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Carbonized Molybdenum Disulfide-Decorated Carbon from Waste Papaya Straws as Counter Electrode for Bifacial Dye-Sensitized Solar Cells
Abstract: Ongoing research efforts are aimed at developing bifacial dye-sensitized solar cells (DSSCs) that are both economically viable and high-performance. In this investigation, molybdenum disulfide-decorated biomass-derived carbon from waste papaya straws (MoS2@PS) was synthesized via a hydrothermal technique, and then subsequently subjected to annealing at various temperatures, referred to as PS26, PS27, and PS28. Annealing MoS2 -decorated PS resulted in an increase in surface area which was confirmed using BrunauerEmmettTeller measurements, revealing type IV isotherms with an H3 hysteresis loop showing the mesoscopic nature of PS28. The maximum recorded photovoltaic conversion efficiency was approximately 6.85% for the PS28 composite counter electrode (CE), highlighting its potential as a platinum-free alternative. Moreover, cyclic voltammetry and Tafel polarization studies confirmed the superior electrocatalytic activity of the MoS2@PS CE in the reduction process of triiodide ions (I3?). Studies on transmittance were conducted to validate the bifacial characteristics of DSSCs. The results from electrochemical impedance spectroscopy indicate that the MoS2@PS CE-based DSSCs exhibit rapid charge transfer at the electrode/electrolyte interface, with a resistance of RCT = 24.27 ? for the PS28 counter electrode. The favourable attributes of optimal conversion efficiency, high transmittance, ease of preparation, rapid charge transfer, and affordability suggest that MoS2@PS counter electrodes hold significant potential for applications in DSSCs. The Minerals, Metals & Materials Society 2025. -
Unusual Generation of Filament-Like Crystal on Vapor-Deposited Sb2Se3 Whiskers Under Ambient Atmosphere
This research article proposes a novel strategy to explore the nucleation and growth mechanism of a filamentary spike-like feature (secondary growth) on vapor-deposited antimony selenide (Sb2S3) whiskers (primary crystallization) due to the influence of electric fields, defects, and ambient atmosphere. Small, ultra-long, branched whiskers were produced by the physical vapor deposition (PVD) method utilizing a homemade tubular furnace. In order to grow these crystal features, a temperature difference (?T) of 180C was maintained by adjusting the temperature in the hot (710C) and cold zones (530C), followed by a fast cooling rate of 12C/min. Optical and scanning electron microscopy, three-dimensional (3D) profilometry, and Raman imaging analysis were utilized to investigate the surface features of the as-grown and electrically activated whiskers under ambient atmosphere. A possible crystallization (secondary growth) mechanism of the filamentary crystals in the defective region under the influence of an electric field was proposed. We noted that the effect of extrinsic impurities like oxygen coupled with an electric field promoted the growth of filamentary crystals on the whiskers, which were probed utilizing x-ray diffraction (XRD), energy-dispersive x-ray analysis (EDAX), x-ray photoelectron spectroscopy (XPS), Raman analysis, thermogravimetric analysis (TGA), and differential thermal analysis (DTA). An orthorhombic crystal structure with unit dimensions of a = 11.632 b = 11.798 and c = 3.987was calculated from the XRD results. This research provides a new growth mechanism and a comprehensive picture of nucleation followed by branching of filamentary crystals on the primary crystallized Sb2Se3 whisker surface. The research output with regard to layered chalcogenide materials (LCMs) will undoubtedly help researchers focus on removing secondary/whisker growth from LCM-based optoelectronic devices. The Minerals, Metals & Materials Society 2025. -
Performance Evaluation of Friction Stir Spot Welding of Al 5754 and Al 6111 using Machine Learning Approaches
This study evaluates advanced machine learning (ML) and deep learning (DL) models for predicting the tensile shear and bending strength of friction stir spot welding joints involving Al 5754 and Al 6111 alloys. ML techniques include Linear Regression, Decision Tree, Random Forest (RF), K-Nearest Neighbors, Support Vector Regression, and XGBoost, while DL models comprise Recurrent Neural Network (RNN) and Backpropagation Neural Network (BPNN). The models were assessed for discrepancies between experimental and predicted results, with the best-performing model identified using R-squared (R2), Root-Mean-Square Error, Mean Square Error, and Mean Absolute Error. The data preprocessing phase included feature scaling and an 85:15 train-test split. Key input process parameters included spindle speed, dwell time, plunge depth, and tool pin profile. The results demonstrate that XGBoost yielded the highest predictive accuracy, achieving an R2 score of 99.99% for both tensile shear and bending strength, while RF offered a strong balance between accuracy and robustness. Other ML models struggled with the datasets complexity, resulting in lower performance. Among DL approaches, the BPNN outperformed the RNN, achieving approximately 99.8% accuracy by effectively capturing complex data patterns. ASM International 2025. -
