and register for COVID-19 vaccines and the associated digital divide (males have greater digital excess than females). Conversely, females’ access to the internet is statistically significant and inversely associated with coverage. This can be attributed to higher vaccine hesitancy among the female population and lower utilization of health services by females.

Human action recognition in videos has become a popular research area in artificial intelligence (AI) technology. In the past few years, this research has accelerated in areas such as sports, daily activities, kitchen activities, etc., due to developments in the benchmarks proposed for human action recognition datasets in these areas. However, there is little research in the benchmarking datasets for human activity recognition in educational environments. Therefore, we developed a dataset of teacher and student activities to expand the research in the education domain. This paper proposes a new dataset, called EduNet, for a novel approach towards developing human action recognition datasets in classroom environments. EduNet has 20 action classes, containing around 7851 manually annotated clips extracted from YouTube videos, and recorded in an actual classroom environment. Each action category has a minimum of 200 clips, and the total duration is approximately 12 h. To the best of our knowledge, EduNet is the first dataset specially prepared for classroom monitoring for both teacher and student activities. It is also a challenging dataset of actions as it has many clips (and due to the unconstrained nature of the clips). We compared the performance of the EduNet dataset with benchmark video datasets UCF101 and HMDB51 on a standard I3D-ResNet-50 model, which resulted in 72.3% accuracy. The development of a new benchmark dataset for the education domain will benefit future research concerning classroom monitoring systems. The EduNet dataset is a collection of classroom activities from 1 to 12 standard schools.

Background Leprosy is an ideal human disease to study T cell regulation as patients show correlation between cytokine skewed Th1-Th2 responses and clinical forms of the disease. The Role of transcription factors on the modulation of Th1 and Th2 responses by M. leprae antigens has not been adequately studied. In the present study, we studied the effect of M. leprae antigens on transcription factors STAT-4, STAT-6 and CREB and their correlation with Th1/Th2 cell mediated immune responses in leprosy. Methods Leprosy patients of both categories of tuberculoid leprosy (BT/TT) and lepromatous leprosy (BL/LL) were selected from the OPD of NJ1L & OMD, (ICMR), Agra and healthy individuals (H) were chosen from the staff and students working in the institute. Peripheral blood mononuclear cells ( PBMCs) of the study subjects were stimulated with M. leprae antigens (WCL, MLSA, and PGL-1). Sandwich ELISA was done in the culture supernatants of healthy and leprosy patients to detect IL-4, IL-10 and IFN-γ. Further, expression of IFN-γ and IL-4 and activation of STAT4, STAT6 and CREB transcription factors in CD4 + T cell with or without stimulation of M. leprae antigens was investigated by flow cytometry. Results Lepromatous leprosy patients showed significantly lower IFN-γ and higher IL-4 levels in culture supernatant and significantly low expression of IFN-γ and higher expression of IL-4 by CD4 + T cells than healthy individuals with or without antigenic stimulation. Antigenic stimulation significantly increased IL-10 in BL/LL patients but not in BT/TT patients or healthy individuals. PGL-1 stimulation led to significantly higher activation of STAT-6 in BT/TT and BL/LL patients in comparison to healthy individuals. All the three antigens led to activation of CREB in healthy and BT/TT patients but not in BL/LL patients. Conclusion Our findings show that M. leprae antigens differentially modulate activation of T cell transcription factors STAT-4/STAT-6 and CREB. These transcription factors are well known to regulate Th1 and Th2 mediated immune response which in turn could play vital role in the clinical manifestations of leprosy. These observations may help to determine how these T cell transcription factors affect the development of immune dysfunction and whether these new pathways have a role in immunomodulation in intracellular diseases like leprosy and TB.

