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This paper introduces the USPDATRO dataset. This is a speech dataset, in the Romanian language, constructed from open data, focusing on under-represented voice types (children, young and old people, and female voices). The paper covers the methodology behind the dataset construction, specific details regarding the dataset, and evaluation of existing Romanian Automatic Speech Recognition (ASR) systems, with different architectures. Results indicate that more under-represented speech content is needed in the training of ASR systems. Our approach can be extended to other low-resourced languages, as long as open data are available.

The airborne relay system is an important support for improving the stability of communication systems in complex electromagnetic environments. As a key component of the airborne relay system, the antenna needs to have characteristics such as high gain, dual circular polarization, wide beam coverage, and miniaturization. Based on the septum circular polarizer, this paper proposes a high-performance circularly polarized antenna structure suitable for airborne relay systems, named AcpAS. The structure consists of a coaxial feed port, a coaxial-to-waveguide transition, a septum circular polarizer, and a parabolic metal housing. Based on this structure, two antennas operating at 19.6 GHz to 21.6 GHz and 29.2 GHz to 31.2 GHz are designed in this paper. Simulated and measured results show that the two antennas have dual circular polarization characteristics, with beam coverage range of more than ±55° for gains higher than 0 dBi, and the radiation patterns exhibit good symmetry and wide-beam coverage characteristics.

At present, data-driven fault diagnosis has made significant achievements. However, in actual industrial environments, labeled fault data are difficult to obtain, making the industrial application of intelligent fault diagnosis models very challenging. This limitation even prevents intelligent fault diagnosis algorithms from being applicable in real-world industrial settings. In light of this, this paper proposes a Collaborative Domain Adversarial Network (CDAN) method for the fault diagnosis of rolling bearings using unlabeled data. First, two types of feature extractors are employed to extract features from both the source and target domain samples, reducing signal redundancy and avoiding the loss of critical signal features. Second, the multi-kernel clustering algorithm is used to compute the differences in input feature values, create pseudo-labels for the target domain samples, and update the CDAN network parameters through backpropagation, enabling the network to extract domain-invariant features. Finally, to ensure that unlabeled target domain data can participate in network training, a pseudo-label strategy using the maximum probability label as the true label is employed, addressing the issue of unlabeled target domain data not being trainable and enhancing the model’s ability to acquire reliable diagnostic knowledge. This paper validates the CDAN using two publicly available datasets, CWRU and PU. Compared with four other advanced methods, the CDAN method improved the average recognition accuracy by 7.85% and 5.22%, respectively. This indirectly proves the effectiveness and superiority of the CDAN in identifying unlabeled bearing faults.

Despite technological growth and worldwide advancements in various fields, the agriculture sector continues to face numerous challenges such as desertification, environmental pollution, resource scarcity, and the excessive use of pesticides and inorganic fertilizers. These unsustainable problems in agricultural field can lead to land degradation, threaten food security, affect the economy, and put human health at risk. To mitigate these global issues, it is essential for researchers and agricultural professionals to promote advancements in smart agriculture by integrating modern technologies such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Wireless Sensor Networks (WSNs), and more. Among these technologies, this paper focuses on UAVs, particularly quadcopters, which can assist in each phase of the agricultural cycle and improve productivity, quality, and sustainability. With their diverse capabilities, quadcopters have become the most widely used UAVs in smart agriculture and are frequently utilized by researchers in various projects. To explore the different aspects of quadcopters’ use in smart agriculture, this paper focuses on the following: (a) the unique advantages of quadcopters over other UAVs, including an examination of the quadcopter types particularly used in smart agriculture; (b) various agricultural missions where quadcopters are deployed, with examples highlighting their indispensable role; (c) the modelling of quadcopters, from configurations to the derivation of mathematical equations, to create a well-modelled system that closely represents real-world conditions; and (d) the challenges that must be addressed, along with suggestions for future research to ensure sustainable development. Although the use of UAVs in smart agriculture has been discussed in other papers, to the best of our knowledge, none have specifically examined the most popular among them, “quadcopters”, and their particular use in smart agriculture in terms of types, applications, and modelling techniques. Therefore, this paper provides a comprehensive survey of quadcopters’ use in smart agriculture and offers researchers and engineers valuable insights into this evolving field, presenting a roadmap for future enhancements and developments.

This paper aims, in the context of the need to reduce the weight and price of vehicles, to present theoretical and experimental results regarding towing systems made of alternative materials (fibrous composite materials and aluminum alloys). The study of the main element of the whole system, namely towbar research, was stressed, presenting comparative results. The FEM will be used to obtain the stress and strain field in the towball and the towing system. The experimental tests validate the theoretical results obtained. This paper studies five towing systems made by different materials and finally presents a series of practical recommendations, useful in the industry, regarding the improvement of the analyzed models.

