Search Results (3,135)

Sustainability education has become a global priority in educational systems, aiming to equip learners with the knowledge, values, and skills necessary to address complex environmental and social challenges. This study specifically aims to understand the role of science education in promoting students’ awareness of sustainability and their understanding of the interconnected relationship between humans and the environment, based on the perceptions and practices of primary science teachers in Al-Ahsa, Saudi Arabia. A qualitative approach was utilized, which included semi-structured interviews complemented by classroom observations as primary data collection instruments. The targeted participants comprised a purposive sample consisting of forty-nine primary-level science instructors from the Al-Ahsa district, located in eastern Saudi Arabia. Emergent concepts from open and axial coding processes by using grounded theory were developed with the gathered data. Based on the findings, teachers perceive science teaching not only as knowledge delivery but as an opportunity to cultivate critical thinking and nurture eco-friendly actions among pupils. Classroom practices that underscore environmental values and principles of sustainability foster a transformative view of the teacher’s role beyond traditional boundaries. The data also highlighted classroom practices that integrate environmental values and sustainability principles, reflecting a transformative perspective on the teacher’s educational role. Full article

Jujubes have a beautiful taste, and high nutritional and economic value. The planting area of dwarf and densely planted jujubes is large and shows an increasing trend; however, the mechanization level and efficiency of fresh jujube harvesting are low. For this reason, our research group conducted a study on mechanical harvesting technology for fresh jujubes. A pneumatic jujube harvester was designed. This harvester is composed of a self-regulating picking mechanism, a telescopic conveying pipe, a negative pressure generator, a cleaning mechanism, a double-chamber collection box, a single-door shell, a control assembly, a generator, a towing mobile chassis, etc. During the harvest, the fresh jujubes on the branches are picked under the combined effect of the flexible squeezing of the picking roller and the suction force of the negative pressure air flow. They then enter the cleaning mechanism through the telescopic conveying pipe. Under the combined effect of the upper and lower baffles of the cleaning mechanism and the negative-pressure air flow, the fresh jujubes are separated from impurities such as jujube leaves and branches. The clean fresh jujubes fall into the collection box. We considered the damage rate of fresh jujubes, impurity rate, leakage rate, and harvesting efficiency as the indexes, and the negative-pressure suction wind speed, picking roller rotational speed, and the inclination angle of the upper and lower baffles of the cleaning and selection machinery as the test factors, and carried out the harvesting test of fresh jujubes. The test results show that when the negative-pressure suction wind speed was 25 m/s, the picking roller rotational speed was 31 r/min, and the inclination angles of the upper and lower baffle plates for cleaning and selecting were −19° and 19.5°, respectively, the breakage rate of fresh jujube harvesting was 0.90%, the rate of impurity was 1.54%, the rate of leakage was 2.59%, and the efficiency of harvesting was 73.37 kg/h, realizing the high-efficiency and low-loss harvesting of fresh jujubes. This study provides a reference for the research and development of fresh jujube mechanical harvesting technology and equipment. Full article

In the globalizing world, raising individuals equipped with 21st-century skills is very important for the economic development of countries. Educational practices that support 21st-century skills are also gaining importance. In this context, STEM education, an interdisciplinary educational practice that develops 21st-century skills, emerges. STEM education aims to contribute to sustainable development by training individuals equipped with 21st-century skills and competencies. In a globalizing world, countries must set sustainable development goals to gain a foothold in the global market. In today’s world, where artificial intelligence also shows itself in every area of human life, it is possible to discuss the importance of artificial intelligence-supported STEM education. This study aims to reveal the educational needs of primary school teachers regarding artificial intelligence-supported STEM education. The study was conducted according to the phenomenological design, and the data were collected using a semi-structured interview form and literature review techniques. The thematic analysis method was used in the analysis of the data. According to the research results obtained from the findings of the study, teachers need training on 21st-century skills, interdisciplinary thinking, technology integration into courses, and artificial intelligence practices in courses to develop their knowledge and skills in the context of artificial intelligence-supported STEM education. Full article