The video camera is essential for reliable activity monitoring, and a robust analysis helps in efficient interpretation. The systematic assessment of classroom activity through videos can help understand engagement levels from the perspective of both students and teachers. This practice can also help in robot-assistive classroom monitoring in the context of human–robot interaction. Therefore, we propose a novel algorithm for student–teacher activity recognition using 3D CNN (STAR-3D). The experiment is carried out using India’s indigenously developed supercomputer PARAM Shivay by the Centre for Development of Advanced Computing (C-DAC), Pune, India, under the National Supercomputing Mission (NSM), with a peak performance of 837 TeraFlops. The EduNet dataset (registered under the trademark of the DRSTATM dataset), a self-developed video dataset for classroom activities with 20 action classes, is used to train the model. Due to the unavailability of similar datasets containing both students’ and teachers’ actions, training, testing, and validation are only carried out on the EduNet dataset with 83.5% accuracy. To the best of our knowledge, this is the first attempt to develop an end-to-end algorithm that recognises both the students’ and teachers’ activities in the classroom environment, and it mainly focuses on school levels (K-12). In addition, a comparison with other approaches in the same domain shows our work’s novelty. This novel algorithm will also influence the researcher in exploring research on the “Convergence of High-Performance Computing and Artificial Intelligence”. We also present future research directions to integrate the STAR-3D algorithm with robots for classroom monitoring.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) has a very important role in repairing intact and injured brain, also known as neuroplasticity. Risk factors may affect neuroplasticity. OBJECTIVES: In this study, our aim was to delineate the levels of BDNF in acute stroke with different etiology and impact of risk factors on its levels. METHODS: In this prospective study, 208 patients with first-ever stroke, between 18 and 75 years, were included. All individuals were assessed for severity and type of stroke, risk factors, levels of BDNF in the acute stroke, and its association with outcome of stroke. RESULTS: The mean age of the patients in our study was 55.29 ± 11.6 years. Compared to healthy controls, a significant decline in the levels of BDNF was observed after stroke (P < 0.01). Patients with National Institutes of Health Stroke Scale (NIHSS) <6 on the 1st day of stroke had significantly higher levels of BDNF than those with NIHSS >6 (9.8 ng/ml ± 3.8; P < 0.01). A significant difference in the levels of BDNF was observed on comparing the stroke patients and healthy individuals of age <55 and >55 years (<55 years: 10.4 ng/ml ± 3.2; >55 years: 9.8 ng/ml ± 4.5 and in healthy individuals <55 years: 22.97 ± 3.8, >55 years: 15.4 ± 4.9; P < 0.01). Risk factors have negative impact on levels of BDNF (diabetics, P = 0.001; alcoholics, P = 0.003; both diabetes mellitus + hypertension, P = 0.002; smokers, P = 0.001). The difference was not significant between hypertensives and nonhypertensives (P = 0.06). CONCLUSION: BDNF level is significantly reduced in acute stroke. The presence of risk factors further affects its level.

BackgroundLongitudinally extensive transverse myelitis (LETM) is a severe inflammatory spinal cord disorder. It is commonly associated with neuromyelitis optica spectrum disorders and other autoimmune conditions like Sjögren syndrome, infectious and idiopathic aetiologies. Interleukin-6 (IL-6) is one of the key pro-inflammatory cytokines implicated in neuroinflammation and may serve as an important biomarker of disease activity.ObjectiveTo evaluate the correlation between cerebrospinal fluid (CSF) IL-6 levels and disease activity in patients with LETM and to assess its prognostic value for short-term functional outcomes.MethodsIn this prospective follow-up study conducted over 18 months, 41 patients with LETM and 21 controls with non-inflammatory neurological conditions were enrolled. Disease severity was assessed using the Expanded Disability Status Scale (EDSS) at admission and at 3-month follow-up. CSF IL-6 levels were analysed and correlated with clinical and radiological variables using Spearman correlation, receiver operating characteristic (ROC) analysis and multivariate regression.ResultsMedian CSF IL-6 levels were significantly higher in LETM patients than controls (11.26 vs 5.02 pg/mL; p<0.001). CSF IL-6 showed a moderate positive correlation with EDSS at admission (ρ=0.34, p=0.027) and follow-up (ρ=0.51, p<0.001). ROC analysis demonstrated fair predictive accuracy for severe disease (AUC=0.70), with a cut-off of approximately 7 pg/mL. Multivariate analysis identified CSF IL-6 as an independent predictor of disease severity.ConclusionsCSF IL-6 correlates with disease activity and disability in LETM and may serve as a useful biomarker for prognostication and treatment planning.

