Search Results (10)

The timeshare is gradually becoming an essential global tourism operation model, especially in rural areas of China, where the leisure industry is developing rapidly. Meanwhile, the environmental issues of the rapidly growing timeshare-related building production have received widespread attention. The existing research on zero-carbon buildings considers carbon emissions as a constant value and cannot adapt to the impact of user changes during the operation phase. Constructing a low-carbon design applicable to timeshare is significant for controlling carbon emissions in the construction industry and responding to the environmental crisis. The practical carbon emissions of touristic apartments depend on the requirement changes in different customer clusters. The timeshare theory reflects the requirement change in different customer clusters based on the timeshare property ownership change. This paper focuses on a dynamic design strategy for zero-carbon building openings to reduce practical carbon emissions. Firstly, this research clarifies the primary customer clusters and conducts a touristic apartment unit model by timeshare property ownership. Then, this research clarifies the changes in customer requirements to analyze the spatial function changes in the operating phase. Finally, the study identifies six dynamic carbon emission indicators, such as the window-to-wall ratio, ventilation rate, and effective daylight area, and through passive design methods, provides 13 variable devices applied in the operating phase to control dynamic carbon emission indicators by customers. This paper also offers a flexible method to effectively decrease and accurately control carbon emissions by reducing the possible device utility. Full article

Lithium-ion batteries play a vital role in modern energy storage systems, being widely utilized in devices such as mobile phones, electric vehicles, and stationary energy units. One of the critical challenges with their use is the thermal runaway (TR), typically characterized by a sharp increase in internal pressure. A thorough understanding and accurate prediction of this behavior are crucial for improving the safety and reliability of these batteries. To achieve this, two new combined models were developed: one to simulate the thermal runaway and another to simulate the internal cell pressure. The thermal model tracks a chain of decomposition reactions that eventually lead to TR. At the same time, the pressure model simulates the proportional increase in pressure due to the evaporation of the electrolyte and the gases produced from the decomposition reactions. What sets this work apart is the validation of the pressure model through experimental data, specifically for prismatic lithium-ion cells using NMC chemistries with varying stoichiometries—NMC111 and NMC811. While the majority of the literature focuses on the simulation of temperature and pressure for cylindrical cells, studies addressing these aspects in prismatic cells are much less common. This article addresses this gap by conducting pressure validation experiments, which are hardly documented in the existing studies. Furthermore, the model’s accuracy and flexibility are tested through two experiments, conducted under diverse conditions to ensure robust and adaptive predictions of cell behavior during failure scenarios. Full article

Accurate estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems, particularly in electric vehicle (EV) applications where real-time monitoring ensures safe and robust operation. This study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and validated using laboratory data from a lithium-ion 18650 battery and simulation data from Matlab/Simulink for a LiCoO₂ battery cell. These models are designed to account for varying temperatures during charge/discharge cycles and the effects of battery aging due to cycling. This paper is the first to estimate the SoC by a deep neural network using a variable current profile that provides the SoC curve during both the charge and discharge phases. The DNN model is implemented in Matlab/Simulink, featuring customizable activation functions, multiple hidden layers, and a variable number of neurons per layer, thus providing flexibility and robustness in the SoC estimation. This approach uniquely integrates temperature and aging effects into the input features, setting it apart from existing methodologies that typically focus only on voltage, current, and temperature. The performance of the DNN model is benchmarked against the GRU and LSTM models, demonstrating superior accuracy with a maximum error of less than 2.5%. This study highlights the effectiveness of the DNN algorithm in providing a reliable SoC estimation under diverse operating conditions, showcasing its potential for enhancing battery management in EV applications. Full article