Visual servoing technology is well developed and applied in many automated manufacturing tasks, especially in tools’ pose alignment. To access a full global view of tools, most applications adopt an eye-to-hand configuration or an eye-to-hand/eye-in-hand cooperation configuration in an automated manufacturing environment. Most research papers mainly put efforts into developing control and observation architectures in various scenarios, but few have discussed the importance of the camera’s location in the eye-to-hand configuration. In a manufacturing environment, the quality of camera estimations may vary significantly from one observation location to another, as the combined effects of environmental conditions result in different noise levels of a single image shot in different locations. In this paper, we propose an algorithm for the camera’s moving policy so that it explores the camera workspace and searches for the optimal location where the image’s noise level is minimized. Also, this algorithm ensures the camera ends up at a suboptimal (if the optimal one is unreachable) location among the locations already searched with the limited energy available for moving the camera. Unlike a simple brute-force approach, the algorithm enables the camera to explore space more efficiently by adapting the search policy by learning the environment. With the aid of an image-averaging technique, this algorithm, in the use of a solo camera, achieves observation accuracy in eye-to-hand configurations to a desirable extent without filtering out high-frequency information in the original image. An automated manufacturing application was simulated, and the results show the success of this algorithm’s improvement in observation precision with limited energy.

This paper investigates the factors influencing the probability of fatality in various types of maritime accidents, including grounding, capsizing, sinking, man overboard incidents, and fatal falls, with a focus on several contributing factors—alcohol consumption, meteorological conditions, and visibility. Through comprehensive analysis, the alcohol consumption was examined in order to show how it impairs judgment and physical abilities, significantly increasing the risk of fatal outcomes in these accidents. The paper explores the interplay between alcohol consumption and other contributing factors, such as time of day (daytime/night) and weather conditions, providing a comprehensive understanding of how these variables collectively influence fatality rates in EU maritime transportation. The findings underscore the critical need for stringent alcohol regulations and enhanced safety protocols to mitigate the heightened risks associated with alcohol-impaired maritime operations.

At present, a large number of rumors are mixed in with various kinds of news, such as current affairs, politics, social economy, and military activities, which seriously reduces the credibility of Internet information and hinders the positive development of various fields. In previous research on rumors, most scholars have focused their attention on the textual features, contextual semantic features, or single-emotion features of rumors but have not paid attention to the chain reaction caused by the hidden emotions in comments in social groups. Therefore, this paper comprehensively uses the emotional signals in rumor texts and comments to extract emotional features and determines the relationship between them to establish dual-emotion features. The main research achievements include the following aspects: (1) this study verifies that, in the field of affective characteristics, the combination of rumor-text emotion and comment emotion is superior to other baseline affective characteristics, and the detection performance of each component is outstanding; (2) the results prove that the combination of dual-emotion features and a semantic-feature-based detector (BiGRU and CNN) can improve the effectiveness of the detector; (3) this paper proposes reconstructing the dataset according to time series to verify the generalization ability of dual affective features; (4) the attention mechanism is used to combine domain features and semantic features to extract more fine-grained features. A large number of data experiments show that the dual-emotion features can be effectively compatible with an existing rumor detector, enhance the detector’s performance, and improve the detection accuracy.

This paper discusses the iteration of a seismic retrofit solution for shear-deficient end regions of 19 reinforced concrete (RC) moment-resisting frame (MRF) T-beams located in a 12-story RC MRF building in downtown Los Angeles, California. Local strengthening with externally bonded (EB) fiber-reinforced polymer (FRP) fabric was chosen as the preferred retrofit strategy due to its cost-effectiveness and proven performance. The FRP-shear-strengthening scheme for the deficient end-hinging regions of the MRF beams was designed and evaluated through large-scale cyclic testing of three replica specimens. The specimens were constructed at 4/5 scale and cantilever T-beam configurations with lengths of 3.40 m or 3.17 m. The cross-sectional geometry was 0.98 × 0.61 m with a top slab of 1.59 m in width and 0.12 m in thickness. Applied to these specimens were three different retrofit configurations, tested sequentially, namely: (a) unanchored continuous U-wrap; (b) anchored continuous U-wrap with conventional FRP-embedded anchors at the ends; and (c) fully closed external FRP hoops made of discrete FRP U-wrap strips and FRP through-anchors that penetrate the top slab and connect both ends of the FRP strips, combined with intermediate crack-control joints. The strengthening concept with FRP hoops precluded the premature debonding and anchor pullout issues of the two more conventional retrofit solutions and, despite a more challenging and labor-intensive installation, was selected for the in-situ implementation. The proposed hooplike EB-FRP shear-strengthening scheme enabled the deficient MRF beams to overcome a 30% shear overstress at the end-yielding region and to develop high-end rotations (e.g., 0.034 rad [3.4% drift] at peak and 0.038 rad [3.8% drift]) at strength loss for a beam that, otherwise, would have prematurely failed in shear. These values are about 30% larger than the ASCE 41 prescriptive value for the Life Safety (LS) performance objective. Energy dissipation achieved with the fully closed scheme was 108% higher than that of the unanchored FRP U-wrap and 45% higher than that of the FRP U-wrap with traditional embedded anchors. The intermediate saw-cut grooves successfully attracted crack formation between the strips and away from the FRP reinforcement, which contributed to not having any discernable debonding of the strips up to 3% drift. This paper presents the experimental evaluation of the three large-scale laboratory specimens that were used as the design basis for the final retrofit solution.

