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Статті в журналах з теми "120202 Building Science and Techniques"
Khair, Nurhayati, M. Y. Nurul Syakima, Suwaibatul Islamiah Abdullah Sani, Puteri Ameera Mentaza Khan, and Azizah Ismail. "Building Performance Evaluation Techniques." Advanced Science Letters 24, no. 6 (June 1, 2018): 4425–28. http://dx.doi.org/10.1166/asl.2018.11618.
Повний текст джерелаAnderson, Kristine L., and Carl M. Moore. "Group Techniques for Idea Building." Contemporary Sociology 17, no. 3 (May 1988): 371. http://dx.doi.org/10.2307/2069666.
Повний текст джерелаKővári, Gábor, and István Kistelegdi Jr. "Building performance simulation modeling techniques." Pollack Periodica 11, no. 2 (August 2016): 135–46. http://dx.doi.org/10.1556/606.2016.11.2.12.
Повний текст джерелаClaudi de Saint Mihiel, Alessandro. "Building envelope: techniques, languages, transparencies." TECHNE - Journal of Technology for Architecture and Environment, no. 24 (July 26, 2022): 278–83. http://dx.doi.org/10.36253/techne-13448.
Повний текст джерелаVolkovich, Vladimir, Jacob Kogan, and Charles Nicholas. "Building initial partitions through sampling techniques." European Journal of Operational Research 183, no. 3 (December 2007): 1097–105. http://dx.doi.org/10.1016/j.ejor.2005.12.045.
Повний текст джерелаFelton, S., M. Tolley, E. Demaine, D. Rus, and R. Wood. "A method for building self-folding machines." Science 345, no. 6197 (August 7, 2014): 644–46. http://dx.doi.org/10.1126/science.1252610.
Повний текст джерелаGorman, Patrick, and Nicolas Pappas. "Techniques for building excellent operator machine interfaces (OMI)." IEEE Aerospace and Electronic Systems Magazine 24, no. 10 (October 2009): 17–22. http://dx.doi.org/10.1109/maes.2009.5317781.
Повний текст джерелаBaldwin, Claudia, and Helen Ross. "Bridging Troubled Waters: Applying Consensus-Building Techniques to Water Planning." Society & Natural Resources 25, no. 3 (March 2012): 217–34. http://dx.doi.org/10.1080/08941920.2011.578120.
Повний текст джерелаRabenstein, R. "Application of model reduction techniques to building energy simulation." Solar Energy 53, no. 3 (September 1994): 289–99. http://dx.doi.org/10.1016/0038-092x(94)90635-1.
Повний текст джерелаCutting-Decelle, A. F., B. P. Das, R. I. Young, K. Case, S. Rahimifard, C. J. Anumba, and N. M. Bouchlaghem. "Building supply chain communication systems: a review of methods and techniques." Data Science Journal 5 (2006): 29–51. http://dx.doi.org/10.2481/dsj.5.29.
Повний текст джерелаДисертації з теми "120202 Building Science and Techniques"
Burianek, Theresa K. (Theresa Kathleen) 1977. "Building a speech understanding system using word spotting techniques." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/81552.
Повний текст джерелаIncludes bibliographical references (p. 63-65).
by Theresa K. Burianek.
M.Eng.
Doban, Nicolae. "Building predictive models for dynamic line rating using data science techniques." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187812.
Повний текст джерелаLee, Jeong Eun. "Sensing Building Structure Using UWB Radios for Disaster Recovery." Thesis, Portland State University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10812182.
Повний текст джерелаThis thesis studies the problem of estimating the interior structure of a collapsed building using embedded Ultra-Wideband (UWB) radios as sensors. The two major sensing problems needed to build the mapping system are determining wall type and wall orientation. We develop sensing algorithms that determine (1) load-bearing wall composition, thickness, and location and (2) wall position within the indoor cavity. We use extensive experimentation and measurement to develop those algorithms.
