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Bremsstrahlung X-ray yield of 204Tl (0.76MeV) and 147Pm (0.225MeV) in in Barium compounds such as BaF2, BaI2, BaS, Ba3N2, BaSe and BaH2 has been measured using NaI(Tl) crystal and is compared with theory. The Z dependence of Bremsstrahlung is also measured and compared with the theory.

Cosmic radiation poses a significant challenge for long-duration space missions by inducing biological damage and degradation of spacecraft materials and electronics. In this study, the radiation shielding performance of tantalum-based polymers, Ta(OC2H5)5 and Ta(OC3H7)5, is investigated using the Stopping and Range of Ions in Matter (SRIM) framework. Key interaction parameters, including stopping power, projected range, and straggling, are evaluated for protons, alpha particles, and representative heavy ions (C, O, and Fe), and compared with a hydrogen-rich reference polymer, C2H4. While hydrogen-rich materials exhibit higher stopping power for light ions, tantalum-based polymers significantly reduce ion penetration depth, enabling compact shielding. The results indicate that tantalum-based polymers are promising candidates for hybrid or multilayer cosmic radiation shielding applications.

The need for real-time and robust monitoring system has become most important with the exponential growth of networked physical and cyber threats. This paper focuses on the design and implementation of an intrusion detection System by using swarm-based intelligent model. This proposed system is capable of detecting the threats in real-time to prompt timely responses by leveraging temporal data analytics. The main objective of this paper is to minimize the potential damages with timely threat identification by developing scalable models so that these models can process and analyze the real-time data. To achieve this objective, we are proposing a multi-layered framework by identifying temporal patterns to improve detection accuracy with low-latency. The proposed approach focuses on the extraction of meaningful features from temporal time series data so that it will help us in enabling dynamic threat identification in multiple domains. From this work, the proposed system for anomaly detection in view of high-speed data, an adaptive threshold mechanism will be considered to reduce the false positives rate by 18%, and a lightweight strategy to ensure capability for low-latency applications. The Swarm-based LSTM achieved accuracy of 98.7 and 96.5% F1 Score with a precision 95.3% demonstrating optimal scalability and efficiency for real-time cybersecurity applications when compared with the vanilla LSTM, GRU, and Bi-LSTM. All these models were evaluated based on the data set KDDcup99.

In recent days, additive manufacturing (AM) plays a vital role in manufacturing a component compared to subtractive manufacturing. AM has a wide advantage in producing complex parts and revolutionizing logistics panorama worldwide. Many researchers compared this emerging manufacturing methodology with the conventional methodology and found that it helps in meeting the demand, designing highly complex components, and reducing wastage of materials, and there are a wide variety of AM processes. The process of making the components in full use of technology with several manufacturing applications to meet the above is studied along with the properties of AM, and subsequently, the advantages of AM over the subtractive methods are described. In this paper, the achievements in this manner with considerable gains are studied and are concluded as a paradigm shift to fulfil the AM potential.

The rising temperatures and levels of carbon dioxide in the atmosphere are anticipated to have a significant impact on the productivity of agricultural crops. Although, the individual effects of elevated CO 2 and temperature have been extensively studied in C3 and C4 crops, there remains a scarcity of research investigating their interactive effects specifically on maize hybrids. The impact of elevated temperature and its interaction with elevated CO 2 on phenology, physiology, biomass, and grain yield of maize hybrids was assessed in a field experiment using Free Air Temperature Elevation (FATE) facility. The results showed that elevated temperature (eT) increased the anthesis silking interval (ASI), while the presence of elevated CO 2 along with elevated temperature (eT + eCO 2 ) mitigated this effect. The differential expression were observed between hybrids depending on their genetic potential. Furthermore, the net photosynthetic rate (A net ), stomatal conductance (g s ), and transpiration rate (Tr) of hybrids decreased under elevated temperature but eT + eCO 2 condition helped in reverting its impact to some extent. In term of leaf composition, the highest level of total soluble sugars (TSS) and starch was observed under eT + eCO 2 conditions, possibly due to improved A net in the presence of elevated eCO 2 . The negative impact of eT was also evident through increased proline and MDA content, but eT + eCO 2 ameliorated the adverse effect of eT. The biomass and grain yield also responded similarly, among the hybrids 900M GOLD recorded superior performance for grain yield at eT condition exceeding 35 °C. On the other hand, DHM117 experienced a significant reduction in grain yield under eT, but performed better under eT + eCO 2 due to its improved physiological response to eCO 2 . The study indicated that elevated levels of carbon dioxide can actually mitigate the detrimental effects of elevated temperature on maize crop. This positive impact on maize crop can be attributed to an enhanced physiological performance in the presence of eCO 2 which enables the plants to maintain satisfactory yield levels despite the challenging environmental conditions.

