Search Results (14,283)

Being able to accurately identify litter in a marine environment is crucial to cleaning up our seas and oceans. Research into object detection techniques to support this identification has been underway for over two decades. However, there have been substantial advancements in the past five years due to the implementation of deep learning techniques. Following the PRISMA-ScR guidelines, we provide an in-depth summary and analysis of recent and significant research contributions to the object detection of macro marine debris. From cross-referencing the results of the literature review, we deduce that there is currently no benchmarked framework for evaluating and comparing computer vision techniques for marine environments. Subsequently, we use the results from our analysis to provide a suggested checklist for future researchers in this field. Furthermore, many of the respected researchers in this field have advocated for a comprehensive database of underwater debris to support research developments in intelligent object detection and identification. Full article

The exhaust duct of aero-engine exhibits complex vibration response characteristics under the influence of temperature fields and vibration loads. Taking the non-axisymmetric exhaust duct of turboshaft engine as the object of study, a finite element model of the exhaust duct was established using three-dimensional finite element analysis methods to analyze the thermal modal and random vibration response characteristics under axial loading for large thin-walled non-axisymmetric exhaust ducts. The simulation analysis method was validated through thermal vibration experiments on the scaled model. In a thermal environment, the shape of the power spectral density curves for displacement and stress of the exhaust duct remains largely unchanged in the low-frequency range; however, the response frequencies exhibit a significant forward shift. When subjected to Y-axial loading, the amplitude of the X- and Z-direction displacement response at 1st order (12.96 Hz) and the stress response at 6th order (30.92 Hz) significantly increase. Random vibration loads excite multiple modes of the exhaust duct, with lower-order modes being more easily stimulated. When subjected to X- and Z-axial loading, 1st order (12.96 Hz) has the greatest impact on the X- and Z-direction displacement responses, while 2nd order (16.93 Hz) and 13th order (82.79 Hz) frequencies have the greatest impact on the displacement response in the Y-direction and equivalent stress response. When subjected to Y-axial loading, the 5th order (22.35 Hz) and 12th order (81.69 Hz) modes have the most significant effects on the displacement responses in the X, Y, and Z directions and equivalent stress responses. Attention to these orders is essential during the design process, along with implementing certain stiffness reinforcement measures to reduce response amplitudes. Full article

Digital Beamforming SAR (DBF-SAR) provides high-resolution wide-swath imaging capability, yet it is affected by inter-channel amplitude, phase and time-delay errors induced by temperature variations and random error factors. Since all elevation channel data are weighted and summed by the DBF module in real time, conventional record-then-compensate approaches cannot meet real-time processing requirements. To resolve the problem, a real-time calibration architecture for Intermediate Frequency DBF (IFDBF) is presented in this paper. The Field-Programmable Gate Array (FPGA) implementation estimates amplitude errors through simple summation, time-delay errors via a simple counter, and phase errors via single-bin Discrete-Time Fourier Transform (DTFT). The time-delay and phase error information are converted into single-tone frequency components through Dechirp processing. The proposed method deliberately employs a reduced-length DTFT implementation to achieve enhanced delay estimation range adaptability. The method completes calibration within tens of PRIs (under 1 s). The proposed method is analyzed and validated through a spaceborne simulation and X-band 16-channel DBF-SAR experiments. Full article

In response to the growing urban–rural dichotomy and escalating human–land conflicts in rural China, this study investigates the role of soundscapes as emotional mediators to enhance environmental satisfaction and foster sustainable human–land symbiosis. To address this need, we carried out a series of systematic field surveys at five representative traditional villages in a major provincial capital city in China, and we implemented a comprehensive questionnaire and surveyed 524 residents about their perceptions of sound, land affection, and environment. We employed a mixed-methods approach combining questionnaire surveys, association rule mining (ARM), and structural equation modeling (SEM) to explore the ‘sound–land–environment’ interaction chain. ARM analysis identified strong associations among tour guide narratives, local dialects, natural sounds (e.g., rustling leaves, birdsong), and tourist-generated sounds (support = 50%, confidence = 78%, lift = 1.33). SEM results revealed that soundscapes significantly and positively influence land dependence (β = 0.952, p < 0.001) and land rootedness (β = 1.812, p < 0.001), which in turn jointly affect environmental satisfaction (β = –0.192, p = 0.027) through a chain mediation pathway. These findings suggest that optimizing rural soundscapes can strengthen emotional bonds between people and land, thereby enhancing environmental satisfaction and promoting performance of sustainable human–land symbiosis. The study contributes theoretically by elucidating the emotional mechanisms linking soundscapes to human–land relationships and offers insights for incorporating soundscape considerations into village planning and developing policies to cultivate land attachment, supporting the sustainable development of traditional villages. Full article

