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Effect of different impact velocities on mechano-luminescence of natural calcite for mechanical sensors
This paper studies the mechanoluminescence (ML) behavior of natural calcite under varying impact velocities to assess its potential use in passive mechanical sensing. Calcite samples obtained from Byrnihat, Meghalaya (2603?03.8?N 9152?11.0?E), were analyzed using X?ray diffraction, field emission scanning electron microscopy, energy-dispersive X?ray spectroscopy, and Fourier-transform infrared spectroscopy. The analysis confirms the formation of the nanocrystalline hexagonal phase with minor impurities that affect its luminescent properties. When subjected to the mechanical impact, the calcite consistently produces asharp ML peak around 17?ms, regardless of the impact speed. The emitted light intensity shows alinear dependence on the impact velocity, suggesting areliable correlation between the mechanical input and optical response. The emission decay follows afirst-order exponential pattern, supporting its usefulness for identifying short-duration force events. Aplot of time against the logarithm of intensity displays aclear negative slope, supporting this kinetic model. These research findings highlight the potential of natural calcite as areliable and environmentally friendly material for mechanical sensor applications. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Reliability estimation of multicomponent stressstrength system with non-identical strength components based on generalized inverted exponential distribution
This research article estimates the reliability of s-out-of-k multicomponent systems subjected to common stress, where the strength components are non-identical and independently distributed. In practical scenarios, it is often unrealistic to assume that all strength components of a system are identical. To address this problem, we estimate the reliability of multicomponent stress strength (MSS) systems under the assumption of non-identical strength components and a common stress component, following independent generalized inverted exponential distributions. The estimation of MSS reliability is performed using both classical and Bayesian approaches. In the classical framework, the maximum likelihood estimation method is employed for point estimation, while asymptotic confidence intervals are constructed for interval estimation. In the Bayesian framework, point and interval estimates are obtained using TierneyKadanes approximation and Markov chain Monte Carlo techniques under an asymmetric loss function, as the Bayes estimator does not have a closed-form expression. A comprehensive numerical simulation study is conducted to compare the performance of the various estimators developed. Finally, the proposed methodologies are illustrated using a real-life dataset, showcasing their practical applicability and effectiveness. From the numerical studies, it is found that the MSEs for the classical and Bayes estimates are less than 0.010, and the coverage probabilities of the interval estimates attain their nominal level of significance, i.e. 95% in most of the cases. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Spotlighting recruitments: is AI dominating human resource practices? Qualitative research using NVIVO
Technology enriches and empowers organizations, making them more competitive and profitable. Any organization function is now technology-driven, helping to make faster and better decisions. Human resource management practices are also automated nowadays. Artificial Intelligence (AI) has gained enough importance in the recruitment process. Most of the literature emphasizes the use of technology and AI in establishing an effective and efficient recruitment process; however, the dominance of AI arouses the need to cast light upon the imbalance created after Al implementation. This study focuses on identifying the adverse impact of automated recruitment processes, which, due to a lack of human consciousness and intelligence, may sometimes not find the right candidate for the vacant position. Thus, a qualitative study was conducted to check the need for a balance of collaboration between humans and AI in recruitment. Primary data was collected through structured interviews with 21 HR professionals of IT companies in India implementing AI in their recruitment process. The findings highlight the positive impact of AI tools on recruitment efficiency but raise concerns about the loss of human connection, the potential misuse of AI, and the need for balanced decision-making in the hiring process. Based on the study's findings, it is recommended that a framework that combines humans and AI can be created. This study enables organizations to develop a customized recruitment process aligned with their vision, optimizing the use of AI and human intelligence to improve procedures and understand the impact of AI on hiring. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Diffusive instability, patterns and limit cycles in a slow-fast generalized SamardzijaGreller model: a multiscale approach