Wistar rats were treated as Group A: Control; Group B: Lead (50 mg kg-1 b wt.); Group C: Lead (50 mg kg-1 b wt.) and jamun seed extract (JSE) (200 mg kg-1 b wt.); Group D: Lead (50 mg kg-1 b wt.) and orange peel extract (OPE) (200 mg kg-1 b wt.); Group E: OPE (200 mg kg-1 b wt.) and Group F: JSE (200 mg kg-1 b wt.). Serum urea, creatinine and serum uric acid levels were analyzed on days 7 and 14. Rats from Group B showed increased serum creatinine from days 7 to 14. Creatinine level decreased in group C and group D at days 7 and 14 as compared to group B. Increased serum urea was recorded in group B rats from 7 days. Rat from group C or group D showed decreased serum urea after days 7 day and 14 as compared to Group B. Lead exposure to rats provoked increased serum uric acid on days 7 and 14. Uric acid decreased in group C and group D on days 7 and 14 as compared to rats of group B. No alteration in creatinine, urea and uric acid level was seen in OPE and JSE treated rats at days 7 and 14.

The economic and agronomic impacts of drip fertigation techniques were evaluated on tuberose (Polianthes tuberosa L.) cultivation in a semi-arid region. Conducted over two growing seasons (2022-2024) at the ICAR-Indian Agricultural Research Institute in New Delhi, the field experiments utilized a split-split plot design with three factors: land configuration (raised bed and flatbed), irrigation schedules (50%, 75%, and 100% pan evaporation), and fertigation schedules (50%, 75%, and 100% recommended dose of fertilizers). Data were collected on flower yield, water productivity, and economic returns. The raised bed system consistently outperformed the flat bed system in water productivity and flower yield. Among the irrigation levels, the highest water productivity and flower yield were observed at 100% pan evaporation. Similarly, the highest fertigation level (100% RDF) resulted in the best outcomes in terms of both yield and economic returns. The economic analysis revealed that the raised bed configuration with higher fertigation and irrigation levels (BI3F3) was the most profitable, with the highest benefit-cost ratios. The study concludes that optimizing fertigation and irrigation practices, particularly using raised bed configurations with higher fertigation and irrigation levels, can significantly enhance tuberose cultivation’s profitability and sustainability in water-scarce regions.

The present study investigated the alternation in liver function parameters such as serum glutamic oxaloacetic transaminase (SGOT), serum glutamic pyruvic transaminase (SGPT), bilirubin (Bil), albumin (Alb), alkaline phosphatases (ALP) and lactate dehydrogenase (LDH) of cadmium exposed Wistar rats. The study also evaluated the possible ameliorative effects of jamun (Syzygium cumini) seed and orange (Citrus sinensis) peel extracts on liver biomarkers in rat treated with cadmium. Six groups of Wistar rats were treated as Group A: Control; Group B: cadmium (10 mg/kg b wt); Group C: cadmium (10 mg/kg b wt.) and jamun seed extract (200mg/kg b wt); Group D: cadmium (10 mg/kg b wt.) and orange peel extract (200 mg/kg b wt.); Group E: orange peel extract (OPE) (200 mg/kg b wt.) and Group F: jamun seed extract (JSE) (200 mg/kg b wt). Liver biomarkers were estimated on day 7 and day 14. Serum SGOT, SGPT, bilirubin, ALP, and LDH were increased on day 7 and 14 after cadmium treatment. Serum albumin levels decreased on day 7 and day 14. The SGOT, SGPT, bilirubin, ALP, LDH and albumin levels showed a tendency for restoration after treatment with JSE and OPE. There were no alterations in these liver parameters after treatment with OPE and JSE. The results clearly indicate that JSE and OPE were effective in recovering liver biomarkers which were altered by cadmium exposure to rats.