The breakup dynamics of ^12 C (400 MeV) and ^16 O (250 MeV) projectiles interacting with the medium-mass targets ^59 Co and ^93 Nb are investigated by comparing Serber-model calculations of double differential cross sections with the available experimental data for emitted α particles and boron ( Z=5 ) fragments over the angular range 10^∘-30^∘ . At forward angles, the calculated spectra reproduce the observed peak positions and the overall spectral shapes, indicating that the high-energy component is qualitatively consistent with the spectator momentum distribution predicted by the Serber model. The agreement gradually deteriorates with increasing emission angle, reflecting the growing influence of additional reaction mechanisms beyond the simplified participant–spectator description. The observed target dependence of the spectral shapes and peak energies further indicates the role of Coulomb and nuclear interactions in modifying the fragment kinematics. The present analysis demonstrates that the Serber model provides a useful baseline description of the forward-angle inclusive fragment spectra while highlighting the need for more comprehensive reaction models to account for the complete experimental distributions.
In the study the researchers evaluated the performance of neural networks on determining the beamforming coefficients for an antenna array based on changes to the communication environment over time. The concept of beamforming has been in existence for a long time; however, it is a computationally intensive task that requires accurate channel information to perform efficiently (accurate channel information can be difficult to obtain in a dynamic communications environment). This study tested whether or not a neural network could predict a relationship between the channel characteristics and the required beamforming coefficients with use of synthetic data. In the study the researchers evaluated the performance of neural networks on determining the beamforming coefficients for an antenna array based on changes to the communication environment over time. The concept of beamforming has been in existence for a long time; however, it is a computationally intensive task that requires accurate channel information to perform efficiently (accurate channel information can be difficult to obtain in a dynamic communications environment). This study tested whether or not a neural network could predict a relationship between the channel characteristics and the required beamforming coefficients with use of synthetic data. Although artificial channel data is used in the research, the designed framework can be used with standardized channel data, such as COST 2100 and QuaDRiGa, for further verification of its efficiency in practical wireless communications systems.
Industries make extensive use of gearboxes in their machinery, and these components must perform under consistent load and varying conditions. Wear and tear in a gearbox’s components can develop over time and go undetected, which may result in sudden failures, production loss, and safety issues. Periodic inspections for maintenance practices are not effective in discovering early-symptoms of failures. Conventional monitoring approaches mainly rely on vibration and other sensor-based measurements, while recent studies have increasingly used machine learning and deep learning techniques for automated fault diagnosis. This paper develops a method for a wear condition diagnosis and health state assessment of gearboxes using sensor data and machine learning. The system determines the wear condition of the gearbox by including relevant operational indicators such as vibrations, temperatures, and lubrication. Lubricant analysis provides an alternative source of information about gearbox degradation, but its use with machine-learning-based wear classification requires further investigation. To assist in more efficient and effective maintenance, supervised learning was used on condition data of gearboxes to create classes of varying levels of wear. Experimental assessment shows that the system can success- fully detect and diagnose the health state of the gearbox and its components more efficiently and effectively than the traditional method of manual inspection. This system provides industry the capability of predictive maintenance which can minimize loss of operational availability and improve gear.