Multivariate statistical optimization of phenolics and antioxidants from nutmeg seeds (Myristica fragrans Houtt)
The present study aimed to optimize the phenolic and antioxidant-rich extract from the nutmeg (Myristica fragrans Houtt) by using a two-factor 26-run central composite design-based response surface methodology tool. The selected parameters were extraction period (2 to 5days), solvent-to-water ratio (v/v) (50100%), and type of solvent (acetone or ethanol). The optimized extract at conditions of 3.14days incubation and 68% (v/v) acetone showed total phenolic content (TPC), total flavonoid content (TFC), and DPPH antioxidant assay as 376.38mg GAE/g DW, 34.40mg QUE/g DW and 842.46mg AAE/g DW, respectively. Among the nineteen (19) compounds identified by the LCMS, myristicin (37.74%) was found to be the highest. Nine (9) alkane-fatty acyl compounds were determined by the GCMS analysis, as well. Additionally, SEM and XRD revealed sheet-like anatomy with the presence of Carbon (C), Oxygen (O) and Potassium (K). The study presented a unique approach to optimizing phenolic-rich antioxidant extracts from nutmeg using response surface methodology, offering valuable insights for more efficient extraction of bioactive compounds with minimal resource waste and potentially enhancing the utilization of nutmeg's nutraceutical properties. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Design space optimization for the extraction of anti-thrombin-rich phenolics and flavonoids from Justicia adhatoda L. using response surface methodology and in silico validation of their anti-thrombin activity
The study focused on optimizing the extraction of anti-thrombin phenolics and flavonoids from the stem and flower ofJusticia adhatoda(JA) using response surface methodology (RSM). Key factors utilized for the optimization included HCl concentration (0.11N), extract concentration (50150mg/mL), methanol proportion (0100%), and incubation temperature (32 2C for hot and 6 2C for cold). Fluorescence microscopy identified plant parts enriched with pharmacologically active compounds. The optimized extracts demonstrated substantial levels of phenolics, flavonoids and thrombin inhibitory activity across all samples. Antioxidant activity was measured using the DPPH and ABTS radical scavenging assays. Furthermore, multivariate optimization enhanced the antioxidant properties of the extracts. LCMS analysis of the optimized extracts fromJA(stem and flower) identified the presence of 22 compounds, with anti-thrombin polyphenols and flavonoids predominating, constituting approximately 30% and 35%, respectively. In silico studies, including molecular docking and dynamic simulations, revealed that the flavonoid Naringenin (?4.868kcalmol?1) had a higher inhibitory potential against the thrombin protein compared to the reference drug Dabigatran (?4.269kcalmol?1). This study demonstrated that flavonoids with significant anti-thrombin activity can be effectively extracted fromJ. adhatodastem and flower using this optimized extraction procedure. The Author(s), under exclusive licence to the Institute of Chemistry, Slovak Academy of Sciences 2025. -
Tailoring natural rubber properties through CaO nanoparticle integration and curing technique
The present study investigates the development of natural rubber nanocomposites reinforced with calcium oxide nanoparticles and cured using pentane-1,5-diylidenediamine, a green crosslinker derived from glutaraldehyde and ammonia. calcium oxide nanoparticles (0.020.16wt%) were incorporated via latex blending, and composites were evaluated in both uncured and pentane-1,5-diylidenediamine -cured forms. Fourier-transform infrared spectroscopy confirmed that the calcium oxide nanoparticles were well dispersed and actively involved in crosslinking with the rubber matrix. Scanning electron microscopy showed that the cured composites had a more uniform surface and better distribution of nanoparticles. Mechanical testing revealed a remarkable tenfold increase in tensile strength from 0.217MPa to 8.478MPa and a significant improvement in elongation at break, rising from 666 to 1317% in the pentane-1,5-diylidenediamine -cured samples. The best mechanical performance was achieved at 0.10wt% calcium oxide. Dielectric measurements further highlighted an increase in permittivity and AC conductivity, especially in the cured composites, attributed to interfacial polarization and the formation of nanoparticle networks. Altogether, these results underline the synergistic benefits of calcium oxide nanoparticles and pentane-1,5-diylidenediamine curing in enhancing the structural, mechanical, and dielectric properties of natural rubber, making it a strong candidate for advanced elastomeric and dielectric applications. The Author(s), under exclusive licence to the Institute of Chemistry, Slovak Academy of Sciences 2026. -
Investigating key biological traits of Malva parviflora influencing its competitive invasion in wheat crops