River sand samples have been collected from Ponnai river, Tamil Nadu, India for characterization of minerals and heavy metals by different spectroscopic techniques. Initially, the samples were subjected by Fourier Transform-Infra Red (FT-IR) spectroscopic technique and infra-red absorption bands values are observed in the range of 515–520, 695–700, 775–780 cm −1 which shows the presence of quartz in all the samples. Similarly, infra-red peaks were absorbed for feldspar, kaolinite, calcite, gibbsite and organic carbon and confirmed by X-Ray diffraction (XRD) technique. Additionally, zircon, aragonite, magnetite and kyanite minerals were identified in the samples using only the XRD method. The concentration of heavy metals such as Pb, Cr, Zn, Ni, Hg, As, Mn, Cu has been determined by flame atomic absorption spectrometry (FAAS). An average metal concentration measured in mg kg −1 were: Pb 0.12, As 0.15, Hg 0.13, Cu 2.80, Zn 10.15 Cr 12.70, Ni 2.86 and Mn 104.94 and hence found in the order of Mn > Cr > Zn > Ni > Cu > As > Hg > Pb. These average values do not exceed the world average value and hence potentially do not affect the quality of sand in the river. In addition to that, presences of heavy metals are confirmed by scanning electron microscope equipped with energy dispersive X-ray spectrometry (SEM/EDS) analysis. In order to understand the possible natural and anthropogenic sources of heavy metals, multivariate statistical techniques such as Pearson correlation, principal component and cluster analysis were performed. Results obtained from the statistical techniques were good agreement with each other.

The green revolution, characterized by intensive farming practices and synthetic agrochemicals, has been associated with concerns about ecological balance and soil health. This study investigated the impact of organic and conventional farming practices on soil quality and microbial diversity in coffee plantations within the Western Ghats. Soil samples from organic and conventional coffee farms in Ponnampet, Kodagu, Karnataka were collected for physical, chemical, and biological analysis. Organic soils had lower bulk density and particle density, suggesting improved structure and porosity. Organic systems had higher levels of organic carbon, nitrogen, and exchangeable calcium and magnesium. Organic coffee farming exhibited the highest soil quality index value of 0.98, which was higher than that of conventional coffee farming practice (0.87). Organic farming systems demonstrated significantly higher soil microbial respiration rates, reflecting a more active and diverse microbial community. Organic coffee farming systems not only promoted higher microbial biomass but also the higher value of Shannon–Wiener’s index, Simpson’s Diversity Index, Shannon and Simpson evenness index enhanced microbial diversity. These findings underscore the potential of organic coffee farming for sustainable agriculture in the Western Ghats, particularly in terms of enhancing soil health, promoting microbial diversity, and improving long-term soil quality compared to conventional practices.