Traditionally, traffic characteristics such as speed, volume, and travel time are obtained from a range of sensors and systems such as inductive loop detectors (ILDs), automatic number plate recognition cameras (ANPR), and GPS-equipped floating cars. However, many issues associated with these data have been identified in the existing literature. Although roadside surveillance cameras cover most road segments, especially on freeways, existing techniques to extract traffic data (e.g., speed measurements of individual vehicles) from video are not accurate enough to be employed in a proactive traffic management system. Therefore, this paper aims to develop a technique for estimating traffic data from video captured by surveillance cameras. This paper then develops a deep learning-based video processing algorithm for detecting, tracking, and predicting highly disaggregated vehicle-based data, such as trajectories and speed, and transforms such data into aggregated traffic characteristics such as speed variance, average speed, and flow. By taking traffic observations from a high-quality LiDAR sensor as ‘ground truth’, the results indicate that the developed technique estimates lane-based traffic volume with an accuracy of 97%. With the application of the deep learning model, the computer vision technique can estimate individual vehicle-based speed calculations with an accuracy of 90–95% for different angles when the objects are within 50 m of the camera. The developed algorithm was then utilised to obtain dynamic traffic characteristics from a freeway in southern China and employed in a statistical model to predict monthly crashes.

Fault ride-through is a prerequisite for ensuring continuous operation of a variable-speed pumped storage unit with a full-size converter (FSC-VSPU) and providing support for the renewable energy and power grid. This paper proposes low-voltage ride through (LVRT) and high-voltage ride through (HVRT) strategies for FSC-VSPU to address this issue. Firstly, the structure of FSC-VSPU and its control strategy under power generation and pumping conditions are described. Subsequently, the fault characteristics of the FSC-VSPU under different operating conditions are analyzed. More stringent fault ride-through technical requirements than those for wind turbines are proposed. On this basis, the fault ride-through strategies of combining fast power drop, rotor energy storage control, power anti-regulation control, dynamic reactive current control, low power protection, and DC crowbar circuit are proposed. Simulation case studies conducted in PSCAD/EMTDC verify the correctness of the theoretical analysis and the effectiveness of the LVRT and HVRT strategies in this paper.

This paper proposes a high-precision interventional pressure measurement catheter with a compact structure for in vivo pressure monitoring. This catheter uses a minimally invasive testing method, and the proposed compact structure reduces pressure loss caused by fluid movement inside the catheter, ensuring accuracy in dynamically varying pressure measurements. The paper conducts a theoretical analysis of the pressure transmission process inside the catheter, fabricates a prototype, and establishes both dynamic and static pressure testing systems. Through experimental research on the effects of the catheter’s dimensions on the accuracy of dynamically varying pressure measurements, it demonstrates that the compact pressure measurement catheter can effectively enhance the precision of dynamically varying pressure measurements. The catheter offers high precision, small size, and simple manufacturing processes. It holds promising applications in fields such as in-body pressure measurement and microfluidic system monitoring.

Walking robots have been widely applied in complex terrains due to their good terrain adaptability and trafficability. However, in some environments (such as disaster relief, field navigation, etc.), although a single strategy can adapt to various environments, it is unable to strike a balance between speed and stability. Existing control schemes like model predictive control (MPC) and traditional incremental control can manage certain environments. However, they often cannot balance speed and stability well. These methods usually rely on a single strategy and lack adaptability for dynamic adjustment to different terrains. To address this limitation, this paper proposes an innovative double-layer reinforcement learning algorithm. This algorithm combines Deep Double Q-Network (DDQN) and Proximal Policy Optimization (PPO), leveraging their complementary strengths to achieve both fast adaptation and high stability in complex terrains. This algorithm utilizes terrain information and the robot’s state as observations, determines the walking speed command of the quadruped robot Unitree Go1 through DDQN, and dynamically adjusts the current walking speed in complex terrains based on the robot action control system of PPO. The speed command serves as a crucial link between the robot’s perception and movement, guiding how fast the robot should walk depending on the environment and its internal state. By using DDQN, the algorithm ensures that the robot can set an appropriate speed based on what it observes, such as changes in terrain or obstacles. PPO then executes this speed, allowing the robot to navigate in real time over difficult or uneven surfaces, ensuring smooth and stable movement. Then, the proposed model is verified in detail in Isaac Gym. Wecompare the distances walked by the robot using six different control methods within 10 s. The experimental results indicate that the method proposed in this paper demonstrates excellent speed adjustment ability in complex terrains. On the designed test route, the quadruped robot Unitree Go1 can not only maintain a high walking speed but also maintain a high degree of stability when switching between different terrains. Ouralgorithm helps the robot walk 25.5 m in 10 s, outperforming other methods.