China is one of the countries severely affected by earthquakes, making precise and timely identification of earthquake precursors essential for reducing casualties and property damage. A novel method is proposed that combines a rock acoustic emission (AE) detection technique with deep learning methods to facilitate real-time monitoring and advance earthquake precursor detection. The AE equipment and seismometers were installed in a granite tunnel 150 m deep in the mountains of eastern Guangdong, China, allowing for the collection of experimental data on the correlation between rock AE and seismic activity. The deep learning model uses features from rock AE time series, including AE events, rate, frequency, and amplitude, as inputs, and estimates the likelihood of seismic events as the output. Precursor features are extracted to create the AE and seismic dataset, and three deep learning models are trained using neural networks, with validation and testing. The results show that after 1000 training cycles, the deep learning model achieves an accuracy of 98.7% on the validation set. On the test set, it reaches a recognition accuracy of 97.6%, with a recall rate of 99.6% and an F₁ score of 0.975. Additionally, it successfully identified the two biggest seismic events during the monitoring period, confirming its effectiveness in practical applications. Compared to traditional analysis methods, the deep learning model can automatically process and analyse recorded massive AE data, enabling real-time monitoring of seismic events and timely earthquake warning in the future. This study serves as a valuable reference for earthquake disaster prevention and intelligent early warning. Full article

This study investigates the foundational causes of unsustainability that obstruct efforts to address global challenges such as climate change, environmental degradation, water crises, and public health deterioration. Using qualitative research with in-depth expert interviews from education, environmental studies, and business, it finds that these global challenges, while visible on the surface, are deeply rooted in worldviews that shape human behavior, societal structures, and policies. Building on this insight, the thematic analysis manifests three interrelated systemic paradigms as the fundamental drivers of unsustainability: a crisis of wholeness, reflected in fragmented identities and collective disorientation; a disconnection from nature, shaped by human-centered perspectives; and the influence of dominant political-economic systems which prioritize growth logics over ecological and social concerns. These paradigms underlie both structural and cognitive barriers to systemic transformation, which influence the design and implementation of education for sustainability. By clarifying a body of knowledge and systemic paradigms regarding unsustainability, this paper calls for transformative education that promotes a holistic, value-based approach, eco-empathy, and critical thinking, aiming to equip future generations with the tools to challenge and transform unsustainable systems. Full article

Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central to our approach is a prioritised action–selection mechanism that facilitates efficient early-stage learning via behaviour cloning (BC), while enabling scalable exploration through reinforcement learning (RL). A high-level decision neural network (DNN) selects between grasping and pushing actions, and two low-level action neural networks (ANNs) execute the selected primitive. The DNN is trained with RL, while the ANNs follow a hybrid learning scheme combining BC and RL. Notably, we introduce an automated demonstration generator based on oriented bounding boxes, eliminating the need for manual data collection and enabling precise, reproducible BC training signals. We evaluate our method on a challenging manipulation task involving five closely packed cubic objects. Our system achieves a completion rate (CR) of 100%, an average grasping success (AGS) of 93.1% per completion, and only 7.8 average decisions taken for completion (DTC). Comparative analysis against three baselines—a grasping-only policy, a fixed grasp-then-push sequence, and a cloned demonstration policy—highlights the necessity of dynamic decision making and the efficiency of our hierarchical design. In particular, the baselines yield lower AGS (86.6%) and higher DTC (10.6 and 11.4) scores, underscoring the advantages of content-aware, closed-loop control. These results demonstrate that our architecture supports robust, adaptive manipulation and scalable learning, offering a promising direction for autonomous skill coordination in complex environments. Full article

