Search Results (1,512)

Super high-rise buildings of 400 m and above are currently rare globally, making their design and construction data invaluable. Due to their enormous size, the structural safety, architectural effect, and construction cost are key concerns of all parties. This study employs parametric analysis to research the lateral force-resisting system and key local structural issues of a 400 m under-construction super-high-rise structure. The overall analysis results show that the 8-mega-column scheme can relatively well balance architectural effect and structural performance; the 5-belt truss design minimizes the steel consumption. The local research results indicate that the inward inclination of bottom columns leads to increased axial forces in floor beams significantly, necessitating reinforcement; horizontal braces directly connected to the core tube enhance folded belt truss integrity under rare earthquakes; failure of bottom gravity columns in the folded secondary frame increases beam bending moments and axial forces substantially. Steel consumption sensitivity analysis shows that when the structural first-order period is reduced by 0.1 s, adjusting the section sizes of the members in the belt truss minimizes the increase in steel consumption, while adjusting steel beams maximizes it. These findings provide essential design insights for similar super-high-rise projects. Full article

The search for more sustainable alternatives to traditional organic solvents, in the frame of the green chemistry approach, is leading to an increasing interest toward the exploration of deep eutectic solvents (DESs), especially natural-based ones (NADESs). The great ferment in the use of DESs as innovative media for many applications and in the research of novel types of DESs is not matched by an equal rigor in their characterization and in the study of their physico-chemical characteristics. Nevertheless, it is evident how comparative studies encompassing the investigation of a wide range of properties in relationship with the DESs structures would be beneficial for a rational development of the field. In this work a panel of DESs featuring choline acetate, L-carnitine and L-proline as hydrogen bond acceptor constituents (HBAs) and ethylene glycol, glycerol and levulinic acid as hydrogen bond donor constituents (HBDs) in 1:2 and 1:3 molar ratios have been prepared and characterized. Their density, viscosity and optical properties have been thoroughly investigated at various temperatures, analyzing the influence of their composition in terms of type of HBA, type of HBD and molar ratio on their properties. All the proposed DESs have also been thermally characterized by TGA and DSC, providing a description of their thermal behavior in a wide range of temperature and determining their thermal stability and thermal degradation profile. Full article

China is the world’s leading producer of apples. However, the current classification of apple maturity is predominantly reliant on manual expertise, a process that is both inefficient and costly. In this study, we utilize a diverse array of apples of varying ripeness levels as the research subjects. We propose a lightweight target detection model, termed BGWL-YOLO, which is based on YOLOv11n and incorporates the following specific improvements. To enhance the model’s ability for multi-scale feature fusion, a bidirectional weighted feature pyramid network (BiFPN) is introduced in the neck. In response to the problem of redundant computation in convolutional neural networks, a GhostConv is used to replace the standard convolution. The Wise-Inner-MPDIoU (WIMIoU) loss function is introduced to improve the localization accuracy of the model. Finally, the LAMP pruning algorithm is utilized to further compress the model size. The experimental results demonstrate that the BGWL-YOLO model attains a detection and recognition precision rate of 83.5%, a recall rate of 81.7%, and an average precision mean of 90.1% on the test set. A comparative analysis reveals that the number of parameters has been reduced by 65.3%, the computational demands have been decreased by 57.1%, the frames per second (FPS) have been boosted by 5.8% on the GPU and 32.8% on the CPU, and most notably, the model size has been reduced by 74.8%. This substantial reduction in size is highly advantageous for deployment on compact smart devices, thereby facilitating the advancement of smart agriculture. Full article