The simultaneous identification and removal of low-concentration contaminants remain important objectives in environmental monitoring and wastewater remediation. Here, a plasmonic Ag/CuFe₂O₄/TiO₂ ternary nanocomposite was fabricated through a modified sol–gel route. Its crystalline phases, surface chemistry, elemental distribution, morphology and porous characteristics were examined using XRD, FTIR, EDX, SEM, and BET analyses. Photocatalytic activity was assessed through visible-light degradation of Congo red at concentration of 20 mg/L. Ternary catalyst achieved approximately 96
In the present work, SUP9 spring steel specimens were reheated to 1000 °C in a controlled muffle furnace to obtain a homogeneous austenitic structure. The specimens were then cooled at different rates (0.1, 5, 100, 375, and 650 °C) to generate varied microstructures. Increasing the cooling rate refined the pearlitic microstructure, resulting in a finer and more uniform grain structure. These heat-treated specimens were then subjected to the wear test to study the influence of cooling rate (dT/dt), load (F), and sliding speed (v) on the response (wear rate). The wear tests were performed under varying conditions of applied load (10, 20, and 30 N) and sliding speed (0.5, 1, and 1.5 m/s) to assess wear behaviour under different operating conditions. A response surface methodology (RSM) model was developed to predict wear rate as a function of cooling rate, applied load, and sliding speed, achieving a good fit with a coefficient of determination (R²) of 94.65
Quantum-mechanical interactions at the nanoscale increasingly govern the behaviour of modern battery and catalytic energy materials, yet a coherent computational framework linking quantum-level descriptors to macroscopic electrochemical performance remains underdeveloped. This study presents a proof-of-concept computational framework that integrates physics-guided quantum-mechanical modelling with data-driven machine learning to explore correlations between quantum characteristics and energy-material performance indicators. Analytical and Python-based simulations were conducted to model quantum tunnelling, quantum confinement, lithium-ion (Li-ion) diffusion, and quantum-corrected catalytic energy surfaces using established textbook-level approximations (Wentzel–Kramers–Brillouin tunnelling, particle-in-a-box confinement, Brownian diffusion, and heuristic charge-transfer descriptors). A synthetic dataset comprising 5,000 hypothetical energy materials was generated from physics-informed statistical distributions, incorporating key electronic-structure features such as effective mass (m*), density of states at the Fermi level (DOS_F), band gap (E_g), particle size (d), and spin polarisation (S). Three ensemble regression models—Random Forest, Gradient Boosting, and Extra Trees—were trained to predict the synthetic specific-capacity target, with the Extra Trees regressor achieving the best fit (R² = 0.894, RMSE = 20.14 mAh g⁻¹). Feature-importance analysis indicated that m* and DOS_F jointly contributed over 84
The Thespesia populnea bark fibre and flaxseed gum were used to fabricate sustainable hybrid composites using epoxy resin as matrix. Different contents of the flaxseed gum (1–5 wt
In this research, we report the induced change in the structural, and linear and non-linear optical properties of PCCCe polymer nanocomposite under the exposure of γ- irradiation from 10 to 50 kGy. The XRD pattern revealed that the structural parameters such as, stacking fault (SF), decreases from 0.01123 to .00399 under the exposure of γ-rays and the intercrystallite distance (R) also shrinkages from 0.47401 to 0.34743. Using the Wemple-DiDomenico model, we calculate the oscillator strength (f) decreases from 16.18 (eV)2 to 2.43 (eV)2, the oscillation energy (Eₒ) changes 65.95 eV to 09.39 eV and the dispersion energy (Ed) decreases from 0.278 eV to 0.258 eV under the exposure of γ-rays. In addition the oscillator wavelength λₒ and oscillator strength Sₒ, optical electro-negativity, static reflection index (nₒ), for the investigated samples were found to be strongly affected by the exposure of γ-rays. The first (M1), third moments (M3) of optical spectra and the first order susceptibility, χ1 (hν → 0) show variation with increasing the dose of γ-radiation from 10 to 50 kGy. The non-linear susceptibilities, χ3 (hν → 0) and the non-linear refractive index (n2) also affected which is good for the non-linear photonic devices. The observed modifications are attributed to the exposure of γ-radiation-induced chain scission, localized cross-linking, defect formation, and structural rearrangement within the nanocomposite, which alter the molecular ordering and electronic structure, leading to the impact on its structural and linear and nonlinear optical properties. Finally, the modification in the structural and linear and non-linear optical characteristics of nanocomposite films make them promising candidate for advanced optoelectronic devices.
