Search Results (3,677)

Both topography and wildfire can exert significant influences on ecosystem processes and functions during boreal forest successions. However, their impacts on ecosystem multifunctionality (EMF) remain unclear. A mega-fire burned an area of 8700 hectares in the Great Xing’an Mountains in 2000, creating a wide range of fire severity levels across various topographic positions. This provided a unique opportunity to explore the impacts of mixed-severity fire disturbance in boreal forests. We evaluated the effect pathways of wildfire and topography on aboveground multifunctionality (AEMF), soil multifunctionality (SEMF), and overall multifunctionality (OEMF). We found that high-severity burning resulted in lower AEMF, SEMF, and OEMF relative to low-severity burning. Topographic positions significantly influenced SEMF and OEMF, but not AEMF. Specifically, both lower SEMF and OEMF were observed on south-facing slopes. The structure equation model analysis showed that aspect had exerted strong indirect effects on AEMF, SEMF, and OEMF by affecting soil moisture and regenerated tree density (RTD). Fire severity had indirect negative effects on AEMF and OEMF by reducing RTD and on SEMF by reducing soil bacterial diversity and RTD. Our study elucidates the necessity of considering postfire site environments to better manage forest ecosystems and, in turn, promote the rapid recovery of boreal ecosystem functions. Full article

The present article is devoted to the issue of studying the patterns of displacement of superincumbent rock over panels of a mine obtained using advanced seismic technologies, allowing for the study of the boundaries of caving zones in the depths of rock mass. A seismic exploration has been performed in local areas of Zhomart mine responsible for the development of Zhaman-Aybat cuprous sandstone deposits in Central Kazakhstan at the stage of repeated mining with pulling of previously non-mined ore pillars and superincumbent rock caving. A 2D field seismic exploration has been accomplished, totaling to 8000-line m of seismic lines using seismic shot point. The survey depth varied from 455 m to 625 m. The state-of-the-art technologies of kinematic and dynamic analysis of wavefield have been widely used during data processing and interpretation targeted at identifying anomalies associated with the structural heterogeneity of the pays and rock mass, engaging modern algorithms and mathematical apparatuses of specialized geodata processing systems. The above effort resulted in new data regarding the location and morphology of the reflectors, characterizing geological heterogeneity of the section, zones of smooth rock displacement, and displacement of strata with significant disturbance of the rocks overlying mined-out productive pay. The potential of the application of modern 2D seismic exploration to studying an underworked zone with altered physical and mechanical properties located over an ore deposit has been assessed. The novelty and practical significance of the research lies in the determination of the boundaries of zones of displacement and superincumbent rock caving over the panels obtained using state-of-the-art technologies of seismic exploration. The deliverables may be used to improve the process of recognizing specific types of technogenic heterogeneities in the rock mass, impacting the efficiency and safety of subsurface ore mining, both for localization and mining monitoring. Full article

Laser based-powder bed fusion (LB-PBF) enables fast, efficient, and cost-effective production of high-performing products. While advanced functionalities are often derived from geometric complexity, the capability to tailor material properties also offers significant opportunities for technical innovation across many fields. This study explores the optimization of the LB-PBF process parameters for producing Ti6Al4V titanium alloy parts with controlled porosity. To this end, cuboid and lamellar samples were fabricated by systematically varying laser power, hatch distance, and layer thickness according to a full factorial Design of Experiments, and the resulting specimens were thoroughly characterized by analyzing envelope porosity, surface roughness and waviness, surface morphology, and surface area. A selection of specimens was further examined using small-angle X-ray scattering (SAXS) and wide-angle X-ray scattering (WAXS) to investigate the atomic structure and nanometric porosity of the material. The results demonstrated the possibility to finely control the porosity and surface characteristics of Ti6Al4V within specific LB-PBF process ranges. The pores were found to be mostly closed even for thin walls, while the surface roughness was recognized as the primary factor impacting the surface area. The lamellar samples obtained by exposing single scan tracks showed nearly an order-of-magnitude increase in both surface area and pore volume, thereby laying the groundwork for the production of parts with optimized porosity. Full article