In today’s fierce market competition, enterprises must quickly attract consumers’ attention to products and prompt them to make purchases. Based on regulatory focus theory, this study examines the impact of the congruence between different types of goal framing in advertising (promotion vs. prevention) and product types (hedonic vs. utilitarian) on individual consumer decision-making, as well as the underlying psychological mechanisms. The findings are as follows: (1) A goal-framing effect was observed, such that individuals allocated more attention and exhibited higher purchase intentions toward products presented with promotion-framed advertising. (2) A matching effect between goal-framing type and product type was identified: promotion framing increased purchase intentions for hedonic products, whereas prevention framing increased purchase intentions for utilitarian products. (3) Processing fluency mediated the effect of goal–product matching on consumer decision-making. (4) The presence of time pressure amplified the goal-framing effect, leading to stronger preferences under promotion-framed advertisements, as reflected in both longer fixation durations and higher purchase intentions. By integrating regulatory focus theory with product type matching, this study leverages eye-tracking data to reveal the cognitive processes underlying consumer decision-making and the moderating role of time pressure on goal-framing effects. The findings enrich the motivational perspective in consumer behavior research and provide empirical guidance for designing differentiated advertising strategies and optimizing advertising copy. Full article

With the steady global progress in integrating technology into healthcare delivery, doctors’ behavioral patterns on online healthcare platforms have increasingly become a focal point in the fields of digital health and healthcare service management. Grounded in Job Crafting Theory, this study constructs a proactive crafting index, which captures doctors’ proactive behaviors on the platform across three dimensions: consultation rate, number of consultations, and response speed. We systematically examine the multidimensional impacts of such behaviors on performance outcomes, including online consultation volume, offline service volume, and user evaluation performance. This study collects publicly available records from a major online healthcare platform in China and conducts empirical analysis using the entropy weight method and econometric techniques. The results reveal that there is an optimal level of proactive engagement: moderate proactivity maximizes online consultation volume, while both insufficient and excessive proactivity reduce it. Offline service volume, in contrast, follows a U-shaped relationship, where moderate proactive engagement minimizes offline visits, while too little or too much engagement leads to more offline service needs. These nonlinear patterns highlight the importance of framing doctors’ proactive behavior to optimize both online engagement and offline service. The findings enrich Job Crafting Theory by identifying boundaries in platform-based service environments and provide actionable insights for platform operators to design behavior management and incentive systems tailored to doctors’ professional rank, patient condition, and regional context. Full article

Background: Colon capsule endoscopy (CCE) has seen increased adoption since the COVID-19 pandemic, offering a non-invasive alternative for lower gastrointestinal investigations. However, inadequate bowel preparation remains a key limitation, often leading to higher conversion rates to colonoscopy. Manual assessment of bowel cleanliness is inherently subjective and marked by high interobserver variability. Recent advances in artificial intelligence (AI) have enabled automated cleansing scores that not only standardise assessment and reduce variability but also align with the emerging semi-automated AI reading workflow, which highlights only clinically significant frames. As full video review becomes less routine, reliable, and consistent, cleansing evaluation is essential, positioning bowel preparation AI as a critical enabler of diagnostic accuracy and scalable CCE deployment. Objective: This CESCAIL sub-study aimed to (1) evaluate interobserver agreement in CCE bowel cleansing assessment using two established scoring systems, and (2) determine the impact of AI-assisted scoring, specifically a TransUNet-based segmentation model with a custom Patch Loss function, on both interobserver and intraobserver agreement compared to manual assessment. Methods: As part of the CESCAIL study, twenty-five CCE videos were randomly selected from 673 participants. Nine readers with varying CCE experience scored bowel cleanliness using the Leighton–Rex and CC-CLEAR scales. After a minimum 8-week washout, the same readers reassessed the videos using AI-assisted CC-CLEAR scores. Interobserver variability was evaluated using bootstrapped intraclass correlation coefficients (ICC) and Fleiss’ Kappa; intraobserver variability was assessed with weighted Cohen’s Kappa, paired t-tests, and Two One-Sided Tests (TOSTs). Results: Leighton–Rex showed poor to fair agreement (Fleiss = 0.14; ICC = 0.55), while CC-CLEAR demonstrated fair to excellent agreement (Fleiss = 0.27; ICC = 0.90). AI-assisted CC-CLEAR achieved only moderate agreement overall (Fleiss = 0.27; ICC = 0.69), with weaker performance among less experienced readers (Fleiss = 0.15; ICC = 0.56). Intraobserver agreement was excellent (ICC > 0.75) for experienced readers but variable in others (ICC 0.03–0.80). AI-assisted scores were significantly lower than manual reads by 1.46 points (p < 0.001), potentially increasing conversion to colonoscopy. Conclusions: AI-assisted scoring did not improve interobserver agreement and may even reduce consistency amongst less experienced readers. The maintained agreement observed in experienced readers highlights its current value in experienced hands only. Further refinement, including spatial analysis integration, is needed for robust overall AI implementation in CCE. Full article

