Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer.

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN-Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.

Numerical study of different ventilation schemes in a classroom for efficient aerosol control.

The air quality is a parameter to be controlled in order to live in a comfortable place. This paper analyzes the trajectory of aerosols exhaled into the environment in a classroom. Three scenarios are investigated; without ventilation, with natural and with mechanical ventilation. A multi-phase computational fluid study based on Eulerian-Lagrangian techniques is defined. Temperature and ambient relative humidity, as well as air velocity, direction and pressure is taken into account. For droplets evaporation, mass transfer and turbulent dispersion have been added. This work tends to be of great help in various areas, such as the field of medicine and energy engineering, aiming to show the path of aerosols dispersed in the air. The results show that the classroom with a mechanical ventilation scheme offers good results when it comes to an efficient control of aerosols. In all three cases, aerosols exhaled into the environment impregnate the front row student in the first 0.5 s. Reaching the time of 4, 2 and 1 s, in the class without ventilation, mechanical and natural ventilation, respectively, the aerosols have been already deposited on the table of the person in the first row, being exposed for longer in the case of no ventilation. Particles with a diameter of less than 20 µm are distributed throughout the classroom over a long period. The air jet injected into the interior space offers a practically constant relative humidity and a drop in temperature, slowing down the process of evaporation of the particles. In the first second, it can be seen that a mass of 0.0025 mg formed by 9 million droplets accumulates, in cases without ventilation and natural ventilation. The room with a mechanical installation accumulated 5.5 million particles of mass 0.0028 mg in the first second. The energy losses generated by natural ventilation are high compared to the other scenarios, exactly forty and twenty times more in the scenario with mechanical ventilation and without ventilation, respectively.

Cooktop Sensing Based on a YOLO Object Detection Algorithm.

Deep Learning (DL) has provided a significant breakthrough in many areas of research and industry. The development of Convolutional Neural Networks (CNNs) has enabled the improvement of computer vision-based techniques, making the information gathered from cameras more useful. For this reason, recently, studies have been carried out on the use of image-based DL in some areas of people's daily life. In this paper, an object detection-based algorithm is proposed to modify and improve the user experience in relation to the use of cooking appliances. The algorithm can sense common kitchen objects and identify interesting situations for users. Some of these situations are the detection of utensils on lit hobs, recognition of boiling, smoking and oil in kitchenware, and determination of good cookware size adjustment, among others. In addition, the authors have achieved sensor fusion by using a cooker hob with Bluetooth connectivity, so it is possible to automatically interact with it via an external device such as a computer or a mobile phone. Our main contribution focuses on supporting people when they are cooking, controlling heaters, or alerting them with different types of alarms. To the best of our knowledge, this is the first time a YOLO algorithm has been used to control the cooktop by means of visual sensorization. Moreover, this research paper provides a comparison of the detection performance among different YOLO networks. Additionally, a dataset of more than 7500 images has been generated and multiple data augmentation techniques have been compared. The results show that YOLOv5s can successfully detect common kitchen objects with high accuracy and fast speed, and it can be employed for realistic cooking environment applications. Finally, multiple examples of the identification of interesting situations and how we act on the cooktop are presented.

Energy and thermal modelling of an office building to develop an artificial neural networks model.

Nowadays everyone should be aware of the importance of reducing CO2 emissions which produce the greenhouse effect. In the field of construction, several options are proposed to reach nearly-Zero Energy Building (nZEB) standards. Obviously, before undertaking a modification in any part of a building focused on improving the energy performance, it is generally better to carry out simulations to evaluate its effectiveness. Using Artificial Neural Networks (ANNs) allows a digital twin of the building to be obtained for specific characteristics without using very expensive software. This can simulate the effect of a single or combined intervention on a particular floor or an event on the remaining floors. In this paper, an example has been developed based on ANN. The results show a reasonable correlation between the real data of the Operative Temperature with the Energy Consumption and their estimates obtained through an ANN model, trained using an hourly basis, on each of the floors of an office building. This model confirms it is possible to obtain simulations in existing public buildings with an acceptable degree of precision and without laborious modelling, which would make it easier to achieve the nZEB target, especially in existing public office buildings.

