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The effect of spatial and intensity level augmentation of structural magnetic resonance images on autism diagnosis model
In deep learning, the robustness and generalizability of models significantly depend on diverse and heterogeneous training data. Acquiring such an extensive dataset is challenging in fields like disorder prediction due to data scarcity, which can be attributed to factors such as privacy concerns, limited patient population, or inadequate facilities. Data augmentation can be an ideal solution to this problem, particularly in the field of disorder prediction, like autism, using medical imaging. Data augmentation can expand and balance datasets by generating high-quality and varied data, thereby improving the generalizability of deep learning models. This study proposed two types of augmentation methods: 1. Spatial level 2. Intensity level augmentation techniques. Eight different levels of augmentations were experimented with across these categories. This study found that the combination of spatial and intensity level augmentations enhanced the model's generalizability and robustness, achieving an AUC value of 0.7433. Additionally, it was observed that the Left to Right flip method, under spatial augmentation, diminished the model's performance, whereas random noise injection, under intensity level augmentation, improved prediction accuracy. 2026 Elsevier B.V. -
FaithfulNet: An explainable deep learning framework for autism diagnosis using structural MRI
Explainable Artificial Intelligence (XAI) can decode the black box models, enhancing trust in clinical decision-making. XAI makes the predictions of deep learning models interpretable, transparent, and trustworthy. This study employed XAI techniques to explain the predictions made by a deep learning-based model for diagnosing autism and identifying the memory regions responsible for children's academic performance. This study utilized publicly available sMRI data from the ABIDE-II repository. First, a deep learning model, FaithfulNet, was developed to aid in the diagnosis of autism. Next, gradient-based class activation maps and the SHAP gradient explainer were employed to generate explanations for the model's predictions. These explanations were integrated to develop a novel and faithful visual explanation, Faith_CAM. Finally, this faithful explanation was quantified using the pointing game score and analyzed with cortical and subcortical structure masks to identify the impaired brain regions in the autistic brain. This study achieved a classification accuracy of 99.74% with an AUC value of 1. In addition to facilitating autism diagnosis, this study assesses the degree of impairment in memory regions responsible for the children's academic performance, thus contributing to the development of personalized treatment plans. 2025 Elsevier B.V. -
Rectifying Whole Brain Segmentation Errors Using a Novel Under-Segmentation Correction Method
Pre-processing is a critical step in any data-driven study, particularly in the field of medical imaging, where it significantly enhances the reliability of disease and disorder diagnosis. In this context, medical image segmentation allows for more precise data analysis by isolating the regions of interest. Accurate segmentation of these regions can reveal influential variabilities in analysis, potentially leading to unique scientific findings. This article presents a novel under-segmentation error correction technique specifically designed for whole-brain segmentation. Additionally, it performs a set of pre-processing steps for the structural magnetic resonance imaging (sMRI) images, which are necessary to maintain the structural integrity and uniformity of MRI scans across different subjects. The proposed algorithm effectively eliminates under-segmentation errors, thereby improving the accuracy of whole-brain segmentation, particularly for structurally intact brain images. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Meta-Teaching on Leveraging the Metaverse for Definitive Efficiency in Learning in Higher Education
One of the most intriguing results of the technology revolution over the past ten years has been virtual reality (VR). This experience is set to be enhanced by the metaverse, the next major technological revolution of our time. The metaverse delivers a fully immersive 3D digital experience that blends virtual and real worlds. The idea is interpreted as the future of the internet, it will allow users to interact with one another in a 3D virtual environment, through gaming or collaborating on projects. In the education sector, metaverse will play a vital role in overcoming learning limitations. Activities that occur in remote locations in the real world can now take place virtually. With VR, students are fully immersed in a simulated environment, free from distractions which enhances the student's ability to learn. Scientific studies show that VR improves spatial memory and cognition. Visual learning can boost student's understanding of more complicated subjects, concepts and languages by allowing them to learn from a first-person perspective and observe everything happening around them. 2025 selection and editorial matter, Kennedy Andrew Thomas, Joseph Chacko Chennattuserry and Joseph Varghese Kureethara; individual chapters, the contributors. -