In order to identify wall types and locations, our research approach uses Received Signal Strength (RSS) measurement between pairs of UWB radios. We create an extensive database of UWB signal propagation data through various wall types and thicknesses. Once the database is built, fingerprinting algorithms are developed which determine the best match between measurement data and database information. For wall mapping, we use measurement of Time of Arrival (ToA) and Angle of Arrival (AoA) between pairs of radios in the same cavity. Using this data and a novel algorithm, we demonstrate how to determine wall material type, thickness, location, and the topology of the wall.
Our research methodology utilizes experimental measurements to create the database of signal propagation through different wall materials. The work also performs measurements to determine wall position in simulated scenarios. We ran the developed algorithms over the measurement data and characterized the error behavior of the solutions.
The experimental test bed uses Time Domain UWB radios with a center frequency of 4.7 GHz and bandwidth of over 3.2 GHz. The software was provided by Time Domain as well, including Performance Analysis Tool, Ranging application, and AoA application. For wall type identification, we use the P200 radio. And for wall mapping, we built a special UWB radio with both angle and distance measurement capability using one P200 radio and one P210 radio.
In our experimental design for wall identification, we varied wall type and distance between the radios, while fixing the number of radios, transmit power and the number of antennas per radio. For wall mapping, we varied the locations of reference node sensors and receiver sensors on adjoining and opposite walls, while fixing cavity size, transmit power, and the number of antennas per radio.
As we present in following chapters, our algorithms have very small estimation errors and can precisely identify wall types and wall positions.
Bouzidi, Khalil Riad. "Aide à la création et à l'exploitation de réglementations basée sur les modèles et techniques du Web sémantique." Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00876366.
Повний текст джерела(11185884), Sang Woo Ham. "Energy Analytics for Eco-feedback Design in Multi-family Residential Buildings." Thesis, 2021.
Знайти повний текст джерелаThe residential sector is responsible for approximately 21% of the total energy use in the U.S. As a result, there have been various programs and studies aiming to reduce energy consumption and utility burden on individual households. Among various energy efficiency strategies, behavior-based approaches have received considerable attention because they significantly affect operational energy consumption without requiring building upgrades. For example, up to 30% of heating and cooling energy savings can be achieved by having an efficient temperature setpoint schedule. Such approaches can be particularly beneficial for multi-family residential buildings because 88% of their residents are renters paying their own utility bills without being allowed to upgrade their housing unit.
In this context, eco-feedback has emerged as an approach to motivate residents to reduce energy use by providing information (feedback) on human behavior and environmental impact. This research has gained significant attention with the development of new smart home technology such as smart thermostats and home energy management systems. Research on the design of effective eco-feedback focuses on how to motivate residents to change their behavior by identifying and notifying implementable actions in a timely manner via energy analytics such as energy prediction models, energy disaggregation, etc.
However, unit-level energy analytics pose significant challenges in multi-family residential buildings tasks due to the inter-unit heat transfer, unobserved variables (e.g., infiltration, human body heat gain, etc.), and limited data availability from the existing infrastructure (i.e., smart thermostats and smart meters). Furthermore, real-time model inference can facilitate up-to-date eco-feedback without a whole year of data to train models. To tackle the aforementioned challenges, three new modeling approaches for energy analytics have been proposed in this Thesis is developed based on the data collected from WiFi-enabled smart thermostats and power meters in a multi-family residential building in IN, U.S.
First, this Thesis presents a unit-level data-driven modeling approach to normalize heating and cooling (HC) energy usage in multi-family residential buildings. The proposed modeling approach provides normalized groups of units that have similar building characteristics to provide the relative evaluation of energy-related behaviors. The physics-informed approach begins from a heat balance equation to derive a linear regression model, and a Bayesian mixture model is used to identify normalized groups in consideration of the inter-unit heat transfer and unobserved variables. The probabilistic approach incorporates unit- and season-specific prior information and sequential Bayesian updating of model parameters when new data is available. The model finds distinct normalized HC energy use groups in different seasons and provides more accurate rankings compared to the case without normalization.
