Silicon nitride (Si₃N₄) is a high-performance ceramic with wide strategic applications in aerospace and thermal-shock environments, owing to its exceptional hardness, thermal stability, and fracture toughness. However, such superior properties render Si₃N₄ extremely difficult to machine, with limited techniques available that provide good surface quality and a high material removal rate (MRR). This work proposes a novel dual-hybrid machining approach for drilling Si₃N₄, termed chemically assisted rotary machining with an ultrasonically vibrated workpiece (CRUWM), which is based on a ductility-dominated ductile–brittle regime mechanism with chemical assistance. In the present approach, ultrasonic vibrations are applied to the workpiece, resulting in distinct mechanical and material removal advantages. In addition, a methanol-based chemically active medium is used to induce a chemo-mechanical effect that suppresses brittle fracture. Substantial improvements in responses, including hole profile accuracy, surface finish, cutting forces, and MRR, are demonstrated in comparison to popular rotary ultrasonic machining (RUM). The experimental results reveal a 75.9
This article presents synthesized Metamaterial Cross Polarizer (MCP) using Binary Wind Driven Optimization (BWDO) technique. BWDO is an advance version of WDO techniques. To achieve wideband response uttermost number of iterations is done at the time of synthesis. The wideband responses below − 10 dB is considered as a bandwidth and its ranges from 7.56 to 11.94 GHz. To achieve best response 50 number of iterations has to be performed. The Polarization Conversion Ratio (PCR) is considered at three distinct peaks i.e., at 8.3, 10.6 and 11.9 GHz with 99.87, 98.93 and 96.71 ϵ _eff μ _eff , whereas the second technique is explained using current distribution. The structure is mounted on dielectric substrate (FR-4). The mounted structure is tested and measured inside the anechoic chamber. The measured result obtained from the chamber are in close agreement with the simulated one which slightly differs due to fabrication tolerance. At last, the presented MCP structure is compared with the past articles and listed in the form of a table.
The gallbladder is a small, hollow organ positioned beneath the liver, primarily responsible for temporarily storing bile. Bile is a fluid formed by the liver that helps digestion. There are various types of gallbladder disease. Early diagnosis and identification are crucial for effective treatment of gallbladder disorders. Poor medical outcomes and improved patient symptoms may result from errors or delays in diagnosis. Numerous symptoms and signs, particularly those associated with gallbladder disorder, might be blurred. As a result, medical specialists must understand and interpret ultrasound images. Considering that ultrasound imaging for diagnosis is labour- and time-consuming, it might be difficult to support financially. Deep learning (DL) is an effective model that could help early identify gallbladder disease using ultrasound (US) images. In this paper, a Hybrid Deep Learning Model with Feature Engineering for the Accurate Diagnosis of Gallbladder Disease Types (HDLMFE-ADGDT) approach is proposed. The primary purpose of the HDLMFE-ADGDT approach is to develop an effective DL-based method for classifying GB disease categories. As an initial step, the HDLMFE-ADGDT technique employs a Non-Local Means (NLM) filter to remove noise and enhance the overall image quality. For feature extraction, the Squeeze-and-Excitation Capsule Network (SE-CapsNet) is employed. At last, the hybrid convolutional neural network with bidirectional long short-term memory (CNN-BiLSTM) is implemented for gallbladder disease diagnosis. A comprehensive set of experiments is conducted to validate the performance of the HDLMFE-ADGDT model on the Gallbladder diseases dataset. The empirical results demonstrate that the HDLMFE-ADGDT model outperformed existing methodologies, achieving 99.09% accuracy, 95.83% precision, 95.87% sensitivity, and 99.49% specificity.
We report on water splitting, disinfection, and pollutant degradation processes assisted by photocatalysis supported by zinc oxide (ZnO) under varied experimental and environmental conditions. In addition, the role of ozonation in the presence of ZnO and its synergistic impact on the effective elimination of organic and inorganic contaminants is discussed, highlighting enhanced reaction kinetics and mineralization efficiency. ZnO nanoparticles (ZnO-NPs) have also emerged as appropriate and versatile tools in drug delivery systems and chemical or biological sensing applications due to their biocompatibility, tunable surface chemistry, and strong photoluminescence response. Owing to its distinctive structural, optical, and electronic properties, ZnO has been extensively employed as an n-type inorganic semiconductor in organic solar cells (OSCs) and hybrid solar cells (HSCs), where it functions efficiently as an electron transport and hole-blocking layer. Its high chemical and thermal stability, non-toxicity, facile synthesis routes, low production cost, and excellent optoelectronic characteristics make ZnO highly attractive for large-scale technological applications. Furthermore, ZnO is widely used as a preservative and functional additive in numerous products and materials, including foundations, ceramics, glass, rubbers, lubricants, plastics, cement, ointments, paints, adhesives, sealants, colorants, ferrites, foods, batteries, food enhancers, fire-retardant systems, and first-aid tapes. These diverse applications underscore the multifunctional nature and industrial relevance of ZnO-based materials.
Stress shielding, the progressive loss of peri-implant bone due to mechanical mismatch between implants and native bone, remains a major cause of long-term orthopaedic implant failure. Despite advances in porous architectures, graded stiffness designs, and bioactive coatings, trade-offs between strength, fatigue resistance and biological efficacy persist. The present manuscript reframes the challenge: implants should function not merely as mechanical matches, but as active mechanobiochemical systems that restore load transfer while modulating local biology. This article synthesises biomechanics (strain energy density, osteocyte lacuno-canalicular mechanotransduction), materials science (porous AM lattices, β-type Ti alloys, polymers, HEAs), and drug-delivery strategies, showing their effects on bone remodelling and implant longevity. Preclinical evidence highlights nitric oxide (NO) as a potent mediator of load-induced bone formation. Accordingly, the article proposes a mechanobiochemical paradigm,i.e., tailored mechanical designs with controlled, local NO release (diazeniumdiolates, S-nitrosothiols, nanomedicine reservoirs) to sustain anabolic signalling at the bone–implant interface. The present article maps the design principles, modelling needs, and translational hurdles, spanning manufacturing reliability, fatigue performance, NO dosing, and safety. A roadmap emerges that combines additive manufacturing, surface engineering, and machine-learning-driven material discovery to transform passive implants into bioresponsive devices that preserve bone, reduce revisions, and extend patient outcomes.
