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Item Static and dynamic analysis of a hyperelastic toroidal air-spring structure(2025) Sahu S.; Roychowdhury S.The present work proposes a novel toroidal air-spring model consisting of two cylindrical elastomeric membranes unlike conventional convoluted air-spring with one rubber bellow. The membranes are attached with two annular plates at top and bottom in circumferential direction, forming a closed space in between. With internally pressurizing the setup, the inflated bellow in the shape of a toroidal air-spring structure is formed. The static and dynamic analysis of the air-spring model is performed under transverse loading on top plate. The static analysis is carried out by compressing the air-spring to different suspension heights, assuming adiabatic compression of the enclosed air. The conditions for impending wrinkling, its prevention measures by choosing suitable design parameters, and the effect using cord-reinforced membranes are explored. The dynamic study under harmonic forcing is performed using the method of assumed modes coupled with a perturbation technique to solve the Eigenvalue problem of the discretized membrane structure. The radial asymmetric perturbations are included in the formulation to explore the symmetry breaking during dynamic study. The Eigen frequencies of the structure are obtained for different inflation pressures of the air-spring. Interestingly, a frequency veering phenomenon is observed between a few Eigen modes associated with closely spaced natural frequencies, where the possibility of mode swapping exists. The forced vibration analysis around a few Eigen frequencies shows beating like responses. The stiffness of the proposed air-spring is found to be linear under both static and dynamic conditions, which is inline with the stiffness nature of the convectional convoluted air-springs. � 2024 Elsevier Masson SASItem Flexible PVDF-Ba0.97Ca0.03TiO3 polymer-ceramic composite films for energy storage, biosensor, mechanosensor, and UV�visible light protection(2025) Elorika P.; Anwar S.; Roy A.; Anwar S.Multifunctional piezoelectric devices, which can detect pressure, store electrostatic energy, block UV radiation, and generate electricity from body movements, are highly beneficial for enhancing individual well-being. To achieve these capabilities, polyvinylidene fluoride (PVDF) composite films with Ba0.97Ca0.03TiO3 (BCT3) filler were prepared, varying the BCT3 content from 0 to 50 wt.%. The BCT3 ceramic, prepared using a modified solid-state reaction, exhibits a tetragonal phase at room temperature with a d33 value of 105 pC/N. X-ray diffraction confirms composite formation. The beta phase ranges from 75 to 86.9 %. At 40 wt.% BCT3, the dielectric constant, energy density, and piezoelectric properties peak, yielding maximum Wrec and Wtot of 138.1 and 284.7 mJ/cm3 (@ 250 kV/cm), respectively. PVDF-BCT3-40 (40 wt.%) shows maximum voltage, current, and power density of 25 V, 26.8 nA, and 19.8 ?W/cm3 under a 50 N load. Increasing BCT3 content enhances UV�visible absorbance, making the composites effective for light shielding. � 2024Item Deep learning neural networks for monitoring early-age concrete strength through a surface-bonded PZT sensor configuration(2025) Jena T.; Raj A.K.; Saravanan T.J.; Bansal T.Monitoring the immediate evolution of concrete's strength is essential to ensure structural integrity and construction efficiency, requiring Forecasting to avoid unforeseen and severe failures during construction. The present investigation introduces an Equivalent structural parametric (ESP) study using a surface-bonded piezo sensor and electromechanical impedance (EMI) methodology to monitor and forecast the strength of concrete by implementing machine learning and deep learning methods. The concrete hydration processes are simulated using COMSOL� 5.5. The concrete cube's hydration is represented by altering Young's modulus and damping ratio to show the rate of curing. Concrete strength development is examined in terms of conductance resonant frequency (CRF) and Conductance resonant peak (CRP). Continuous conductance signature monitoring and data analysis show that CRF and CRP increase with compressive strength. The system's mechanical impedance is measured, and EMI signatures vs. frequency plots within a specific frequency range are compared to healthy impedance graphs. A mass-spring-damper system with identical properties is identified, and relevant structural parameters are computed. Modern ML algorithms include linear regression (LR), interaction LR, fine, medium, and coarse Gaussian SVM, etc. reliably predict strength with an error rate of less than 2%. Convolutional neural networks (CNN) have advanced image-based recognition, but their usage in EMI-based structural strength assessment is still being studied. A unique strategy using 2D CNN, 2D CNN� Long short-term memory (LSTM), and 2D CNN Bidirectional-LSTM to forecast concrete structure compressive strength shows the potential of deep learning. The proposed 2D CNN-Bi-LSTM model excels in compressive strength prediction, obtaining an R2 value of 0.99 and stabilizing loss error over a few epochs. � 2024 Elsevier LtdItem Assessing cement paste strength evolution under curing: An experimental and numerical investigation through equivalent stiffness parameter identified by embedded piezo sensors(2025) Bansal T.; Azam A.; Morwal T.; Talakokula V.; Jothi Saravanan T.Equivalent stiffness is one of the most important mechanical parameters of any concrete structure which allows engineers to predict the behaviour of the structure. This paper identifies the equivalent stiffness parameter through an embedded piezo sensor (EPS) in the strength development of cement paste using the electro-mechanical impedance (EMI) technique through experimental and numerical investigation. The experiments were conducted on the cement paste specimensItem Empirical mode decomposition and Hessian LLE in Fluorescence spectral signal analysis for Cervical cancer detection(2025) Deo B.S.; Nayak S.; Pal M.; Panigrahi P.K.; Pradhan A.Cervical cancer is a significant cause of female mortality worldwide. The timely and accurate identification of different stages of cervical cancer has the capacity to significantly improve both treatment efficacy and patient survival duration. Fluorescence spectroscopy acts as a significantly sensitive technique for identifying the biochemical changes that occur during the advancement of cancer. Fluorescence spectral data was collected from a diverse set of 110 human cervix samples in our study. The spectral data underwent an initial preprocessing step that included data normalization. Subsequently, empirical mode decomposition (EMD) was utilized to decompose the signal into several intrinsic mode functions within the spectral domain. Thereafter, various nonlinear dimensionality reduction methods, including Isomap, Local Linear Embedding (LLE), and Hessian LLE, were applied to extract more informative features in a lower-dimensional representation. Furthermore, a 1D convolutional neural network (CNN) was employed to categorize the lower dimensional spectral signals into three classes: normal, pre-cancerous, and cancerous. The proposed methodology attained the best evaluation metrics using the Hessian LLE dimensionality reduction technique. A mean classification accuracy, precision, recall, F1-score, and specificity of 98.72%, 98.02%, 98.61%, 98.00%, and 99.30%, respectively, were obtained through a 5-fold cross-validation technique. The combination of fluorescence spectroscopy and machine learning holds promise for detecting cancer at earlier stages than current diagnostic methods. � 2024 Elsevier LtdItem Bio-inspired powder bed fusion 3D printed surfaces for enhanced critical heat flux and boiling heat transfer(2025) Laskar N.; Pancholi R.; Das M.K.The thermal performance and sizing of two-phase heat exchangers employed in various industries can be significantly improved by modifying the surface topology of tubes. The advancement in metal 3D printing technology enables the modification of surfaces to generate distinct patterns that enhance the heat transfer coefficient. This study aligns with these advances by investigating the boiling heat transfer performance of 3D printed surfaces using methanol as the working fluid. The enhanced surfaces studied include four bio-inspired 3D printed surfaces (Mushroom, Cactus, Petal, and Torus), each strategically designed to improve the heat transfer coefficient (HTC) and delay the onset of critical heat flux (CHF). The primary objective of this research is to discuss and analyze the heat transfer mechanism in terms of bubble dynamics, particularly focusing on the interaction between surface morphology and bubble behavior. Experimental results reveal significant enhancements in CHF and HTC for 3D printed surfaces compared to plain surfaces. Among the 3D printed surfaces, the Mushroom surface exhibits the highest enhancement in CHF of 88 % compared to the plain surface. Similarly, the maximum HTC enhancements are 138 %, 86 %, 65 %, and 46 % for the Mushroom, Cactus, Petal, and Torus surfaces, respectively, compared to the plain surface. The methodologies and findings of this study are poised to have a lasting impact on the field, potentially opening new avenues for improving the efficiency and effectiveness of various two-phase heat exchangers. � 2024Item A note on kernel functions of Dirichlet spaces(2025) Gehlawat S.; Jain A.; Sarkar A.D.For a planar domain ?, we consider the Dirichlet spaces with respect to a base point ??? and the corresponding kernel functions. It is not known how these kernel functions behave as we vary the base point. In this note, we prove that these kernel functions vary smoothly. As an application of the smoothness result, we prove a Ramadanov-type theorem for these kernel functions on ?