Generative models have shown excellent results in aspect-based sentiment analysis tasks by predicting quadruples by setting specific template formats. The existing research predicts sentiment elements and enhances the dependency between elements using the multi-template prompting method, but it does not realize the information interaction in the generation process, and it ignores the dependency between the prompt template and the aspect terms and opinion terms in the input sequence. In this paper, we propose a Dual-template Prompted Mutual Learning (DPML) generative model to enhance the information interaction between generation modules. Specifically, this paper designs a dual template based on prompt learning and, at the same time, develops a mutual learning information enhancement module to guide each generated training process to interact with iterative information. Secondly, in the decoding stage, a label marking the interactive learning module is added to share the explicit emotional expression in the sequence, which can enhance the ability of the model to capture implicit emotion. On two public datasets, our model achieves an average improvement of 5.3% and 3.4% in F1 score compared with the previous state-of-the-art model. In the implicit sentiment analysis experiment, the F1 score of the proposed model in the data subset containing implicit words is increased by 2.75% and 3.42%, respectively.

The increase in traffic caused by new development affects the change in traffic conditions on the surrounding roads, and shopping centres are significant traffic generators. The development of local travel generation rates and their characteristics for individual land uses from the aspect of traffic demand is a reliable way to plan traffic in order to come up with preventive solutions to traffic problems, that is, prevention of possible negative consequences on traffic conditions in the street network occurring due to the construction of shopping centres. One of the main aims of this paper is to develop a model for objective assessment of the generated traffic demand for significant changes in land use, such as the construction of shopping centres in medium-sized towns. All these would be steps in the right direction for the promotion of reliable traffic planning and adoption of TIA for every new development before a decision regarding the change in land purpose has been made. This kind of process still has not been established systematically in either Bosnia and Herzegovina and the Republic of Serbia, or in surrounding countries. This paper focuses on the formulation of a model for determining the volume of traffic generated by shopping centres in medium-sized towns in two countries of the Southeast Europe region. The survey was conducted in eight different locations (cities) where there are shopping centres with common facilities. The analysis showed that the number of visitors and vehicles attracted by the shopping centre zone can be determined by a model based on a linear regression analysis. The analysis included exploring several different factors of trip generation in shopping centres, including the relationship between trip generation and combinations of several independent variables. The verification of the model was conducted in real conditions of the traffic flow generated by a shopping centre which was not the analysis subject when forming the forecasting model. In this way, the validity of the proposed model is credibly assessed. The developed model can be applied in the procedures of planning the construction of shopping centres in medium-sized cities in the Republic of Serbia and Bosnia and Herzegovina, and wider, in the region of Southeast Europe, in order to estimate the volume of generated traffic demand, that is, its impact on the conditions of traffic on the surrounding traffic network.

Masked face recognition (MFR) has emerged as a critical domain in biometric identification, especially with the global COVID-19 pandemic, which introduced widespread face masks. This survey paper presents a comprehensive analysis of the challenges and advancements in recognizing and detecting individuals with masked faces, which has seen innovative shifts due to the necessity of adapting to new societal norms. Advanced through deep learning techniques, MFR, along with face mask recognition (FMR) and face unmasking (FU), represents significant areas of focus. These methods address unique challenges posed by obscured facial features, from fully to partially covered faces. Our comprehensive review explores the various deep learning-based methodologies developed for MFR, FMR, and FU, highlighting their distinctive challenges and the solutions proposed to overcome them. Additionally, we explore benchmark datasets and evaluation metrics specifically tailored for assessing performance in MFR research. The survey also discusses the substantial obstacles still facing researchers in this field and proposes future directions for the ongoing development of more robust and effective masked face recognition systems. This paper serves as an invaluable resource for researchers and practitioners, offering insights into the evolving landscape of face recognition technologies in the face of global health crises and beyond.

This paper presents theoretical notions with practical applications for establishing the lifetime of a dental bur (durability and reliability) based on the wear of the active part and considering a series of statistical indicators. To justify applying theoretical notions in practice, a conical–cylindrical dental bur is studied experimentally to obtain a series of data of the work process necessary for statistical calculation. The parameter taken into consideration in this paper is mass lost through the wear of the dental bur active part, based on testing. This is useful for dental bur lifetime establishment and its extension or even optimization in operation. The loss of mass of the dental bur active part is analyzed in the work process using the results and experimental data obtained and validated by statistical–mathematical numerical calculation. The numerical calculation approximates the mass lost through wear at different rotation speeds and operation times, and based on a comparison with the experimentally determined ones, the lifetime was established. The results show that the dental bur works with high yield in the first 20 h of work, after which it should be replaced with a new one. Theoretically, the studied dental bur can work over 21 h. Practically, it can work up to 18 h without major risk of failure, but the lifetime can be extended up to 20 h, at which point the failure risk can reach 10% and it is recommended to replace the bur.

