Minimize Resource Consumption Research Articles

An Investigation of Further Strategies to Optimize Early Treatment Gains in Brief Therapies for Insomnia

ABSTRACT Objectives Identifying those who are most (and least) likely to benefit from a stepped-care approach to cognitive behavioral therapy for insomnia (CBT-I) increases access to insomnia therapies while minimizing resource consumption. The present study investigates non-targeted factors in a single-session of CBT-I that may act as barriers to early response and remission. Methods Participants (N = 303) received four sessions of CBT-I and completed measures of subjective insomnia severity, fatigue, sleep-related beliefs, treatment expectations, and sleep diaries. Subjective insomnia severity and sleep diaries were completed between each treatment session. Early response was defined as a 50% reduction in Insomnia Severity Index (ISI) scores and early remission was defined by < 10 on the ISI after the first session. Results A single-session of CBT-I significantly reduced subjective insomnia severity scores and diary total wake time. Logistic regression models indicated that lower baseline fatigue was associated with increased odds of early remission (B = −.05, p = .02), and lower subjective insomnia severity (B = −.13, p = .049). Only fatigue was a significant predictor of early treatment response (B = −.06, p = .003) Conclusions Fatigue appeared to be an important construct that dictates early changes in perceived insomnia severity. Beliefs about the relationship between sleep and daytime performance may hinder perceived improvements in insomnia symptoms. Incorporating fatigue management strategies and psychoeducation about the relationship between sleep and fatigue may target non-early responders. Future research would benefit from further profiling potential early insomnia responders/remitters.

Analyzing the Attractiveness of Businesses to Receive Investments for a Creative and Innovative Transition to a Circular Economy: The Case of the Textile and Fashion Industry

Excessive and often compulsive consumption has given the textile–fashion industry the reputation of being one of the industries causing the most pollution in today’s world. For this reason, there is a necessity for a transition from a linear to a circular approach in the textile–fashion industry. However, this is not an easy task, especially when considering the investments that must be made to put a circular economy structure into practice. In this sense, the transition to a circular economy in the textile–fashion industry presents a unique opportunity for businesses to attract investments to support this transition by leveraging creativity and innovation to reduce waste, minimize resource consumption, and increase the longevity of products and materials. Therefore, this study sets out to develop a multicriteria decision support model to measure the attractiveness of businesses to receive investments that aim at aiding the transition to the circular economy. The model uses the “play card” from Simos’ procedure and the Normalize software that provide a comprehensive, consistent, and transparent approach to decision making, which can help investors to evaluate the attractiveness of investment opportunities and identify businesses that have the potential for long-term success in the circular economy. Hence, catalyzing and obstructing factors of the circular economy discussed in the literature were selected to underpin the analysis model and to draw up robust investment recommendations to the investors. In addition to the scientific contributions of the model, indications are also provided to the private sector, public policy makers, and society on how sustainability can be driven by the circular economy.

Need to adopt scaled decentralized systems in the water infrastructure to achieve sustainability and build resilience

Abstract Urban water infrastructure (UWI) in cities faces enormous pressure to cope with increased water demands, handle extreme events and improve the service with minimum resource consumption and environmental impacts. The current study presents an approach for addressing the challenges in UWI, specifically in water supply and sewerage. The article argues a need for a paradigm shift that simultaneously includes the sustainability and resilience aspects throughout the life cycle of UWI. The article further highlights the issues in the prevailing approach of centralized infrastructure and demonstrates the necessity of moving away from such an approach and shifting towards decentralized infrastructure. Understanding the factors accelerating decentralization to attain a paradigm shift to decentralization is necessary. Hence, the study first identifies the drivers of decentralization. Secondly, the need for an appropriate scale to be considered while implementing decentralized UWI is highlighted in this study. Furthermore, the effect of the scale of infrastructure is discussed through the trade-offs between life-cycle costs, ease of governance, resilience and recycling benefits. The approach of scaled decentralization outlined in the study will be useful for developing countries to plan new infrastructure and also for developed countries to replace the ageing UWI to create future sustainable and resilient urban systems

Securing the Cyber Resilience of a Blockchain-Based Railroad Non-Stop Customs Clearance System.

