Search Results (1,761)

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

With the rapid development of smart manufacturing, the optimization of real-time monitoring in operating procedures has become a crucial issue in modern industry. Traditional disassembly and assembly (D/A) work, relying on human experience and visual inspection, lacks immediacy and a quantitative basis, further affecting operating quality and efficiency. This study aims to develop a thin-film force sensor and an inertial measurement unit (IMU)-integrated wearable device for monitoring and analyzing operators’ behavioral characteristics during D/A tasks. First, by having operators wear self-made smart gloves and 17 IMU sensors, the work tables with three different heights are equipped with a mechatronics machine for the D/A experiment. Common D/A motions are designed into the experiment. Several subjects are invited to execute the standardized operating procedure, with upper limbs used to collect data on operators’ hand gestures and movements. Then, the measured data are applied to verify the performance measure functional best path of machine D/A. The results reveal that the system could effectively identify various D/A motions as well as observe operators’ force difference and motion mode, which, through the theory of performance indicator optimization and the verification of data analysis, could provide a reference for the best path planning, D/A sequence, and work table height design in the machine D/A process. The optimal workbench height for a standing operator is 5 to 10 cm above their elbow height. Performing assembly and disassembly tasks at this optimal height can help the operator save between 14.3933% and 35.2579% of physical effort. Such outcomes could aid in D/A behavior monitoring in industry, worker training, and operational optimization, as well as expand the application to instant feedback design for automation and smartization in a smart factory. Full article

This paper contributes to the field of urban waste collection systems, which are crucial for advancing sustainability, urban cleanliness, and the aesthetic quality of cities. Specifically, it introduces a novel framework designed to support planners and decision makers in the design of efficient and responsive textile waste collection systems, aligned with both environmental objectives and citizen engagement. To this end, the framework exploits a hybrid simulation platform that realistically models the logistics infrastructure in a spatially explicit environment. Also, within the framework, citizens are represented as adaptive agents whose environmental attitudes evolve through personal experience, social influence, and perceived service quality. The behavioural layer is the core element of the framework. It enables dynamic analysis of the two-way feedback between citizen participation and service effectiveness to underscore the often-overlooked role of citizen behaviour in shaping overall system performance. The model was tested in a representative urban scenario under varying operational conditions. The results highlight how policy incentives and smart collection infrastructure can significantly boost participation, while social segregation may hinder the adoption of sustainable practices. The framework ultimately offers a generalisable decision-support tool to explore the behavioural dimension of circular economy initiatives and develop robust, scenario-based strategies. Full article

Methane (CH₄) emissions from the wetlands of the Tibetan Plateau (TP) remain poorly quantified, particularly regarding their historical dynamics, spatial heterogeneity, and response to climate change. This study provides the high-resolution, observation-driven reconstruction of TP wetland CH₄ emissions over the past four decades, integrating a machine learning model with 108 flux measurements from 67 sites. This unique combination of field-based data and fine-scale mapping enables unprecedented accuracy in quantifying both emission intensity and long-term trends. We show that current TP wetlands emit 5.87 ± 1.43 g CH₄ m⁻² yr⁻¹, totaling 97.3 Gg CH₄ yr⁻¹, equivalent to 7.8% of East Asia’s annual wetland emissions. Despite a climate-driven increase in per-unit-area CH₄ fluxes, a 19.8% (8432.9 km²) loss of wetland area since the 1980s has reduced total emissions by 15%, counteracting the enhancement from warming and moisture increases. Our comparative analysis demonstrates that existing land surface models (LSMs) substantially underestimate TP wetland CH₄ emissions, largely due to the inadequate representation of TP wetlands and their dynamics. Projections under future climate scenarios indicate a potential 8.5–21.2% increase in emissions by 2100, underscoring the importance of integrating high-quality, region-specific observational datasets into Earth system models. By bridging the gap between field observations and large-scale modeling, this work advances understanding of alpine wetland–climate feedback, and provides a robust foundation for improving regional carbon budget assessments in one of the most climate-sensitive regions on Earth. Full article