The SamardzijaGreller model is an extension of the classical LotkaVolterra predatorprey system, and this paper investigates the multiscale dynamics in a modified SamardzijaGreller model to take into account the slower timescales in predator reactions rather than prey. We present a comprehensive local stability analysis, pattern formation through diffusive instability and fractional Hopf bifurcations. The analysis of the spatio-temporal model reveals the effects of diffusion coefficients and parameter variations on the dynamical behavior of the slow-fast system. By analyzing the systems response to changes in the self-diffusion rate of the prey (dX), the intra-species competition rate of the first predator (d1) and the interaction parameter a, we observe chaotic patterns for small values of a, particularly when the prey exhibits strong diffusion. Increases in a lead to the emergence of regular, periodic patterns that are homogeneous in space. We discuss in detail how fractional-order models create memory effects that inhibit chaotic transitions, potentially being delayed or avoided in the temporal model. The study shows clear differences in the dynamical regimes between the integer-order and the fractional-order models. The latter model gives more significance to the stabilization effect of the fractional-order derivative on ecological systems and improves our understanding of predatorprey interactions under different parameter settings. Findings clarify the potential to derive ecological stability from emergent patterns and transition into a better understanding of complex ecological processes. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Control of chaos and intermittent periodic motions in Rayleigh-Bard convection using a feedback controller
Control of regular convective motion, chaos and periodic motion in the Rayleigh-Bard system is studied by considering a feedback control mechanism that considers the dependence of the heating (cooling) of the two boundary plates on one another. This set up ensures that the different flow regimes (convective, chaotic and periodic) in the system have no mechanical interference and the control remains an external mechanism. The rheostatic influence of feedback control on these flows is demonstrated by investigating in detail the critical Rayleigh number in the case of regular convective motion and the Hopf-Rayleigh number in the case of chaotic motion. For mild coupling between lower and upper boundary temperatures, periodic motions are intermittently observed in an otherwise chaotic regime at times when the system arrives at a situation (fuelling zone) wherein it needs to conserve energy in order to sustain chaos at subsequent times. For strong coupling between the boundary temperatures, an interesting situation arises wherein chaos makes a delayed and brief appearance and gives way to a prolonged spell of periodic motion. Features of the classical Rayleigh-Bard system are retained but each regime makes a delayed appearance. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Spatial variations of landslide severity with respect to meteorological and soil related factors
Landslides, a prevalent natural disaster, wreak havoc on both human lives and vital infrastructure, making them a significant global concern. Their devastating impact is immeasurable, necessitating proactive measures to minimize their occurrence. The ability to accurately forecast the severity of a landslide, including its potential fatality rate and the scale of destruction it may cause, holds tremendous potential for prevention and mitigation to reduce the risk and the damage caused by a landslide to infrastructure and life. In this study, the spatial variability in severity of landslides (in terms of mortality rates) and its dependence on various meteorological, geographical and soil composition has been attempted to be established. To do this, Ordinary Least Squares (global) and various Geographically Weighted (local) models have been employed to observe the varying relation between mortality rates and its various causative factors. Existence of geographical heterogeneity in the relationships is also investigated. The spatial pattern of landslide mortality and its associations with various causative variables in the South Asian Region are investigated and analysed. Through this, insights into targeting of prevention and mitigation measures for landslides based on a given location can be obtained by studying the various forms of heterogeneous spatial associations observed. The outcomes highlight that the local models in the form of Gaussian GWR and Poisson GWR outperform their global counterparts by a huge margin with better R2 and Adj R2 values. In comparison with Poisson GWR and Gaussian GWR, it is seen that Poisson GWR outperforms Gaussian GWR in terms of Mean Absolute Error, Mean Squared Error and Corrected Akaike Information Criterion. Furthermore, several intriguing local relationships patterns are also noted. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Dual ion-imprinted chitosan-stabilized platinum nanoparticles for simultaneous electrochemical detection of Cd2? and Pb2? in water samples
