Search Results (10,387)

Vertical addition to already-existing structures is an approach to energy-efficient building renovation. It presents an opportunity for the densification of built-up areas and the construction of new usable spaces. While many studies have dealt with the subject of renovating buildings with a focus on energy efficiency, far fewer studies have specifically examined the potential of vertically extending existing buildings, an approach which could be introduced in new sustainable building policies. The objective of this study is to redevelop optimal timber–glass upgrade modules, considering the ideal proportions of glazing for all cardinal directions, by using discrete Mixed-Integer Non-Linear Programming optimization. The novelty of the suggested method resides in the synchronous optimization of the upgrade modules’ daylighting and energy-efficiency performance, resulting in the creation of optimization methods that can determine the optimal glazing proportions for all cardinal directions and incorporate rational design and window measurement. The impact of the developed Mixed-Integer Non-Linear Programming-optimized upgrade modules is compared to previously designed optimized upgrade modules. Finally, a methodology for estimating the energy efficiency of building renovations incorporating vertical additions using the timber–glass upgrade modules was developed, supporting the quick assessment of the reduction in hybrid buildings’ energy consumption for heating and cooling according to boundary conditions, presuming that they undergo the suggested renovations. The findings are applicable (not exclusively) to Slovenia’s residential building stock, which makes up around 20% of the country’s current housing stock and was mainly constructed between 1946 and 1970. This offers a substantial opportunity to improve the overall sustainability and energy efficiency of the country’s housing stock. The proposed approach offers a holistic solution to drive sustainable development in the built environment by incorporating all three pillars of sustainability (environmental, social, and economic). Full article

In recent years, the fusion of hyperspectral and multispectral images in remote sensing image processing still faces challenges, primarily due to their complexity and multimodal characteristics. Diffusion models, known for their stable training process and exceptional image generation capabilities, have shown good application potential in this field. However, when dealing with multimodal data, it may prove challenging for the models to fully capture the intricate relationships between the modalities, which may result in incomplete information integration and a small amount of remaining noise in the generated images. To address these problems, we propose a new model, KanDiff, for hyperspectral and multispectral image fusion. To address the differences in modal information between multispectral and hyperspectral images, KANDiff incorporates Kolmogorov–Arnold Networks (KAN) to guide the inputs. It helps the model understand the complex relationships between the modalities by replacing the fixed activation function in the traditional MLP with a learnable activation function. Furthermore, the image generated by the diffusion model may exhibit a small amount of the remaining noise. Convolutional Neural Networks (CNNs) effectively extract local features through their convolutional layers and achieve noise suppression via layer-by-layer feature representation. Therefore, the MergeCNN module is further introduced to enhance the fusion effect, resulting in smoother and more accurate outcomes. Experimental results on the public CAVE and Harvard datasets indicate that KanDiff has achieved improvements over current high-performance methods across several metrics, particularly showing significant enhancements in the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM), thus demonstrating superior performance. Additionally, we have created an image fusion dataset of the lunar surface, and KANDiff exhibits robust performance on this dataset as well. This work introduces an effective solution for addressing the challenges posed by missing high-resolution hyperspectral images (HRHS) data, which is essential for tasks including landing site selection and resource exploration within the realm of deep space exploration. Full article

In this paper, the asymptotic behavior of the modified Mellin transform ${\mathcal{Z}}_2\left(s\right)$, $s=\sigma +it$, of the fourth power of the Riemann zeta function is characterized by weak convergence of probability measures in the space of analytic functions. The main results are devoted to probability measures defined by generalized shifts ${\mathcal{Z}}_2(s+i\phi \left(\tau \right))$ with a real increasing to $+\infty$ differentiable functions connected to the growth of the second moment of ${\mathcal{Z}}_2\left(s\right)$. It is proven that the mass of the limit measure is concentrated at the point expressed as $h\left(s\right)\equiv 0$. This is used for approximation of $h\left(s\right)$ by ${\mathcal{Z}}_2(s+i\phi \left(\tau \right))$. Full article

The conventional Structural Health Monitoring (SHM) framework focuses on individual structures. However, preliminary studies are required at a large territorial scale to effectively identify the most vulnerable elements. This becomes particularly challenging in urban settings, where numerous buildings of varied shapes, ages, and structural conditions are closely spaced from one another. A twofold task is therefore required: the automated identification and differentiation of various structures, coupled with a ranking system based on perceived structural risk, here assumed to be linked to their deformation patterns. It integrates displacement measurements acquired through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique, specifically employing the full-resolution Small Baseline Subset (SBAS) approach coupled with Hierarchical Clustering. The effectiveness of this method is successfully demonstrated and validated in two selected areas of Rome, Italy, serving as case studies. The results of this vast-area scale monitoring can be used to select the constructions that need a more in-depth assessment. Full article