In recent years, China’s rapid economic growth and urbanization have heightened the conflict between economic development and resource sustainability, leading to severe urban water challenges, including scarcity and environmental degradation. This study proposes a quantitative model that integrates the “Human-Water-City” (HWC) feedback mechanisms to assess and measure urban comprehensive water resources and environmental carrying capacity (CWRECC), aimed at addressing urban water sustainability challenges. The CWRECC integrates water quantity and quality dimensions following the principles of the “Cannikin Law”—selecting the lower envelope between water resources and water environment carrying capacities, which emphasizes the importance of weaknesses in enhancing the overall system. The maximum sustainable population and Gross Domestic Product (GDP) under the CWRECC constraints can be obtained using this quantitative method. A case study was conducted in Wuhan City. The results show that Wuhan has abundant water resources. From 2013 to 2020, if only considering the water quantity aspect, the water resources carrying capacity could support a population ranging from 22.63 to 61.17 million and a GDP between 1946.6 and 7988.9 billion yuan, maintaining a sustainable state throughout the period. However, when considering both water quantity and quality, the CWRECC revealed an overloaded state in 2013, 2014, 2018, and 2019, primarily attributable to significant water environmental issues. In 2013, 2014, 2018, and 2019, the quantified CWRECC could sustain populations of 9.88 million, 10.01 million, 10.33 million, and 10.57 million people, and support a GDP of 849.5 billion, 976.5 billion, 1402.9 billion, and 1538.9 billion yuan, respectively. Both the population and GDP capacities fell short of the actual recorded values for those years. The findings demonstrate that Wuhan needs to make greater efforts in water environmental protection to sustain the harmonious development within the HWC. This empirical study highlights the model’s potential to provide a scientific foundation for urban water resources management and environmental protection strategies. Full article

Despite its success in measuring air–sea exchange, the Woods Hole Oceanographic Institution’s (WHOI) X-Spar Buoy faces operational limitations due to energy constraints, motivating the integration of an energy harvesting apparatus to improve its deployment duration and capabilities. This work explores the feasibility of an augmented, self-powered system in two parts. Part 1 presents the collaborative design between X-Spar developers and wave energy researchers translating user needs into specific functional requirements. Based on requirements like desired power levels, deployability, survivability, and minimal interference with environmental data collection, unsuitable concepts are pre-eliminated from further feasibility study consideration. In part 2, we focus on one of the promising concepts: an internal rigid body wave energy converter. We apply control co-design methods to consider commercial of the shelf hardware components in the dynamic models and investigate the concept’s power conversion capabilities using linear 2-port wave-to-wire models with concurrently optimized control algorithms that are distinct for every considered hardware configuration. During this feasibility study we utilize two different control algorithms, the numerically optimal (but acausal) benchmark and the optimized damping feedback. We assess the sensitivity of average power to variations in drive-train friction, a parameter with high uncertainty, and analyze stroke limitations to ensure operational constraints are met. Our results indicate that a well-designed power take-off (PTO) system could significantly extend the WEC-Spar’s mission by providing additional electrical power without compromising data quality. Full article

Spinal health depends on the dynamic interplay between mechanical forces, biochemical signaling, and cellular behavior. This review explores how key molecular pathways, including integrin, yeas-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ), Piezo, and Wingless/Integrated (Wnt) with β-catenin, actively shape the structural and functional integrity of spinal tissues. These signaling mechanisms respond to physical cues and interact with inflammatory mediators such as interleukin-1 beta (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α), driving changes that lead to disc degeneration, vertebral fractures, spinal cord injury, and ligament failure. New research is emerging that shows scaffold designs that can directly harness these pathways. Further, new stem cell-based therapies have been shown to promote disc regeneration through targeted differentiation and paracrine signaling. Interestingly, many novel bone and ligament scaffolds are modulating anti-inflammatory signals to enhance tissue repair and integration, as well as prevent scaffold degradation. Neural scaffolds are also arising. These mimic spinal biomechanics and activate Piezo signaling to guide axonal growth and restore motor function. Scientists have begun combining these biological platforms with brain–computer interface technology to restore movement and sensory feedback in patients with severe spinal damage. Although this technology is not fully clinically ready, this field is advancing rapidly. As implantable technology can now mimic physiological processes, molecular signaling, biomechanical design, and neurotechnology opens new possibilities for restoring spinal function and improving the quality of life for individuals with spinal disorders. Full article