The selection of attribute weights is a critical aspect of multi-attribute decision-making (MADM), particularly when decision problems involve both qualitative and quantitative attributes. Determining appropriate weights for qualitative attributes remains a significant challenge in many existing MADM methods. This paper presents the Best Holistic Adaptable Ranking of Attributes Technique (BHARAT-2) as a simple, transparent, and computationally efficient approach for attribute weight determination and alternative ranking. The proposed method is capable of handling both qualitative and quantitative attributes and is applicable to individual as well as group decision-making environments. Attribute weights are derived from decision-makers’ preference rankings using the rank relation matrix, and the alternatives are subsequently evaluated using normalized performance values and weighted aggregation. The effectiveness of the proposed method is demonstrated through six Industry 4.0 case studies. The first case study concerns make-to-order manufacturing involving 18 attributes and three alternatives. The second considers vehicle technology selection with 13 attributes and five alternatives. The third evaluates Industry 4.0 readiness in small and medium-sized enterprises using eight attributes and fifteen alternatives. The fourth addresses a transportation service selection problem with five attributes and five alternatives. The fifth focuses on cloud service selection involving four attributes and four alternatives, while the sixth evaluates IIoT platforms for smart manufacturing using fourteen attributes and three alternatives. The rankings obtained using BHARAT-2 are compared with those generated by established MADM approaches. The results demonstrate that the proposed method is simple, effective, easy to implement, and capable of providing reliable decision support across diverse industrial applications.
Current study aims to present an Machine Learning (ML) based model for prediction of convective heat transfer features in Tube Bank Systems using physics driven synthetic data. Instead of embedding physical governing equations within the Machine Learning training process, the proposed framework incorporates physical knowledge during synthetic dataset generation using established empirical Nusselt number (Nu) correlations. The generated physics-consistent dataset is subsequently used to train and evaluate conventional supervised ML algorithms for predicting heat transfer behaviour in Tube Bank arrangements. The study uses a synthetic dataset comprising 500 data points, generated using well established empirical Nu correlations of the form. The correlation accounts for variations in convective heat transfer behaviour across multiple tube rows and Reynolds numbers (Re) ranging from 300 to 6000. Gaussian noise was added to the dataset to simulate experimental data variability. The synthetic dataset was internally verified by recalculating the Nu using the same empirical correlation, resulting in negligible reconstruction error. This confirmed the numerical consistency of the synthetic data generation procedure. Five supervised ML algorithms, namely Linear Regression (LR), Support Vector Regression (SVR), Decision Tree (DT), Random Forest, and Gradient Boosting (GB), were developed and evaluated for predicting the Nu using Reynolds number and tube row as input parameters. Performance of proposed ML models was evaluated using Mean Absolute Percentage Error (MAPE). The ensemble-based ML models performed well in predicting the Nu. Out of all the analysed algorithms, the highest prediction accuracy was obtained by the GB algorithm with test MAPE of 1.09
The disposal of cotton cloth waste generated by the textile industry presents significant environmental challenges. This study investigates the feasibility of utilizing grounded cotton cloth waste (GCW) as a partial replacement for fine aggregate in interlocking blocks, aiming to promote sustainable waste management and circular economy practices in the construction sector. Interlocking blocks were produced using Portland Pozzolana Cement (PPC), Ground Granulated Blast Furnace Slag (GGBS), chemical admixtures, and varying levels of GCW as a replacement for fine aggregate. Three replacement levels were considered: 0
This study investigates the dielectric behaviour of alkali-treated kenaf fiber reinforced epoxy composites hybridized with Terminalia Chebula filler at varying loadings (0, 0.5, 1, 2, and 4 wt
The increasing demand for sustainable, biodegradable materials and Engineering sectors has driven the development of eco-friendly composites that combine mechanical robustness, wear resistance, and antibacterial properties. This study presents the fabrication and optimization of novel hybrid epoxy composites reinforced with Sansevieria cylindrica fibers (SCs) and titanium dioxide (TiO₂) nanoparticles for durable engineering and tribological applications. Using hand layup followed by compression moulding, composite samples were prepared by varying SC (20, 30, 40 wt
The present research aimed to examine the effects of integrating zirconium nitride (ZrN) at concentrations of 0 and 10 wt