Correction: Lopez-Vazquez et al. Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories. Sensors 2020, 20, 726.

The authors wish to correct the following error in the original paper [...].

Delamination Fracture Behavior of Unidirectional Carbon Reinforced Composites Applied to Wind Turbine Blades.

One of the materials that is used widely for wind turbine blade manufacturing are fiber-reinforced composites. Although glass fiber reinforcement is the most used in wind turbine blades, the use of carbon fiber allows larger blades to be manufactured due to their better mechanical characteristics. Some turbine manufacturers are using carbon fiber in the most critical parts of the blade design. The larger rotors are exposed to complex loading conditions in service. One of the most relevant structures on a wind turbine blade is the spar cap. It is usually manufactured by means of unidirectional laminates, and one of its major failures is the delamination. The determination of material features that influence delamination initiation and advance by appropriate testing is a fundamental topic for the study of composite delamination. The fracture behavior is studied across coupons of carbon fiber reinforcement epoxy laminates. Fifteen different test conditions have been analyzed. Fracture surfaces for different mode ratios have been explored using optical microscope and scanning electron microscope. Experimental results shown in the paper for critical fracture parameters agree with the theoretically expected values. Therefore, this experimental procedure is suitable for wind turbine blade material characterizing at the initial coupon-scale research level.

Temperature based maximum power point tracking for photovoltaic modules.

In this article authors propose a temperature based Maximum Power Point Tracking algorithm (MPPT). Authors show that there is an optimal current vs maximum power curve that depends on photovoltaic (PV) module temperature. Therefore, the maximum power point (MPP) can be achieved in very few commutation steps if the control forces the PV module to work in temperature dependent optimal curve. Authors shows how this PV module temperature based MPPT is stable and converges to MPP for each temperature. In order to proof its stability, authors propose a Lyapunov energy function. This Lyapunov energy function has positive values for all values except into MPP given the PV module temperature. This Lyapunov energy function has negative increment along each time step. Hence, the stability of temperature based MPPT can be demonstrated. The proposed MPPT algorithm proposes a current set point. This current set point is obtained with instantaneous PV module power and temperature dependent maximum power vs optimal current curve. Stability is analysed for different temperature levels. Optimal current vs maximum power curve has been modelled by a line. The lines' coefficients depend on PV module temperature. Proposed Lyapunov energy function is not symmetric about equilibrium or MPP because MPPT algorithm and PV module dynamic have no symmetric behaviour about this equilibrium point.

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories.

An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.

Arm Orthosis/Prosthesis Movement Control Based on Surface EMG Signal Extraction.

This paper shows experimental results on electromyography (EMG)-based system control applied to motorized orthoses. Biceps and triceps EMG signals are captured through two biometrical sensors, which are then filtered and processed by an acquisition system. Finally an output/control signal is produced and sent to the actuators, which will then perform the actual movement, using algorithms based on autoregressive (AR) models and neural networks, among others. The research goal is to predict the desired movement of the lower arm through the analysis of EMG signals, so that the movement can be reproduced by an arm orthosis, powered by two linear actuators. In this experiment, best accuracy has achieved values up to 91%, using a fourth-order AR-model and 100ms block length.

Lynx: Automatic Elderly Behavior Prediction in Home Telecare.

This paper introduces Lynx, an intelligent system for personal safety at home environments, oriented to elderly people living independently, which encompasses a decision support machine for automatic home risk prevention, tested in real-life environments to respond to real time situations. The automatic system described in this paper prevents such risks by an advanced analytic methods supported by an expert knowledge system. It is minimally intrusive, using plug-and-play sensors and machine learning algorithms to learn the elder's daily activity taking into account even his health records. If the system detects that something unusual happens (in a wide sense) or if something is wrong relative to the user's health habits or medical recommendations, it sends at real-time alarm to the family, care center, or medical agents, without human intervention. The system feeds on information from sensors deployed in the home and knowledge of subject physical activities, which can be collected by mobile applications and enriched by personalized health information from clinical reports encoded in the system. The system usability and reliability have been tested in real-life conditions, with an accuracy larger than 81%.