The usage of Reclaimed Asphalt Pavement (RAP) material is a highly resource-conservative, economical, and sustainable practice in flexible pavement construction. However, its usage in hot mix asphalt (HMA) is capped at 25% by the majority of state transportation agencies due to its aging levels, stiffness characteristics, and handling capabilities, which may result in early-age pavement distress. Though researchers suggest methodologies to increase RAP usage, higher RAP percentages in asphalt pavements require the support of state authorities. The main objective of this paper is to provide information on how different states design their mixtures with high RAP percentages. This study reviewed the current state of practice of fifty (50) state DOTs in the United States (US) with respect to RAP usage and the factors governing its regulations. It was observed that the limit of RAP content is mainly governed by traffic levels, gradation, binder content, and stiffness contributed by RAP and layer position in a pavement structure. The specifications also suggest that apart from volumetric and performance justification, blending charts, fractionation, and virgin binder grade selection would facilitate the use of higher RAP content in HMA. Controlled mixture design abiding by state specifications can increase the allowable RAP to 40–100%. Full article

Demand-responsive control of electrically heated hot water storage tanks (HWSTs) is one solution, already present in the building stock, to stabilise volatile energy networks and markets. This has been put into sharp focus with the current energy crisis in Europe due to reduced access to natural gas. Furthermore, increasing proportions of intermittent renewable energy will likely add to this volatility. However, the adoption of demand response (DR) by consumers is highly dependent on the economic benefit. This study assesses the economic potential of DR of centralised HWSTs through both an analysis of spot price data and an optimisation algorithm approximating DR control. The methods are applied to a case study apartment building in Norway using current pricing models and examine the effect of the demand profile, electricity prices, heating power and storage capacity on energy cost and energy flexibility. Unit cost savings from DR are closely linked to the variation in unit energy price during the optimisation period. Increasing the storage capacity or the heating power increases the flexibility with a diminishing rate of return. However, increasing storage capacity does not result in cost savings as additional heat losses are greater than the saving from shifting demand, except for during highly volatile electricity price periods. Changing the minimum setpoint temperature improves the cost curve as a greater thermal storage capacity can be achieved without increasing heat loss. Systems utilising a smaller heating power are more economical due to the dominant role of the monthly price related to the peak energy demand of the system. Full article

The COVID-19 crisis and new pandemic-oriented everyday life have emphasized the urge to reorganize the way we live and reside, additionally highlighting the already existing socio-spatial problematic that Montenegrin society has been experiencing for thirty years. Since residential space is considered to be vital for physical, mental and social wellbeing, this sudden and global paradigm shift presents an opportunity to redefine the current housing concepts towards greater long-term resilience in the context of present, pandemic and future challenges. The results of the survey of housing users in Podgorica confirmed the need to address this issue. The article discusses a possible model of resilient adaptation of residential buildings and neighborhoods in Podgorica, Montenegro. The model addresses the flexibility of the apartment units, existence of indoor and outdoor common areas for social activities within the building and immediate contact with nature, and it is applicable in the wider territorial context as well. Formulated with the aim of long-term improvement of the concept of housing, the model presents a significant framework for the planning and design of future buildings. The resilient residential model is tested by its application to specific buildings of residential area Blok 5 in Podgorica. This leads to the conclusion that the earlier concepts of housing present in theory and practice in the second half of the twentieth century in Montenegro and the region were more adequate in the context of resilience. As we believe that such improved resilient housing would consequently increase the resilience of the community regarding the challenges it faces currently due to COVID-19, we consider this a long-term contribution of this research. Full article

The underlying infrastructure paradigms behind the novel usage scenarios and services are becoming increasingly complex—from everyday life in smart cities to industrial environments. Both the number of devices involved and their heterogeneity make the allocation of software components quite challenging. Despite the enormous flexibility enabled by component-based software engineering, finding the optimal allocation of software artifacts to the pool of available devices and computation units could bring many benefits, such as improved quality of service (QoS), reduced energy consumption, reduction of costs, and many others. Therefore, in this paper, we introduce a model-based framework that aims to solve the software component allocation problem (CAP). We formulate it as an optimization problem with either single or multiple objective functions and cover both cases in the proposed framework. Additionally, our framework also provides visualization and comparison of the optimal solutions in the case of multi-objective component allocation. The main contributions introduced in this paper are: (1) a novel methodology for tackling CAP-alike problems based on the usage of model-driven engineering (MDE) for both problem definition and solution representation; (2) a set of Python tools that enable the workflow starting from the CAP model interpretation, after that the generation of optimal allocations and, finally, result visualization. The proposed framework is compared to other similar works using either linear optimization, genetic algorithm (GA), and ant colony optimization (ACO) algorithm within the experiments based on notable papers on this topic, covering various usage scenarios—from Cloud and Fog computing infrastructure management to embedded systems, robotics, and telecommunications. According to the achieved results, our framework performs much faster than GA and ACO-based solutions. Apart from various benefits of adopting a multi-objective approach in many cases, it also shows significant speedup compared to frameworks leveraging single-objective linear optimization, especially in the case of larger problem models. Full article

