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Performance of rotary ultrasonic machining (RUM) system greatly influences by appropriate design of ultrasonic horn. Ultrasonic stepped horn gives high amplitude of vibration and better cutting efficiency but design and integration of horn with RUM system is highly intricate. Therefore, systematic study on design and implementation of ultrasonic stepped horn was needed in order to achieve better efficiency of RUM process. This paper focuses on design aspects of ultrasonic stepped horn by theoretical, FE simulations and modeling techniques. The designed horn was integrated with RUM system, performance was measured in terms of ultrasonic resonant frequency through FE simulations and modeling on ANSYS workbench. Finally, fabricated ultrasonic stepped horn was validated by performing experiments on rotary ultrasonic machine tool for Nomex honeycomb composites (NHCs). FE simulations and experimental results prove that the designed ultrasonic stepped horn achieves reasonable vibration amplitude at desired resonant frequency to perform RUM process on NHCs materials.

The exact etiology of dementia is still unclear, but both genetic and lifestyle factors are thought to be key drivers of this complex disease. The recognition of familial patterns of dementia has led to the discovery of genetic factors that have a role in the pathogenesis of dementia, including the apolipoprotein E (APOE) genotype and a large and still-growing number of genetic variants1,2. Beyond genetic architecture, several modifiable risk factors have been implicated in the development of dementia3. Prevention trials of measures to halt or delay cognitive decline are increasingly recruiting older individuals who are genetically predisposed to dementia. However, it remains unclear whether targeted health and lifestyle interventions can attenuate or even offset increased genetic risk. Here, we leverage long-term data on both genetic and modifiable risk factors from 6,352 individuals aged 55 years and older in the population-based Rotterdam Study. In this study, we demonstrate that, in individuals at low and intermediate genetic risk, favorable modifiable-risk profiles are related to a lower risk of dementia compared to unfavorable profiles. In contrast, these protective associations were not found in those at high genetic risk.

Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.

Endosulfan, an organochlorine insecticide, is known to cause detrimental effects to the environment and human health due to its excessive usage. Its highly toxic nature calls for an environmental-friendly approach for its detoxification. Environmental transformation of Endosulfan was assessed through biodegradation by isolated and cultured soil microbes (Bacillus subtilis (BS), Aspergillus niger (AN), Aspergillus flavus (AF) and Penicillium chrysogenum (PC)). Degradation of 10 mg/L Endosulfan was determined in aqueous solution at regular time intervals and analysed by gas chromatography–mass spectrometry for 35 days. BS and AN displayed substantial potential to degrade Endosulfan and subsequently transform it into its daughter products (95 and 77%, respectively). Endosulfan transformation followed first-order reaction kinetics. Chromatogram peaks revealed less toxic metabolites by Endosulfan transformation (Endosulfan diol, Endosulfan ether, Endosulfan hydroxyether and Endosulfan lactone). Half-life of Endosulfan obtained by various strains utilised in the experiments was in the order, PC (69) > AF (34.6) > AN (17.3) > BS (11.5) days. Statistical analysis was performed in MINITAB to evaluate the significance of results. Bioaugmentation of contaminated sites with such efficient microbes can facilitate rapid pesticide transformation and decontamination of the environment.

Accurate radiogenomic classification of brain tumors is important to improve the standard of diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we propose a novel two-stage MGMT Promoter Methylation Prediction (MGMT-PMP) system that extracts latent features fused with radiomic features predicting the genetic subtype of glioblastoma. A novel fine-tuned deep learning architecture, namely Deep Learning Radiomic Feature Extraction (DLRFE) module, is proposed for latent feature extraction that fuses the quantitative knowledge to the spatial distribution and the size of tumorous structure through radiomic features: (GLCM, HOG, and LBP). The application of the novice rejection algorithm has been found significantly effective in selecting and isolating the negative training instances out of the original dataset. The fused feature vectors are then used for training and testing by k -NN and SVM classifiers. The 2021 RSNA Brain Tumor challenge dataset (BraTS-2021) consists of four structural mpMRIs, viz. fluid-attenuated inversion-recovery, T1-weighted, T1-weighted contrast enhancement, and T2-weighted. We evaluated the classification performance, for the very first time in published form, in terms of measures like accuracy, F 1 -score, and Matthews correlation coefficient. The Jackknife tenfold cross-validation was used for training and testing BraTS-2021 dataset validation. The highest classification performance is (96.84 ± 0.09)%, (96.08 ± 0.10)%, and (97.44 ± 0.14)% as accuracy, sensitivity, and specificity respectively to detect MGMT methylation status for patients suffering from glioblastoma. Deep learning feature extraction with radiogenomic features, fusing imaging phenotypes and molecular structure, using rejection algorithm has been found to perform outclass capable of detecting MGMT methylation status of glioblastoma patients. The approach relates the genomic variation with radiomic features forming a bridge between two areas of research that may prove useful for clinical treatment planning leading to better outcomes.

Research on the sustainable development goals (SDGs) has brought attention to the significance of small and medium enterprises (SMEs) due to their substantial contributions to economic growth. However, SMEs still need to develop integrated frameworks to assess the implications of sustainable operations while managing scarce resources. In this study, we investigate how top managers of SMEs utilize leadership competencies to balance and allocate resources for SDGs in a turbulent environment. To test the model, the analysis was conducted on 254 SMEs operating in an emerging market. The findings indicate that resource commitment plays a partially mediating role between leadership competencies and SDGs, while environmental uncertainty does not moderate the relationship between leadership competencies and resource commitment. These insights suggest that SMEs with competent leaders commit resources to SDGs regardless of environmental conditions. This research recommends that SMEs focus on cultivating competent leaders to navigate resource constraints and contribute to the SDGs in a turbulent environment. Further implications are discussed.

