Recent Patents on Engineering - Online First

Piano tuning plays a very important role in piano playing and music education. Regular tuning can prolong the service life of the piano, improve the sound saturation, and make the sound more concentrated and full. Automated mechanical equipment, controllers, sensors, and other equipment are used to automatically tune pianos to solve the problems of human error, overtone resonance interference, string breakage, etc., occurring in manual tuning. To solve the above problems, based on the synthesis of related patents and research results, the patents on automatic tuning system and methods for modulating piano are introduced in detail. An automatic tuning system can replace manual tuning, simplify the complex process of tuning, and consider the problem of overtone interference caused by the vibration of the strings to improve the accuracy of tuning; it can, at the same time, perform real-time monitoring of the string tension, protecting the strings from breaking due to tuning. The automatic tuning system has a simple structure and a high degree of automation; its method is highly accurate and it can replace professional tuning technicians, being highly innovative and widely practical.

Background: The escalating expansion of global telecom towers since the 1990s has led to an ageing infrastructure, with towers exceeding 30 years. Rust, a pervasive threat, jeopardizes structural integrity, posing risks of collapse and harm to riggers. Recognizing the importance of innovation in addressing this challenge, rust detection and tower maintenance patents play a crucial role in advancing the field.

Objective: This paper focuses on detecting and evaluating rust levels on ageing telecom towers. Beyond addressing the immediate concerns, the objective includes exploring patented technologies in rust detection and prevention. The aim is to amalgamate innovative solutions into the algorithmic framework for automated rust detection, thereby enhancing the robustness and novelty of the proposed approach.

Methods: The study involves a comprehensive literature review to identify patented technologies related to rust detection on structures like telecom towers. By integrating patented methods into the image processing code developed in Python, the algorithm gains an edge in accuracy and efficiency. The three-tier classification of rust severity aligns with patented preventative maintenance strategies, ensuring a holistic approach to tower care.

Result: The amalgamation of patented technologies with the image processing code enhances the accuracy of rust detection on telecom towers. The results guide targeted treatments, informed by patented preventative maintenance strategies, including patented rust inhibitors, protective coatings, and corrosion-resistant materials.

Conclusion: This paper introduces an innovative algorithm for rust detection and emphasizes the integration of patented technologies. Incorporating patented solutions into preventative maintenance strategies makes the approach more effective and technologically advanced. This holistic method not only benefits telecom companies in accident prevention and cost reduction but also positions the developed algorithm as a significant contribution to patented rust detection and tower maintenance technologies.

Advanced technologies on the internet create an environment for information exchange among communities. However, some individuals exploit these environments to spread false news. False News, or Fake News (FN), refers to misleading information deliberately crafted to harm the reputation of individuals, products, or services. Identifying FN is a challenging issue for the research community. Many researchers have proposed approaches for FN detection using Machine Learning (ML) and Natural Language Processing (NLP) techniques. In this article, we propose a combined approach for FN detection, leveraging both ML and NLP techniques. We first extract all terms from the dataset after applying appropriate preprocessing techniques. A Feature Selection Algorithm (FSA) is then employed to identify the most important features based on their scores. These selected features are used to represent the dataset documents as vectors. The term weight measure determines the significance of each term in the vector representation. These document vectors are combined with vector representations obtained through an NLP technique. Specifically, we use the Bidirectional Encoder Representations from Transformers (BERT) model to represent the document vectors. The BERT small case model is employed to generate features, which are then used to create the document vectors. The combined vector, comprising ML-based document vector representations and NLP-based vector representations, is fed into various ML algorithms. These algorithms are used to build a model for classification. Our combined approach for FN detection achieved the highest accuracy of 96.72% using the Random Forest algorithm, with document vectors that included content-based features of size 4000 concatenated with outputs from the 9th to 12th BERT encoder layers.

Primarily, concrete undergoes wide self-desiccation, autogenous shrinkage, and other mechanical properties that may be influenced by inadequate curing methods. Various researchers have explored different materials for self-curing in concrete, including lightweight aggregates, water-retaining admixtures, superplasticizers, polymers, natural fibers, and pozzolanic materials, each with patented formulations capable of withstanding high-water content. These patented self-curing agents serve as infill material within the concrete, aiding in its internal curing process. Numerous tests conducted by different researchers have evaluated the efficiency and scope of self-curing in concrete, focusing on physical, mechanical, durability, and microstructural properties. Results consistently demonstrate improved concrete properties through the adoption of patented self-curing processes. The majority of research in this area has concentrated on high-performance concrete, aiming to address shrinkage issues resulting from low water-to-cement ratios. In the current research, emphasis is placed on experimentally enhancing the mechanical properties of M45 grade concrete using patented internal curing agents such as Calotropis Gigantea fibers and Polyethylene Glycol admixtures, alongside bamboo reinforcement. Calotropis Gigantea fibers, maintained at a length of 10 mm, will be added at varying percentages (0.5%, 1%, and 1.5%) by weight of the concrete. A battery of tests will be conducted to analyze different properties of concrete, which includes workability, compressive strength, flexural strength, degree of hydration, and comparative assessments against conventional concrete. Additionally, optimization of admixture dosages, intended for internal curing, will be undertaken.