While governments around the world are embarking on the path to recovery from the COVID-19 crisis, sustainable tourism planning is crucial, in particular in the hospitality sector, which enhances the resilience of destinations. However, many destination management models overlook the role of urban zoning. Little is known about the impacts of land-use zoning on the hospitality and property industries, especially with the current disruption of short-term peer-to-peer accommodation like Airbnb. Euclidean zoning, also known as effects-based planning, has long been criticised in destination management for its exclusionary nature and lack of flexibility. With exclusionary zoning, property owners may only be able to use their land sub-optimally, and cities will be less efficient in responding to market changes in short-term and long-term accommodation demands, but planning intentions can be better controlled, and the property supply can be more stable. Taking Hong Kong as a noteworthy case, this study puts forward a conceptual framework that enables comparison of a novel zoning approach with the traditional zoning approach. This novel zoning approach encompasses both the short- and long-term rental sectors as a continuum of accommodation, ranging from hotels and serviced apartments to Airbnb and rental housing units under a unified regulatory and planning regime to enhance the switching options value. This novel zoning system can gear up the tourism sector with the rapid growth of the sharing economy and aligns with sustainable tourism to ensure long-term socioeconomic benefits to related stakeholders. We extract the data of Airbnb listings to construct the first Airbnb ADR Index (ADRI) by Repeat-sales method, and the results support our Switching Option Hypothesis. Full article

To cope with the net load variability in real time, sufficient ramp capability from controllable resources is required. To address the issue of insufficient ramp capacity in real time operations, flexible ramping products (FRPs) have been adopted by some Independent System Operators (ISOs) in the USA as a new market design. The inherent variability and uncertainty caused by renewable energy sources (RESs) call for new FRP providers apart from conventional generating units. The so-called interruptible load (IL) has proved to be useful in maintaining the supply-demand balance by providing demand relief and can be a viable FRP provider in practice. Given this background, this work presents a stochastic real-time unit commitment model considering ramp requirement and simultaneous provision of IL for FRP and demand relief. Load serving entities (LSEs) are included in the proposed model and act as mediators between the ISO and multiple ILs. In particular, incentive compatible contracts are designed to encourage customers to reveal their true outage costs. Case studies indicate both the system and LSEs can benefit by employing the proposed method and ILs can gain the highest profits by signing up a favorable contract. Full article

The disease triangle is a basic and highly flexible tool used extensively in forest pathology. By linking host, pathogen, and environmental factors, the model provides etiological insights into disease emergence. Landscape ecology, as a field, focuses on spatially heterogeneous environments and is most often employed to understand the dynamics of relatively large areas such as those including multiple ecosystems (a landscape) or regions (multiple landscapes). Landscape ecology is increasingly focused on the role of co-occurring, overlapping, or interacting disturbances in shaping spatial heterogeneity as well as understanding how disturbance interactions mediate ecological impacts. Forest diseases can result in severe landscape-level mortality which could influence a range of other landscape-level disturbances including fire, wind impacts, and land use among others. However, apart from a few important exceptions, these disturbance-disease interactions are not well studied. We unite aspects of forest pathology with landscape ecology by applying the disease-triangle approach from the perspective of a spatially heterogeneous environment. At the landscape-scale, disturbances such as fire, insect outbreak, wind, and other events can be components of the environmental ‘arm’ of the disease triangle, meaning that a rich base of forest pathology can be leveraged to understand how disturbances are likely to impact diseases. Reciprocal interactions between disease and disturbance are poorly studied but landscape ecology has developed tools that can identify how they affect the dynamics of ecosystems and landscapes. Full article