The development of highly selective and ultrasensitive electrochemical sensors for trace-level heavy metal monitoring remains a critical challenge in environmental analysis. In this work, a novel dual ion-imprinted, chitosan-stabilized platinum nanoparticle (PtNP)modified glassy carbon electrode (GCE) is reported for the simultaneous electrochemical detection of Cd2? and Pb2? in aqueous media. The synthesized PtNPs provide a large electroactive surface area and are uniformly stabilized within a chitosan matrix enriched with oxygen- and nitrogen-containing functional groups, which act as selective coordination sites for target metal ions. The incorporation of dual ion-imprinting generates specific recognition cavities that promote selective adsorption of Cd2? and Pb2? through electrostatic and coordination interactions, leading to significantly enhanced sensitivity and selectivity. Under optimized conditions, the sensor exhibits wide linear detection ranges of 44.04pM0.18nM for Pb2? and 79.4pM0.18nM for Cd2?, with remarkably low detection limits of 13.2pM and 23.83pM, respectively. The proposed sensing platform demonstrates excellent anti-interference capability and reliable performance in real water samples, confirming its applicability for practical environmental monitoring. This study highlights the synergistic effect of dual ion-imprinting and chitosan-stabilized PtNPs, offering a robust and efficient strategy for multi-ion electrochemical sensing of toxic heavy metals. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
Enhanced image encryption using fractional-order chaotic systems and neural network-based optimization for secure multimedia applications
The rapid expansion of multimedia data in fields like healthcare and finance necessitates robust image encryption to protect sensitive content. Conventional chaotic encryption, based on integer-order systems, is hindered by restricted key spaces (e.g., and suboptimal parameter choices, exposing vulnerabilities. This work introduces an innovative encryption method that merges a fractional-order chaotic Logistic map with neural network optimization to overcome these shortcomings and enhance security. Utilizing the Grunwald-Letnikov derivative, the fractional-order Logistic map produces a complex, unpredictable sequence for encryption. A feedforward neural network fine-tunes parameters (,), elevating the Lyapunov exponent from 0.5032 to 0.6540, signifying heightened chaos. This integration harnesses fractional-order memory effects and neural network adaptability, surpassing traditional integer-order encryption constraints. The method achieves a key space of, entropy of 7.9962, and horizontal correlation of 0.0028. Parameter sensitivity tests show significant output variation with minor changes. Security analysis yields NPCR at 99.60% and UACI at 33.45%. Neural network training achieves a low mean squared error of 0.0032912 by epoch 100, with high correlation. Encryption of 256256 images in 0.21 seconds and 720p video at 41.67 fps (0.024 s/frame) supports real-time applications. By combining fractional-order chaos with machine learning, this approach delivers superior image encryption, addressing integer-order system limitations. It provides a scalable framework for secure multimedia communications. Future efforts will extend the technique to color images and video, incorporating advanced machine learning for greater resilience. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Whispered speech emotion recognition with gender detection using hybridopti-gendernet
The Whispered Speech Emotion Recognition (SER) presents special challenges because it does not involve the vibration of vocal folds and less acoustic information, which makes it more difficult to identify the nuances of emotions and gender-related peculiarities than in normal speech. The research presents a new system, HybridOpti-GenderNet, of concurrent whispered speech emotion and gender recognition. It is a framework of six stages are data collection, pre-processing, feature extraction, feature selection, gender detection, and emotion recognition (ER). A Red Butterfly Optimization Algorithm (RBOA), which uses Butterfly Optimization Algorithm (BOA) and Red Kite Optimization Algorithm (ROA) by adjusting the weight to achieve optimal feature selection, is proposed to achieve a better balance between exploration and exploitation. Gender identification is carried out with the proposed HybridOpti-GenderNet, a new hybridization of Bi-LSTM and DCNN, and ER is accomplished with the help of the Attention-Enhanced Hybrid Belief Network (AHBN) that incorporates Deep Belief Network (DBN), Feedforward Neural Network (FNN) & a tailor-made attention mechanism to detect delicate emotional signals. The innovativeness of the given work is that the gender-informed recognition is combined with a high-level optimization and a hybrid deep learning pipeline that enhances the robustness of the analysis of whispered speech significantly. The system was tested in Python and tested on the GeWEC and EMO-DB data, showing superior performance in cases of privacy preservation, noisy conditions, and real-life human-computer interaction. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Lung cancer detection and classification with optimal feature selection and two-fold-deep-learning-classifiers
The respiratory system is undoubtedly hampered by lung disorders. Also, one of the important reasons for death among people all around the world has been lung cancer. Early discovery can advance human survival probabilities. As a result, a unique ensemble-deep-learning paradigm for lung cancer detection and classification is established in the present research effort. The projected model includes five major phases: (a) image augmentation, (b) pre-processing, (c) segmentation, (d) feature extraction, (e) feature selection, and (f) lung cancer detection and classification, respectively. The collected raw CT images are augmentation with SMOTE. The augmented images are pre-processed via Median Filtering (for noise removal) and Contrast-limited adaptive histogram equalization (CLAHE) (for image contrast enhancement). Subsequently, from the pre-processed data, the ROI is identified via optimized U-NETS. The activation function (hyper-parameter) of U-NETS is optimized via a new hybrid optimization model-Digging Tunaswarm Optimizer (DTO). This DTO is the conceptual amalgamation of two standard meta-heuristic optimization models, namely Honey Badger Algorithm (HBA) and Tuna Swarm Optimization (TSO) models, respectively. Then, from the selected ROI area, the features like texture features (Manhattan Distance-based-GLCM, GLRM), Color features (Color Histogram), and Shape features (Moments, Area, Perimeter) are extracted. Among the extracted features, the optimal features are selected using DTO. This optimal feature selection reduces the computational complexity of the projected model. Finally, using these extracted optimal features, the two-fold-deep-learning-classifier framework is trained. This two-fold-deep-learning-classifiers framework encapsulates the Bidirectional long-short term memory (Bidirectional LSTM) and the Recurrent Neural Network (RNN) and the Modified Convolutional Neural Network (M-CNN). In the first phase, the Bi-LSTM and RNN are clamped, and they are trained with the selected optimal factors. The outcome from Bi-LSTM and also RNN was fed as input to M-CNN. Final detected findings based on the existence or absence of lung cancer are acquired from the M-CNN, whose loss function has been modified with RMSE. Finally, a comparative evaluation is undergone to validate the efficiency of the projected model. The proposed model has a higher overall accuracy (92.4%) detecting modelling accuracy (96.3%) and classification accuracy (92.4%) compared to other models such as HBA, TSO, CNN, 3D CenterNet, and TSCNN. The use of a two-fold deep learning framework is responsible for these improvements, and the model also has lower failure rates (FPR and FNR) in detecting lung cancer. It is suggested that the proposed approach is effective in early-stage lung cancer detection. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Design of automatic follicle detection and ovarian classification system for ultrasound ovarian images
Polycystic Ovary Syndrome (PCOS) is a common reproductive and metabolic disorder characterized by an increased number of ovarian follicles. Accurate diagnosis of PCOS requires detailed ultrasound imaging to assess follicles size, number, and position. However, noise often needs to be improved on these images, complicating manual detection for radiologists and leading to potential misidentification. This paper introduces an automated diagnostic system for integration with ultrasound imaging equipment to enhance follicle identification accuracy. The system consists of two main stages: preprocessing and follicle segmentation. Preprocessing employs an adaptive Frost filter to reduce noise, while follicle segmentation utilizes a region-based active contour combined with a modified Otsu method. Unlike the conventional Otsu method, where the threshold value is selected manually, the modified Otsu method automatically selects initial threshold values using an iterative approach. After segmentation, features are extracted from the segmented results. An SVM classifier then categorizes the ovarian image as normal, cystic, or polycystic. Experimental results demonstrate that the proposed methods Follicle Identification Rate is 96.3% and the False Acceptance Rate is 2%, which significantly improves classification accuracy, highlighting its potential advantages for clinical application. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Multitask EfficientNet affective computing for student engagement detection
