Search Results (74,788)

The design of product packaging significantly impacts consumer behavior by attracting attention and influencing purchasing choices within a competitive market environment. This research explores the effects of visual packaging elements, such as branding, imagery, and descriptive text, on consumer preferences and decisions. By examining regular, organic, and character-themed milk packaging, the study seeks to determine which features effectively capture attention and drive engagement. Employing eye-tracking tools (Tobii Pro Spectrum and Tobii Pro Lab) alongside survey analysis, the study evaluates consumer focus through fixation durations and heatmap visualizations. The findings indicate that character-themed packaging draws the most attention, with colorful illustrations and prominently displayed brand names playing a central role. Fixation data highlight brand names as the most consistently engaging feature across all packaging types. Regression analysis further reveals that cultural familiarity influences preferences, as international participants favored designs resembling those from their home regions or familiar global brands. These insights underscore the importance of visually appealing and culturally relevant designs in building consumer engagement and brand loyalty. The study offers valuable guidance for marketers and designers aiming to create packaging that resonates with diverse audiences and market demands. Full article

Pluvial floods are increasingly affecting urban areas worldwide. Despite growing media attention and clear owner responsibility for reducing climate-related risk for buildings in Swedish national adaptation policy, adaptation action remains slow. Understanding how different property owner categories view and act on flood risks is key for developing better incentive structures and support for accelerating adaptation action. While tenant-owned housing is a common form of housing tenure in Sweden, studies are lacking. This study enhances understanding of pluvial flood risk and adaptation views and actions by tenant-owned housing associations in two Swedish cities. It is based on assessments of 69 apartment buildings within eleven tenant-owned associations and semi-structured interviews with their eleven chairpersons. The study indicates that tenant-owned associations grossly underestimate their flood risks and responsibilities for climate adaptation, even though many buildings studied are at significant risk, and most associations have been impacted by floods, some severely and recurrently. The patronization of flood risk and responsibility for adaptation is attributed to several factors: underestimating risks and consequences, devaluing the benefit of one’s own adaptation actions, lacking knowledge about climate adaptation measures for buildings, and (overly) generous insurance terms. The findings confirm low adaptation action among housing associations, even those with recurring floods, which is concerning given the strong reliance on property-owner adaptation in national adaptation policy. Full article

The paper analyses multi-variant energy simulations carried out in IDA ICE 4.8 software for a newly designed single-family building within the framework of the 4E Idea. This idea assumes the use of energy-saving, ecological, ergonomic, and economic solutions in construction and building operation. Energy simulations were conducted to evaluate the annual energy-saving potential of the developed architectural house concept, which incorporates ergonomic analyses and cost-effective construction solutions. Analyses were conducted to optimise the non-renewable primary energy index by selecting mechanical ventilation system (CAV or VAV) with heat recovery; the configuration of photovoltaic module installation in terms of their location and orientation; the exposure and type of solar thermal collectors (flat and vacuum); and the use of two types of heat pumps (air- and ground-source). The most favourable energy performance of the building was achieved with an HVAC system equipped with a VAV mechanical ventilation system with heat recovery, an on-grid photovoltaic installation, vacuum solar thermal collectors, and a ground-source heat pump with a horizontal heat exchanger. This configuration resulted in a primary energy index value of 2 kWh/m²/year. The results of the analyses carried out for the 4E building concept may serve as a reference point for future energy-efficient building designs aspiring to meet higher standards of sustainable development. Full article

This study examines existing buildings in Haikou in China under tropical island climate conditions. It presents three retrofit design models for greenhouses roofs (GHR), green roofs (GR) and photovoltaic roofs (PVR). The carbon cost of each retrofit roof model is calculated in the production and transportation phases of building materials, construction, and demolition. The changes in electricity consumption, water consumption, and plant carbon reduction are coupled to calculate the carbon reduction generated by each phase of the use of the retrofitted roofs. The carbon reduction per unit area for GHR, GR and PVR over the life cycle (20 years) is then comprehensively calculated. The life cycle carbon reduction per unit area is 262.57 kg·m⁻² for GHR, 127.41 kg·m⁻² for GR and 2567.12 kg·m⁻² for PVR. Among the three retrofit methods, PVR has the greatest potential for reducing carbon emissions. The study can as a guide for implementing carbon reduction measures in tropical island areas. Domestic research on rooftop greenhouses also focuses on technology, yield, and energy consumption, mostly for northern regions with cold winters, and less research on rooftop greenhouses applied to regions with hot summers and warm winters. But domestic and foreign studies on the potential of rooftop greenhouses to reduce emissions have not yet been combined with plant cultivation of hydroelectric carbon emissions and plant carbon sequestration. Full article