Hyperspectral imaging for gas plume identification holds significant potential for applications in industrial emission control and environmental monitoring, including critical needs in livestock farm environments. Conventional pixel-by-pixel spectral identification methods primarily rely on spectral information, often overlooking the rich spatial distribution features inherent in hyperspectral images. This oversight can lead to challenges such as inaccurate identification or leakage along the edge regions of gas plumes and false positives from isolated pixels in non-gas areas. While infrared imaging for gas plumes offers a new perspective by leveraging multi-frame image variations to locate plumes, these methods typically lack spectral discriminability. To address these limitations, we draw inspiration from the multi-frame analysis framework of infrared imaging and propose a novel hyperspectral gas plume identification method based on image background modeling and suppression. Our approach begins by employing background modeling and suppression techniques to accurately detect the primary gas plume region. Subsequently, a representative spectrum is extracted from this identified plume region for precise gas identification. To further enhance the identification accuracy, especially in the challenging plume edge regions, a spatial-spectral combined judgment operator is applied as a post-processing step. The effectiveness of the method was validated through experiments using both simulated and real-world measured data from ammonia and methanol gas releases. We compare its performance against classical methods and an ablation version of our model. Results consistently demonstrate that our method effectively detects low-concentration, thin, and diffuse gas plumes, offering a more robust and accurate solution for hyperspectral gas plume identification with strong applicability to real-world industrial and livestock farm scenarios. Full article

Stroke-related impairment remains a leading cause of long-term disability, limiting individuals’ ability to perform daily activities. While wearable sensors offer scalable monitoring solutions during rehabilitation, they struggle to distinguish functional from non-functional movements, and manual annotation of sensor data is labor-intensive and prone to inconsistency. In this paper, we propose a novel framework that uses large language models (LLMs) to generate activity descriptions from video frames of therapy sessions. These descriptions are aligned with concurrently recorded accelerometer signals to create labeled training data. Through exploratory analysis, we demonstrate that accelerometer signals exhibit distinct temporal and statistical patterns corresponding to specific activities, supporting the feasibility of generating natural language narratives directly from sensor data. Our findings lay the foundation for future development of sensor-to-text models that can enable automated, non-intrusive, and scalable stroke rehabilitation monitoring without the need for manual or video-based annotation. Full article

In the digital era, online search patterns provide a practical way to track changes in the public interest in sustainability. This study analyzes monthly Google Trends data in the United States (January 2019–December 2024) for five keywords: two institutional (“ESG”, “carbon neutral”), and three consumer-oriented (“eco friendly”, “zero waste”, and “plastic free”). Drawing on agenda-setting theory and the diffusion-of-innovations framework, we test the directional links between institutional and consumer attention. The methods include Granger causality tests, impulse response functions, and cross-correlation analysis. The findings reveal a consistent lead–lag structure in which institutional terms precede consumer-oriented searches, but the timing and persistence of influence vary across concepts. A broad discourse such as ESG produces slower, yet more sustained, effects, whereas action-oriented concepts like carbon neutrality generate quicker but shorter-lived responses. Seasonal analysis also shows recurring peaks in consumer interest around events such as Earth Day and Plastic-Free July, underscoring the cyclical nature of attention to sustainability. By integrating communication theory with multi-year digital trace data, this study provides evidence of how institutional messaging diffuses into consumer behavior, while highlighting the roles of timing and message framing. The results contribute to sustainability communication research and offer practical insights for policymakers, NGOs, and marketers relevant to aligning campaigns with evolving public attention. Full article