The remote sensing-based inversion of sound speed profile (SSP) enables the acquisition of high-spatial-resolution SSP without in situ measurements. The spatial division of the inversion grid is crucial for the accuracy of results, determining both the number of samples and the consistency of inversion relationships. The result of our research is the introduction of a physics-inspired self-organizing map (PISOM) that facilitates SSP inversion by clustering samples according to the physical perturbation law. The linear physical relationship between sea surface parameters and the SSP drives dimensionality reduction for the SOM, resulting in the clustering of samples exhibiting similar disturbance laws. Subsequently, samples within each cluster are generalized to construct the topology of the solution space for SSP reconstruction. The PISOM method significantly improves accuracy compared with the SOM method without clustering. The PISOM has an SSP reconstruction error of less than 2 m/s in 25% of cases, while the SOM method has none. The transmission loss calculation also shows promising results, with an error of only 0.5 dB at 30 km, 5.5 dB smaller than that of the SOM method. A physical interpretation of the neural network processing confirms that physics-inspired clustering can bring better precision gains than the previous spatial grid. Full article

The aim of this article is to introduce and study a new class of fractional integro nonlocal boundary value problems involving the p-Laplacian operator and generalized fractional derivatives. The existence of solutions in Banach spaces is investigated with the aid of the properties of Kuratowski’s noncompactness measure and Sadovskii’s fixed-point theorem. Two illustrative examples are constructed to guarantee the applicability of our results. Full article

Infectious diseases have profoundly impacted global health and daily life. To control virus transmission, countries worldwide have implemented various preventive measures. A critical pathway for infection spread is cross-infection within households, especially among family members in the same or adjacent rooms. This study uses numerical simulations to examine aerosol transmission characteristics in adjacent spaces in home settings and assess associated infection risks. The study evaluated the effects of factors such as outdoor wind speed, door gap leakage, and door opening actions on aerosol concentration and infection risk across various areas. Key conclusions include the following: Under prolonged lack of ventilation, aerosol leakage through the door gap is minimal, with the average aerosol concentration outside the bedroom remaining low (<0.04). In the absence of ventilation, aerosol accumulation primarily occurs within the bedroom. Under ventilated conditions, door gap leakage may increase infection risk in adjacent areas, suggesting a stay duration of no more than 75 min to keep infection risk below 30%. The findings provide practical recommendations for airtight design and activity area selection within residential spaces, offering valuable guidance for effective infection control measures. Full article

Urbanization transforms landscapes from natural ecosystems to configurations of impervious surfaces and green spaces, leading to urban heat island effects that impact health and ecosystem sustainability. This study in Xiamen City, China, categorizes urban areas into functional zones, employs Random Forest and Stepwise Regression models to assess thermal differences, and proposes optimization measures for the building–green space landscape. The optimization involves altering the characterization of the building–green space landscape pattern. Results indicate: (1) due to the spatial heterogeneity of the building–green space landscape pattern in different functional zones, the surface temperature also shows strong spatial heterogeneity in different functional zones; (2) different optimization measures for the building–green space pattern are needed for different functional zones; taking the urban residential zone as an example, the Normalized Difference Vegetation Index (NDVI) in the hot spot area can be adjusted according to the value range of the cold spot area; (3) considering the solar radiation process, Sun View Factor (S_unVF) plays an important role in indicating the change in surface temperature in the commercial service area, and as S_unVF increases, the surface temperature of the functional zone tends to rise. This research offers insights into urban thermal environment improvement and landscape pattern optimization. Full article

Phase-change random access memory (PcRAM) faces significant challenges due to the inherent instability of amorphous Ge₂Sb₂Te₅ (GST). While doping has emerged as an effective method for amorphous stabilization, understanding the precise mechanisms of structural modification and their impact on material stability remains a critical challenge. This study provides a comprehensive investigation of elastic strain and stress in crystalline lattices induced by various dopants (C, N, and Al) through systematic measurements of film thickness changes during crystallization. Through detailed analysis of cross-sectional electron microscopy data and theoretical calculations, we reveal distinct behavior patterns between interstitial and substitutional dopants. Interstitial dopants (C and N) generate substantial elastic strain energy (~9 J/g) due to their smaller atomic radii (0.07–0.08 nm) and ability to occupy spaces between lattice sites. In contrast, substitutional dopants (Al) produce lower strain energy (~5 J/g) due to their similar atomic radius (0.14 nm) to host atoms. We demonstrate that N doping achieves higher elastic strain energy compared to C doping, attributed to its preferential formation of Ge-N bonds and resulting lattice distortions. The correlation between dopant properties, structural features, and induced strain energy provides quantitative insights for optimizing dopant selection. These findings establish a fundamental framework for understanding dopant-induced thermodynamic stabilization in GST materials, offering practical guidelines for enhancing the reliability and performance of next-generation PcRAM devices. Full article