Ancient paintings, as a vital component of cultural heritage, encapsulate a profound depth of cultural significance. Over time, they often suffer from different degradation conditions, leading to damage. Existing ancient painting inpainting methods struggle with semantic discontinuities, blurred textures, and details in missing areas. To address these issues, this paper proposes a generative adversarial network (GAN)-based ancient painting inpainting method named RG-GAN. Firstly, to address the inconsistency between the styles of missing and non-missing areas, this paper proposes a Regional Attention-Style Transfer Module (RASTM) to achieve complex style transfer while maintaining the authenticity of the content. Meanwhile, a multi-scale fusion generator (MFG) is proposed to use the multi-scale residual downsampling module to reduce the size of the feature map and effectively extract and integrate the features of different scales. Secondly, a multi-scale fusion mechanism leverages the Multi-scale Cross-layer Perception Module (MCPM) to enhance feature representation of filled areas to solve the semantic incoherence of the missing region of the image. Finally, the Global Context Perception Discriminator (GCPD) is proposed for the deficiencies in capturing detailed information, which enhances the information interaction across dimensions and improves the discriminator’s ability to identify specific spatial areas and extract critical detail information. Experiments on the ancient painting and ancient Huaniao++ datasets demonstrate that our method achieves the highest PSNR values of 34.62 and 23.46 and the lowest LPIPS values of 0.0507 and 0.0938, respectively.

The preparation of traditional wood-based panels mostly uses adhesives such as urea-formaldehyde resin and phenolic resin, which not only consumes petrochemical resources but also releases formaldehyde, posing potential health risks to the human body. Lignin, a natural adhesive in plant cells, is characterized by high reactivity, and it is expected to aid in the development of a new generation of green formaldehyde-free adhesives. However, current studies of lignin adhesives have revealed that while strides have been made in reducing formaldehyde emissions, its residual presence remains a concern, an issue which is compounded by inadequate water resistance. Dehydrogenated Polymer (DHP) has a lignin-like structure and good water resistance, offering a new option for the development of formaldehyde-free adhesives. In this paper, DHP and glucuronic acid were reacted with each other in a simulated hot-pressing environment to obtain DHP-glucuronic acid complex, and then the structure of the complex was characterized by infrared nuclear magnetic resonance to verify whether DHP can be efficiently connected with hemicellulose components under hot-pressing conditions. The results showed that the thermal condensation reaction of DHP and glucuronic acid can generate ester bonds at the Cα position in a simulated hot-pressing environment. This paper explores the thermal condensation mechanism of DHP and glucuronic acid, which is helpful for understanding the bonding process between adhesives and components of wood-based panels in the hot-pressing process, and provides key theoretical support for the design of more sustainable lignin adhesives.

Natural gas dehydration is a critical process in natural gas extraction and transportation, and the membrane separation method is the most suitable technology for gas dehydration. In this paper, based on molecular dynamics theory, we investigate the performance of a metal–organic composite membrane (ZIF-90 membrane) in natural gas dehydration. The paper elucidates the adsorption, diffusion, permeation, and separation mechanisms of water and methane with the ZIF-90 membrane, and clarifies the influence of temperature on gas separation. The results show that (1) the diffusion energy barrier and pore size are the primary factors in achieving the separation of water and methane. The diffusion energy barriers for the two molecules (CH4 and H2O) are ΔE(CH4) = 155.5 meV and ΔE(H2O) = 50.1 meV, respectively. (2) The ZIF-90 is more selective of H2O, which is mainly due to the strong interaction between the H2O molecule and the polar functional groups (such as aldehyde groups) within the ZIF-90. (3) A higher temperature accelerates the gas separation process. The higher the temperature is, the faster the separation process is. (4) The pore radius is identified as the intrinsic mechanism enabling the separation of water and methane in ZIF-90 membranes.

In the face of escalating environmental challenges driven by human activities, the quest for innovative solutions to counter pollution, contamination, and ecological degradation has gained paramount importance. Traditional approaches to environmental remediation often fall short in addressing the complexity and scale of modern-day environmental problems. As industries transition towards sustainable paradigms, the exploration of novel materials and technologies becomes crucial. Lignin nanoparticles have emerged as a promising avenue of exploration in this context. Once considered a mere byproduct, lignin’s unique properties and versatile functional groups have propelled it to the forefront of environmental remediation research. This review paper delves into the resurgence of lignin from an environmental perspective, examining its pivotal role in carbon cycling and its potential to address various environmental challenges. The paper extensively discusses the synthesis, properties, and applications of lignin nanoparticles in diverse fields such as water purification and soil remediation. Moreover, it highlights the challenges associated with nanoparticle deployment, ranging from Eco toxicological assessments to scalability issues. Multidisciplinary collaboration and integration of research findings with real-world applications are emphasized as critical factors for unlocking the transformative potential of lignin nanoparticles. Ultimately, this review underscores lignin nanoparticles as beacons of hope in the pursuit of cleaner, healthier, and more harmonious coexistence between humanity and nature through innovative environmental remediation strategies.