This article focuses on modern methods of documentation and visualization for a historic object. Digital photogrammetry and terrestrial laser scanning (TLS), which are essential tools for documenting cultural heritage in view of their rapid development in recent years, were used, compared, and analyzed. Furthermore, the use of available 3D computer graphics technologies for visualization is described and an optimal procedure for converting the object into VR and AR is proposed and implemented. The technologies presented in this article were tested within the context of a project on the reconstruction of the shrine of the Prophet Nahum in the city of Alqosh in northern Iraq, taking the shrine as a case study. Funded by ARCH Int. and provided by GemaArt Int., the restoration project started in 2018 and was completed in 2021. The ongoing documentation was prepared by the CTU and it used the materials for research purposes. Accurate documentation using photogrammetry, drones, and TLS was key to the restoration. Leica BLK360, Faro Focus S150, and GeoSlam laser scanners were used, as well as photogrammetric methods. In particular, the documentation process involved the creation of 3D textured models from the photogrammetry, which were compared to the TLS data to ensure accuracy. These models were necessary to track changes during the reconstruction phases and to calculate the volumes of rubble removed and materials added. Our data analysis revealed significant differences between the construction logs and the analysis of the accurate 3D models; the results showed an underestimation of the displaced material statements by 13.4% for removed material and 4.6% for added material. The use of heat maps and volumetric analyses helped to identify areas of significant change that guided the reconstruction and documented significant changes to the building for the investor. These findings are important for use in the construction industry with respect to historic sites as well as for further research focused on visualization using VR (virtual reality) and AR (augmented reality). The conversion of existing 3D models into VR and AR is rapidly evolving and significant progress was made during this project. The Unreal Engine (UE) game engine was used. Despite the significantly improved performance of the new UE 5 version, the data for conversion to VR and AR needs to be decimated to reduce the amount—in our case, this was by up to 90%. The quality appearance of the objects is then ensured by textures. An important outcome of this part of the research was the debugged workflow developed to optimize the 3D models for VR, which was essential for creating a virtual museum that shows the restoration process. Full article

Global warming is a significant threat to the future of humankind. It is caused by greenhouse gases that accumulate in the atmosphere. CO₂ emissions are one of the main drivers of global warming, and the energy sector is one of the main contributors to CO₂ emissions. Recent technological advances in artificial intelligence (AI) have accelerated the adoption of AI in numerous applications to solve many problems. This study carries out a scoping review to understand the use of AI solutions to reduce CO₂ emissions in the energy sector. This paper follows the PRISMA-ScR guidelines in reporting the findings. The academic search engine Google Scholar was utilized to find papers that met the review criteria. Our research question was “How is artificial intelligence used in the energy sector to reduce CO2 emissions?” Search phrases and inclusion criteria were decided based on this research question. In total, 186 papers from the search results were screened, and 16 papers fitting our criteria were summarized in this study. The findings indicate that AI is already used in the energy sector to reduce CO₂ emissions. Three main areas of application for AI techniques were identified. Firstly, AI models are employed to directly optimize energy generation processes by modeling these processes and determining their optimal parameters. Secondly, AI techniques are utilized for forecasting, which aids in optimizing decision-making, energy transmission, and production planning. Lastly, AI is applied to enhance energy efficiency, particularly in optimizing building performance. The use of AI shows significant promise of reducing CO₂ emissions in the energy sector. Full article

A hybrid probabilistic road map (PRM) path planning algorithm based on historical automatic identification system (AIS) information and Douglas–Peucker (DP) compression is proposed to address the issues of low path quality and the need for extensive sampling in the traditional PRM algorithm. This innovative approach significantly reduces the number of required samples and decreases path planning time. The process begins with the collection of historical AIS data from the autonomous vessel’s navigation area, followed by comprehensive data-cleaning procedures to eliminate invalid and incomplete records. Subsequently, an enhanced DP compression algorithm is employed to streamline the cleaned AIS data, minimizing waypoint data while retaining essential trajectory characteristics. Intersection points among various vessel trajectories are then calculated, and these points, along with the compressed AIS data, form the foundational dataset for path searching. Building upon the traditional PRM framework, the proposed hybrid PRM algorithm integrates the B-spline algorithm to smooth and optimize the generated paths. Comparative experiments conducted against the standard PRM algorithm, A*, and Dijkstra algorithms demonstrate that the hybrid PRM approach not only reduces planning time but also achieves superior path smoothness. These improvements enhance both the efficiency and accuracy of path planning for maritime autonomous surface ships (MASS), marking a significant advancement in autonomous maritime navigation. Full article