Given the increasing prevalence of anxiety and depression, this research aims to identify design features that enhance the sense of restoration, with the goal of supporting mental and behavioral healthcare facility design. This study employed both screen-based and virtual reality (VR) stimuli to evaluate the perceived restorativeness of different interior settings. The key variables analyzed included window view access, view content, materiality, and room geometry. Thirty-five undergraduate and graduate students assessed 16 distinct interior environments. Findings indicate that the VR presentations generally produced higher restorativeness scores compared with screen-based presentations, though this effect varied across stimuli. Repeated-measures ANOVA revealed that larger windows consistently correlated with higher restorativeness scores in both presentation modes. Views of water were rated as most restorative, followed by wooded areas. Natural materials were perceived as significantly more restorative than other materials, particularly in VR presentations. Varied ceiling designs, especially vaulted ceilings, were associated with evaluations of higher restorativeness compared with flat ceiling designs, with this effect more pronounced in VR. This research underscores the potential of VR technology to simulate and assess interior design interventions, offering insights into creating more effective and personalized restorative environments in mental health treatment facilities. The findings can inform evidence-based design strategies for healthcare spaces, supporting treatment processes and patient well-being. Full article

The geometric feature characterization of fruit trees plays a role in effective management in orchards. LiDAR (light detection and ranging) technology for object detection enables the rapid and precise evaluation of geometric features. This study aimed to quantify the height, canopy volume, tree spacing, and row spacing in an apple orchard using a three-dimensional (3D) LiDAR sensor. A LiDAR sensor was used to collect 3D point cloud data from the apple orchard. Six samples of apple trees, representing a variety of shapes and sizes, were selected for data collection and validation. Commercial software and the python programming language were utilized to process the collected data. The data processing steps involved data conversion, radius outlier removal, voxel grid downsampling, denoising through filtering and erroneous points, segmentation of the region of interest (ROI), clustering using the density-based spatial clustering (DBSCAN) algorithm, data transformation, and the removal of ground points. Accuracy was assessed by comparing the estimated outputs from the point cloud with the corresponding measured values. The sensor-estimated and measured tree heights were 3.05 ± 0.34 m and 3.13 ± 0.33 m, respectively, with a mean absolute error (MAE) of 0.08 m, a root mean squared error (RMSE) of 0.09 m, a linear coefficient of determination (r²) of 0.98, a confidence interval (CI) of −0.14 to −0.02 m, and a high concordance correlation coefficient (CCC) of 0.96, indicating strong agreement and high accuracy. The sensor-estimated and measured canopy volumes were 13.76 ± 2.46 m³ and 14.09 ± 2.10 m³, respectively, with an MAE of 0.57 m³, an RMSE of 0.61 m³, an r² value of 0.97, and a CI of −0.92 to 0.26, demonstrating high precision. For tree and row spacing, the sensor-estimated distances and measured distances were 3.04 ± 0.17 and 3.18 ± 0.24 m, and 3.35 ± 0.08 and 3.40 ± 0.05 m, respectively, with RMSE and r² values of 0.12 m and 0.92 for tree spacing, and 0.07 m and 0.94 for row spacing, respectively. The MAE and CI values were 0.09 m, 0.05 m, and −0.18 for tree spacing and 0.01, −0.1, and 0.002 for row spacing, respectively. Although minor differences were observed, the sensor estimates were efficient, though specific measurements require further refinement. The results are based on a limited dataset of six measured values, providing initial insights into geometric feature characterization performance. However, a larger dataset would offer a more reliable accuracy assessment. The small sample size (six apple trees) limits the generalizability of the findings and necessitates caution in interpreting the results. Future studies should incorporate a broader and more diverse dataset to validate and refine the characterization, enhancing management practices in apple orchards. Full article

