The people of all ages are changing the way they live as technology advances, while younger people are experiencing more bone injuries from activity and accidents, seniors are still more prone to broken bones and joint issues. Biomaterials, which are natural or created materials such as titanium, nickel, cobalt, and stainless steel alloys, are used to repair damaged body parts. Stainless steel is a frequently utilized biomaterial due to its ready availability, reasonable price, and ease of shaping. However, it is susceptible to corrosion, suffers from wear, and doesn’t always interact well with the body; furthermore, its properties are considerably altered by the conditions within a human being. Corrosion is particularly dangerous for stainless steel biomaterials, as it releases harmful materials which could damage health and cause numerous serious complications. This analysis offers a thorough evaluation of how stainless steel biomaterials corrode, the specific difficulties with various stainless steel types, and methods to alter the surface of steel to improve durability, corrosion protection, compatibility with the body and how long an implant will last.
In this study, heterojunction nanostructures S1-TiO2 and S2-TiO2/NiTiO3—were successfully synthesized via a microwave-assisted solution combustion technique. The heterojunction nanostructures were comprehensively characterized to assess their physicochemical properties relevant to photocatalysis. Structural and morphological analyses using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDAX), and Brunauer–Emmett–Teller (BET) surface area measurements confirmed the formation of well-crystallized materials. XRD results revealed an average crystallite size of approximately 30 nm. UV–Vis diffuse reflectance spectroscopy (UV-DRS) demonstrated a narrowed optical band gap of 2.69 eV for the S2-TiO2/NiTiO3 composite, indicative of enhanced visible-light absorption. SEM imaging showed a sheet-like NiTiO3 framework decorated with spherical TiO2 nanoparticles ranging from 20 to 50 nm. The S2−TiO2/NiTiO3 sample exhibited a significantly higher surface area (65.215 m2/g) compared to S1-TiO2, correlating with its superior photocatalytic performance. Photocatalytic experiments conducted under natural sunlight using Eriochrome Black T (EBT) and Methyl Red (MR) as model azo dyes confirmed the enhanced activity of the S2-TiO2/NiTiO3 heterostructure. These results establish the S2-TiO2/NiTiO3 composite as a highly effective and environmentally sustainable photocatalyst for the remediation of dye-contaminated water.
The increasing adoption of deep learning for Computed Tomography (CT) image classification has significantly improved diagnostic accuracy in medical imaging. However, traditional centralized approaches require transferring large volumes of medical data to a central server, leading to high bandwidth consumption, increased latency, and serious privacy concerns, particularly in wireless healthcare environments. Federated Learning (FL) offers a promising solution by enabling collaborative model training without sharing raw patient data. Nevertheless, conventional FL methods suffer from substantial communication overhead due to frequent transmission of large model updates, limiting their applicability in bandwidth-constrained networks. To address these challenges, this paper proposes a Communication-Efficient Federated Learning (CEFL) framework for distributed CT image classification. The proposed approach integrates gradient sparsification, model quantization, and adaptive communication scheduling to significantly reduce the size and frequency of model updates. The framework is implemented using a multi-layer architecture comprising medical imaging, edge computing, wireless communication, and federated aggregation layers. Experiments are conducted on the LIDC-IDRI CT dataset under simulated bandwidth-constrained conditions. The results demonstrate that the proposed CEFL framework reduces communication overhead by up to 40-60% compared to conventional FL methods such as FedAvg, while achieving improved classification accuracy of approximately 90%. Furthermore, latency is significantly reduced, making the system suitable for real-time wireless healthcare applications. These findings highlight the effectiveness of communication-efficient strategies in enabling scalable, privacy-preserving medical image analysis
This study explores the thermal performance enhancement of a double-slope solar still (DSSS) though the integration of paraffin wax (PCM_56) and silicon dioxide (SiO2) nanoparticles as thermal energy storage media. Experimental investigations were conducted under thee distinct configurations: a conventional system without storage, a setup incorporating only PCM_56, and a hybrid configuration combining PCM_56 with SiO2 nanoparticles. The conventional system achieved a maximum distillate yield of 1.4 kg h−1 with 36
This paper presents the design and implementation of a comprehensive smart shopping cart system that integrates Radio-Frequency Identification (RFID) and Quick Response (QR) code technologies to revolutionize the retail shopping experience. Built around the powerful ESP32 microcontroller, the system automates the entire shopping process from product scanning to payment processing and inventory management. Customers can seamlessly scan product QR codes to add items to their virtual cart, with payment authorization handled automatically through pre-registered RFID cards. The system features a sophisticated web dashboard that provides real-time monitoring of transactions, inventory levels, and user balances for both customers and store administrators. Additionally, GSM functionality enables instant transactional SMS alerts. This innovative solution effectively addresses critical challenges in modern retail environments, including reducing checkout queues, minimizing human error, enhancing operational efficiency, and providing valuable data analytics for business intelligence. The system represents a significant advancement in IoT-based retail automation, offering scalability, cost-effectiveness, and improved customer satisfaction.