Plant invasion is a major concern for ecologists and agriculturists. Early detection of potential invaders (weeds) would save energy and resources that would otherwise be used to tackle them after they had spread. A study was initiated at the ICAR-Indian Agricultural Research Institute, New Delhi, on the basis of the early detection and rapid response (EDRR) strategy. For this study, we choose the little mallow (Malva parviflora L.), a newly introduced Malvaceae family weed in the agricultural fields of Delhi and adjoining regions of India. The above-ground allometric parameters ofM. parviflora populations in the main field and the field boundary were compared. The findings revealed that the EDRR approaches established by this study provided useful information to corroborate the weed species' invasion. The canopy diameter, plant height, and the number of leaves M. parviflora differed between the field boundaries (25.72cm, 24.40cm, 58.97, respectively) and main field (12.79cm, 49.08cm, 18.85, respectively) populations in all three locations, except the canopy diameter was comparable in location 2. Furthermore, neighborhood analysis showed that the M. parviflora had greater acclimatization with a variety of neighbors (38 plant species), i.e., legumes, noxious weeds, and seasonal dominant weeds. Malva parviflora has become a dominant weed along the field boundary. However, it has the potential to spread to the main field and become a serious weed in winter crops in the future. The EDRR methodologies developed in this study can be used to assess the invasion of new weeds in a variety of habitats. Plant Science and Biodiversity Centre, Slovak Academy of Sciences (SAS), Institute of Zoology, Slovak Academy of Sciences (SAS), Institute of Molecular Biology, Slovak Academy of Sciences (SAS) 2025. -
An improved atom search optimization algorithm based on ranking strategy and sine cosine algorithm for epileptic seizure detection
Epilepsy is a serious neurological disorder that remains difficult to detect with high accuracy. Automated seizure detection using EEG signals has gained increasing attention, and optimization algorithms are often applied to improve system performance. Atom Search Optimization (ASO) has strong global search ability but frequently suffers from premature convergence and limited local search efficiency. To address these issues, this study proposes a hybrid algorithm that combines ASO with the SineCosine Algorithm (SCA) and a ranking strategy (RSHASOSCA). ASO provides effective global exploration, SCA enhances local exploitation, and the ranking strategy stabilizes convergence, together creating a more balanced and reliable search process. The method was evaluated on the CHB-MIT scalp EEG dataset. Features were extracted using Wavelet Packet Transform (WPT) and refined with the KruskalWallis test (p ? 0.001). Comparative experiments against twelve established optimization algorithms showed that the RSHASOSCA framework achieved superior performance. When applied with an SVM classifier, it reached 99.13% accuracy and an AUC of 1. These findings highlight the value of integrating ASO, SCA, and ranking strategy, and demonstrate the potential of the proposed framework for reliable and efficient seizure detection in clinical practice. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025. -
Optimal setting of arc welding robot and laser sensor variables for getting maximal weld quality, positional accuracy, and smooth trajectory
Abstract: For seam-finding applications, a robotic welding system and laser sensor can be coupled to achieve improved repeatability and shorter cycle times. This manuscript investigates the impact of several robot variables, including robot orientation, robot travel speed, and focal length of the laser sensor, on three key factors: positioning error, associated joint jerk-torque rate, and weld quality. An Enhanced Multi-Objective NSGA-II (EMONSGA-II) is proposed, which combines NSGA-II with Nelder Mead local search to find the best values for robot and sensor variables. The goal is to acquire the lowest values for joint jerk-torque rate, positional error, and maximum weld quality metrics. The maximized weld quality is represented by maximized ultimate strength, yield strength, and penetration of weld joint, as minimized weld bead height and width. Fuzzy logic has been used to transform the multi-performance weld characteristics into one term of the weld quality. The experiments have been performed using the Arc 50 series welding system with AccuFast point laser sensor integrated MOTOMAN MA 1440 arc welding robot system. Finally, the optimal setting of the robot and sensor parameters have been validated through experimentation to observe the weld quality and positional accuracy. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2025. -
In Vitro Cytotoxic Potential and Integrated Network Pharmacology, Molecular Docking and Molecular Dynamic Approaches to Decipher the Mechanism of Gymnostachyum febrifugum Benth., in the Treatment of Breast Cancer