Current railroad customs clearance systems are problematic in that the movement of trains is occasionally restricted for extended periods during inspections to verify cargo integrity at customs clearance. Consequently, significant human and material resources are consumed to obtain customs clearance to the destination, considering different processes exist for cross-border trade. Therefore, we developed a cross-border blockchain-based non-stop customs clearance (NSCC) system to address these delays and reduce resource consumption for cross-border trains. The integrity, stability, and traceability of blockchain technology are used to construct a stable and reliable customs clearance system to address these problems. The proposed method connects diverse trade and customs clearance agreements in a single blockchain network, which ensures integrity and minimal resource consumption, and includes railroads, freight vehicles, and transit stations in addition to the current customs clearance system. The integrity and confidentiality of customs clearance data are protected using sequence diagrams and the blockchain to strengthen the resilience of the NSCC process against attacks; the blockchain-based NSCC system structurally verifies the attack resilience based on matching sequences. The results confirm that the blockchain-based NSCC system is time- and cost-efficient compared with the current customs clearance system and offers improved attack resilience.

A hunger-based scheduling strategy for distributed crawler

Distributed crawling is one of the mainstream text data collection technologies, which is essential for mining boundless data available on the Internet for users. Internet information is clustered by the correlation of keywords, and users employ search engines to retrieve relevant keywords to get the information they care about. Distributed crawlers are used to mine the Internet information by simulating users’ behavior, the more important keywords that users care about, the higher the correlation of data to keywords. In order to preferentially collect information that users care about with minimal resource consumption, in this paper, we design a scheduling framework and propose a novel scheduling strategy based on hunger for distributed crawler. We first define the load capacity of distributed crawler as hunger which reflects the ability to complete tasks and divide keywords queues into sub-queues based on the hunger of distributed crawlers. Then, we use a vector space model and cosine similarity algorithm to learn the correlation of keywords to text data and apply the optimized logistic algorithm to measure the importance of keywords. Meanwhile, we design a comprehensive evaluation algorithm to quantify the contribution of keywords, so that updating sub-queues order. Finally, new sub-queues are used in the deeper scheduling to preferentially get data that users desire and sacrifice the least number of resources. Experimental results demonstrate that our method optimizes the scheduling procedures and makes crawling more efficient with less run time.

DESIGNING TOOLS FOR AUTOMATED DATA COLLECTION FOR IoT ROAD CONDITION MONITORING SYSTEMS

The object of the research is the data collection processes on road conditions in monitoring and maintenance systems based on Internet of Things (IoT) technologies. A concept of data collection and aggregation from sensors is proposed to ensure their effective processing at the IoT network edge, with the possibility of integration into smart city systems based on IoT. The proposed approach of automated data collection using embedded devices in vehicles enables dynamic monitoring of road conditions with minimal resource consumption. To implement the proposed concept, a complex of hardware and software has been developed in the form of an experimental prototype of an IoT road condition monitoring system. Experiments have proven that the proposed tools enable rapid response to road defects in any terrain, and the aggregated data format is appropriate for further analysis, including intelligent analysis. The proposed tools can be used in smart city maintenance systems to improve driving quality and prevent critical situations related to poor road conditions.

DADCNF: Diagnoser design for Duplicate Address Detection threat using Conjunctive Normal Form

Duplicate Address Detection is the protocol that confirms the uniqueness of an IPv6 host′s identification when it joins a new network. DAD scheme is susceptible to neighbor advertisement and neighbor solicitation spoofing attacks because ICMPv6 control messages lack in authentication. Existing strategies against the DAD attack have major drawbacks, e.g., high computation, non-scalability, requirement of protocol modification etc. This paper presents a strategy for detecting DAD attack using Conjunctive Normal Form-based Discrete Event System diagnoser. DES-based intrusion detection system have shown effective results in terms of features, like no change in protocol, low overhead etc. against network attacks.The primary drawback of the DES-based IDS is the diagnosis process, which necessitates synchronization of two or more state-based models, making it computationally expensive. To address this issue, CNF-based DES diagnoser scheme is proposed that has all the advantages of DES-based IDS and at the same time addresses the complexity issues. The CNF diagnoser also facilitates the diagnosability verification. The proposed CNF-based IDS for DAD attack is implemented in a testbed and experimental results illustrate the effectiveness in terms of minimal resource consumption, 100% accuracy, high detection rate, less processing time and low traffic overhead.