Accurate load forecasting is of vital importance for improving the energy utilization efficiency and economic profitability of intelligent buildings. However, load forecasting is restricted in the popularization and application of conventional load forecasting techniques due to the great difficulty in obtaining numerical weather prediction data at the hourly level and the requirement to conduct predictions on multiple time scales. Under the condition of lacking meteorological forecast data, this paper proposes to utilize a temporal convolutional network (TCN) to extract the coupled spatial features among multivariate loads. The reconstructed features are then input into the long short-term memory (LSTM) neural network to achieve the extraction of load time features. Subsequently, the self-attention mechanism is employed to strengthen the model’s ability to extract feature information. Finally, load forecasting is carried out through a fully connected network, and a multi-time scale prediction model for building multivariate loads based on TCN–LSTM–self-attention is constructed. Taking a hospital building as an example, this paper predicts the cooling, heating, and electrical loads of the hospital for the next 1 h, 1 day, and 1 week. The experimental results show that on multiple time scales, the TCN–LSTM–self-attention prediction model proposed in this paper is more accurate than the LSTM, CNN-LSTM, and TCN-LSTM models. Especially in the task of predicting cooling, heating, and electrical loads on a 1-week scale, the model proposed in this paper achieves improvements of 16.58%, 6.77%, and 3.87%, respectively, in the RMSE indicator compared with the TCN-LSTM model. Full article

Coal gangue is a kind of typical by-product emitted during the coal mining and washing process. With the increase in coal resource utilization, a large amount of coal gangue was not reasonably utilized, causing environmental pollution and resource waste. The main purpose of this article is to introduce the surface structural features and compositional characteristics of coal gangue and to summarize the utilization of coal gangue in the fields of building materials, energy production, agricultural utilization, and high-value-added areas such as catalysts and adsorbents. Secondly, this review discussed the environmental challenges and technical difficulties derived from the process of coal gangue utilization and how to solve these problems through innovative methods and technological improvements. Finally, the article proposed the development direction and strategies for the future resource utilization of coal gangue, emphasizing the importance of coal gangue as a sustainable resource and its significant role in achieving a circular economy for reducing environmental pollution. By analyzing the potentiality of coal gangue for resource utilization systematically, this article aims to provide valuable references and insights for researchers and decision-makers in related fields. Full article

The escalating issue of air pollution contributes to an alarming number of premature fatalities each year, thereby posing a significant threat to global health. The focus of recent research has shifted towards understanding its potential association with neurodegenerative diseases, specifically Alzheimer’s disease (AD). AD is recognised for its characteristic deposition of toxic proteins within the brain, leading to a steady deterioration of cognitive capabilities, memory failure, and, ultimately, death. There is burgeoning evidence implying that air pollution may be a contributing factor to this protein build up, thereby intensifying the course of AD. It has been demonstrated that the olfactory system, responsible for smell perception and processing, acts as a potential gateway for airborne pollutants to inflict brain damage. This review aims to elucidate the relationship between air pollution, olfactory deterioration, and AD. Additionally, this review aims to highlight the potential mechanisms through which pollutants might instigate the development of AD and the role of the olfactory system in disease pathogenesis. Moreover, the diverse model systems employed in exploring the correlation, public health policy ramifications, and prospective directions for future research will be discussed. Full article

Scientific identification of ecological sources and corridors is crucial in constructing an ecological security pattern (ESP). To develop an ESP tailored to the scale of central urban areas in megacities, this study takes Central Beijing as the research object. It innovatively integrates the integrated valuation of ecosystem services and tradeoffs (InVEST), the morphological spatial pattern analysis (MSPA), and the Conefor software to identify ecological sources. Seven indicators related to topographic, natural conditions, and human disturbance factors are selected to build the ecological resistance surface, which is then combined with circuit theory to construct the ESP. The results show the following: (1) Central Beijing contains 157 ecological sources, primarily distributed in the western, northern, and eastern regions, with woodland as the dominant land type. (2) A total of 439 ecological corridors were extracted, including 317 key ecological corridors and 122 inactive ecological corridors. (3) The identified ecological pinch points are mainly the Jingmi Diversion Canal and the West Moat. (4) The identified ecological barriers are spread throughout the entire study area. The results of this study are highly significant for improving the quality of ecological security and protecting biodiversity in the study area and other urban centers. Full article

Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture. Objectives: This research explores the capabilities of the CNNs and transformers to build a robust classification model that can handle both short- and long-range dependencies with global features to accurately classify food images and enhance food image recognition for better nutritional analysis. Methods: Our approach, which combines CNNs and Vision Transformers (ViTs), begins with the RestNet50 backbone model. This model is responsible for local feature extraction from the input image. The resulting feature map is then passed to the ViT encoder block, which handles further global feature extraction and classification using multi-head attention and fully connected layers with pre-trained weights. Results: Our experiments on five diverse datasets have confirmed a superior performance compared to the current state-of-the-art methods, and our combined dataset leveraging complementary features showed enhanced generalizability and robust performance in addressing global food diversity. We used explainable techniques like grad-CAM and LIME to understand how the models made their decisions, thereby enhancing the user’s trust in the proposed system. This model has been integrated into a mobile application for food recognition and nutrition analysis, offering features like an intelligent diet-tracking system. Conclusion: This research paves the way for practical applications in personalized nutrition and healthcare, showcasing the extensive potential of AI in nutritional sciences across various dietary platforms. Full article

This article points out the kinematics in tracing a Bézier curve defined by control mass points. A mass point is a point with a non-positive weight, a non-negative weight or a vector with a null weight. For any Bézier curve, the speeds at endpoints can be modified at the same time for both endpoints. The use of a homographic parameter change allows us to choose any arc of the curve without changing the degree but not offer to change the speeds at both endpoints independently. The homographic parameter change performs weighted points with any non-null real number as weight and also vectors. The curve is thus called a rational Bézier curve with control mass points. In order to build independent stationary points at endpoints, a quadratic parameter change is required. Adding null vectors in the Bézier representation is also an answer. Null vectors are obtained when converting any power function in a rational Bézier curve and their inverse. The authors propose a new approach on placing null vectors in the representation of the rational Bézier curve. It allows us to break free from projective geometry where there is no null vector. The paper ends with some examples of known curves and some perspectives. Full article

In recent years, firefighter accidents and people injured by the collapse of steel structures during a fire have occurred frequently, which has attracted the attention of the National Emergency Management Department and the Fire and Rescue Bureau. It is urgent to carry out research on early warning systems for building collapse during a fire. Existing early warning methods mainly use characteristic parameters such as temperature, vibration, and structural deformation. Due to the complexity of an actual fire, it is difficult to accurately predict the critical temperature of fire−induced instability in columns and the failure mode after the instability, and there are deviations in the collapse warnings. In this study, changes in ultrasonic transverse and longitudinal wave velocities at high temperatures are used to monitor the stiffness degradation of columns in fire in real time and improve the accuracy of early warning systems. In this study, four common collapse modes of portal frames are obtained by using the results of parametric numerical analysis. According to key displacements and the displacement rates of simple key measuring points, the elastic modulus threshold of a three−level early warning for portal frame collapse with different collapse modes is obtained. Combined with an ultrasonic experiment, the theoretical relationships between the transverse and longitudinal wave velocities and the elastic modulus of steel at high temperatures are verified, and the relationship between the transverse and longitudinal wave velocities and the overall damage of the portal frame is further constructed; then, a new early warning method for portal frame stability during a fire is proposed. Based on the change in wave velocity, a three-level early warning index for predicting portal frame stability during a fire is determined. When the collapse mode of a portal frame is an overall inward collapse, transverse and longitudinal wave velocities are reduced to 2635 m/s and 5308 m/s, respectively. At a second-level warning, they are reduced to 2035 m/s and 4176 m/s, respectively. At 1504 m/s and 3030 m/s, respectively, third-level warnings are issued. This research shows that the real−time monitoring of wave velocities provides an effective way for early warning systems to identify structural collapse. The proposed early warning method can be used as a quick and efficient early warning system for the collapse of portal frames during a fire, and its accuracy and applicability are verified by experiments. Full article

Ceramic detachments in cladding systems are indicative of adhesion loss between the ceramic tiles and the substrate or its adhesive mortar due to inadequate quality workmanship, the quality of the adhesive mortar or that of the ceramic material, whether acting simultaneously or not. The shear stresses resulting from the ceramic tiles’ expansion due to humidity accelerate this process. There is a shortage of studies on the quality of ceramic tiles and adhesive mortars. This study conducted elemental, physical and microstructural characterization tests on ceramic tiles and adhesive mortars that showed detachment up to two years after being laid. At first glance, the adhesive mortar samples had adequate traits and degree of hydration. The ceramic tiles, on the other hand, showed high porosity and high levels of amorphous and poorly sintered materials, with no crystalline phase. In a second analysis, scanning electron microscopy (SEM) tests associated with boiling plus autoclave moisture expansion tests executed on unused ceramic pieces of the same conformation proved to be more suitable for predicting expansion potential than standard tests. Due to the costs and difficulties in accessing and analyzing the SEM tests, chemical analysis of the ceramic tiles was executed using X-ray fluorescence (XRF) to assess the presence of the amorphous silica (free quartz) and alkaline oxides. Together with pressure and temperature determination tests (autoclave), they may represent another alternative that is easier to access and more cost-effective for predicting future expansion. Full article