�?. This extends the previously known convergence results of these kernel functions. In fact, we have made these observations in a more general setting, that is, for weighted kernel functions and their higher-order counterparts. � 2024 Elsevier Inc.Item Does climate-smart agriculture technology improve farmers' subjective well-being? Micro-level evidence from Odisha, India(2025) Sahoo D.; Mohanty P.; Mishra S.; Behera M.K.; Mohapatra S.Since the global population is expected to reach 9.7 billion by 2050, food production must increase by 70% in the next 30 years to provide food security in the face of climate change. Implementing climate-smart agriculture technology (CSAT) is essential for ensuring food security and promoting economic growth in the context of sustainable agriculture. Climate change and weather patterns significantly affect agricultural yield, necessitating the implementation of more efficient, productive, and climate-resilient techniques. However, the use of CSAT is a behavioural decision that affects the subjective well-being of the users. Using smart agricultural practices reduces climate change's impact on agricultural productivity and promotes sustainable agriculture, improving adopters' welfare. This study examines how the use of CSAT affects rural households' subjective well-being in Odisha, India. The result of the study shows that the use of CSAT significantly affects the subjective well-being of the farmers. The measured impact is 0.149, 0.181, and 0.144 for farmers whose intensity is 0.251�0.500, 0.501�0.750, and 0.751 and above, respectively, as compared to farmers whose intensity is 0.0�0.250. This implies greater satisfaction for farmers who engage in the moderate use of CSAT practices. Low utilization of technology may not yield benefits for farmers, while the adoption of advanced technology may not be economically viable. Additionally, CSAT is not easily available to households residing in low-lying areas, preventing them from improving their well-being. Only a small number of landowners in impoverished areas utilize CSAT. Therefore, it is necessary to evaluate government regulations regarding land and tenancy as well as develop measures for farmers to adapt to new technologies. � 2024 The AuthorsItem Artificial intelligence-based visual inspection system for structural health monitoring of cultural heritage(2024) Mishra M.; Barman T.; Ramana G.V.The United Nations aims to preserve, evaluate, and conserve cultural heritage (CH) structures as part of sustainable development. The design life expectancy of many CH structures is slowly approaching its end. It is thus imperative to conduct frequent visual inspections of CH structures following conservation guidelines to ensure their structural integrity. This study implements a custom defect detection, and localization supervised deep learning model based on the you only look once (YOLO) v5 real-time object detection algorithm by implementing a case study of the Dadi-Poti tombs in Hauz Khas Village, New Delhi. The custom YOLOv5 model is trained to automatically detect four defects, namely, discoloration, exposed bricks, cracks, and spalling, and tested on a dataset comprising 10291 images. The validity and performance of the custom YOLOv5 model are compared with a ResNet 101 architecture-based faster region-based convolutional neural network (R-CNN), and conventional manual visual inspection methods are used to convey the significance of the developed artificial intelligence-based model. The maximum average precision (mAP) of the custom YOLOv5 model and faster R-CNN is 93.7% and 85.1%, respectively. � Springer-Verlag GmbH Germany, part of Springer Nature 2022.Item Parametric Investigation of Droplet Generation Inside T-Junction Microchannel(2024) Jena S.K.; Srivastava T.; Kondaraju S.Droplet generation inside the microchannel and its manipulation�are one of essential droplet microfluidics processes. Many researchers study the physics behind droplet generation inside the T-junction microchannel; still, a thorough understanding of the concepts behind droplet formation is missing. The current investigation focuses on the parametric study of aqueous droplets in oil for a T-channel. This study examines the impact of numerous parameters, including the continuous phase capillary number (Cac), flow rate ratio (?), and various fluid combinations, as well as the dispersed phase Weber number (Wed), on the droplet size and frequency of droplet formation within a T-channel. Different flow patterns like squeezing, dripping, and jetting regimes are observed from flow visualization. The variation of droplet length due to Wed is studied for a constant Cac. In-house experiments are conducted for Cac = 0.0004�0.2, flow rate ratios (? = 1, 0.5, 0.25), and for varying Wed. � The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.