Comprehensive integration of mass and energy utilization for refinery and synthetic plant of chemicals

Expensive industrial mass and energy mediums, including hydrogen, carbon oxides, and steam, heat and electricity derived from resources of coal, and/or petroleum and/or natural gas, are extensively consumed in refinery and synthetic plant of chemicals (SPC) to simultaneously manufacture value added products and generate emissions. Mass is the carrier of energy while energy is the driving force of mass conversion. Based on such dialectical relationship in chemical conversions, a mass and energy comprehensive integration methodology is proposed to minimize resource consumption and carbon emission. A hierarchical superstructure consisting of resources to intermediate feedstocks (IFs) and IFs to final conversions for refinery and SPC is constructed and a mixed integer non-linear programming (MINLP) model is established to perform optimization. Case study results indicate that with intact productivity as individual networks, comprehensive integration can conserve coal consumption by 23.9%, natural gas consumption by 7.6% and total CO2 emission by 14.4%. The methodology in this work demonstrates that the interaction of mass and energy in chemical conversions is crucial to resource conservation and emission reduction and thus comprehensive integration is great progress in process integration.

Incremental Entity Blocking over Heterogeneous Streaming Data

Web systems have become a valuable source of semi-structured and streaming data. In this sense, Entity Resolution (ER) has become a key solution for integrating multiple data sources or identifying similarities between data items, namely entities. To avoid the quadratic costs of the ER task and improve efficiency, blocking techniques are usually applied. Beyond the traditional challenges faced by ER and, consequently, by the blocking techniques, there are also challenges related to streaming data, incremental processing, and noisy data. To address them, we propose a schema-agnostic blocking technique capable of handling noisy and streaming data incrementally through a distributed computational infrastructure. To the best of our knowledge, there is a lack of blocking techniques that address these challenges simultaneously. This work proposes two strategies (attribute selection and top-n neighborhood entities) to minimize resource consumption and improve blocking efficiency. Moreover, this work presents a noise-tolerant algorithm, which minimizes the impact of noisy data (e.g., typos and misspellings) on blocking effectiveness. In our experimental evaluation, we use real-world pairs of data sources, including a case study that involves data from Twitter and Google News. The proposed technique achieves better results regarding effectiveness and efficiency compared to the state-of-the-art technique (metablocking). More precisely, the application of the two strategies over the proposed technique alone improves efficiency by 56%, on average.

Spatial decision support system for low-carbon sustainable cities development: An interactive storytelling dashboard for the city of Turin

The evaluation of city sustainability considering economic, social, and environmental subsystems is a fundamental field in urban planning. It directly affects the quality of urban economies toward efficiency and innovation with minimal resource consumption. This requires the use of appropriate SDSS (Spatial Decision Support Systems) which take into account inclusive approaches to engage local stakeholders. The study aims at developing an interactive dashboard to aid decision-makers in sustainable urban planning processes to co-create future transition strategies with the employment of spatialized outputs. The dashboard enables the evaluation of the sustainability performance of the city by the selection and assessment of KPIs (Key Performance Indicators) as a part of the MOLOC European Interreg Project. The study proposes a multidisciplinary GIS-based framework for the spatial evaluation of KPIs which can support scenario development by providing targeted suggestions for local development planning. Outcomes represent a useful interactive SDSS tool within a storytelling dashboard which is combinable with multi-criteria decision models at the city scale. The spatial distribution of results evidences the strengths and weaknesses of the city and facilitates the implementation of low-carbon action plans, avoiding unnecessary investments. The methodology illustrated was applied to the city of Turin as the case study, however, it can be implemented in other European urban contexts intending to achieve sustainable spatial planning since the methodological framework modulates to the territorial specificities of a place, always including all aspects of sustainability to be assessed.

Comparing the importance of farming resource endowments and agricultural livelihood diversification for agricultural sustainability from the perspective of the food–energy–water nexus

Sustainable agricultural development, which aims to optimize income and minimize resource consumption and environmental impacts, is strongly influenced by farming resource endowments and agricultural livelihood strategies. However, quantifying the impacts of these factors on agricultural sustainability remains a challenge. Here, we proposed a framework that incorporated the agricultural food–energy–water (FEW) nexus into a sustainable livelihood framework, to explore agricultural sustainability. We then applied a partial least squares–structural equation model (PLS-SEM), based on household survey data from MiYun Reservoir watershed, China, to identify the complex pathways of the impact of household farming resource endowments and livelihood diversification on agricultural sustainability. Our study indicated that diversified farming achieved a better performance in the FEW nexus via the mediating factor of farming inputs (β = 0.335, p < 0.01). Meanwhile, diversified farming had positive effects on economic profits and environmental sustainability (β = 0.531, p < 0.01). Increasing farming resource endowments also had a better performance in the FEW nexus (β = 0.211, p < 0.01); however, that may lead to economic and environmental costs (β = −0.445, p < 0.01). The framework can be used to identify the relationship between household livelihoods and the FEW nexus to better achieve resource security and sustainable development goals.