In the era of digital interconnectivity, user-generated reviews on platforms such as TripAdvisor have become a valuable source of social feedback, reflecting collective experiences and perceptions of airline services. However, aggregating such feedback presents several challenges: evaluations are typically expressed using linguistic ordinal scales, are subjective, often incomplete, and influenced by opinion dynamics within social networks. To effectively deal with these complexities and extract meaningful insights, this study proposes an information-driven decision-making framework that integrates Fuzzy Belief Structures with the TOPSIS method. To handle the uncertainty and imprecision of linguistic ratings, user opinions are modeled as fuzzy belief distributions over satisfaction levels. Rankings are then derived using TOPSIS by comparing each airline’s aggregated profile to ideal satisfaction benchmarks via a belief-based distance measure. This framework presents a novel solution for measuring synthetic satisfaction in complex social feedback systems, thereby contributing to the understanding of information flow, belief aggregation, and emergent order in digital opinion networks. The methodology is demonstrated using a real-world dataset of TripAdvisor airline reviews, providing a robust and interpretable benchmark for service quality. Moreover, this study applies Shannon entropy to classify and interpret the consistency of customer satisfaction ratings among Star Alliance airlines. The results confirm the stability of the Airline Satisfaction Index (ASI), with extremely high correlations among the five rankings generated using different fuzzy utility function models. The methodology reveals that airlines such as Singapore Airlines, ANA, EVA Air, and Air New Zealand consistently achieve high satisfaction scores across all fuzzy model configurations, highlighting their strong and stable performance regardless of model variation. These airlines also show both low entropy and high average scores, confirming their consistent excellence. Full article

Concrete quality on construction sites depends on maintaining certain workability during transportation. However, traditional slump testing is manual and cannot assess concrete in transit. This study presents a cyber-physical system (CPS) integrating IoT sensors with machine learning models to estimate concrete workability in real time during delivery. The proposed approach first correlates traditional slump test parameters with sensor measurements, allowing for automated replication of established workability evaluation. The CPS continuously captures IoT sensor data, such as drum rotation, vibration, internal pressure, and temperature, processes these data in real time, and infers workability before unloading. The developed CPS was validated in a real-world case study, achieving high workability prediction accuracy with an average error of approximately 0.9 cm and timely, automated feedback of below 200 ms. These results enable continuous in-transit monitoring of concrete workability and lay a practical foundation for data-driven operational improvements in construction logistics. Full article

Heart failure, a significant global health burden, is divided into heart failure with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF), characterized by systolic dysfunction and diastolic stiffness, respectively. While HFrEF benefits from pharmacological and device-based therapies, HFpEF lacks effective treatments, with both conditions leading to high rehospitalization rates and reduced quality of life, especially in older adults with comorbidities. This review explores the role of artificial intelligence (AI) in advancing autonomic neuromodulation for heart failure management. AI enhances patient selection, optimizes stimulation strategies, and enables adaptive, closed-loop systems. In HFrEF, vagus nerve stimulation and baroreflex activation therapy improve functional status and biomarkers, while AI-driven models adjust stimulation dynamically based on physiological feedback. In HFpEF, AI aids in deep phenotyping to identify responsive subgroups for neuromodulatory interventions. Clinical tools support remote monitoring, risk assessment, and symptom detection. However, challenges like data integration, ethical oversight, and clinical adoption limit real-world application. Algorithm transparency, bias minimization, and equitable access are critical for success. Interdisciplinary collaboration and ethical innovation are essential to develop personalized, data-driven, patient-centered heart failure treatment strategies through AI-guided neuromodulation. Full article

The construction industry faces significant challenges due to the physically demanding and hazardous nature of tasks such as manual filling of expansion joints with mastic. Automating mastic filling presents additional difficulties due to the variability of mastic density with temperature, which creates a constantly changing environment that requires adaptive control strategies to ensure consistent application quality. This pilot study focuses on testing a new human–robot collaborative approach for automating the mastic application in concrete expansion joints. The system learns the task from demonstrations performed by expert construction operators teleoperating the robot. This study evaluates the usability, efficiency, and adoption of robotic assistance in joint-filling tasks compared to traditional manual methods. The study analyzes execution time and joint quality measurements, psychophysiological signal analysis, and post-task user feedback. This multi-source approach enables a comprehensive assessment of task performance and both objective and subjective evaluations of technology acceptance. The findings underscore the effectiveness of automated systems in improving safety and productivity on construction sites, while also identifying key areas for technological improvement. Full article