Second, this Thesis presents a real-time modeling approach to predict the HC energy consumption of individual units in a multi-family residential building. The model has a state-space structure to capture the building thermal dynamics, includes the setpoint schedule as an input, and incorporates real-time state filtering and parameter learning to consider uncertainties from unobserved boundary conditions (e.g., temperatures of adjacent spaces) and unobserved disturbances (i.e., window opening, infiltration, etc.). Through this real-time form, the model does not need to be re-trained for different seasons. The results show that the median power prediction of the model deviates less than 3.1% from measurements while the model learns seasonal parameters such as the cooling efficiency coefficient through sequential Bayesian update.
Finally, this Thesis presents a scalable and practical HC energy disaggregation model that is designed to be developed using data from smart meters and smart thermostats available in current advanced metering infrastructure (AMI) in typical residential houses without additional sensors. The model incorporates sequential Bayesian update whenever a new operation type is observed to learn seasonal parameters without long-term data for training. Also, it allows modeling the skewed characteristics of HC and non-HC power data. The results show that the model successfully predicts disaggregated HC power from 15-min interval data, and it shows less than 12% of error in weekly HC energy consumption. Finally, the model is able to learn seasonal parameters via sequential Bayesian update and gives good prediction results in different seasons."Feature Selection Techniques for Effective Model Building and Estimation on Twitter Data to Understand the Political Scenario in Latvia with Supporting Visualizations." Master's thesis, 2016. http://hdl.handle.net/2286/R.I.40214.
Повний текст джерелаDissertation/Thesis
Masters Thesis Computer Science 2016
(9681032), Xiaoqi Liu. "Exploration of Intelligent HVAC Operation Strategies for Office Buildings." Thesis, 2020.
Знайти повний текст джерелаCommercial buildings not only have significant impacts on occupants’ well-being, but also contribute to more than 19% of the total energy consumption in the United States. Along with improvements in building equipment efficiency and utilization of renewable energy, there has been significant focus on the development of advanced heating, ventilation, and air conditioning (HVAC) system controllers that incorporate predictions (e.g., occupancy patterns, weather forecasts) and current state information to execute optimization-based strategies. For example, model predictive control (MPC) provides a systematic implementation option using a system model and an optimization algorithm to adjust the control setpoints dynamically. This approach automatically satisfies component and operation constraints related to building dynamics, HVAC equipment, etc. However, the wide adaptation of advanced controls still faces several practical challenges: such approaches involve significant engineering effort and require site-specific solutions for complex problems that need to consider uncertain weather forecast and engaging the building occupants. This thesis explores smart building operation strategies to resolve such issues from the following three aspects.
First, the thesis explores a stochastic model predictive control (SMPC) method for the optimal utilization of solar energy in buildings with integrated solar systems. This approach considers the uncertainty in solar irradiance forecast over a prediction horizon, using a new probabilistic time series autoregressive model, calibrated on the sky-cover forecast from a weather service provider. In the optimal control formulation, we model the effect of solar irradiance as non-Gaussian stochastic disturbance affecting the cost and constraints, and the nonconvex cost function is an expectation over the stochastic process. To solve this optimization problem, we introduce a new approximate dynamic programming methodology that represents the optimal cost-to-go functions using Gaussian process, and achieves good solution quality. We use an emulator to evaluate the closed-loop operation of a building-integrated system with a solar-assisted heat pump coupled with radiant floor heating. For the system and climate considered, the SMPC saves up to 44% of the electricity consumption for heating in a winter month, compared to a well-tuned rule-based controller, and it is robust, imposing less uncertainty on thermal comfort violation.