Obstacle avoidance during high-speed, low-altitude flight remains a significant challenge for unmanned aerial vehicles (UAVs), particularly in unfamiliar environments where prior maps and heavy onboard sensors are unavailable. To address this, we present an entropy-aware deep reinforcement learning framework that enables fixed-wing UAVs to navigate safely using only monocular onboard cameras. Our system features a lightweight, single-frame depth estimation module optimized for real-time execution on edge computing platforms, followed by a reinforcement learning controller equipped with a novel reward function that balances goal-reaching performance with path smoothness under fixed-wing dynamic constraints. To enhance policy optimization, we incorporate high-quality experiences from the replay buffer into the gradient computation, introducing a soft imitation mechanism that encourages the agent to align its behavior with previously successful actions. To further balance exploration and exploitation, we integrate an adaptive entropy regularization mechanism into the Proximal Policy Optimization (PPO) algorithm. This module dynamically adjusts policy entropy during training, leading to improved stability, faster convergence, and better generalization to unseen scenarios. Extensive software-in-the-loop (SITL) and hardware-in-the-loop (HITL) experiments demonstrate that our approach outperforms baseline methods in obstacle avoidance success rate and path quality, while remaining lightweight and deployable on resource-constrained aerial platforms. Full article

The growing use of unmanned aerial vehicles (UAVs) for real-time video surveillance in smart city and smart region infrastructures requires reliable and delay-aware data transmission models. In urban environments, UAV communication links are subject to stochastic variability, leading to jitter, packet loss, and unstable video delivery. This paper presents a novel approach based on the Graphical Evaluation and Review Technique (GERT) for modeling the transmission of video frames from UAVs over uncertain network paths with probabilistic feedback loops and lognormally distributed delays. The proposed model enables both analytical and numerical evaluation of key Quality-of-Service (QoS) metrics, including mean transmission time and jitter, under varying levels of channel variability. Additionally, the structure of the GERT-based framework allows integration with artificial intelligence mechanisms, particularly for adaptive routing and delay prediction in urban conditions. Spectral analysis of the system’s characteristic function is also performed to identify instability zones and guide buffer design. The results demonstrate that the approach supports flexible, parameterized modeling of UAV video transmission and can be extended to intelligent, learning-based control strategies in complex smart city environments. This makes it suitable for a wide range of applications, including traffic monitoring, infrastructure inspection, and emergency response. Beyond QoS optimization, the framework explicitly accommodates security and privacy preserving operations (e.g., encryption, authentication, on-board redaction), enabling secure UAV video transmission in urban networks. Full article

We study the effects of the time evolution of the matter-gravity coupling on the luminosity distance, showing it can provide a natural explanation to the apparent Hubble tension. The gravitational coupling evolution induces a modification of the Friedman equation with respect to the $\mathsf{\Lambda }$CDM model, which we study in both the Einstein and Jordan frame. We consider a phenomenological parametrization of the low redshift variation of the coupling in a narrow redshift shell, showing how it can affect the distance of the anchors used to calibrate supernovae (SNe), while higher redshift background observations are not affected. This effect is purely geometrical, and it is not related to any change of the intrinsic SNe physical properties. The effects of a time varying gravity coupling only manifest on sufficiently long time scales, such as in cosmological observations at different redshifts, and if ignored lead to apparent tensions in the values of cosmological parameters estimated with observations from different epochs of the Universe history. Full article