Implementation of OpenId connect and O Auth 2.0 to create SSO for educational institutes
Increase in the number of users is directly proportional to the need of verifying them. This means that any user using any website or application has to be authenticated first; this leads to the creation of multiple credentials of one user. Now if these different websites or applications are connected or belong to one single organization like a college or school, a lot of redundancy of data is there. Alo ng with this, each user has to remember a wide range of credentials for different applications/websites. So in this paper, we addre ss the issue of redundancy and user related problems by introducing SSO using OpenId Connect in educational institutes. We aim to mark the di fference between the traditional system and proposed login by testing it on a group of users. 2018 Authors. -
Smart city initiatives and disaster resilience of cities through spatial planning in Pune city, India
Cities are attracting populations at alarming rate. Cities provide the need of populations in every way from livelihoods to livability. In doing so it is exhausting its resources resulting in increasing threats of risk. An initiative like Smart City Mission is aiming to enhance the capacities of the cities to increase livability and quality of life for its population and decrease threats of risk. This study examines the impact of smart city initiatives on resilience to earthquakes and floods through a spatial planning perspective for the city of Pune in State of Maharashtra through series of structured interviews with key stakeholders. The findings suggest that smart city initiative is still in its primary stage and requires assimilation with the development strategy to contribute to the resilience of the city. The study further proposes the need to integrate the smart city initiative with all the current and future developmental projects. 2023, World Research Association. All rights reserved. -
Mitigation of harmonics for five level multilevel inverter with fuzzy logic controller
Introduction. The advantages of a high-power quality waveform and a high voltage capability of multilevel inverters have made them increasingly popular in recent years. These inverters reduce harmonic distortion and improve the voltage output. Realistically speaking, as the number of voltage levels increases, so does the quality of the multilevel output-voltage waveform. When it comes to industrial power converters, these inverters are by far the most critical. Novelty. Multilevel cascade inverters can be used to convert multiple direct current sources into one direct current. These inverters have been getting a lot of attention recently for high-power applications. A cascade H-bridge multilevel inverter controller is proposed in this paper. A change in the pulse width of selective pulse width modulation modulates the output of the multilevel cascade inverter. Purpose. The total harmonic distortion can be reduced by using filters on controllers like PI and fuzzy logic controllers. Methods. The proposed topology is implemented with MATLAB/Simulink, using gating pulses and pulse width modulation methodology and fuzzy logic controllers. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage and number of DC sources are analyzed with conventional topologies. Practical value. The proposed topology has been very supportive for implementing photovoltaic based multilevel inverter, which is connected to large demand in grid and industry. M.S. Sujatha, S. Sreelakshmi, E. Parimalasundar, K. Suresh. -
Efficient Method for Personality Prediction using Hybrid Method of Convolutional Neural Network and LSTM
Users' contributions and the emotions conveyed in status updates may prove invaluable to studies of human behavior and character. A number of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user's personality traits based on their social media profiles. Among the many customer services now available on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users' inner personalities, is significant. However, there hasn't been much work done on the psychological analysis that's needed to deduce the user's inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is better at capturing long-term dependencies. combination of features for personality prediction, the LSTM-CNN model is superior to the individual models. 2023 IEEE. -
Application of response surface methodology to optimize lead(Ii) ion adsorption by activated carbon fabricated from de oiled soya