Understanding the growth dynamics and ecological constraints of Porphyra spp. is essential for optimizing sustainable seaweed aquaculture. However, most existing models lack physiological detail and exhibit limited performance under variable environmental conditions. This study developed a mechanistic Dynamic Energy Budget (DEB) model to simulate structural biomass accumulation, carbon and nitrogen reserve dynamics, and blade area expansion of Porphyra under natural environmental conditions in Korean coastal waters. The model incorporates temperature, irradiance, and nutrient availability (NO₃⁻ and CO₂) as environmental drivers and was implemented using a forward difference numerical scheme. Field data from Beein Bay were used for model calibration and validation. Simulations showed good agreement with the observed biomass, reserve content, and blade area, with root-mean-square error (RMSE) typically within ±10%. Sensitivity analysis identified temperature-adjusted carbon assimilation and nitrogen uptake as the primary drivers of growth. The model was further used to estimate dynamic carrying capacity, revealing seasonal thresholds for sustainable biomass under current farming practices. Although limitations remain—such as the exclusion of reproductive allocation and tissue loss—the results demonstrate that DEB theory provides a robust framework for modeling Porphyra aquaculture. This approach supports scenario testing, spatial planning, and production forecasting, and it is adaptable for ecosystem-based management including integrated multi-trophic aquaculture (IMTA) and climate adaptation strategies. Full article

Summer Youth Employment Programs (SYEPs) operate in most major U.S. cities and are known to build social–emotional and job skills in youth while reducing crime. Integrating STEM learning and summer employment offers a promising way to increase youth engagement in STEM—and allow leaders to access funding not typically used for education. Using a connected learning framework, we examined how STEM-focused SYEPs support STEM pathways, the practices they implement, and their connections with schools. Our study explored 10 diverse STEM programs (e.g., robotics, renewable energy, coding) within a citywide employment initiative in summer 2015. Through 22 staff interviews and focus groups with 59 youth, we found that these programs provided meaningful and engaging STEM experiences. They combined interest-driven exploration with hands-on, real-world learning in supportive environments. Many included mentors from groups underrepresented in STEM fields. While collaboration with schools was generally limited to recruitment and shared facilities, opportunities for deeper partnerships were evident. Our findings led to a list of ten promising practices for STEM-focused SYEPs. This study underscores the importance of lifelong, lifewide, and connected approaches to STEM learning through summer employment initiatives. Full article

Accurately quantifying corrosion damage in reinforced concrete (RC) beams is a significant challenge for structural health monitoring. This study introduces a novel damage identification method that integrates the Sparrow Search Algorithm (SSA)-optimized Extreme Learning Machine (ELM) to address this issue. By utilizing dynamic characteristics, including natural frequencies and mode shapes, as input features, the model predicts three critical damage indicators: the mass corrosion ratio (η_s), flexural capacity reduction factor (α), and flexural stiffness reduction factor (β). Validation through ABAQUS finite element simulations demonstrated the superior performance of the SSA-ELM approach compared to conventional ELM, achieving a 60–70% reduction in mean square error (MSE). Specifically, the MSE for η_s decreased from 2.1062 to 0.3174. The experimental validation conducted on seven RC beams with corrosion levels ranging from 0% to 14.1% confirmed the method’s reliability, with prediction errors for α and β ranging from 5 to 10%. This represents a 50% improvement in accuracy compared to conventional ELM, which exhibited errors in the range of 9–20%. SSA-ELM is a novel and more effective solution to the challenges (e.g., early convergence and convergence speed) faced by existing optimized ELM methods (especially GWO-ELM and GA-ELM). Furthermore, the practical implementation of the proposed framework includes a MATLAB R2024a-based graphical user interface (GUI) with Docker containerization, enabling efficient field deployment for structural assessment. Overall, this study establishes SSA-ELM as a promising tool for post-corrosion safety evaluation of RC structures. Full article