Barriers to the adoption of integrated sustainable-green-lean-six sigma-agile manufacturing system (ISGLSAMS): a literature review

PurposeScarcity of resources, ecological imbalance, global warming, rising energy prices and the ever-changing need for variety have attracted the government and manufacturers for sustainable development of the industries. The integrated sustainable-green-lean-six sigma-agile manufacturing system (ISGLSAMS) provides a solid platform for meeting both the customers’ variety needs and business sustainability requirements. Many organizations opted for ISGLSAMS, but still due to various barriers organizations are not able to fully implement ISGLSAMS. The purpose of this paper is to identify the barriers to the ISGLSAMS, so that a more sustainable industrial manufacturing system and industrial symbiosis can be developed.Design/methodology/approachA literature review, from the Web of Science and Google Scholar database, has been carried out to identify the various barriers to the implementation of ISGLSAMS in the entire value chain. A total of 168 research papers have been reviewed for identifying the ISGLSAMS barriers.FindingsThis paper elaborates the concept of the ISGLSAMS, its attributes and various barriers and contributes to a better understanding and successful implementation of ISGLSAMS to meet business’ sustainability and market performance goals in the entire value chain. The paper also projects the future research framework and directions for the ISGLSAMS, integrated sustainable-green-lean-six sigma-agile (ISGLSA) product and ISGLSA supply and value chain.Practical implicationsThe study contributes to a better understanding of ISGLSAMS’ barriers. The government, stakeholders and policymakers may plan the policy, road map and strategies to overcome the ISGLSAMS’ barriers. In-depth knowledge of subclauses of ISGLSAMS’ barriers will help the practitioners to overcome the ISGLSAMS’ barriers strategically. By overcoming the ISGLSAMS barriers, a more sustainable 7 Rs based market focused manufacturing system can be designed. This will also increase the opportunities to enhance the industrial ecology, industrial symbiosis and better recovery of the product, process and supply chain residual value. This will reduce the waste to the ecosystem.Originality/valueThis work has been carried out in search of a more sustainable manufacturing system, i.e. ISGLSAMS (which is 7 Rs based, i.e. 6 Rs of sustainability with 7th R, reconfiguration) to meet the customer variety needs along with sustainability in the ever-changing customer market. This study adds value to the practitioners to identify and prioritize the ISGLSAMS’ industry-specific barriers and design the solution for the more sustainable development of (1) industries, (2) the industrial symbiosis system and (3) the ISGLSA product, process, system and supply value chain with minimum resource consumption and environmental impact. The research also contributes to the (a) ISGLSAMS (b) ISGLSA supply chain (c) reconfigurable, sustainable and modular products and (d) redesign, recovery and refurbishing of the product to increase the product life cycle.

MIDAS: Safeguarding IoT Devices Against Malware via Real-Time Behavior Auditing

The number of IoT devices on the Internet has surged recently, accompanied by a barrage of large-scale IoT malware infections breakouts. Designing security mechanisms for IoT devices poses significant challenges due to constantly changing malware variants that have numerous camouflage strategies, limited hardware resources, and heterogeneous architectures. In this article, we propose MIDAS, an adaptive safeguard framework for Linux-based IoT devices to defend against malwares with the real-time behavior auditing mechanism. First, we construct a stable and abstract behavior paradigm through behavioral characteristic extraction of 115 970 malwares. Then, based on the behavior paradigm, MIDAS can: 1) monitor suspicious behaviors of break-in programs in real-time driven by our built-in SELinux policy customized for malware defense; 2) aggregate behaviors of the program’s submodules with homology tracing; and 3) summarize these behaviors into abstract behavior pairs to unveil a possible IoT malware. Using the aforementioned real-time behavior auditing, MIDAS can constrain mutating and camouflaged malwares to protect discrepant IoT devices from being compromised while maintaining low overheads. We thoroughly evaluated the defense capabilities of MIDAS. On the benchmark dataset, MIDAS successfully constrained up to 94.46%, 91.79%, and 88.34% of 115970 malware samples on ARM, MIPS, and MIPSEL architectures, with less than 1.8 MiB of memory consumption and 0.54% CPU usage. Furthermore, we deployed virtual IoT devices worldwide to examine the performance of MIDAS when defending against real-world attacks. Over a duration of 25 days, these devices suffered from 971 951 attacks originating from 71 979 intruding malwares and 48 805 unique IPs distributed in 167 countries. For devices with MIDAS protection, the number of compromised incidents decreases by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$343.1\times $ </tex-math></inline-formula> , and the duration of continuous operation is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$179.2\times $ </tex-math></inline-formula> greater than devices without MIDAS on average. The evaluation results demonstrate that MIDAS can effectively safeguard IoT devices with minimal resource consumption.

Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation

We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices. It aims to ensure differential privacy before data becomes visible to a service provider while maintaining high data accuracy and minimizing resource consumption on users’ devices. To achieve this, we revisit distributed differential privacy based on recent results in secure multiparty computation, and we design a scalable and adaptive distributed differential privacy approach for location analytics. Evaluation on public location datasets shows that this approach successfully generates metropolitan-scale heatmaps from millions of user samples with a worstcase client communication overhead that is significantly smaller than existing state-of-the-art private protocols of similar accuracy.

Resource consumption and security-aware multi-tenant service function chain deployment based on hypergraph matching

Network function virtualization (NFV) technology enables cloud service providers (CSPs) to deploy their service function chains (SFCs) into the common shared physical infrastructures to operate the network or cloud service. However, designing a suitable placement scheme for multi-tenant SFCs to balance resource consumption with performance and security is a tricky issue. In this paper, we first proposed security constraints based on physical isolation and formulated the problem of reasonably placing multi-tenant SFCs subject to multiple constraints to minimize resource consumption. The proposed problem is more intractable than previous works because of the extra introduced security constraints. To solve it, a new non-uniform weighted hypergraph construction approach is proposed by defining hyperedge weights to represent the resource consumption on one SFC. A hypergraph matching algorithm is proposed to find the maximum weight subset of hypergraphs whose vertices do not intersect to give a high degree of physical isolation by dealing with the conflict graphs . Simulation results show that the proposed hypergraph matching algorithm outperforms the comparison schemes in terms of the resource consumption while satisfying the security requirements.

Trends in contemporary culture in chemistry and pharmaceutical

One of the main trends of chemistry and pharmaceutical present development is miniaturization technologies introduction, that allows to create mechatronics systems with rare technical and technological characteristics: minimal weight-size parameters, little energy and resources consumption, mild wear, maximum sensitivity, increased accuracy parameters and operation speed, reliability and operations security. This article contains estimation results of mechatronics current development trends. The author considers that today mechatronics is not just a simple aggregation of two sciences: it shall be analyzed as the new technology, whereby the device with mechanical operating actuators comprises software. Now the mechatronics development is essential to the development of countries. Mechatronics suggests direct use of intelligent systems paradigm.

Delay-aware and resource-efficient service function chain mapping in inter-datacenter elastic optical networks

Network function virtualization (NFV) is emerging as a promising paradigm for network architectures. By migrating network functions from dedicated hardware appliances to software instances running in a virtualized environment, NFV promises to offer a more flexible way to deploy and manage service function chains (SFCs). When deploying these SFCs to users, the network operators require not only the user’s demands (e.g., end-to-end delay) to be satisfied, but require the cost of SFC mapping to be minimized (e.g., resource consumption). To fulfill these two goals, in this paper, we have investigated how to realize the delay-aware and resource-efficient SFC mapping in inter-datacenter elastic optical networks. We first formulate an integer linear programming (ILP) model to solve the problem exactly. The main optimization goal in the ILP model is to jointly minimize resource consumption and end-to-end delay to achieve optimal virtual network function placement. Then, a delay-aware and load-balancing mapping algorithm (DALB-MA) is proposed to obtain a near-optimal solution in a reasonable amount of time. Finally, we evaluate the proposed ILP model and heuristic algorithms via extensive simulations. The results indicate that the proposed ILP model and the DALB-MA outperform the benchmarks in terms of block rate, average cost, number of CPUs used, maximum frequency slot index, and delay margin gain.