Gymnostachyum febrifugum, a less-known ethnomedicinal plant from the Western Ghats of India, is used to treat various diseases and serves as an antioxidant and antibacterial herb. The present study aims to profile the cytotoxic phytochemicals in G. febrifugum roots using GCMS/MS, in vitro confirmation of cytotoxic potential against breast cancer and an in silico study to understand the mechanism of action. Phytochemical profiling using GCMS/MS showed the presence of eight cytotoxic molecules with lupeol in high abundance. A potent cytotoxic effect of G. febrifugum roots against breast cancer was also observed with antiproliferation, antimigration, inhibition in colony formation and death of breast cancer cells. Further, the cytotoxic potential of the plant was confirmed with the apoptosis of cells as observed in the flow cytometry. In silico network pharmacology, GO and KEGG analysis suggested the modulation of proteins of MAPK, PI3K-AKT and apoptosis pathways by lupeol to induce cytotoxicity in breast cancer. Further, dynamic simulation revealed MAPK and AKT as the major targets for lupeol. Our studies comprehensively elucidated the role of lupeol, a major phytochemical in G. febrifugum to induce cytotoxicity against breast cancer by targeting major cancer signaling pathways, providing a promising strategy and scientific basis to explore lupeol in targeted cancer therapy. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
A method for identification of restarted radio sources from large radiosurveys
Active galaxies hosting radio jets can exhibit distinct active phases marked by two sets of radio lobes. Typically, these episodic radio sources have been identified through morphological observations. In addition, spectral characteristics-based methods are also employed wherever multi-frequency deep radio observations are available. However, these methods are inefficient in detecting restarted radio sources that do not exhibit a clear morphology. To address this, a method of using the spectral curvature (SPC=?150MHz1400MHz-?74MHz150MHz) to identify restarted radio sources is presented. This is based on the fact that restarted radio sources with significant remnant emission are expected to have concave spectra in contrast to the convex or straight spectra observed in most radio sources. We use available wide area radio surveys in the range of frequencies from 74MHz to 1.4GHz to search for episodic radio sources and to shortlist 9,405 sources based on the criteria of SPC?0.5. The candidates thus identified can be followed up for detailed morphological and spectral index studies. This method will find application in the automated identification of episodic radio sources in large radio sky surveys from telescopes like LOFAR and SKA. Indian Academy of Sciences 2025. -
CLASS onboard Chandrayaan-2: Five years around the Moon
Chandrayaan-2 Large Area soft X-ray Spectrometer (CLASS) is a remote X-ray Fluorescence experiment to map the lunar surface elemental abundances. With its large effective area and low energy threshold, CLASS generates the highest spatial resolution maps of all major rock-forming elements on the Moon, such as Mg, Al, Si, Ca, Ti, and Fe. Five years of operation in lunar orbit has resulted in global coverage. With several lunar missions planned for this decade for in situ exploration and sample returns, the 155 km geochemical maps from CLASS will serve as an important dataset. This article highlights the scientific results of CLASS in the last five years and discusses its potential applications. Indian Academy of Sciences 2025. -
Fabrication of liquid-crystal retarders for solar polarimetry: A facile method
A high-precision polarimeter for simultaneous multi-line spectropolarimetric Sun observations is under development at Indian Institute of Astrophysics. Towards this end, we plan to use liquid-crystal retarders as the polarization modulators. A prototype liquid-crystal variable retarder (LCVR) is fabricated and characterized. A solution-processed method is adapted to fabricate the LCVR using commercially available E7 nematic liquid-crystal material. Thickness of the alignment layer of the LC retarder was optimized to achieve uniformity. The fabricated LCVR demonstrates spatial uniformity of retardance comparable to a commercial waveplate. The device is found to have a low-range of operational voltage of <20 V and a very short response time of <1 ms. Also, the device shows consistent operational stability. Indian Academy of Sciences 2025. -
UVIT data release version 7: Regenerated high-level UVIT data products
Ultra-Violet Imaging Telescope (UVIT) on board AstroSat is an active telescope capable of high-resolution far-ultraviolet imaging (<1.5??) and low-resolution (?/???100) slitless spectroscopy with a field-of-view as large as ? 0.5?. Now almost a decade old, UVIT continues to be operational and generates valuable data for the scientific community. UVIT is also capable of near-ultraviolet imaging (<1.5??); however, the near-ultraviolet channel stopped working in August 2018 after providing data for nearly 3years. This paper gives an overview of the latest version (7.0.1) of the UVIT pipeline and UVIT data release version 7. The high-level products generated using pipeline versions having a major ver. no. 7 will be called UVIT data release version 7. The latest pipeline version overcomes two limitations of the previous version (6.3), namely: (a) inability to combine all episode-wise images; and (b) failure of the astrometry module in a large fraction of the observations. The procedures adopted to overcome these two limitations as well as a comparison of the performance of this new version over the previous one, are presented in this paper. The UVIT data release version 7 products are available at the Indian Space Science Data Center of the Indian Space Research Organization for archival and dissemination from 1 June 2024. New pipeline version is open source and made available on GitHub. Indian Academy of Sciences 2025. -