With the intensification of the global energy crisis, green, low-carbon, and environmentally friendly biomass materials have become the focus of research. Among them, biomass-derived carbon dots (B-CDs), a novel class of sustainable zero-dimensional carbon nanomaterials, attract significant interest due to their environmental friendliness, low toxicity, and unique optical properties. Research findings indicate that B-CDs, utilizing biomass materials as carbon sources, demonstrate significant potential in numerous application fields through structural design and photo-functionalization. However, the underlying mechanisms and formation processes of B-CDs remain inadequately elucidated, and systematic summarization still requires further refinement. Therefore, this review systematically summarizes the synthesis methods, precursor structures, formation mechanisms, luminescent properties, and prevailing applications of B-CDs, with a particular emphasis on recent advances in their use for sensing, anti-counterfeiting, bioimaging, and optronics. In addition, the challenges encountered in performance-oriented controllable preparation and large-scale production were also clarified. This comprehensive review provides a theoretical foundation for further research and multidisciplinary applications of B-CDs, thereby contributing to promoting large-scale commercialization and industrial implementation. Full article

This study explores the extent to which civil engineers and architects in Spain perceive sustainable leadership practices in their organizations. The main aim is to understand how leadership approaches in the built environment sector can be aligned with long-term, ethical, and stakeholder-focused principles. A total of 200 middle and senior managers participated in a survey that used a Likert-scale questionnaire that was designed to assess key elements of sustainable leadership; the various responses were analyzed in order to determine the prevalence of Honeybee-type (sustainable) versus Locust-type (short-term, exploitative) leadership traits. The findings showed that while participants generally endorse sustainable leadership values, especially regarding environmental responsibility and employee well-being, many organizations still exhibit practices associated with short-term priorities, fragmented communication, and resistance to innovation. A hybrid leadership approach seems to be quite common, considering that it combines sustainable intentions with structural or cultural barriers that limit full implementation. This study contributes to the sustainable leadership literature by providing sector-specific insights from civil engineering and architecture; it also suggests the need for organizational strategies that might strengthen internal communication, prioritize workforce development and facilitate a cultural shift toward sustainability. All the findings have serious practical implications for leadership development and for human resource practices in high-pressure technical fields. Full article

The influence of stress state multiaxiality on the propagation velocity of Rayleigh waves is explored through a detailed numerical study. The study uses the Murnaghan model to capture nonlinear elastodynamics in the material behavior, necessitating consideration of third-order elastic constants. Various invariant stress variables are compared for their suitability to describe the relationship between multiaxiality of the stress state and change in propagation velocity. The results are interpreted physically and provide information about the interaction between stress state multiaxiality and wave propagation. Finite element simulations are conducted using Abaqus/Explicit, with the material behavior implemented via a VUMAT user subroutine. Transformation relations for rotated axes are used to understand how the stress state affects the directional dependence of wave velocity. This study offers valuable insights into the complex relationship between stress state and Rayleigh wave propagation, essential for applications in reconstruction of residual stress fields. The results show that the change in propagation velocity is best described by models that include the principal stresses. Different stress states lead to different distortion of the propagation front. The numerical results are compared and validated with the semianalytical solution. The results show good agreement. Full article

Sunflower seed hulls and meal are among the most abundant by-products of the food industry. They are an example of waste and, at the same time, a plentiful biomass that cannot be utilized directly in human and animal diets due to their hard digestibility and low nutritional value. Besides their main compounds—carbohydrates, lipids, and proteins—they possess valuable constituents such as vitamins, minerals, and especially phenolics that contribute to their antioxidant capacity. Numerous benefits can be retrieved from such by-products. Since sunflower meal and seed hulls are cheap renewable sources of beneficial substances, their potential in relation to the improvement in our daily life needs to be studied. This is the reason why, in recent years, there has been such a serious interest in their utilization and valorization towards the concept of a circular bio-based economy and process sustainability. This review aims to trace the potential applications and implementation of sunflower meal and hulls in the different fields of industry and environmental protection strategies. Full article