Lead(II) ion a heavy metal is known for its toxicity. An initiative has been taken in this study, to adsorb toxic lead(II) ion using activated carbon made of de oiled soya, by an aqueous solution through batch adsorption methodology. Adsorption process variables such as adsorbent dose, contact time, solution pH, and lead(II) ion concentration were optimized by central composite design (CCD). To find the interaction between process variables, response surface plots were utilized using response surface methodology. Design-Expert software version 7 was resorted to in this experiment. It was observed that the components from the analysis of variance of the CCD revealed that the selective process independent variables had significant control over adsorption capacity. Desirability function was used to appraise the factors and response in adsorption experiments to find an optimum point where the preferred adsorption could be obtained. Adsorption process with the application of activated carbon developed from de-oiled soya meritoriously removed lead(II) ion with an optimum adsorption capacity of 26.279 mg/g for an initial concentration of lead(II) at 60 mg/L. 2021 Desalination Publications. All rights reserved. -
A critical review of Cr(VI) ion effect on mankind and its amputation through adsorption by activated carbon
A toxic heavy metal is a one which is plausibly dense metal or metalloid that is eminent for its prospective toxicity, particularly in environmental context. Heavy metal poisoning may crop up as an upshot of air or water contamination, exposure to industrial activities, foodstuffs, medicines, coarsely coated food containers, etc. The present review highlights various issues related to the effects of Cr (VI) heavy metal toxicity to human health and its adsorption from wastewater using low cost adsorbents. Many researchers have lay their endeavor to ascertain low-priced adsorbents that are effortlessly available and have power over the sensible adsorption capacity. It is perceptible from the literature survey that the revealed adsorbents have established stupendous removal capabilities for Cr (VI) metal ions. As the convention of heavy metal Cr (VI) is increased, it is implicit that there is a strong need for research to remove Cr (VI) heavy metal ions from wastewater to trim down the problem of soaring anthropogenic pressure and burly tendency to mount up in living organisms. 2020 Elsevier Ltd. All rights reserved. -
Investigation of detoxification nature of activated carbons developed from Manilkara zapota and de oiled soya
Heavy metals are poisonous and detrimental water contaminant. Their existence affects human beings, animals and vegetation as a outcome of their mobility in aqueous ecosystem, toxicity and nonbiodegradability. This work aimed at the development of new adsorbent in the detoxification of heavy metals using Manilkara zapota tree wood and de oiled soya. The study completely focused on the characterization of the developed activation in the view of using it as a adsorbent. The characterization of activated carbon was effected SEM analysis, FTIR, XRD analysis and surface area determination. Both the activation carbon have showed a tremendous characterization in their employability as adsorbent in adsorption of heavy metals in aqueous solution. 2019 Elsevier Ltd. All rights reserved. -
Exclusion of Chromium(VI) Ion in Grueling Activated Carbon Fabricated from Manilkara zapota Tree Wood by Adsorption: Optimization by Response Surface Methodology
The current paper makes obvious the elimination of chromium(VI) ion, from wastewater via adsorption technique with activated carbon generated from Manilkara zapota tree (MZTWAC). Preliminarily MZTWAC has undergone characterization studies which uncovered the suitability of MZTWAC to expel chromium(VI) from aqueous solution. Batch adsorption experimentation was premeditated with the competence of central composite design (CCD) and it was executed. Response surface methodology (RSM) was the key optimization software to appraise the adsorptive chattels of MZTWAC engaged in removing chromium(VI) ion in aqueous solution which explored the interactions flanked between four expounding variables explicitly initial concentration of chromium(VI) ion, pH of the solution, MZTWAC dose and time of exposure, and contact time. The response variable that was concentrated in the study was adsorption capacity. It was deduced a polynomial in quadratic equation was documented amid the adsorption capacity and variables influencing the adsorption with R2=0.9792 which was projected as the best suit for the adsorption process. ANOVA that is expanded as analysis of variance judged the connotation of adsorption process variables. 0.2 g of MZTWAC dosage has removed 87.629% chromium(VI) from aqueous solution. The enhancement of adsorption process reclined on the attainment of maximum adsorption capacity which further depends on the optimization of variables under consideration. This criterion was accomplished by the desirability function optimizing the process variables. 2022 S. Sujatha et al. -