Second, this thesis explores user-interactive thermal environment control systems that aim to increase energy efficiency and occupant satisfaction in office buildings. Towards this goal, we present a new modeling approach of occupant interactions with a temperature control and energy use interface based on utility theory that reveals causal effects in the human decision-making process. The model is a utility function that quantifies occupants’ preference over temperature setpoints incorporating their comfort and energy use considerations. We demonstrate our approach by implementing the user-interactive system in actual office spaces with an energy efficient model predictive HVAC controller. The results show that with the developed interactive system occupants achieved the same level of overall satisfaction with selected setpoints that are closer to temperatures determined by the MPC strategy to reduce energy use. Also, occupants often accept the default MPC setpoints when a significant improvement in the thermal environment conditions is not needed to satisfy their preference. Our results show that the occupants’ overrides can contribute up to 55% of the HVAC energy consumption on average with MPC. The prototype user-interactive system recovered 36% of this additional energy consumption while achieving the same overall occupant satisfaction level. Based on these findings, we propose that the utility model can become a generalized approach to evaluate the design of similar user-interactive systems for different office layouts and building operation scenarios.
Finally, this thesis presents an approach based on meta-reinforcement learning (Meta-RL) that enables autonomous optimal building controls with minimum engineering effort. In reinforcement learning (RL), the controller acts as an agent that executes control actions in response to the real-time building system status and exogenous disturbances according to a policy. The agent has the ability to update the policy towards improving the energy efficiency and occupant satisfaction based on the previously achieved control performance. In order to ensure satisfactory performance upon deployment to a target building, the agent is trained using the Meta-RL algorithm beforehand with a model universe obtained from available building information, which is a probability measure over the possible building dynamical models. Starting from what is learned in the training process, the agent then fine-tunes the policy to adapt to the target building based on-site observations. The control performance and adaptability of the Meta-RL agent is evaluated using an emulator of a private office space over 3 summer months. For the system and climate under consideration, the Meta-RL agent can successfully maintain the indoor air temperature within the first week, and result in only 16% higher energy consumption in the 3rd month than MPC, which serves as the theoretical upper performance bound. It also significantly outperforms the agents trained with conventional RL approach.
Sandanayake, Malindu. "Models and toolkit to estimate and analyse the emissions and environmental impacts of building construction." Thesis, 2016. https://vuir.vu.edu.au/36411/.
Повний текст джерела(10292846), Zhipeng Deng. "RECOGNITION OF BUILDING OCCUPANT BEHAVIORS FROM INDOOR ENVIRONMENT PARAMETERS BY DATA MINING APPROACH." Thesis, 2021.
Знайти повний текст джерела(11191899), Jie Ma. "A SEQUENTIAL APPROACH FOR ACHIEVING SEPARATE SENSIBLE AND LATENT COOLING." Thesis, 2021.
Знайти повний текст джерелаCurrent air conditioning systems generally operate with a relatively fixed moisture removal capacity, and indoor humidity conditions are usually not actively controlled in most buildings. If we focus only on sensible heat removal, an air conditioning system could operate with a fairly high evaporating temperature, and consequently a high coefficient of performance (COP). However, to provide an acceptable level of dehumidification, air conditioners typically operate with a much lower evaporating temperature (and lower COP) to ensure that the air is cooled below its dew point to achieve dehumidification. The latent (moisture related) loads in a space typically only represent around 20-30% of the total load in many environments; however, the air conditioning system operates 100% of the time at a low COP to address this small fraction of the load. To address issues associated with inadequate dehumidification and high energy consumption of conventional air conditioning systems, the use of a separate sensible and latent cooling (SSLC) system can dramatically increase system COP and provide active humidity control. Most current SSLC approaches that are reported in the literature require the installation of multiple components or systems in addition to a conventional air conditioner to separately address the sensible and latent loads. This approach increases the overall system installation and maintenance costs and complicates the controller design.