This paper presents a physics-informed digital twin designed for real-time thermal monitoring and visualization of a metallic plate. The system comprises a physical layer consisting of an aluminum plate equipped with thermistors to capture boundary conditions, a computational layer that implements the steady-state Laplace equation using the finite difference method, and an embedded execution framework deployed on a microcontroller that utilizes Direct Memory Access-driven ADC for efficient concurrent acquisition. The computed thermal field is transmitted through a serial interface and displayed in real time using a Python-based visualization interface. The Steinhart–Hart model was used to experimentally characterize the sensors, ensuring accuracy in the boundary condition acquisition. While the current formulation is restricted to steady-state conditions, it enables accurate spatial reconstructions with acceptable error margins and demonstrates operational concurrency with the physical system. The compact and modular architecture allows adaptation to other physical domains governed by elliptic PDEs, making it suitable for educational applications, diagnostic prototyping, and embedded edge deployments. Full article

The Industry Foundation Classes (IFC) standard has been widely implemented as an open data standard in the architecture, engineering, and construction (AEC) industry. IFC enables robust information representation and facilitates cross-disciplinary collaboration, serving as a critical data foundation for future intelligent development in the engineering field. However, current IFC research topics remain relatively fragmented, and there are still several challenges in the practical implementation of IFC. Therefore, this paper provides a comprehensive review of IFC research over the past two decades. The research progress is systematically summarized in three key areas: IFC applications, interoperability, and data processing. Through this review, the limitations in IFC development have been discussed, and future research directions are proposed. This paper aims to provide a comprehensive perspective on addressing data-related challenges in the AEC industry and contributes to facilitating the deep integration of emerging technologies such as artificial intelligence within the AEC domain. Full article

The shape and position information of towed streamers is crucial for both implementing marine seismic exploration operations and analyzing exploration data. Streamer positioning accuracy directly impacts the quality and reliability of seismic imaging. Existing polynomial curve models exhibit deviations between the calculated and actual shapes during streamer turning. This paper proposes a segmented fitting positioning model based on spline curves. It is mathematically rigorous and applicable to complex scenarios. First, the specific function expression of the spline curve model is constructed. Then, using a cubic spline as an example, the segmented fitting method is explained, incorporating smoothness constraints at the connection points. The error equations for positioning observations and the calculation processes for curve parameters and hydrophone coordinates are derived. Finally, the model is verified through simulations and field tests. The experimental results show that, compared with the polynomial curve model, the spline curve model improves positioning accuracy by 47.1% in simulations involving six streamers and by 20.0% and 35.0% in field tests with six and ten streamers, respectively. In straight scenarios, both models perform similarly. Thus, the spline model can effectively reduce the modeling errors of the polynomial curve model under high-curvature conditions. Full article

The discovery of penicillin led to remarkable progress in the treatment of diseases and far-reaching advancements in novel antibiotics’ development and use. However, the uncontrolled use and abuse of antibiotics in subsequent years have led to the emergence of the antimicrobial resistance (AMR) crisis, which now threatens modern medicine. There is an increasing number of emerging and reemerging infectious diseases, which have worsened the state of AMR and pose a serious threat to global health. The World Health Organization (WHO) reports the inadequacy of the drug development pipeline to meet the needs of the pharmaceutical sector in the face of AMR, and this poses a significant challenge in the treatment of diseases. Natural products (NPs) represent a promising group of antibiotic alternatives that can potentially mitigate AMR, as they bypass the pharmacodynamics of traditional antibiotics, thereby making them immune to the mechanisms of AMR. NPs, including plant derivatives, bacteriophages, metals, antimicrobial peptides, enzymes, and immune modulators, as monotherapies or in synergism with existing antibiotics, are gaining attention in a bid to reconstruct the antibiotic pipeline. Harnessing these as antimicrobial agents to curb AMR can help to provide sufficient defence against these infectious pathogens. The current review provides a comprehensive overview of the state of AMR and the potential of the above-mentioned antibiotic alternatives. Additionally, we discuss progress made and research breakthroughs in the application of these alternative therapies in humans, exploring findings from clinical trials and experimental models. The review further evaluates the advancement in technology, interdisciplinary approaches to the formulation and utilisation of NPs, and collaborations in alternative drug development. The research gaps present in this ever-evolving field are highlighted and evaluated together with regulatory issues, safety concerns, and technical difficulties in implementation. Full article