Modern energy systems are finding new applications for magnetohydrodynamic rheological bio-inspired pumping systems. The incorporation of the electrically conductive qualities of flowing liquids into the biological geometries, rheological behavior, and propulsion processes of these systems was a significant effort. Additional enhancements to transport properties are possible with the use of nanofluids. Due to their several applications in physiology and industry, including urine dynamics, chyme migration in the gastrointestinal system, and the hemodynamics of tiny blood arteries. Peristaltic processes also move spermatozoa in the human reproductive system and embryos in the uterus. The present research examines heat transport in a two-dimensional deformable channel containing magnetic viscoelastic nanofluids by considering all of these factors concurrently, which is vulnerable to peristaltic waves and hall current under ion slip and other situations. Nanofluid rheology makes use of the Sutterby fluid model, while nanoscale effects are modeled using the Buongiorno model. The current study introduces an innovative numerical computing solver utilizing a Multilayer Perceptron feed-forward back-propagation artificial neural network (ANN) with the Levenberg–Marquardt algorithm. Data were collected for testing, certifying, and training the ANN model. In order to make the dimensional PDEs dimensionless, the non-similar variables are employed and calculated by the Homotopy perturbation technique. The effects of developing parameters such as Sutterby fluid parameter, Froude number, thermophoresis, ion-slip parameter, Brownian motion, radiation, Eckert number, and Hall parameter on velocity, temperature, and concentration are demonstrated. The machine learning model chooses data, builds and trains a network, and subsequently assesses its performance using the mean square error metric. Current results declare that the improving Reynolds number tends to increase the pressure rise. Improving the Hall parameter is shown to result in a decrease in velocity. When raising a fluid's parameter, the temperature profile rises.

Understanding the burden and pattern of mental disorders as well as mapping the existing resources for delivery of mental health services in India, has been a felt need over decades. Recognizing this necessity, the Ministry of Health and Family Welfare, Government of India, commissioned the National Mental Health Survey (NMHS) in the year 2014-15. The NMHS aimed to estimate the prevalence and burden of mental health disorders in India and identify current treatment gaps, existing patterns of health-care seeking, service utilization patterns, along with an understanding of the impact and disability due to these disorders. This paper describes the design, steps and the methodology adopted for phase 1 of the NMHS conducted in India. The NMHS phase 1 covered a representative population of 39,532 from 12 states across 6 regions of India, namely, the states of Punjab and Uttar Pradesh (North); Tamil Nadu and Kerala (South); Jharkhand and West Bengal (East); Rajasthan and Gujarat (West); Madhya Pradesh and Chhattisgarh (Central) and Assam and Manipur (North East). The NMHS of India (2015-16) is a unique representative survey which adopted a uniform and standardized methodology which sought to overcome limitations of previous surveys. It employed a multi-stage, stratified, random cluster sampling technique, with random selection of clusters based on Probability Proportionate to Size. It was expected that the findings from the NMHS 2015-16 would reveal the burden of mental disorders, the magnitude of the treatment gap, existing challenges and prevailing barriers in the mental-health delivery systems in the country at a single point in time. It is hoped that the results of NMHS will provide the evidence to strengthen and implement mental health policies and programs in the near future and provide the rationale to enhance investment in mental health care in India. It is also hoped that the NMHS will provide a framework for conducting similar population based surveys on mental health and other public health problems in low and middle-income countries.

Tuberculosis (TB) remains a significant global health challenge, necessitating innovative approaches for effective treatment. Conventional TB therapy encounters several limitations, including extended treatment duration, drug resistance, patient noncompliance, poor bioavailability, and suboptimal targeting. Advanced drug delivery strategies have emerged as a promising approach to address these challenges. They have the potential to enhance therapeutic outcomes and improve TB patient compliance by providing benefits such as multiple drug encapsulation, sustained release, targeted delivery, reduced dosing frequency, and minimal side effects. This review examines the current landscape of drug delivery strategies for effective TB management, specifically highlighting lipid nanoparticles, polymer nanoparticles, inorganic nanoparticles, emulsion-based systems, carbon nanotubes, graphene, and hydrogels as promising approaches. Furthermore, emerging therapeutic strategies like targeted therapy, long-acting therapeutics, extrapulmonary therapy, phototherapy, and immunotherapy are emphasized. The review also discusses the future trajectory and challenges of developing drug delivery systems for TB. In conclusion, nanomedicine has made substantial progress in addressing the challenges posed by conventional TB drugs. Moreover, by harnessing the unique targeting abilities, extended duration of action, and specificity of advanced therapeutics, innovative solutions are offered that have the potential to revolutionize TB therapy, thereby enhancing treatment outcomes and patient compliance. Graphical Abstract