In the present work, the modeling of hoop strain in a structural steel pipe embedded with a semi-ellipsoidal internal corrosion defect subjected to internal hydrostatic pressure was performed using a statistical response surface methodology (RSM) model. The independent variables used in the modeling were the internal corrosion defect’s length (z), width (x), and depth (y), as well as internal hydrostatic pressure (P). The developed analytical expression for predicting hoop strain demonstrated a high predicted coefficient of determination (R² = 97.91
Nanofluids exhibits the improved thermal properties as compared the conventional type fluids. In present study, heat transfer performance has been evaluated of flat tube with hybrid nanofluids. The study has been carried out 0.1
Information on the remaining useful life (RUL) of rotating equipment allows for the optimization of asset utilization through cost-effective and strategically timed preventive maintenance. However, RUL estimation models have traditionally relied on significant processing capabilities and costly high-frequency data acquisition hardware. It also requires post-collection data analysis by a predictive model. Furthermore, as a moving time-series, the training data must also be carefully constructed to avoid data leakage and time proximities. In this paper, we explore an architecture that performs RUL estimation and health stage classification locally at the edge using an ESP32 microcontroller with an MPU6050 sensor. We investigate carefully chosen classification models to identify a cost-effective solution for the detection of gearbox degradation stages. A strict 5-fold cross-validation pipeline is used to ensure generalization to an unseen test set. In order to isolate the physical elements of the degradation process, a feature ablation study sequentially excludes the operating time-history from the set of input features. The methodology is tested on a laboratory test rig subjected to a run-to-failure experiment with a compound fault. The input feature of vibration magnitude and rotational speed is the sole physical sensor data used in the classification and RUL estimation. The baseline Random Forest architecture accurately models the non-linear impact of gear mesh resonance on the vibration data and attains a coefficient of determination of 0.7147 ± 0.0677 and a multi-class classification accuracy of 96.95
The performance of Horizontal Axis Wind Turbines (HAWTs) is strongly influenced by the velocity of the incoming wind, with low wind speeds resulting in reduced aerodynamic loading and power generation. This study proposes an umbrella scoop attachment to enhance the effective airflow reaching the rotor of a 5 MW HAWT. The umbrella scoop is designed as a conical frustum and is positioned axially upstream of the rotor to concentrate and accelerate the incoming airflow. Experimental and Computational Fluid Dynamics (CFD) analyses were conducted for umbrella scoop angles of 15°, 20°, 30°, and 45° under different inlet wind speeds. The experimental results showed a progressive increase in outlet velocity with increasing scoop angle. At an inlet velocity of 12.5 m/s, the outlet velocity increased from 20.3 m/s at 15° to 30.1 m/s at 20°, 42.9 m/s at 30°, and 56.7 m/s at 45°. The CFD analysis similarly demonstrated increased aerodynamic moment with the introduction of the scoop. At 12.5 m/s, the maximum moment increased from 1012655.8 Nm without the umbrella scoop to 2,156,957 Nm with the 30° umbrella scoop, representing an improvement of approximately 113.1
Recently, the adsorption process has been used at industrial level for the purification and chemical separation method gain a lot of attention due to their simplicity, ease of use, and environment friendly nature. In the given experimental study, metal–organic framework (MOF) obtained beads have been inspected to remove rhodamine B (RhB) dye from aqueous solutions via batch adsorption experiments. A bimetallic MOF namely Fe/Cu-BTC synthesized at room temperature was transformed into highly robust millimetre-sized beads using polyvinylidene fluoride (PVDF). The present study focuses on the synthesis of Fe–Cu bimetallic metal–organic framework (MOF)-based microscopic beads and their application as an efficient adsorbent for the removal of Rhodamine B (RhB) dye from contaminated water. The incorporation of iron and copper within the MOF framework is expected to enhance the surface area, porosity, active adsorption sites, and structural stability of the material. Conversion of the MOF powder into microscopic beads facilitates easy separation, recovery, and reuse of the adsorbent after treatment. The properties of the Fe/Cu-BTC @PVDF composite beads were determined using several characterization techniques. A key novelty of this work lies in the fabrication of Fe–Cu BTC embedded within poly(vinylidene fluoride) (PVDF) macroscopic beads, which provide a mechanically robust, easily recoverable, and scalable platform for practical applications. The PVDF bead matrix overcomes the handling and separation challenges associated with powdered MOFs while preserving the catalytic/adsorptive activity of Fe–Cu BTC, thereby enhancing operational stability and facilitating real-world implementation. The incorporation of Fe–Cu BTC into PVDF-based macroscopic beads represents a novel strategy to improve material recoverability, mechanical stability, and process scalability, addressing key limitations of conventional powdered MOF systems. The effect of parameters such as initial pH, adsorption time, and initial RhB concentration on the adsorption process was also investigated. After 70 min of contact time, the dye adsorption equilibrium was achieved by dye removal of 85.2