This paper aims to explore the possibility of utilizing vision transformers (ViTs) for on-edge medical diagnostics by experimenting with the Kvasir-Capsule image classification dataset, a large-scale image dataset of gastrointestinal diseases. Quantization techniques made available through TensorFlow Lite (TFLite), including post-training float-16 (F16) quantization and quantization-aware training (QAT), are applied to achieve reductions in model size, without compromising performance. The seven ViT models selected for this study are EfficientFormerV2S2, EfficientViT_B0, EfficientViT_M4, MobileViT_V2_050, MobileViT_V2_100, MobileViT_V2_175, and RepViT_M11. Three metrics are considered when analyzing a model: (i) F1-score, (ii) model size, and (iii) performance-to-size ratio, where performance is the F1-score and size is the model size in megabytes (MB). In terms of F1-score, we show that MobileViT_V2_175 with F16 quantization outperforms all other models with an F1-score of 0.9534. On the other hand, MobileViT_V2_050 trained using QAT was scaled down to a model size of 1.70 MB, making it the smallest model amongst the variations this paper examined. MobileViT_V2_050 also achieved the highest performance-to-size ratio of 41.25. Despite preferring smaller models for latency and memory concerns, medical diagnostics cannot afford poor-performing models. We conclude that MobileViT_V2_175 with F16 quantization is our best-performing model, with a small size of 27.47 MB, providing a benchmark for lightweight models on the Kvasir-Capsule dataset.

Throughout the development process of the education industry, the core competitiveness of education focuses on the output of high-quality content, and the emergence of a virtual human provides a more efficient carrier for the education industry, which can fundamentally help the education industry transform to improve efficiency. By combining virtual reality technology with artificial intelligence, this paper designs a virtual teacher based on the VOCA model for real-time interaction. The learned model, VOCA (voiceoperated character animation) takes any speech signal as input and realistically animates a wide range of adult faces. Compared with the traditional virtual teacher based on text or speech, the virtual teacher in this paper provides human-like interaction, which is a new teaching form for people involved in the field of artificial intelligence. According to the appearance, movement, and behavior characteristics of real teachers, the virtual teacher image is designed, and the interaction mode of virtual teachers is enriched from facial expression, body posture, voice, and speech. A virtual teacher with personalized, interactive, and intelligent characteristics is developed by combining voice, image and natural language-processing technology. It enables virtual teachers to interact with students in a more intuitive and personalized way, provide real-time feedback and personalized guidance, and provide better learning support and teaching experience for online education.

As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames.

This paper combines self-organizing mapping (SOM) and a long short-term memory network (SOM-LSTM) to construct an audio-based motor-fault diagnosis system for identifying the operating states of a rotary motor. This paper first uses an audio signal collector to measure the motor sound signal data, uses fast Fourier transform (FFT) to convert the actual measured sound–time-domain signal into a frequency-domain signal, and normalizes and calibrates the frequency-domain signal to ensure the consistency and accuracy of the signal. Secondly, the SOM is used to further analyze the characterized frequency-domain waveforms in order to reveal the intrinsic structure and pattern of the data. The LSTM network is used to process the secondary data generated via SOM. Dimensional data aggregation and the prediction of sequence data in long-term dependencies accurately identify different operating states and possible abnormal patterns. This paper also uses the experimental design of the Taguchi method to optimize the parameters of SOM-LSTM in order to increase the execution efficiency of fault diagnosis. Finally, the fault diagnosis system is applied to the real-time monitoring of the motor operation, the work of identifying the motor-fault type is performed, and tests under different loads and environments are attempted to evaluate its feasibility. The completion of this paper provides a diagnostic strategy that can be followed when it comes to motor faults. Through this fault diagnosis system, abnormal conditions in motor equipment can be detected, which can help with preventive maintenance, make work more efficient and save a lot of time and costs, and improve the industry’s ability to monitor motor operation information.

The democratization of artificial intelligence (AI) involves extending access to AI technologies beyond specialized technical experts to a broader spectrum of users and organizations. This paper provides an overview of AI’s historical context and evolution, emphasizing the concept of AI democratization. Current trends shaping AI democratization are analyzed, highlighting key challenges and opportunities. The roles of pivotal stakeholders, including technology firms, educational entities, and governmental bodies, are examined in facilitating widespread AI adoption. A comprehensive framework elucidates the components, drivers, challenges, and strategies crucial to AI democratization. This framework is subsequently applied in the context of scenario analyses, offering insights into potential outcomes and implications. The paper concludes with recommendations for future research directions and strategic actions to foster responsible and inclusive AI development globally.