Recently, deep learning algorithms have been increasingly applied in construction for activity recognition, particularly for excavators, to automate processes and enhance safety and productivity through continuous monitoring of earthmoving activities. These deep learning algorithms analyze construction videos to classify excavator activities for earthmoving purposes. However, previous studies have solely focused on single-source external videos, which limits the activity recognition capabilities of the deep learning algorithm. This paper introduces a novel multi-modal deep learning-based methodology for recognizing excavator activities, utilizing multi-stream input data. It processes point clouds and RGB images using the two-stream long short-term memory convolutional neural network (CNN-LSTM) method to extract spatiotemporal features, enabling the recognition of excavator activities. A comprehensive dataset comprising 495,000 video frames of synchronized RGB and point cloud data was collected across multiple construction sites under varying conditions. The dataset encompasses five key excavator activities: Approach, Digging, Dumping, Idle, and Leveling. To assess the effectiveness of the proposed method, the performance of the two-stream CNN-LSTM architecture is compared with that of single-stream CNN-LSTM models on the same RGB and point cloud datasets, separately. The results demonstrate that the proposed multi-stream approach achieved an accuracy of 94.67%, outperforming existing state-of-the-art single-stream models, which achieved 90.67% accuracy for the RGB-based model and 92.00% for the point cloud-based model. These findings underscore the potential of the proposed activity recognition method, making it highly effective for automatic real-time monitoring of excavator activities, thereby laying the groundwork for future integration into digital twin systems for proactive maintenance and intelligent equipment management. Full article

In December 2023, infections of western equine encephalitis virus (WEEV) within Argentina were reported to the World Health Organization (WHO). By April 2024, more than 250 human infections, 12 of which were fatal, and 2500 equine infections were identified in South America. Laboratory diagnosis and surveillance in affected countries were hindered by a lack of facilities equipped with BSL-3 laboratories, as confirmatory serodiagnosis for WEEV requires live virus in the plaque reduction neutralization test (PRNT). To expand serodiagnosis for WEEV in the Americas, we developed a virus chimera composed of vesicular stomatitis virus (VSV) engineered to display the E2-E1 glycoproteins of WEEV (VSV/WEEV) in place of the VSV glycoprotein (G). PRNT₉₀ and IC₉₀ values of parental WEEV and VSV/WEEV were analogous using sera collected from mice, horses, and chickens. VSV/WEEV rapidly formed plaques with clear borders and reduced the assay readout time by approximately 8 h compared to the parental virus. Overall, we demonstrate that chimeric VSV/WEEV is a suitable surrogate for WEEV in a diagnostic PRNT. Use of chimeric VSV/WEEV in place of authentic WEEV will dramatically expand testing capacity by enabling PRNTs to be performed at BSL-2 containment, while simultaneously decreasing the health risk to testing personnel. Full article

With the rapid technological advancements in wearable devices, motion and health management have significantly improved, enabling the measurement of various biometric data with compact equipment. Our research focuses on motion measurement but, in general, full-body motion estimation requires motion capture systems or multiple inertial sensors, making it necessary to directly measure movement itself. In this study, we propose estimating full-body posture using inverse kinematics based on trunk posture and limb-end information collected through wearable devices. To enhance estimation accuracy in this underdetermined problem, we employ Physics-Informed Neural Networks (PINNs), which efficiently learn using physical laws as a loss function, along with a high-precision inverse kinematics model of a digital human. Through this approach, we enable high-accuracy full-body posture estimation even with wearable devices in underdetermined scenarios. Full article

The stable operation of electrical equipment is critical for industrial safety, yet traditional anomaly detection methods often suffer from limitations, such as high resource demands, dependency on expert knowledge, and lack of real-world capabilities. To address these challenges, this article proposes an unsupervised anomaly detection method for electrical equipment, utilizing audio latent representation and a parallel attention mechanism. The framework employs an autoencoder to extract low-dimensional features from audio signals and introduces a phase-aware parallel attention block to dynamically weight these features for an improved anomaly sensitivity. With adversarial training and a dual-encoding mechanism, the proposed method demonstrates robust performance in complex scenarios. Using public datasets (MIMII and ToyADMOS) and our collected real-world wind turbine data, it achieves high AUC scores, surpassing the best baselines, which demonstrates our framework design is suitable for industrial applications. Full article