In the realm of education, feedback emerges as a pivotal component, serving to foster engagement and interaction while also facilitating the refinement of teaching methods to capture and maintain student attention. Traditional classroom assessment methods often struggle to accurately gauge the degree of comprehension among students during lectures, relying on manual comment collection that inherently carries the risk of inaccuracies. In response to this challenge, a novel system has been proposed, harnessing the power of Facial Emotion Recognition (FER) technology to capture student feedback. Within this framework, students are given a unique avenue to convey their emotions and reactions, employing facial expressions and gestures as the means to communicate. This innovative approach enables the analysis of students emotional responses and thereby provides invaluable insights into their comprehension levels, as well as the overall quality and engagement experienced during lectures. The approach takes shape through the utilization of Computer Vision techniques, with a particular focus on an unobtrusive methodology for assessing students overall engagement. Overcoming limitations of traditional assessment, our approach integrates compound scaling, employing the proposed Multitask EfficientNetB0 model recognized for its proved accuracy in emotion recognition (95.7%) and behavior analysis (96.3%) across diverse datasets (DAiSEE, iSED, iSAFFE). The behavioral classification system categorizes students into Engaged and Disengaged classes within a multi-class framework, providing nuanced insights into comprehension and Student engagement. Assessment metrics, including ROC Curves, Precision, Recall, and F1-Score, ensure a thorough evaluation. Our systems adaptability is demonstrated across varied educational environments, showcasing real-world efficacy in classrooms, laboratories, and seminar halls. The inclusion of MTCNN enhances face detection capabilities, facilitating robust analysis in dynamic scenarios. Expanding its applicability, the model has been put to the test in a range of educational settings, including classrooms, laboratory environments, and seminar halls, offering dual-capability analysis of both emotions and behavior. This comprehensive approach yields nuanced insights into student engagement and interaction, and its performance has been validated through real-world deployment within classrooms and seminars The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Test case reduction and SWOA optimization for distributed agile software development using regression testing
Regression testing is a well-established practice in software development, but its position and importance have shifted in recent years as agile approaches have grown in popularity, emphasizing the fundamental role of regression testing in preserving software quality. In previous techniques, the challenge to address is determining the number and size of clusters and optimization to stabilize the cost and efficacy of the strategy. To overcome all the existing drawbacks; this research study proposes test case reduction and Support-based Whale Optimization Algorithm (SWOA) for distributed agile software development using regression testing. The purpose of this research study is to look into regression testing strategies in agile development teams and to find out what they are optimum clustered test cases. The proposed strategy is divided into two stages: prioritization as well as selection. Prioritization and selection are carried out once the test instances have been retrieved and grouped. The test case clusters are sorted and prioritized in this stage to ensure that the most critical instances are chosen first. During this stage, the test case clusters undergo sorting and prioritization to guarantee that the most essential cases are selected initially. Second, the SWOA is used to choose test cases with a greater frequency of failure or coverage criterion. The results of the assessment metrics show that the proposed approach outperforms other current regression testing strategies substantially. Based on experimental findings, our proposed approach betters existing methods in terms of information performance. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
EASM: An efficient AttnSleep model for sleep Apnea detection from EEG signals