Image pre-processing is crucial for large fleet management. Many traffic videos are collected by closed-circuit television (CCTV), which has a fixed area monitoring for image analysis. This paper adopts the front camera installed in large vehicles to obtain moving traffic images, whereas CCTV is more limited. In practice, fleets often install cameras with different resolutions due to cost considerations. The cameras evaluate the front images with traffic lights. This paper proposes fuzzy enhancement with RGB and CIELAB conversions to handle multiple resolutions. This study provided image pre-processing adjustment comparisons, enabling further model training and analysis. This paper proposed fuzzy enhancement to deal with multiple resolutions. The fuzzy enhancement and fuzzy with brightness adjustment produced images with lower MSE and higher PSNR for the images of the front view. Fuzzy enhancement can also be used to enhance traffic light image adjustments. Moreover, this study employed You Only Look Once Version 9 (YOLOv9) for model training. YOLOv9 with fuzzy enhancement obtained better detection performance. This fuzzy enhancement made more flexible adjustments for pre-processing tasks and provided guidance for fleet managers to perform consistent image-enhancement adjustments for handling multiple resolutions.

The real-time monitoring and evaluation of water quality provides a scientific basis for water resource management and promotes regional sustainable development. This study established a database using Landsat-8 satellite data and water quality data from the Ningxia Yellow River basin in China, spanning 2021 to 2023, and this paper proposes a custom residual convolutional neural network model with a hybrid attention mechanism, referred to as PCWA-ResCNN. The accuracy of the model in predicting turbidity, permanganate, ammonia nitrogen, and dissolved oxygen concentration was more than 95%. Compared to convolutional neural networks and long short-term memory models, this model performed better in predicting water quality parameters with significantly improved prediction performance. In terms of spatial distribution, the pollution degree in the middle reaches of the basin is relatively serious. However, the overall water quality is good, being mainly Class I and Class II water quality. The hybrid model established in this paper can better capture the complex nonlinear relationship between the observed values and the surface water reflectance, showing strong robustness. This model can be used for the water quality monitoring of complex inland rivers and lakes, and it can also provide effective support for relevant government departments to formulate scientific and reasonable water quality management policies.

Additive manufacturing resins used in dental prosthetics may retain uncured monomers post-polymerization, posing potential long-term patient exposure risks. Understanding the biological safety of these materials is crucial, particularly for 3D-printed acrylic-based prosthodontic devices such as occlusal nightguards, complete and partial dentures, and temporary fixed prostheses. This paper reviews the literature evaluating the cytotoxicity of such materials. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we conducted a scoping review using the MESH keywords related to population (P), intervention (I), comparison (C), and outcome (O) across databases, including OVID Medline, EMBASE, and SCOPUS. Our search, limited to peer-reviewed English language articles from 2015 to 2023, resulted in 22 papers. These studies, utilizing digital light processing (DLP) or stereolithography (SLA) printing methods, varied in examining different 3D-printed materials, as well as washing and post-curing protocols. The primary experimental cells used were human gingival fibroblasts (HGF) and mouse fibroblasts (L929). There are no statistical differences in biocompatibility regarding different commercially available resins, washing solutions, or methods. Improvements in cell viability were related to an increase in washing time, as well as post-curing time. After the polishing procedure, 3D resin-based printed occlusal devices perform similarly to milled and conventionally processed ones. Our findings underline the importance of appropriate washing and post-curing protocols in minimizing the cytotoxic risks associated with these 3D-printed resin-based devices.

This paper proposes a modular protocol for the designing of experimental studies to analyze exoskeletons used in industrial settings to support manual material handling (MMH). Despite exoskeleton technologies starting to be highly commercialized and present in workplaces, research still lacks a standardized procedure for analyzing the impact of these devices on workers. The protocol presented in this paper outlines a step-by-step procedure, including the parameters to be collected and analyzed in a research study. Moreover, the approach could be easily adapted to meet the specificity of a wide range of exoskeletons. The main novelty of the protocol is thus to support the experimental design and analysis of studies assessing the overall impact of exoskeletons on workers. To implement the protocol, the selected case study concerned a palletizing task involving the MMH of 12 cardboard boxes, weighing 10 kg. The results from physiological signals and pressure insoles show that the protocol is comprehensive and can be utilized by researchers evaluating different occupational exoskeletons for assistance during MMH (both active and passive), with modifications to specific parts based on the type of exoskeleton being assessed or the variables of interest.

The vibrations that a shaft suffers when rotating affect both the friction and subsequent wear of the shaft. The main objective of this paper is to present an academic and experimental prototype that allows controlling the vibrations of a rotating shaft through magnetic levitation. The control was carried out with a microcontroller, electromagnets, and proximity sensors. Other sensors and regulators were also added to the model that allow parameterization and obtaining data for subsequent analysis. According to the experiments performed in the laboratory, the proposed digital controller is capable of mitigating vibrations of a rotating shaft. This theoretical-practical methodology enables students to acquire extensive knowledge of mechanics, electronics, computing, and control in an effective manner. Although this paper presents only an academic prototype, the same technology could be applied to a wide variety of mechanisms that require rotation shafts with friction problems.