The single-phase titanium ß-alloy Ti10V2Fe3Al (Ti-1023) has been widely used in the aerospace industry due to its unique mechanical properties, which include high fatigue strength and fracture toughness, as well as high corrosion resistance. On the other hand, these unique properties significantly hinder the cutting processes of this material, especially those characterized by a closed machining process area, such as drilling. This paper is devoted to the study of the short hole drilling process of the above-mentioned titanium alloy using direct measurements and numerical modeling. Measurements of the cutting force components in the drilling process and determination of the resultant cutting force and total cutting power were performed. The macro- and microstructure of chips generated during drilling were analyzed, and the dependence of the chip compression ratio and the distance between neighboring segments of serrated chips on cutting speed and drill feed was determined. Experimental studies were supplemented by determining the temperature on the lateral clearance face of the drill’s outer cutting insert in dependence on the cutting modes. For the modeling of the drilling process using the finite element model, the parameters of the triad of component submodels of the numerical model were determined: the machined material model, the model of contact interaction between the tool and the machined material, and the fracture model of the machined material. The determination of these parameters was performed through the DOE sensitivity analysis. The target values for performing this analysis were the total cutting power and the distance between neighboring chip segments. The maximum deviation between the simulated and experimentally determined values of the resulting cutting force is no more than 25%. At the same time, the maximum deviation between the measured values of the temperature on the lateral clearance face of the drill’s outer cutting insert and the corresponding simulated values is 26.1%. Full article

Sugarcane is a high-impact crop used in the majority of global sugar production, with India being the second largest global producer. Understanding the timing and length of sugarcane growth stages is critical to improving the sustainability of sugarcane management. Earth observation (EO) data have been shown to be sensitive to the variation in sugarcane growth, but questions remain as to how to reliably extract sugarcane phenology over wide areas so that this information can be used for effective management. This study develops an automated approach to derive sugarcane growth stages using EO data from Landsat-8 and Sentinel-2 satellite data in the Indian state of Andhra Pradesh. The developed method is then evaluated in the State of Telangana. Normalised difference vegetation index (NDVI) EO data from Landsat-8 and Sentinel-2 were pre-processed to filter out clouds and to harmonise sensor response. Pixel-based cloud filtering was selected over filtering by scene in order to increase the temporal frequency of observations. Harmonising data from two different sensors further increased temporal resolution to 3–6 days (70% of sampled fields). To automate seasonal decomposition, harmonised signals were resampled at 14 days, and low-frequency components, related to seasonal growth, were extracted using a fast Fourier transform. The start and end of each season were extracted from the time series using difference of Gaussian and were compared to assessments based on visual observation for both Unit 1 (R² = 0.72–0.84) and Unit 2 (R² = 0.78–0.82). A trapezoidal growth model was then used to derive crop growth stages from satellite-measured phenology for better crop management information. Automated assessments of the start and the end of mid-season growth stages were compared to visual observations in Unit 1 (R² = 0.56–0.72) and Unit 2 (R² = 0.36–0.79). Outliers were found to result from cloud cover that was not removed by the initial screening as well as multiple crops or harvesting dates within a single field. These results demonstrate that EO time series can be used to automatically determine the growth stages of sugarcane in India over large areas, without the need for prior knowledge of planting and harvest dates, as a tool for improving sustainable production. Full article

Recent studies have highlighted the growing integration of Natural Language Processing (NLP) techniques and Large Language Models (LLMs) in healthcare. These technologies have shown promising outcomes across various healthcare tasks, especially in widely studied languages like English and Chinese. While NLP methods have been extensively researched, LLM applications in healthcare represent a developing area with significant potential. However, the successful implementation of LLMs in healthcare requires careful review and guidance from human experts to ensure accuracy and reliability. Despite their emerging value, research on NLP and LLM applications for Arabic remains limited particularly when compared to other languages. This gap is largely due to challenges like the lack of suitable training datasets, the diversity of Arabic dialects, and the language’s structural complexity. In this study, a panel of medical experts evaluated responses generated by LLMs, including ChatGPT, for Arabic healthcare inquiries, rating their accuracy between 85% and 90%. After fine tuning ChatGPT with data from the Altibbi platform, accuracy improved to a range of 87% to 92%. This study demonstrates the potential of LLMs in addressing Arabic healthcare queries especially in interpreting questions across dialects. It highlights the value of LLMs in enhancing healthcare communication within the Arabic-speaking world and points to a promising area for further research. This work establishes a foundation for optimizing NLP and LLM technologies to achieve greater linguistic and cultural adaptability in global healthcare settings. Full article