This paper addresses the crucial task of automatic sleep stage classification to assist sleep experts in diagnosing sleep disorders such as sleep apnea and insomnia. The proposed solution presents a novel attention-based deep learning model called, Efficient Attention-sleep Model (EASM), designed specifically for sleep apnea detection using EEG signals. EASM incorporates a streamlined architecture that includes a modified Muti-Resolution Convolutional Neural Network (MRCNN), Adaptive Feature Recalibration (AFR), and a simplified Temporal Context Encoder (TCE) module to reduce complexity. To mitigate overfitting, ridge regression is utilized, which incorporates a penalty term to enhance model generalization. Furthermore, the proposed EASM utilizes a class-balanced focal loss function to address data imbalance issues. The effectiveness of EASM is evaluated on two publicly available datasets, SLEEP EDF-20 and SLEEP EDF-78. Comparative analysis of EASM against state-of-the-art models demonstrates its superior performance in terms of accuracy, training time, and model complexity. Notably, the proposed model achieves a 50% reduction in training time and a 55.7% decrease in complexity compared to the Attnsleep model. The EASM achieves a classification accuracy of 85.8% with minimum loss when compared to the Attnsleep model. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
EGMM: removal of specular reflection with cervical region segmentation using enhanced Gaussian mixture model in cervix images
Colposcopy is a crucial imaging technique for finding cervical abnormalities. Colposcopic image evaluation, particularly the accurate delineation of the cervix region, has considerable medical significance.Before segmenting the cervical region, specular reflection removal is an efficient one. Because, cervical cancer can be found using a visual check with acetic acid, which turns precancerous and cancerous areas whiteand these could be viewed as signs of abnormalities. Similarly, bright white regions known as specular reflections obstruct the identification of aceto-whiteareas and should therefore be removed. So, in this paper, specular reflection removal with segmentingthe cervix region ina colposcopy image is proposed. The proposed approach consists of two main stages, namely, pre-processing and segmentation. In the pre-processing stage, specular reflections are detected and removed using a swin transformer. After that, cervical regions are segmented using an enhanced Gaussian mixture model (EGMM). For better segmentation accuracy, the best parameters of GMM are chosen via the adaptive Mexican Axolotl Optimization (AMAO) algorithm. The performance of the proposed approach is analyzed based on accuracy, sensitivity, specificity, Jaccard index, and dice coefficient, and the efficiency of the suggested strategy is compared with various methods. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
ANFIS-Based Multi-Sensor Data Fusion Model for Optimized Autonomous Vehicle Navigation Using Big Data and Filtering Techniques
The navigation of an autonomous vehicle depends mostly on the integration of multi-sensor data from sources such as LiDAR, GPS, radar, and cameras. Issues like sensor noise, data asynchrony, and fusion inaccuracies hamper reliable real-time decision-making. This paper proposes an optimized multi-sensor data fusion framework integrating big data analytics with modern filtering techniques to increase navigation accuracy and system robustness. The proposed model integrates Kalman Filter (KF), Extended Kalman Filter (EKF), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for dynamic state estimation and adaptive noise accommodation. In addition, sensor reliability and position tracking are enhanced via Bayesian data fusion and Particle Filter. Simulation results show that the proposed technique is evidently superior to existing models in accuracy (1.5 RMSE), convergence time (0.98s), and latency (50 ms). The fusion system enhances stability and responsiveness in autonomous navigation and offers an intelligent transportation framework that can be deployed efficiently at a real-time scale. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Elevating pyrrole derivative synthesis: a three-component revolution
Pyrrole is an essential chemical with considerable relevance as a pharmaceutical framework for many biologically necessary medications. The growing demand for biologically active compounds calls for a simple one-pot method for generating novel pyrrole derivatives. Nots surprisingly, several multicomponent reactions (MCRs) aim to synthesize pyrrole derivatives. However, this review presents the three-component synthesis of pyrrole derivatives, highlighting the significance of multicomponent reaction in synthesizing eclectic multi-functionalised pyrrole covering the selected literature on the three-component synthesis of substituted pyrrole from 2016 to late 2023. Furthermore, this article classifies the reactions based on the starting material with functional groups involved in the pyrrole ring formation. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Navigating Hope and Despair: The Agonizing Boat Journeys of the Sri Lankan Tamil Refugees