Free vibration studies of box type laterite masonry structures
Vol.39, N0.3, /august -September 2012 pp 332-346 -
Studies on Uniaxial Compressive Strength of Laterite Masonry Prisms
Vol-04, No.02, April ISNN: 0974-5904 -
Harnessing MOF Derived Frustrated Lewis Pair-CeO2 Nano Catalyst for CO2-Activated Soft Oxidation of Furfural to Furoic Acid
CO2 as a soft oxidant is scarcely explored in literature for furfural oxidation to furoic acid, and where CeO2, capitalizes on the synergistic effect of FLPs for enhanced CO2 reduction and furfural oxidation. This study presents a comparative analysis by the synthesis method to produce hydrothermal and MOF-derived CeO2 nanocatalyst and their effect on the formation of oxygen vacancies and metastable Ce3+ ions. The normalized surface concentration derived from XPS At% and specific surface area effectively quantifies accessible Ce3+ sites and oxygen vacancies, capturing FLP sites. Oxygen vacancies near surface Ce3+ sites (FLPs) critically modulated the catalytic performance, and evidenced by high turnover number (TON). FTIR adsorption study revealed that CO2 forms bidentate carbonate species and furfural, bi-coordination via ?2- (C, O) mode on the catalyst surface. Further active site masking strategy helped to understand and validate the crucial role of FLPs. The Central Composite Design model was utilized to optimize the reaction conditions and obtained high furfural conversion (99%) and furoic acid selectivity (99%). The catalyst was resistant to active site leaching and exhibited excellent stability, recyclability, and robustness. The findings highlight a potential pathway for the catalytic soft oxidation of furfural to furoic acid by CO2 utilization. 2025 Wiley-VCH GmbH. -
Utilizing Highly Reactive Lewis Pairs Generated by Oxygen Vacancies in the Cu3Mo2O9 Solid Catalyst for Cycloaddition of CO2 to 1,2-Propanediol
This work emphasizes generating highly reactive Lewis pair sites on CuMo oxides for CO2 activation and utilization in the cyclization reaction to produce propylene carbonate from 1,2-propanediol. The CuMo oxides were synthesized by enabling the oxygen vacancies that enhance the catalytically active sites, resulting in the formation of metastable cations (Mo5+ and Cu1+) and oxygen vacancies. Under ethanol-PEG-400 medium, the pure phase of Cu3Mo2O9 obtained at 500 C exposed maximum defects without any secondary phase compared to other screened catalysts. The experimental and theoretical investigations provide evidence for determining and correlating the characteristics of active sites with catalytic performance. The catalysts were extensively characterized along with density functional theory (DFT) studies, which revealed the presence of defect centers as one of the key factors in the enhanced activity. From the chemical bonding analysis, i.e., Crystal Orbital Hamiltonian Population (COHP) and Electron Localization Function (ELF), the CO2 molecule is known to form a strong chemisorption interaction with the catalyst surface that is facilitated by the oxygen vacancy/Lewis pairs. The Cu-Mo oxide catalyst achieved 99% conversion of 1,2-propanediol and 97% yield of propylene carbonate, outperforming previously reported catalysts. Thus, Cu-Mo oxide was shown to be highly efficient catalyst with good recyclability for 1,2-propanediol and the CO2 reaction. 2025 American Chemical Society. -
Global Trade and Food Security
Global trade can increase food availability and improve food security by facilitating food movement between countries and regions. However, it also poses challenges to food security, including the displacement of small-scale farmers and food producers, food safety risks, dependence on imports, and the distortion of global trade through agricultural subsidies. To ensure food security in global trade, a coordinated and multi-sectoral approach is needed that promotes sustain-able and equitable food systems, local food production, and food safety. Addressing these challenges is critical for ensuring the adequacy and accessibility of sufficient, safe, and nutritious food while fostering economic development and environmental sustainability. Economic development and government policies are crucial deter-minants of a countrys ability to engage in global trade and ensure food security. Climate and weather patterns, agricultural practices, infrastructure and transporta-tion, and market demand influence food security significantly. Furthermore, interna-tional conflicts disrupt trade and impact food security. Policymakers must consider all these factors while developing policies promoting trade and ensuring food secu-rity. To solve these complicated concerns and promote sustainable agriculture and trade