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0.542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

Heritage Building Information Modeling (HBIM) has emerged as a key tool in advancing heritage conservation and sustainable management. Preceding reviews had typically concentrated on specific technical aspects but did not provide sufficient bibliometric analysis. This study aims to integrate existing HBIM research to identify key research patterns, emerging trends, and forecast future directions. A total of 1516 documents were initially retrieved from the Web of Science Core Collection using targeted search terms. Following a relevance screening, 1175 documents were related to the topic. CiteSpace 6.4.R1, VOSviewer 1.6.20, and Bibliometrix 4.1, three bibliometric tools, were employed to conduct both quantitative and qualitative assessments. The results show three historical phases of HBIM, identify core journals, influential authors, and leading regions, and extract six major keyword clusters: risk assessment, data acquisition, semantic annotation, digital twins, and energy and equipment management. Nine co-citation clusters further outline the foundational literature in the field. The results highlight growing scholarly interest in workflow integration and digital twin applications. Future projections emphasize the transformative potential of artificial intelligence in HBIM, while also recognizing critical implementation barriers, particularly in developing countries and resource-constrained contexts. This study provides a comprehensive and systematic framework for HBIM research, offering valuable insights for scholars, practitioners, and policymakers involved in heritage preservation and digital management. Full article

In the field of tunnel engineering, the precise determination of the spatial coordinates of karst water-rich pipelines represents a critical area of research for disaster prevention and control. Traditional detection methods often exhibit limitations, including inadequate accuracy and low efficiency, which can significantly compromise the safety and quality of tunnel construction. To enhance the accuracy of the spatial coordinate estimation for karst water-rich pipelines, this study introduces a novel method grounded in a strapdown inertial navigation system (SINS). This approach involves the deployment of sensing equipment within the karst water-rich pipeline to gather motion state data. Consequently, it provides spatial coordinate information pertinent to the karst water-rich pipeline within the tunnel site, thereby augmenting the completeness and accuracy of the spatial coordinate estimation results compared to conventional detection methods. This study employs ESKF filtering to process the data collected by the SINS, ensuring the robustness and accuracy of the data. The research integrates theoretical analysis, model testing, and numerical simulation. It systematically examines the operational principles and error characteristics associated with the SINS, develops an error model for this technology, and employs a comparative selection method to design the spatial coordinate sensing equipment based on the SINS. Full article

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software—investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project “Accelerometer Measurements in Rail Passenger Transport Vehicles”, pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral (x) and vertical (z) accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula—accounting for Earth’s curvature—and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s², and extreme values approaching 1 m/s². The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app’s data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors. Full article

A particularly important activity carried out by railway infrastructure managers to maintain railway devices in full working order is the diagnostic process. It increases the level of railway safety. The diagnostic process involves collecting information about the equipment through inspections, tests, functional trials, parameter measurements, and analysis of the working environment, followed by comparing the obtained information with the required parameters or permissible conditions. This activity also enables the formulation of a technical diagnosis regarding the current ability of the devices to perform its intended functions, taking into account the impact of its technical condition on railway traffic safety. This is especially important in the case of railway traffic control devices, as these devices are largely responsible for ensuring railway traffic safety. The collection of data on the condition of railway traffic control devices in the form of Big Data sets and diagnostic inference is an effective factor in making operational decisions for such devices. It enables the acquisition of complete information about the actual course of the exploitation process and allows for obtaining reliable information necessary to manage this process, particularly in the areas of diagnostics forecasting of devices conditions, renewal, and organization of maintenance and repair facilities. To support this, a service data acquisition and analysis system for railway traffic control devices (SADEK) was developed. This system can serve as a software platform for maintenance needs in the railway sector. Full article