A multi-frequency study of the candidate doubledouble radio galaxy J2349?0003 with a possible misalignment
We present a multi-frequency analysis of the candidate doubledouble radio galaxy (DDRG) J2349?0003, exhibiting a possible lobe misalignment. High-resolution uGMRT observations at Bands 3 and 4 reveal a complex radio morphology featuring a pair of inner and outer lobes, and the radio core, while the Band 5 image detects the core and the compact components. The positioning of both pairs of lobes with the central core supports its classification as a DDRG. Spectral age estimates for the inner and outer lobes indicate two distinct episodes of active galactic nucleus (AGN) activity interspaced by a short quiescent phase. The possible compact steep-spectrum nature of the core, together with its concave spectral curvature, suggests ongoing or recent jet activity, suggesting the possibility that J2349?0003 may be a candidate triple-double radio galaxy. With a projected linear size of 1.08 Mpc, J2349?0003 is classified as a giant radio galaxy (GRG), although its moderate radio power (?1024 WHz-1) suggests a sparse surrounding environment. Arm-length (R?) and flux density ratios (RS) indicate environmental influences on source symmetry. The observed lobe misalignment and the presence of nearby galaxies in the optical image suggest that merger-driven processes may have played a key role in shaping the sources evolution. Indian Academy of Sciences 2025. -
Quintessence and false vacuum: Two sides of the same coin?
We studied the late-time acceleration scenarios using a quintessence field initially trapped in a metastable false vacuum state. The false vacuum has non-zero vacuum energy and can drive exponential expansion if not coupled with gravity. Upon decay of the false vacuum, the quintessence field is released and begins to evolve. We assumed conditions where the effective scalar potential gradient must satisfy ?Veff>A, characterised by a pressure term approximately ?p/p>O(?) invoking the string swampland criteria. We then derived the effective potential of the scalar with an upper bound on the coupling constant ?<0.6. Further analysis revealed that Veff shows a slow-roll behaviour for 0.1>?>-0.04 in the effective dark energy equation of state (EoS) -0.8 -
A novel mathematical investigation of carbon emissions, economic growth, carbon taxation and renewable energy dynamics: stability analysis and forecasting
The main cause of global warming is carbon dioxide (CO2) emissions, acting as a significant greenhouse gas. These emissions stem from various sources and significantly contribute to climate change. Fortunately, we have countermeasures like carbon taxes to curb CO2 output. Carbon taxes incentivise a reduction in CO2 production and a shift towards cleaner energy sources by placing a cost on emissions. This paper investigates the interplay between carbon tax policy, carbon emissions, economic output (GDP) and renewable energy consumption. A system of differential equations is constructed to model these relationships based on a comprehensive literature review. Parameter estimation based on real-world data yielded successful fits for the variables. However, the fit for the carbon tax equation is less conclusive, suggesting a more complex relationship with carbon emissions. Stability analysis and the boundedness of the system are carried out. Auto-regressive integrated moving average (ARIMA) forecasting is employed to predict future trends. The results suggest a projected increase in GDP and renewable energy consumption over the next ten years, indicating a potential for a cleaner energy transition. Furthermore, the forecasts anticipate a rise in carbon tax implementation. This analysis emphasises how important carbon taxes are for cutting emissions and advancing renewable energy. Results indicate that carbon taxes can promote decarbonisation and economic growth, despite the complicated link between them and CO2 emissions. Both GDP growth and the use of renewable energy are anticipated to increase. However, policies must be improved to combat climate change effectively. Future studies should improve parameters and investigate other relevant elements to promote a low-carbon future. Indian Academy of Sciences 2025. -
Convolutional bi-directional autoencoder assisted generative adversarial de-blurring framework for palm leaf character analysis
