The work presents a structural computational model developed for investigation of heat and air—water vapor mass exchange between the multilayer textile packages and the human body. Each fabric layer is represented by a single element, characterized by its air and water vapor permeability, thermal resistance and heat capacity, as well as, water vapor absorption properties. While characteristics of each layer are obtained experimentally, the computational model enables the prediction of the variation in time of temperature and humidity in the gaps between each pair of adjacent layers. The finite element, which represents the forced ventilation layer made of three-dimensional textile material, is created and may be used as a structural element within the overall structural model of the textile package. The element equations are derived on the basis of ideal gas state equation. The results of the measured textile layers characteristics and the numerical data are presented. Though the condensation of water vapor was not considered in this model, the time point of initiation of sweating at given heat and water vapor generation rates could be predicted.

Urban change detection is an important part of sustainable urban planning, regional development, and socio-economic analysis, especially in regions with limited access to economic and demographic statistical data. The goal of this research is to create a strategy that enables the extraction of indicators from large-scale orthoimages of different resolution with practically acceptable accuracy after a short training process. Remote sensing data can be used to detect changes in number of buildings, forest areas, and other landscape objects. In this paper, aerial images of a digital raster orthophoto map at scale 1:10,000 of the Republic of Lithuania (ORT10LT) of three periods (2009–2010, 2012–2013, 2015–2017) were analyzed. Because of the developing technologies, the quality of the images differs significantly and should be taken into account while preparing the dataset for training the semantic segmentation model DeepLabv3 with a ResNet50 backbone. In the data preparation step, normalization techniques were used to ensure stability of image quality and contrast. Focal loss for the training metric was selected to deal with the misbalanced dataset. The suggested model training process is based on the transfer learning technique and combines using a model with weights pretrained in ImageNet with learning on coarse and fine-tuning datasets. The coarse dataset consists of images with classes generated automatically from Open Street Map (OSM) data and the fine-tuning dataset was created by manually reviewing the images to ensure that the objects in images match the labels. To highlight the benefits of transfer learning, six different models were trained by combining different steps of the suggested model training process. It is demonstrated that using pretrained weights results in improved performance of the model and the best performance was demonstrated by the model which includes all three steps of the training process (pretrained weights, training on coarse and fine-tuning datasets). Finally, the results obtained with the created machine learning model enable the implementation of different approaches to detect, analyze, and interpret urban changes for policymakers and investors on different levels on a local map, grid, or municipality level.

This study presents a computational model for investigation of the air permeability coefficient and water-vapor resistance coefficient through 3D textile layer. The effective values of the coefficients are highly dependent on the volumetric internal structure of the layer. The computational study of air and water vapor flow has been performed a microscale finite element model of the representative volume of the textile layer by taking into account the real configuration the yarns and filaments. The effective values of the coefficients were obtained by using taking average values of air and water vapor fluxes through the thickness of the representative volume. The steady state computational models in micro scale are based on Navier-Stokes and Brinkman partial differential equations. The simulations were performed in Comsol Multiphysics 5.3a software environment by using laminar flow (.spf) application mode. Numerical results for the air permeability of the samples were obtained, analyzed and validated by comparing against experimental data. Good agreement with experimental data has been achieved.

Automatic climate management enables us to reduce repetitive work and share knowledge of different experts. An artificial intelligence-based layer to manage climate in white button mushroom growing hall was presented in this article. It combines visual data, climate data collected by sensors, and technologists’ actions taken to manage climate in the mushroom growing hall. The layer employs visual data analysis methods (morphological analysis, Fourier analysis, convolutional neural networks) to extract indicators, such as the percentage of mycelium coverage and number of pins of different size per area unit. These indicators are used to generate time series that represent the dynamics of the mushroom growing process. The incorporation of time synchronized indicators obtained from visual data with monitored climate indicators and technologists’ actions allows for the application of a supervised learning decision making model to automatically define necessary climate changes. Whereas managed climate parameters and visual indicators depend on the mushroom production stage, three different models were created to correspond the incubation, shock, and fruiting stage of the mushroom production process (using decision trees, K-nearest neighbors’ method). An analysis of the results showed that trends of the selected visual indicators remain similar during different cultivations. Thus, the created decision-making models allow for the definition of the majority of the cases in which the climate change or transition between the growing stages is needed.

The heat resistance of fabric enhanced by bio-ceramic additives (BCAs) is investigated theoretically and experimentally in order to determine the influence of modification of the infrared (IR) absorption property of the fabric. The enhanced IR sensitivity of textiles improves the thermoregulatory processes when worn in cold environments. The finite element model has been developed by taking into account the coupled phenomena of heat conduction, surface convection and the interaction of the fabric with IR power flux by employing heat transfer differential equations and the Stefan–Boltzmann law. Evaluations of IR absorptivity, reflectivity and transmissivity, the temperature transients during the hot plate chamber test and heat retaining properties of the fabric heated by an IR lamp have been obtained experimentally and simulated by means of the developed finite element model. The values of model parameters have been found, which provided a satisfactory match between the computation and the experiment in all considered cases. Simultaneously, the obtained values were reasonably close to rough theoretical estimations. Efforts have been made to distinguish from each other the influence of diffusive and radiative components of heat transfer, which affect the results of thermal resistance tests. The comparative analysis of contributions of different heat exchange mechanisms allows a better understanding of the peculiarities of standard heat resistance measurement procedures applied to BCA-enhanced fabrics and facilitates the validation of the computational models.

Hydromorphology of rivers assessed through direct measurements is a time-consuming and relatively expensive procedure. The rapid development of unmanned aerial vehicles and machine learning (ML) technologies enables the usage of aerial images to determine hydromorphological units (HMUs) automatically. The application of various direct and indirect data sources and their combinations for the determination of river HMUs from aerial images was the main aim of this research. Aerial images with and without the Sobel filter, a layer of boulders identified using Yolov5x6, and a layer of direct measurements of depth and streamflow velocity were used as data sources. Three ML models were constructed for the cases if one, two, or three data sources were used. The ML models for HMU segmentation were constructed of MobileNetV2 pre-trained on ImageNet data for the feature extraction part and U-net for the segmentation part. The stratified K-fold cross-validation with five folds was carried out to evaluate the performance of the model due to the limited dataset. The analysis of the ML results showed that the measured metrics of segmentation using direct measurements were close to the ones of the model trained only on the combination of boulder layer and aerial images with the Sobel filter. The obtained results demonstrated the potential of the applied approach for the determination of HMUs only from the aerial images, and provided a basis for further development to increase its accuracy.