The human cochlea is undeniably one of the most amazing organs in the body. One of its most intriguing features is its unique capability to convert sound waves into electrical nerve impulses. Humans can generally perceive frequencies between 20 Hz and 20 kHz with their auditory systems. Several studies have been conducted on building an artificial basilar membrane for the human cochlea (cochlear biomodel). It's possible to mimic the active behavior of the basilar membrane using micro-electromechanical systems (MEMS). This paper proposes an array of MEMS bridge beams that are mechanically sensitive to the perceived audible frequency. It was designed to operate within the audible frequency range of a set of bridge beams with 0.65 μm thickness, width of 50 μm and varying lengths between 200 μm and 2000 μm. As the material for bridge beam structures, Platinum (Pt), Molybdenum (Mo), Chromium (Cr), and Aluminium (Al) have been considered. For the cochlear biomodel, platinum has proven to be the best material, closely mimicking the basilar membrane, based on the finite element (FE) and lumped element (LE) models.

Degrading air quality has been a matter of concern nowadays, and monitoring the air quality helps us keep a check on it. Air pollution is a pressing global issue with far-reaching impacts on public health and the environment. The need for effective and real-time monitoring systems has become increasingly apparent to combat this growing concern. Here, an innovative air pollution monitoring system (APMS) that leverages internet of things (IoT) technology to enable comprehensive and dynamic air quality assessment is introduced. The proposed APMS employs a network of IoT-enabled sensors strategically deployed across urban and industrial areas. These sensors are equipped to measure various pollutants, including particulate matter (PM2.5 and PM10), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), carbon monoxide (CO), and volatile organic compounds (VOCs). Here, a regression model is created to forecast air quality using sensor data while taking into account variables including weather information, traffic patterns, and pollutants. Additionally, air quality categories (such as good, moderate, and harmful) are classified using classification algorithms based on preset thresholds. The IoT architecture facilitates seamless data transmission from these sensors to a centralized cloud-based platform. The developed APMS monitors the air quality using a MQ-135 gas sensor, and the data are shared over a web server using the internet. An alarm will trigger when the air quality goes below a certain level. Also, the air quality, which is measured in parts per million (PPM), is displayed on the unit connected to it. Further, an alert message is sent to the air pollution control board when the PPM goes beyond a certain level, which takes preventive measures to control the pollution and also alerts the people, which helps each person in that society save their environment from pollution and have a good air quality environment. Additionally, the APMS offers user-friendly interfaces, accessible through web and mobile applications, to empower citizens with real-time air quality information. The effectiveness of the IoT-based air pollution monitoring system has been validated through successful field trials in urban and industrial environments, and it has the ability to provide real-time data insights and empower stakeholders in promoting environmental sustainability and fostering citizen engagement.

Tool condition monitoring (TCM) systems are essential in milling operations to guarantee the product’s quality, and when those are paired with indirect measure techniques, such as vibration or acoustic emission sensors, the monitoring can happen without sacrificing productivity. Some more advanced techniques in tool wear estimation are based on supervised machine learning algorithms, like several other applications in the Industry 4.0’s context, however, a satisfactory performance can be obtained with simple techniques and low computational power. This work focuses on an application of tool wear estimation using a simple backpropagation neural network in a milling dataset. Statistics techniques, i.e., the mean, variance, skewness and kurtosis were used as features extracted of indirect measurements from vibration and acoustic emission sensors’ data in a real milling testbench dataset containing multiple experiments with sensor data and a direct measure of the flank wear (VB) in most instances. The data was preprocessed, specifically to acquire clean and normalized values for the neural network training, assuming the VB measure as the target variable to predict tool wear, and all incomplete samples without a VB measure, as well as outliers, were removed beforehand. The train and test subsets were chosen randomly after making sure that the maximum values of every variable were represented in the training subset. A multiple topology approach was implemented to test multiple backpropagation neural networks’ configurations to determine the most suitable one based on two performance criteria, i.e., Mean Absolute Percent Error (MAPE) and variance. Although only a simple backpropagation algorithm was used, the results were adequate to demonstrate a balance between accuracy and computational resource usage.

The exploration of the Solar system from Earth in search of new living spaces will provide new options and possibilities for the survival and development of humankind, where the understanding of their environment, and their resource utilization are essential stages to develop. In the future, when space technology is highly developed and the cost of interplanetary transportation is greatly reduced, planets such as Saturn, Mars and Jupiter will become the "islands" in the Solar system for human settlements. The identification of such resources usually done through exploration, monitoring with sensors and spectral analysis is needed. The development of techniques for satellite surveying to organize the surface of such celestial bodies to generate maps with resource information is part of the first steps for the exploration. For the purpose of a highly accurate 3D modeling of the celestial body and a flat 2D map presentation, the surface structure of the celestial body should be understood by using geometry as well as different types of map projection methods, that will be exploited by the satellites. Different geometrical models and algorithms together with information from sensing systems can be used as much as possible in order to accurately locate the position of on-surface vehicles that could perform in-situ analysis of surface samples. Thus, the combination of the 2D/3D techniques with localization information obtained from sensors, and its use through the satellites, creates a map of the distribution of the celestial body resources. In this article, a projection method based on such a combination and other conventional techniques to achieve better accuracy and efficiency during the process of mapping and projection of a celestial body surface is presented.

Over the past few decades, a significant interest in space exploration has emerged, driven by the lack of resources and the quest for answers to issues like climate change on Earth. New technologies have brought us closer to the possibility of exploring and mapping our solar system and its surroundings in greater detail. Alongside these new horizons, important challenges also emerge. Current methods of space communications operate with a notable lack of efficiency. The vast distances between celestial bodies within our solar system and Earth result in data transmission and reception that fall far short of real-time capabilities. Factors such as bandwidth asymmetry contribute to disruptions in the satellite communications network, e.g., intermittency of service. This paper proposes the development of an interplanetary satellite communication network, which is built upon a communications protocol featuring dynamic routing. This network architecture aims to optimize information transportation by adapting its communications algorithm to environment conditions as quickly as possible. The envisioned satellite infrastructure involves strategically placing satellites at key Lagrange points around each moon and planet within the asteroid belt. The elements within the infrastructure must be aware of their position in space through the integration of sensing capabilities and intelligent algorithms. Near each planet, a satellite with more capabilities will be tasked with gathering and transmitting the information from nanosatellites orbiting a planet, which will relay the respective signals back to our planet. This architecture will enable faster decision-making processes based on exploration data of the most significant celestial bodies within the asteroid belt, providing valuable insights such as constant monitoring of the dark side of the moon and difficult to reach zones in the Solar system.