Palm leaf manuscripts have historically educated people on a variety of topics, including astronomy, mathematics, astrology and medicine. These manuscripts are constructed from dried palm leaves, which contain a wealth of information that remains largely untapped due to the challenges of digitalization and transcription. Recognizing the characters found in palm leaf manuscripts is a complex problem because blurred images of these manuscripts often conceal critical information. The present study proposes an automated de-blurring model to effectively identify exact Malayalam characters in palm leaf manuscripts. The input images are gathered from a real-time dataset to address this challenge. The Weighted Guided Image Filtering (WG_IF) method is employed to extract the detail layer, which not only reveals important information about the characters but also eliminates unwanted noise. The detailed layer is then input into the proposed Convolutional Bi-directional AutoEncoder assisted Generative Adversarial De-blurring (CBiAE_GADeblur) framework. This framework comprises two main blocks: the generator and the discriminator. The generator block uses the Convolutional Bi-directional Long Short Term Memory with AutoEncoder (CBLSTM_AE) method to produce de-blurred images. The discriminator block classifies these images as real or fake, enhancing the prediction accuracy of the palm characters. The proposed method demonstrates a superior accuracy rate of 98.25% in Prasavachikilsa and Vishavydyam datasets and exhibits lower time complexity. The motivation behind this research is to overcome the significant barriers posed by blurred palm leaf images, thereby unlocking and preserving the invaluable knowledge contained within these historical documents. Indian Academy of Sciences 2025. -
Ageing with Disability and Family Dynamics: A Social Constructionist Reading of Rohinton Mistrys Family Matter
This article examines how disability and ageing intersect within family structures and cultural expectations, foregrounding the role of ableism and ageism in shaping individual and collective experience. By examining Rohinton Mistrys Family Matters through the frameworks of social constructionism and ageism, the research underscores how age and disability are socially constructed and often associated with dependency and burden. Through the character of Nariman Vakeel, who experiences the dual challenges of old age and physical disability, this article critically examines how disability and ageing shape his health and wellbeing, access to care, and reliance on family and community support. The analysis highlights the ethics of caregiving and family responsibility, encouraging more inclusive and empathetic approaches to ageing and disability across cultural and familial contexts. Mistrys narrative mirrors real-world dynamics of care, responsibility, and economic burden within families. By situating Family Matters within broader discourses on ageing with disability, this article contributes to the growing field of interdisciplinary ageing studies and underscores the value of literature as a cultural text that reflects and critiques social realities. The study emphasises the importance of intergenerational understanding, encouraging families to recognise the emotional and social value of the elderly beyond their physical limitations. Finally, by demonstrating how literature reflects lived realities, the study highlights the role of cultural narratives in shaping more empathetic attitudes towards ageing and disability. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
Analytical and AINN-based investigation of surface wave propagation in dry long bones with initial stress, magnetic field, and rotation effects
This study presents a comprehensive analytical and artificial intelligence neural network (AINN)-based investigation of wave propagation in dry long bones, modeled as an orthotropic hollow cylindrical structure. The proposed model incorporates the combined effects of initial stress, magnetic field, and rotational motion to capture the complex behavior of bone-like media under coupled physical influences. The governing equations are formulated within a continuum mechanics framework and solved analytically using displacement potential methods, yielding solutions in terms of Bessel functions that satisfy the cylindrical geometry and boundary conditions. Two distinct cases, corresponding to the absence and presence of rotation, are examined to assess the influence of rotational effects on wave dynamics. A detailed parametric analysis is carried out to evaluate the variation of phase velocity and frequency with respect to wave number, initial stress, magnetic parameter, density, and geometric ratios. The analytical results indicate that initial stress enhances wave propagation, while magnetic effects introduce damping and rotation significantly modifies dispersion characteristics. To improve computational efficiency and predictive capability, an AINN model is developed and trained using analytically generated data. The AINN predictions show excellent agreement with the analytical results, as confirmed through parity plots, error analysis, residual distribution, and loss convergence behavior. The novelty of the study lies in the unified analytical-AINN framework that integrates mechanical, electromagnetic, and rotational effects within a single model. The findings provide important insights for wave-based characterization of bone structures and have potential applications in non-destructive evaluation, ultrasonic diagnostics, and advanced biomedical sensing systems. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026.