Background/Objectives: Pompe disease (PD) is a rare autosomal recessive disorder caused by a deficiency of the lysosomal acid α-1,4-glucosidase (GAA) encoded by the GAA gene, leading to muscular dysfunctions due to pathological accumulation of glycogen in skeletal and cardiac muscles. PD has been reported in several animals and Japanese quails (JQ; Coturnix japonica), but a causative mutation has yet to be found in JQs with PD. Here, we aimed to identify a pathogenic mutation in JQs associated with PD. Methods: Paraffin-embedded skeletal muscle blocks from four JQs stored since the 1970s were used in this study. After confirming the histopathological phenotypes of PD, Sanger sequencing was performed to identify a pathological mutation in the GAA I gene of JQs. A genotyping survey was conducted using a real-time polymerase chain reaction assay targeting a candidate mutation using DNA samples extracted from 70 new-hatched JQs and 10 eggs from commercial farms. Results: Microscopic analysis confirmed the presence of the PD phenotype in three affected JQs based on abnormal histopathological changes and accumulated glycogen in the affected muscles, while one JQ was unaffected and served as a control. Sanger sequencing revealed that the three affected JQs were homozygous for the deletion of guanine at position 1096 in the open reading frame (c.1096delG). A genotyping survey of 70 JQs and 10 eggs from commercial farms showed that none carried this deletion mutation. Conclusions: This study identified c.1096delG as the pathogenic mutation for PD in JQs. This mutation induces a frameshift and substitution of amino acids at position 366 (alanine to histidine), resulting in premature termination at the 23rd codon (p.A366Hfs*23). This suggests that this mutation causes the deficient activity of GAA in JQs with PD. The identification of the c.1096delG mutation enabled the systematic maintenance of the flock colony in the PD model. Furthermore, this PD model can be used to clarify unknown aspects of PD pathogenesis and develop therapeutic strategies. Full article

Video stabilization is a critical technology for enhancing visual content quality in dynamic shooting scenarios, especially with the widespread adoption of mobile photography devices and Unmanned Aerial Vehicle (UAV) platforms. While traditional digital stabilization algorithms can improve frame stability by modeling global motion trajectories, they often suffer from excessive cropping or boundary distortion, leading to a significant loss of valid image regions. To address this persistent challenge, we propose the View Out-boundary Synthesis Algorithm (VOSA), a symmetry-aware spatio-temporal consistency framework. By leveraging rotational and translational symmetry principles in motion dynamics, VOSA realizes optical flow field extrapolation through an encoder–decoder architecture and an iterative boundary extension strategy. Experimental results demonstrate that VOSA enhances conventional stabilization by increasing content retention by 6.3% while maintaining a 0.943 distortion score, outperforming mainstream methods in dynamic environments. The symmetry-informed design resolves stability–content conflicts and outperforms mainstream methods in dynamic environments, establishing a new paradigm for full-frame stabilization. Full article

With the exponential growth of Internet of Things (IoT) and wearable devices for home automation and industrial applications, vast volumes of data are continuously generated, requiring efficient data collection methods. IoT devices, being resource-constrained and typically battery-dependent, require lightweight protocols that optimize resource usage and energy consumption. Among such IoT devices, this article focuses on Bluetooth-based beacons due to their low latency and the advantage of not requiring pairing for communications. Specifically, to tackle the limitations of beacons in terms of bandwidth and transmission frequency, this article proposes a protocol that modifies beacon frames to include up to three parameters per frame and that allows for making use of configurable beaconing intervals based on the specific requirements of the communications scenario. Moreover, the use of the proposed protocol leads to increased data rates for beaconing transmissions, providing a low latency and a flexible configuration that permits adjusting different parameters. The proposed solution enables end-to-end interoperability in Opportunistic Edge Computing (OEC) networks by integrating a lightweight bridge module to transparently manage BLE advertisement segments. To demonstrate the performance of the devised opportunistic protocol, it is evaluated across multiple scenarios (i.e., in a short-distance reference scenario, inside a home with diverse obstacles, inside a building, outdoors and in an industrial scenario), showing its flexibility and ability to collect substantial data volumes from heterogeneous IoT devices. Full article