Circularity evaluation as guidance for building design

Resource scarcity and global warming call for ambitious strategies in the construction sector to meet the ever-growing demand for indoor spaces with minimal resource consumption and positive environmental impacts. In line with the need to introduce circular economy in the construction sector, the German Public Sustainability Certification System (BNB) is revising its indicator for disassembly, separation and reuse. The proposed assessment is intended to guide in the planning process and to point out challenges and potentials of the circular economy by making the complex interactions and requirements comprehensible in detail. The continuity of the assessment from the building material to the building component to the entire building allows users to track the impact of changes made at each level of aggregation. Based on extensive background research, end-of-life categories are assigned to building materials according to their reusability, taking into account assembly techniques and adjacent, associated materials. Example building components illustrate the method and show the impact of design changes. At the building level, the quantity determination of materials in the end-of-life condition allows transparent comparison of different design variants and documents in detail the material inventory for use in building material passports. Future developments envision the inclusion of building services in the circularity assessment, benchmarking of circularity at the building level, and integration of circular qualities into life cycle analysis calculations.

High-frequency oscillations in the ripple bands and amplitude information coding: Toward a biomarker of maximum entropy in the preictal signals.

Intracranial electroencephalography (iEEG) can directly record local field potentials (LFPs) from a large set of neurons in the vicinity of the electrode. To search for possible epileptic biomarkers and to determine the epileptogenic zone that gives rise to seizures, we investigated the dynamics of basal and preictal signals. For this purpose, we explored the dynamics of the recorded time series for different frequency bands considering high-frequency oscillations (HFO) up to 240 Hz. We apply a Hilbert transform to study the amplitude and phase of the signals. The dynamics of the different frequency bands in the time causal entropy-complexity plane, H × C, is characterized by comparing the dynamical evolution of the basal and preictal time series. As the preictal states evolve closer to the time in which the epileptic seizure starts, the, H × C, dynamics changes for the higher frequency bands. The complexity evolves to very low values and the entropy becomes nearer to its maximal value. These quasi-stable states converge to equiprobable states when the entropy is maximal, and the complexity is zero. We could, therefore, speculate that in this case, it corresponds to the minimization of Gibbs free energy. In this case, the maximum entropy is equivalent to the principle of minimum consumption of resources in the system. We can interpret this as the nature of the system evolving temporally in the preictal state in such a way that the consumption of resources by the system is minimal for the amplitude in frequencies between 220-230 and 230-240 Hz.

Classification of heating conditions in terms of smart control of indoor heating with the use of uncontrolled electric heaters

Purpose. To reduce specific energy consumption for heating municipal and industrial buildings by introducing smart indoor temperature control taking into consideration individual dependences of characteristics of each person as a consumer of energy resources on specific heating conditions. Methodology. The energy-efficient and smart control of indoor heating is based on the fact that a control system is to elaborate and provide a compromise solution as for comfortable perception of proper conditions of someones staying indoors and minimum consumption of energy resources. To do that, first of all the problem should be solved concerning recognition of different heating conditions by a smart control system aimed at providing a process of system learning and database formation. To complete this task, the parameters of one-dimensional dynamic models describing heat-exchange processes are proposed to be used as the information signs for the classification of situations in terms of heating relative to the uncontrolled electric heaters; the input value is the heater capacity, and the output value is the air temperature within the local indoor zone. Within the framework of the development of a method for classifying indoor heating conditions, dependences of the parameters of dynamic models of local indoor heating zones on the characteristics of local heating zones were analysed. Besides, certain regularities of a control process for heaters were determined; that helped provide accurate identification of the models of local heating zones without considerable changes in a preset temperature mode. Computational experiments made it possible to evaluate the accuracy of determination of information signs for the classification of heating conditions while representing real characteristics of indoor heat-exchange processes. Findings. The studies resulted in the development of a method for identifying dynamic properties of indoor heat zones for the cases of using uncontrolled electric heaters with two states. Originality. For the first time, certain regularities have been identified concerning a capacity control process for electric heaters with two states and a process of temperature measurement within the local indoor zones. The regularities made it possible to determine the parameters of dynamic models of indoor heat-exchange processes with high accuracy and without considerable changes in the preset temperature mode, and to use these parameters as information signs while classifying the heating conditions. Practical value. The obtained regularities of the processes of heater control and temperature measurement allowed developing a method for identification of dynamic properties of local indoor heat zones, which makes up the basis for a classification procedure of heating conditions.