Background/Objectives: Empowerment has been associated with several positive outcomes in healthcare; however, there is limited insight on empowerment levels within the adolescent population of those with a chronic condition/disability. The aim of this scoping review was to identify gaps in the existing literature on empowerment levels within this population. Methods: Five databases (MEDLINE [Ovid], EMBASE [Ovid], PsycINFO [Ebsco], CINHAL [Ebsco] and Web of Science [UBC]) and grey literature were searched. Results: A total of 67 studies were included and used for data extraction including descriptive numerical analysis followed by a narrative review. Extracted data were divided into demographic characteristics (e.g., ethnicity/ancestry), type of disability/condition (e.g., type 1 diabetes), interventions used to increase empowerment or empowerment-adjacent elements, quantitative and qualitative tools used to measure empowerment (e.g., questionnaires and/or interviews), domains/outcomes associated with empowerment (e.g., self-control), and review articles. Several interventions were shown to have positive effects on empowerment levels in adolescents with a chronic condition/disability. Conclusions: Gaps were identified in the consideration of ethnicity/ancestry and socioeconomic status, demonstrating a need for future research in this space to focus on the intersection of disability, ethnicity/ancestry, and socio-economic status and the implementation of interventions promoting empowerment. Full article

This article introduces a frequency-adaptive control strategy for a three-phase shunt active power filter, aimed at improving energy efficiency and ensuring high power quality in consumer-oriented power systems. The proposed control system utilizes real-time frequency estimation to dynamically adjust the gain of a proportional-integral–resonant (PIR) controller, facilitating precise harmonic compensation under challenging unbalanced grid conditions, such as unbalanced three-phase loads, grid impedance variations, and diverse nonlinear loads like three-phase rectifiers and induction motors. These scenarios often increase total harmonic distortion (THD) at the point of common coupling (PCC), degrading the performance of connected loads and reducing the efficiency of induction motors. The PIR controller integrates both proportional-integral (PI) and proportional-resonant (PR) control features, achieving improved stability and reduced overshoot. A novel voltage sensorless control method is proposed, requiring only current measurements to determine reference currents for the inverter, thereby simplifying the implementation. Validation of the frequency adaptive control scheme through MATLAB/Simulink simulations and real-time experiments on a dSPACE (DS1202) platform demonstrates significant improvements in harmonic compensation, energy efficiency, and system stability across varying grid frequencies. This approach offers a robust consumer-oriented solution for managing power quality, positioning the SAPF as a key technology for advancing sustainable energy management in smart applications. Full article

This work is devoted to an experiment studying the regularities of the propagation of hydroacoustic low-frequency signals in the conditions of the sea at intermediate depth and deep in terms of their transformation into vibrations in the upper layer of the Earth’s crust. This experiment belongs to the field of acoustic tomography and is aimed at solving the problems of non-contact methods for studying the geological structure of the shelf areas of the World Ocean. The novelty and uniqueness of the work lies in the use of a harmonic low-frequency hydroacoustic signal with a frequency of 22 Hz of high power, capable of creating Rayleigh surface waves at the “water–bottom” interface. The surface waves propagating at the bottom are registered by a coastal laser-interference measuring system capable of recording deformations in the upper crustal layer with an accuracy of 0.01 nm. The experimental results showed that the radiated hydroacoustic energy is not localized in the liquid half-space and propagates predominantly according to the law close to spherical divergence, even when the shelf depth is comparable to the wavelength of the radiated signal. Full article

With shifts in lifestyle and dietary patterns, obesity has become an increasing health issue among younger demographics, particularly affecting young adults. This trend is strongly associated with a heightened risk of developing chronic diseases, especially cardiovascular conditions. However, conventional health monitoring systems are often limited to basic parameters such as heart rate, pulse pressure (PP), and systolic blood pressure (SBP), which may not provide a comprehensive assessment of cardiac health. This study introduces an intelligent heart health monitoring system that leverages the Internet of Things (IoT) and advanced sensor technologies. By incorporating IoT-based sensors, this system aims to improve the early detection and continuous monitoring of cardiac function in young obese women. The research employed a TERUMO ES-P2000 to measure blood pressure and a PhysioFlow device to assess noninvasive cardiac hemodynamic parameters. Through precise sensor data collection, the study identified key indicators for monitoring cardiovascular health. Machine learning models and big data analysis were utilized to predict cardiac index (CI) values based on the sensor-derived inputs. The findings indicated that young obese women showed significant deviations in blood pressure (SBP and PP) and cardiac hemodynamic metrics (SVI, EDV, and ESV) at an early stage. The implementation of signal processing techniques and IoT sensors enhanced the CI prediction accuracy from 33% (using basic parameters like heart rate, PP, and SBP) to 66%. Moreover, the integration of extra sensor-based parameters, such as Stroke Volume Index (SVI) and Cardiac Output (CO), along with the use of color space transformations, successfully improved the prediction accuracy of the original data by 36.68%, increasing from 53.33% to 90.01%. This represents a significant improvement of 30.01% compared to the existing technology’s accuracy of 60%. These results underscore the importance of utilizing sensor-derived parameters as critical early indicators of cardiac function in young obese women. This research advances smart healthcare through early cardiovascular risk assessment using AI and noninvasive sensors. Full article