The use of components fabricated by laser powder bed fusion (LPBF) requires the development of processing parameters that can produce high-quality material. Manipulating the most commonly identified critical build parameters (e.g., laser power, laser scan speed, and layer thickness) on LPBF equipment can generate acceptable parts for established materials and moderately intricate part geometries. The need to fabricate increasingly complex parts from unique materials drives the limited research into LPBF process control using underutilized parameters, such as atmosphere composition and pressure. As presented in this review, manipulating atmosphere composition and pressure in laser beam welding has been shown to expand processing windows and produce higher-quality welds. The similarities between laser beam welding and laser-based AM processes suggest that this atmosphere control research could be effectively adapted for LPBF, an area that has not been widely explored. Tailoring this research for LPBF has significant potential to reveal novel processing regimes. This review presents the current state of the art in atmosphere research for laser beam welding and LPBF, with a focus on studies exploring cover gas composition and pressure, and concludes with an outlook on future LPBF atmosphere control systems. Full article

This paper presents a highly efficient, low-power, compact mixed-signal sinusoidal current generator (CG) integrated circuit (IC) designed for bioelectrical impedance spectroscopy (BIS) with low total harmonic distortion (THD). The proposed system employs a 9-bit sine wave lookup table (LUT) which is simplified to a 4-bit data stream through a third-order digital delta–sigma modulator (ΔΣM). Unlike conventional analog low-pass filters (LPF), which statically limit bandwidth, the finite impulse response (FIR) filter attenuates high-frequency noise according to the operating frequency, allowing the frequency range of the sinusoidal signal to vary. Additionally, the output of the FIR filter is applied to a 6-bit capacitive digital-to-analog converter (CDAC) with data-weighted averaging (DWA), enabling dynamic capacitor matching and seamless interfacing. The sinusoidal CG IC, fabricated using a 65 nm CMOS process, produces a 5 μA amplitude and operates over a wide frequency range of 0.6 to 20 kHz. This highly synthesizable CG achieves a THD of 0.04%, consumes 19.2 μW of power, and occupies an area of 0.0798 mm². These attributes make the CG IC highly suitable for compact, low-power bio-impedance applications. Full article

Hot surfaces in industrial processes and automotive systems present a remarkable fire hazard. Lubricating oil is a widely used oil in these scenarios. Quantifying the ignition characteristics and flame behavior of lubricating oil on hot surfaces is critical for enhancing fire safety in energy-related applications. This paper utilizes a self-developed experimental platform for the hot surface ignition to systematically conduct combustion tests on lubricating oil with varying volumes at different surface temperatures. Through statistical analysis and image processing, the ignition temperature, flame height, flame propagation velocity, and flame temperature were examined to assess the fire risk of a hot surface ignition. The results demonstrate that the ignition and combustion process of lubricating oil on hot surfaces can be categorized into five stages. The ignition temperature decreases as the oil volume increases. The flame height and flame propagation velocity are positively correlated with the hot surface temperature. The maximum flame height increases with the increase in the oil volumes. When the flame height reaches the maximum value, the flame area is the largest, and the average flame temperature is 1540.30 °C, showing a greater fire risk. When the oil content is 0.2 mL, the flame propagation velocity is the fastest, reaching 3.81 m/s. Meanwhile, the flame is very close to the oil pipe, which may cause a secondary fire. Therefore, hot surface ignition of lubricating oil poses a direct threat to vehicle safety. Full article

This study adapted the mean age of air, a time scale widely utilized in evaluating indoor ventilation, to assess the impact of building layouts on urban ventilation capacity. To distinguish it from its applications in enclosed indoor environments, the adapted index was termed the effective mean age of air (${\overline{\tau }}_E$). Based on an experimentally validated method, computational fluid dynamic (CFD) simulations were performed for parametric studies on four generic parameters that describe urban morphologies, including building height, building density, and variations in the heights or frontal areas of adjacent buildings. At the breathing level (z = 1.7 m), the results indicated three distinct distribution patterns of insufficiently ventilated areas: within recirculation zones behind buildings, in the downstream sections of the main road, or within recirculation zones near lateral facades. The spatial heterogeneity of ventilation capacity was emphasized through the statistical distributions of ${\overline{\tau }}_E$. In most cases, convective transport dominates the purging process for the whole canopy zone, while turbulent transport prevails for the pedestrian zone. Additionally, comparisons with a reference case simulating an open area highlighted the dual effects of buildings on urban ventilation, notably through the enhanced dilution promoted by the helical flows between buildings. This study also serves as a preliminary CFD practice utilizing ${\overline{\tau }}_E$ with the homogenous emission method, and demonstrates its capability for assessing urban ventilation potential in urban planning. Full article

Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure. Full article

Data collection using drones is a widely used practice today. This capability can be used to develop a drone-based road surface inspection system aimed at increasing user (driver) safety and providing information to the owners and maintainers to assess the status of their infrastructure assets. This paper presents an overview of the Turkish–Hungarian U-SOAR project, aimed at the prototype level development of such system. While the system is technically feasible, two key aspects were identified that requires focused further evaluation and development: the business model definition and the management of conflicts in operation. The paper highlights the key findings in these two areas. First, the methodology used to assess the possible business approaches is demonstrated, showing that in the context of these two countries, service provision providing processed data has the highest potential. Second, the requirements towards conflict management as a key part of safe operations are presented. This paper shows the proposed automated conflict management solution and the initial development and testing of the system. Full article

This review provides a comprehensive overview of recent advancements in the synthesis, properties, and applications of organic materials in the optoelectronics sector. The study emphasizes the critical role of organic materials in the development of state-of-the-art optoelectronic devices such as organic solar cells, organic thin-film transistors, and OLEDs. The review further examines the structure, operational principles, and performance metrics of organic optoelectronic devices. Organic materials have emerged as promising candidates due to their low-cost production and potential for large-area or flexible substrate applications. Additionally, this review highlights the physical mechanisms governing the optoelectronic properties of high-performance organic materials, particularly photoinduced processes relevant to charge carrier photogeneration. It discusses the unique benefits of organic materials over traditional inorganic materials, including their light weight, simple processing, and flexibility. The report delves into the challenges related to stability, scalability, and performance, while highlighting the wide range of electronic properties exhibited by organic materials, which are critical for their performances in optoelectronic devices. Furthermore, it addresses the need for further research and development in this field to achieve consistent performance across different types of devices. Full article

Human Activity Recognition (HAR) is an essential area of research in Artificial Intelligence and Machine Learning, with numerous applications in healthcare, sports science, and smart environments. While several advancements in the field, such as attention-based models and Graph Neural Networks, have made great strides, this work focuses on data augmentation methods that tackle issues like data scarcity and task variability in HAR. In this work, we investigate and expand the use of mixup and cutout data augmentation methods to sensor-based and skeleton-based HAR datasets. These methods were first widely used in Computer Vision and Natural Language Processing. We use both augmentation techniques, customized for time-series and skeletal data, to improve the robustness and performance of HAR models by diversifying the data and overcoming the drawbacks of having limited training data. Specifically, we customize mixup data augmentation for sensor-based datasets and cutout data augmentation for skeleton-based datasets with the goal of improving model accuracy without adding more data. Our results show that using mixup and cutout techniques improves the accuracy and generalization of activity recognition models on both sensor-based and skeleton-based human activity datasets. This work showcases the potential of data augmentation techniques on transformers and Graph Neural Networks by offering a novel method for enhancing time series and skeletal HAR tasks. Full article

AMHSs (Automated Material Handling Systems) are widely used in major Fabs (semiconductor fabrication plants). The OHT in an AMHS is responsible for handling the FOUP (Front Opening Unified Pod) within the Fabs. Due to the unidirectional track, the movement path of the OHT aims to avoid congested areas caused by operations or malfunctions as much as possible, to improve the overall FOUP handling efficiency. To do so, we propose a dynamic path planning method, MI-DDQN (Map Information Double Deep Q-Network), driven by deep reinforcement learning and based on map information. Firstly, we design and establish a map information state space model based on the core elements of the OHT path planning in the AMHS. Then, we design an OHT motion simulator to simulate the position coordinate transformation of the OHT, providing real-time coordinate update data for the OHT during the algorithm training process. We design a deep reinforcement learning algorithm structure based on map information model and a convolutional neural network model structure and use the algorithm to train the network model. Finally, the designed task generation module and OHT motion simulator are used to randomly generate the starting position and task position of the OHT during the training process to enhance the richness of the data. The addition of a “fault” OHT verifies the method’s ability to plan routes in complex road conditions such as congestion that may occur at any time. Full article