Hybrid composites of metal sulphides conjugated graphene oxide have engrossed the scientific community recently owing to their enhanced characteristics. Present research describes the synthesis of (Zr–Ni)sulphide-GO composite thin film by diethyldithiocarbamate ligand deposited through E-beam deposition. Synthesized thin films were characterized through XRD, UV–visible spectrophotometer, FTIR and SEM–EDX analysis. The cubic Zr2NiS4-GO thin film possessed a direct bandgap of 3.4 eV, and a hexagonal crystal system. The efficient photocatalytic degradation property of the thin film was investigated with an enhanced removal of pesticide and phenol, whereas a moderate degradation of dye was observed. Photocatalytic pesticide removal was 83% under 60 min whereas ~ 70% up to four successive cycles. A high specific capacitance of 438.5 F g−1 proved the thin film as an excellent electrode for supercapacitors. The impressive photocatalytic and electrochemical properties of the [ZrNi]S-GO thin film present it as a superlative material for practical use in electrical and environmental remediation devices.

Over the past numerous decades, unrelenting attention has been dedicated for sustainable design and synthesis of electrochemical metal oxide with controlled morphology and nano scale structural complexity to enhance their electrochemical properties. Here in, we reported the sol–gel synthesis of molybdenum trioxide NPs using bioactive compounds of Euphorbia cgnata Boiss as fuel. The current study demonstrated the stabilization of molybdenum trioxide nanoparticles by Benzeneethanamine and Benzenemethanol. The chemical and structural intrinsic features with low band gap energy of 2.02 eV rendered the MoO 3 NPs suitable for electrochemical applications. On this account, we fabricated MoO 3 nickel foam electrode to investigate its supercapacitor properties. MoO 3 electrode revealed the moderate capacitance of 63.5 F/g at 2 mV/s with energy density of 1.8 Wh/kg and high power density of 4551.3 W/kg. Therefore, the overall results are proposing the potential of fabricated electrode toward energy storage device like supercapacitor. Highlights Bio template assisted MoO 3 NPs. The identification of incorporated phyto-organic compounds in the MoO 3 NPs. Tailoring of surface chemistry by bio-organic functional groups. Effects of surface modification on electrochemical properties.

In this study, the effects of nonlinear thermal radiation, Arrhenius activation energy, and chemical reactions on the flow and heat transfer of a water-based hybrid nanofluid containing SWCNT- & MWCNT- nanoparticles over a rotating disk are examined. The investigation highlights the combined influence of nonlinear radiation and nanoparticle shape factors on the transport properties of the hybrid fluid. Given that the thermal and structural performance of nanomaterials is strongly dependent on their morphology, special attention is devoted to assessing the role of particle shape variations. The objective of this work is to advance the fundamental understanding of how nonlinear radiative processes, activation energy, and nanoparticle geometry interact in rotating disk flows, thereby contributing to the development of efficient nanofluid based thermal management systems. These materials find applications in energy storage, thermal stability, transistors, and electromagnetic shielding. Given the growing demand for nanotechnology, understanding these effects is crucial for enhancing performance in engineering and energy systems. The governing PDEs are simplified into dimensionless ODEs using similarity transformations. The Successive Over-Relaxation method, executed through a custom MATLAB code, is used to obtain the solutions of these equations. The effects of different parameter values on radial and transversal velocity, as well as heat and mass transfer, are examined using graphical analysis. In addition, tabular data are presented to evaluate the behavior of skin friction, Nusselt number, and Sherwood number under various parametric conditions. The results reveal that velocity diminishes with increasing magnetic parameter values, whereas nonlinear radiation enhances heat transfer. Activation energy augments both concentration and mass transfer, although the latter is influenced by the Schmidt number and the chemical reaction rate. Conversely, temperature decreases with a rise in the Prandtl number. Radial skin friction decreases by about 44% as the magnetic parameter increases, while tangential skin friction magnitude rises by nearly 78% at low suction and around 37% at high suction. Furthermore, the heat transfer rate improves from 25.27% at Rd = 0.5 to 37.18% at Rd = 1.4, indicating an overall enhancement of 11.91%. These outcomes hold practical significance for optimizing fluid behavior and heat transfer in rotating systems, with potential applications in energy systems, heat exchangers, and advanced cooling technologies.

Alachlor interaction with soil has been evaluated by batch equilibrium method on nine soils, geographically distant, from hilly to desert areas. Different soil samples have been investigated for sorption and removal mechanism of Alachlor. Linear and Freundlich soil–pesticide sorption isotherms were used to study the adsorption phenomena. Distribution coefficient (Kd) shifted extraordinarily from 94.9 to 6.15 µg mL−1. Statistical analysis showed a negative correlation between soil pH and Kd (R2 = − 0.70) and a positive relationship with organic matter content (R2 = 0.80). The adsorption results gave a C-type isotherm. The results were additionally investigated by univariate ANOVA and their accuracy was checked through residual plots. Activated carbon prepared from walnut shells (Juglans regia) was utilized for green remediation of Alachlor-contaminated soils, proved to be a cost-effective absorbent. The influence of two parameters including pesticide concentration and contact time for the abatement of Alachlor were investigated. Most prominent removal in 5 ppm Alachlor concentration was 84% while in 7.5 ppm most astounding removal was 72% from soils. The utilization of Juglans regia shells for decontamination of soils makes this technique environmental friendly, economical and easily applicable.