A sequential SSLC system is proposed and described in this work takes full advantage of readily available variable speed technology and utilizes independent speed control of both the compressor and evaporator fan, so that a single direct expansion (DX) air-conditioning (A/C) system can be operated in such a way to separately address the sensible and latent loads in a highly efficient manner. In this work, a numerical model of DX A/C system is developed and validated through experiential testing to predict the performance under varied equipment speeds and then used to investigate the energy saving potential with the implementation of the proposed sequential SSLC system. To realize the sequential SSLC system approach, various corresponding control strategies are proposed and explained in this work that minimizes energy consumption while provides active control over both space temperature and relative humidity. At the end of this document, the benefits of applying the SSLC system in a prototype residential building under different typical climate characteristics are demonstrated.
Книги з теми "120202 Building Science and Techniques"
Techniques and applications of expert systems in the construction industry. Chichester, West Sussex, England: E. Horwood, 1989.
Знайти повний текст джерелаSpence, William Perkins. Construction methods, materials, and techniques. 2nd ed. Clifton Park, N.Y: Thomson Delmar Learning, 2006.
Знайти повний текст джерелаConstruction methods, materials, and techniques. 2nd ed. Clifton Park, NY: Thomson Delmar Learning, 2006.
Знайти повний текст джерелаConstruction methods, materials, and techniques. Albany, N.Y: Delmar Publishers, 1997.
Знайти повний текст джерелаJ, Hammond David, ed. Lessons from the Oklahoma City bombing: Defensive design techniques. New York, N.Y: ASCE Press, 1997.
Знайти повний текст джерелаH, Moore John. Building scientific apparatus. 4th ed. Cambridge, UK: Cambridge University Press, 2009.
Знайти повний текст джерелаPro ASP.NET SharePoint 2010 solutions: Techniques for building SharePoint functionality into ASP.NET applications. [Berkeley, Calif.]: Apress, 2010.
Знайти повний текст джерелаGuggenheim, Jaenet. Triassic Hall: Building the Triassic exhibit from the ground up. Santa Fe, N.M: Azro Press, 2011.
Знайти повний текст джерелаSmithsonian Institution. Snakes, snails and history tails: Building discovery rooms and learning labs at the Smithsonian Institution. Washington, D.C: The Institution, 1991.
Знайти повний текст джерелаNorman, Fisher. Project monitoring and control - by contractors: A pilot study to develop model building techniques (supportedby the Science and Engineering Research Council). [Reading]: University of Reading, Dept. of Construction Management, 1986.
Знайти повний текст джерелаЧастини книг з теми "120202 Building Science and Techniques"
Rossi, Cesare, and Flavio Russo. "Some Ancient Building Techniques." In History of Mechanism and Machine Science, 381–408. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44476-5_22.
Повний текст джерелаCaferra, Ricardo, and Nicolas Peltier. "Decision procedures using model building techniques." In Computer Science Logic, 130–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61377-3_35.
Повний текст джерелаRaina, Vineet, and Srinath Krishnamurthy. "Techniques and Technologies: An Overview." In Building an Effective Data Science Practice, 159–62. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7419-4_12.
Повний текст джерелаRockloff, Matthew J. "Bedrock: Building Multi-Agent Simulation Applications." In Tools and Techniques for Social Science Simulation, 115–30. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-51744-0_7.
Повний текст джерелаWatson, Ian. "Applying Knowledge Management: Techniques for Building Organisational Memories." In Lecture Notes in Computer Science, 6–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46119-1_2.
Повний текст джерелаSalamó, Maria, and Elisabet Golobardes. "Deleting and Building Sort Out Techniques for Case Base Maintenance." In Lecture Notes in Computer Science, 365–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46119-1_27.
Повний текст джерелаLouchet, Jean, Michael Boccara, David Crochemore, and Xavier Provot. "Building new tools for synthetic image animation by using evolutionary techniques." In Lecture Notes in Computer Science, 273–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61108-8_44.