practices, governments, international organizations, and the corporate sector must work in concert to improve food security. Food security has a significant impact on global trade, as the availability and accessibility of food directly affect a countrys ability to engage in international trade. When a country experiences food shortages may reduce or halt its exports, causing a ripple effect in the global economy and leading to higher food prices and insecurity in other countries that rely on imported food. Nevertheless, economies with high levels of food security and surplus food production are better positioned to engage in global trade. They export food to other countries, generating revenue and supporting economic growth by creating trade barriers, as importing countries may reject or restrict such food products, nega-tively impacting global trade. In addition to the economic impacts, food security also has broader societal and environmental implications. For example, food insecu-rity can lead to malnutrition, affecting individuals physical and cognitive develop-ment, particularly children. Moreover, unsustainable agricultural practices can lead to environmental degradation, affecting biodiversity, soil quality, and water resources, affecting food security. The determinants of food security in global trade and its impact are analyzed in this chapter. Since food security is an essential factor in inter-national trade, impacting trade flows, economic growth, and public health. Promoting sustainable agriculture and trade practices and building robust food security systems can support a more resilient and equitable global food system, benefiting producers and consumers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Navigating the digital frontier: Trends, innovation, and challenges in industry 5.0
Industry 5.0 is a shift in industrial processes involving robots and AI, enhancing efficiency and customization. International organizations are implementing Industry 5.0, leveraging cognitive virtual minds, AI, additive technologies, and 5G networks. However, Industry 5.0 presents challenges such as job displacement by low adaptability machines and requires substantial investments to transform existing industrial setups. Creative thinking, cybersecurity, and human-machine collaboration are essential for overcoming these challenges. Cyber-Physical system integration, cybersecurity, IoT, AI, and human-machine collaboration are key strategies. Continuous learning, virtual training, and digital twin technology are essential for a skilled workforce. Collaboration is essential for tackling complex digital issues, and organizations must anticipate future trends and prepare for technological breakthroughs. Ethical thinking and responsible AI practices are also crucial. Industry 5.0 is a transformative journey involving advanced technologies into manufacturing processes. 2024, IGI Global. All rights reserved. -
Indias Outward FDI: Macro-economic Determinants of Home Country
Nevertheless, a gap in the literature remains on the choice of investment destination and rationale backing the investment of Indian MNEs. The study examines the diverse home country determinants of outward FDI from low-and middle-income economies also the motive behind the investment of MNEs, which gained little attention in empirical studies. The role of home country determinants investigated for the most recent period, 1991-2019, using a panel data econometric framework. Results indicate that the home country's economic development level, globalization, political risk and science and technology investments significantly correspond to outward FDI from low-and middle-income countries. The present study analysis recommended that low and middle income governments provide incentivesto attract and retain FDI. Indian Institute of Finance. -
Transformation of India as investor of outward fdi: A systematic investigation of literature
Besides the economic transformation and industrial up-gradation, Indian enterprises have steadily intensified their overseas investment venture during recent years. A systematic literature review performed to inspect the strategic motives and Outward FDI (OFDI) impact on emerging economies like India. This paper explores relevant theories, strategic rationale, and economic policies that propel the present OFDI trend from India. The effort taken by the Indian government to promote innovations were Cross border commercial and industrial collaboration. These efforts flagged the way for more Outward FDI possibilities in the future (Welch, 1988). This study comprises the literature works till the year 2019, which includes research journals and reports. The analysis observes that knowledge-based industries drive India's Outward FDI and examine whether knowledge-based industries contribute to sustaining long-term domestic and international growth (Pradhan J.P., 2005; Narayanan, 2016). Indian Institute of Finance.