This study investigates the psychological experiences of Sri Lankan Tamil Refugees (SLTRs) involving boat journeys and the refugee lives that follow. Thirty participants from rehabilitation camps in Tamil Nadu, India, were interviewed. Reflexive thematic analysis was used to analyze the responses. The two overarching themes were 'the motives and consequences of exile' and 'the complexities of refugee life.' The findings reveal that the participants experienced psychosomatic symptoms immediately upon arrival, reflecting the inner conflicts resulting from war trauma and boat crossings. They reported serious bouts of trauma during and after their crossing. The first- and second-generation participants recounted nightmares pertaining to boat journeys which contributed to hauntedness, which is a state of emotional or mental disturbance often attributed to past trauma. Refugee life is complex, encompassing hopelessness and haunted memories which are passed down to subsequent generations, leading to intergenerational trauma. The boat journey in itself is an ambivalent phenomenon blending hope and profound agony. This study is a novel attempt to gain coherent insights into the boat travel experiences of the SLTR, the dynamics of the interplay of collective unconscious mechanisms, and anxieties in exile. These insights can play a seminal role in facilitating psychological reconstruction and developing effective coping strategies. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Wavefield analysis of nano-scale surface/interface effects on dynamic stress response in biphasic laminated media with circular defects
The dynamic stress concentration around a nanoscale circular hole located at the centre of a two-phase circular laminated medium subjected to localized anti-plane SH-wave loading is examined in this paper. The model (used in this paper) is developed using the complex variable function method combined with wavefield superposition and multipolar expansion. In this framework, GurtinMurdoch (GM) surface and interface elasticity is incorporated at both the material interface and the free surface of the nanohole, resulting in a coupled two-surface formulation that has not been previously reported for biphasic geometries. This leads to non-classical traction-jump conditions and modified stress-free boundary conditions. The resulting infinite system of linear equations is then solved through series truncation. Numerical results reveal that nanoscale surface and interface effects significantly reduce the dynamic stress concentration factor (DSCF) around the hole, with the most substantial attenuation occurring at low wavenumber ratios and low shear modulus ratios. Conversely, the stress reaches its maximum amplification under long-wavelength excitation or when the outer layer is relatively soft. Overall, these findings offer new insights into nanoscale toughening mechanisms in realistic multilayered systems, providing a solid foundation for defect detection, lifetime prediction, and the damage-tolerant design of laminated nanocomposites and coreshell nanostructures. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
KleinGordon nonlocal dynamics of porous piezo-thermoelastic medium with surface irregularity under fractional-order modified LS model
The miniaturization of devices alongside advances in thermal management technologies necessitates the generalization of heat conduction and thermal elastic coupling to faithfully represent material responses at ultrashort temporal scales. Motivated by viscoelastic mechanical analogies, this work develops an analytical framework for investigating vibrational behavior in an orthotropic, size-dependent piezo-thermoelastic substrate featuring voids, modeled within the Modified LordShulman (MLS) thermoelasticity theory augmented by fractional derivatives. Employing the KleinGordon nonlocal elasticity formulation, the governing equations of motion are rigorously derived. The normal mode method facilitates the examination of coupled thermoelectro-mechanical excitation phenomena. Emphasis is placed on a corrugated interface contiguous to a vacuum, where comprehensive boundary conditions encompassing thermal, electrical, mechanical, and stress equilibria are imposed to determine fundamental field variables. The study systematically evaluates the influence of pivotal parameters, including temporal evolution, nonlocality characteristics, and spatial coordinates, on the thermomechanical and electrical responses, with outcomes substantiated through detailed graphical representations. Although previous investigations have addressed vibrations in porous piezo-thermoelastic media under varying theoretical constructs, the current research uniquely elucidates the dynamic response of a size-dependent porous piezo-thermoelastic medium with a corrugated surface within the fractional-order modified LordShulman framework, marking a significant advancement in the modeling of smart microstructured materials. The Author(s), under exclusive licence to Springer Nature B.V. 2026.