Повний текст джерелаShetty, Ashvitha R., and B. Krishna Mohan. "Building Extraction in High Spatial Resolution Images Using Deep Learning Techniques." In Computational Science and Its Applications – ICCSA 2018, 327–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95168-3_22.
Повний текст джерелаZinovievich, Aronov Iosif, Maksimova Olga Vladimirovna, and Grigoryev Vadim Iosifovich. "Analysis of Consensus-Building Time in Social Groups Based on the Results of Statistical Modeling." In Advanced Mathematical Techniques in Science and Engineering, 1–31. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003337034-1.
Повний текст джерелаChen, Jingchao. "Building a Hybrid SAT Solver via Conflict-Driven, Look-Ahead and XOR Reasoning Techniques." In Lecture Notes in Computer Science, 298–311. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02777-2_29.
Повний текст джерелаТези доповідей конференцій з теми "120202 Building Science and Techniques"
ZEROLA, Michal, Roman BARTAK, Jerome LAURET, and Michal Sumbera. "Building Efficient Data Planner for Peta-scale Science." In 13th International Workshop on Advanced Computing and Analysis Techniques in Physics Research. Trieste, Italy: Sissa Medialab, 2011. http://dx.doi.org/10.22323/1.093.0025.
Повний текст джерелаJaradat, Farah, and Damian Valles. "A Human Detection Approach for Burning Building Sites Using Deep Learning Techniques." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00277.
Повний текст джерелаKahfi, Fani Fatimah. "Design and Building of Learning Tutorial Using Multimedia Based E-Learning Techniques." In First International Conference on Science, Technology, Engineering and Industrial Revolution (ICSTEIR 2020). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210312.033.
Повний текст джерелаOcchipinti, Annalisa, and Claudio Angione. "A Computational Model of Cancer Metabolism for Personalised Medicine." In Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.
Повний текст джерелаHauke, Krzysztof, Mievzyslaw L. Owoc, and Maciej Pondel. "Building Data Mining Models in the Oracle 9i Environment." In 2003 Informing Science + IT Education Conference. Informing Science Institute, 2003. http://dx.doi.org/10.28945/2697.
Повний текст джерелаJenett, Benjamin, Neil Gershenfeld, and Paul Guerrier. "Building Block-Based Assembly of Scalable Metallic Lattices." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6442.
Повний текст джерелаSadman, Nafiz, Sumaiya Tasneem, Ariful Haque, Md Maminur Islam, Md Manjurul Ahsan, and Kishor Datta Gupta. "“Can NLP techniques be utilized as a reliable tool for medical science?” - Building a NLP Framework to Classify Medical Reports." In 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2020. http://dx.doi.org/10.1109/iemcon51383.2020.9284834.
Повний текст джерелаGandotra, Rahil, and Levi Perigo. "GPF: A Green Power Forwarding Technique for Energy-Efficient Network Operations." In 2nd International Conference on Machine Learning Techniques and Data Science (MLDS 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111808.
Повний текст джерелаPatsiomitou, Stavroula. "The Development of Students Geometrical Thinking through Transformational Processes and Interaction Techniques in a Dynamic Geometry Environment." In InSITE 2008: Informing Science + IT Education Conference. Informing Science Institute, 2008. http://dx.doi.org/10.28945/3235.
Повний текст джерелаSankey, Maxim L., Sheldon M. Jeter, Trevor D. Wolf, Donald P. Alexander, Gregory M. Spiro, and Ben Mason. "Continuous Monitoring, Modeling, and Evaluation of Actual Building Energy Systems." In ASME 2014 8th International Conference on Energy Sustainability collocated with the ASME 2014 12th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/es2014-6610.
Повний текст джерелаЗвіти організацій з теми "120202 Building Science and Techniques"
Rudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, July 2022. http://dx.doi.org/10.32370/iaj.2710.
Повний текст джерела