Aiming at the weakness of “two kinds of thinking; lack of integration” between Chinese culture and green technology in the field of urban and rural construction, as well as the high-quality construction needs of China’s new urbanization, using system thinking, genetic factors, and iteration theory, the components of genetic and variation factors and the integrative and iterative mechanisms of Chinese culture and green technology were studied. Chinese culture is rich in humanistic and green science and technology feelings of “reciprocity between heaven and mankind” and “matching nature”, believing that the ways of science and technology is inherently consistent with the ways of nature and man. Chinese culture endorses green science and technology with humanity and soul, and green science and technology promote the evolution and rebirth of Chinese culture. The genetic factors of Chinese culture and the variation factors of green science and technology constitute the inheritance and renewal system of integration, the unity of opposites, and the coordination of conflicts between them, deducing the integrated architectural design mechanism in which science and technology are “culturized” and culture is “technologized”. The iterative mechanism of mathematical operation is the logical thinking of architectural design in the repeated feedback process of cultural and scientific spatial graphics. It approximates the design thinking law of design conditions and requirements through a cyclic iterative scaling operation and can use computer-aided iterative design. According to the function, level, and structural relationship between Chinese culture space graphics and green science and technology space graphics, the iterative mechanism of collocation accumulation is the iterative design law of level accumulation and optimized collocation. There are two kinds of deduction paths, forward and reverse fusion, in the iteration–fusion design mechanism, which form the integration and iteration logic of mutual carrier and object. Full article

This study conducts a simulation-based approach to improve microwave (MW) convective drying using a temperature-responsive pulse ratio (TRPR) method. Traditional fixed-time pulse ratio (TimePR) techniques often result in uneven heating and reduced product quality due to uncontrolled temperature spikes. To address this, a physics-based model was developed using COMSOL Multiphysics 6.3, executed on a high-performance computing (HPC) platform. The TRPR algorithm dynamically adjusts MW ON/OFF cycles based on internal temperature feedback, maintaining the maximum point temperature below a critical threshold of 75 °C. The model geometry, food materials (apple) properties, and boundary conditions were defined to reflect realistic drying scenarios. Simulation results show that TRPR significantly improves temperature and moisture uniformity across the sample. The TRPR method showed superior thermal stability over time-based regulation, maintaining a lower maximum COV of 0.026 compared to 0.045. These values are also well below the COV range of 0.05–0.26 reported in recent studies. Moreover, the TRPR system maintained a constant microwave energy efficiency of 40.7% across all power levels, outperforming the time-based system, which showed lower and slightly declining efficiency from 36.18% to 36.29%, along with higher energy consumption without proportional thermal or moisture removal benefits. These findings highlight the potential of the temperature-responsive pulse ratio (TRPR) method to enhance drying performance, reduce energy consumption, and improve product quality in microwave-assisted food processing. This approach presents a scalable and adaptable solution for future integration into intelligent drying systems. Full article

Wire Arc Additive Manufacturing holds promise for on-board metal part production in maritime settings, yet its implementation remains limited due to the vibrational instability inherent to shipborne environments. This study addresses this critical technological barrier by analyzing the effects of marine vibrations on process stability and proposing an integrated solution based on adaptive process control, gyrostabilized platforms, and real-time monitoring systems. The research establishes specific technical requirements for WAAM instrumentation under maritime conditions and evaluates the capabilities and limitations of existing hardware and software tools. A set of engineering recommendations is presented for improving digital modeling, thermal–mechanical monitoring, and feedback control systems. Additionally, the study highlights material-related challenges by examining the influence of alloy properties on print quality under dynamic loads. The proposed approach enhances WAAM process resilience, laying the groundwork for reliable, high-quality additive manufacturing at sea. These findings are particularly relevant to shipboard maintenance, repair, and remote fabrication tasks, marking a significant step toward the industrial adoption of WAAM in marine engineering. Full article

Background/Objectives: Hypodermoclysis has gained increasing recognition as a safe, effective, and minimally invasive method for administering medication and fluids in palliative care. Despite its advantages, its adoption remains limited, primarily due to a lack of structured training resources for healthcare professionals. This study aimed to develop and validate an educational tool for training clinical nurses in hypodermoclysis administration in palliative care. Methods: This is a methodological study with a multi-methods approach. Study development involved a needs assessment with 48 professionals, a literature review, and the creation of a manual enriched with visual aids. Results: The material was validated by expert judges, technical reviewers, and the target audience. Organized into 21 chapters, the manual comprehensively addresses technical, theoretical, and ethical dimensions of the practice. Content validation by 14 experts yielded an outstanding global Content Validity Index (CVI) of 0.95. An independent evaluation of visual design by four communication specialists produced consistently high scores (91–96%), classifying the material as “superior” in quality. Feedback from target users (12 nurses) highlighted the manual’s clarity, applicability, and relevance. All constructive suggestions were incorporated into the final version. Conclusions: The resulting manual demonstrates strong validity as an educational resource, with significant potential to standardize and enhance hypodermoclysis training in palliative nursing, promoting both safety and humanized care. Full article