This article explores the world of dependable systems, specifically focusing on system design, software solutions, and architectural decisions that facilitate collaborative work on shared text documents across multiple users in near real time. It aims to dive into the intricacies of designing robust and effective document collaboration software focusing on understanding the requirements of such a system, the working principle of collaborative text editing, software architecture, technology stack selection, and tooling that can sustain such a system. To examine the pros and cons of the proposed system, the paper will detail how collaborative text editing software can benefit from such an architecture regarding availability, elasticity, and scaling. The intricate nature of this system renders this paper a valuable resource for prospective investigations within the domain of dependable systems and distributed systems. This research first examines the requirements of a real-time collaboration system and the necessary core features. Then, it analyzes the design, the application structure, and the system organization while also considering key architectural requirements as the necessity of scaling, the usage of microservices, cross-service communications, and client–server communication. For the technology stack of the implementation, this research considers the alternatives at each layer, from client to server. Once these decisions are made, it follows system development while examining possible improvements for the issues previously encountered. To validate the architecture, a testing strategy is developed, to examine the key capabilities of the system, such as resource consumption and throughput. The conclusions review the combination of modern and conventional application development principles needed to address the challenges of conflict-free document replication, decoupled and stateless event-driven architecture, idempotency, and data consistency. This paper not only showcases the design and implementation process but also sets a foundation for future research and innovation in dependable systems, collaborative technologies, sustainable solutions, and distributed system architecture.

The safety of high-potential risk facilities concerned with fault displacement hazards is a complex technical issue, especially if the fault is revealed beneath the facility during the operation. Applying simple conservative engineering hazard evaluation methods is rational if an urgent decision should be made to continue operation or implement protective measures. Engineering methods are being published for strike-slip on-fault sites and structures. Their crucial point is to estimate the probability of the rupture at the site intersection and consider the displacement distribution over the rupture length relative to the site’s on-fault location. It is shown in the paper that strict geometrical relations between the site location, length and initial point of the rupture determine whether the principal fault displacement intersects the site. The paper considers these geometrical parameters as independent random variables and applies a screening of ruptures contributing to the hazard. Magnitude- and on-fault coordinate-dependent empirical relations have been analysed and selected to evaluate the site displacements. The procedure resulted in realistic but conservative hazard curves for different on-fault site locations using data from the Paks site in Hungary. The results were compared to those obtained by the conservative engineering method for the same site and some published analyses.

The fault diagnosis of rolling bearing acoustic radiation signals holds significant importance in industrial equipment maintenance. It effectively prevents equipment failures and downtime, ensuring the smooth operation of the production process. Compared with traditional vibration signals, acoustic radiation signals have the advantage of non-contact measurement. They can diagnose faults in special conditions where sensors cannot be installed and provide more comprehensive equipment status information. Therefore, to extract the fault characteristic information of rolling bearings from complex acoustic signals, this paper proposes an advanced deep learning model combining Gramian Angular Field (GAF), ResNet1D, ResNet2D, and multi-head attention mechanism, named CRAMNet (Combined ResNet Attention Multi-Head Net), to diagnose the faults of rolling bearing acoustic radiation signals. Firstly, this method includes converting one-dimensional signals into GAF images and performing data standardization and segmentation. Then, the method utilizes ResNet1D to extract features from one-dimensional signals and ResNet2D to extract features from GAF images. Further, it combines the multi-head attention mechanism to enhance feature representation and capture dependencies between different channels. Finally, this paper compares the proposed method with several traditional models (including CNN, LSTM, DenseNet, and CNN-Transformers). Experimental results show that the proposed method performs outstandingly in terms of accuracy and robustness. The combination of residual networks and multi-head attention mechanism in the model significantly enhances its ability to accurately diagnose rolling bearing faults, proving the superiority of the algorithm.

This research paper provides a comprehensive overview of the use of layered double hydroxides (LDH) in the removal of selenium species from contaminated water sources. Key studies on sorption mechanisms and the impact of competing ions on selenium removal are presented, and the effectiveness of LDH is compared across different structures and compositions. Scholarly sources extensively document the application of conventional LDH for effective selenium removal, with notable advancements achieved through innovative synthesis approaches. Comparative studies between LDH synthesized through various methods reveal the potential of tailored LDH for enhanced selenium adsorption. The paper further explores the influence of competing anions on LDH efficacy, emphasizing the impact of sulfate on selenium removal. Additionally, investigations into calcined LDH and commercially available variants underscore the potential for industrial applications. Beyond conventional LDH, the paper delves into iron-based LDH, LDH with intercalated thiomolybdate anions, and layered rare earth hydroxides, exploring their effectiveness in separating different selenium species. The role of pH in the removal of selenium species and the impact of three-metal cation LDH are also discussed. The study extends to nanocomposites, combining LDH with zero-valent iron, carbon-based materials, and organic compounds, illustrating their potential for selenium species immobilization. The presented findings offer valuable insights for researchers and practitioners in environmental science, addressing the growing demand for efficient selenium remediation strategies.

This paper presents an experimental analysis of a high-response piezoelectric actuator system for the modular design of hydraulic digital fluid control units. It focuses on determining static and dynamic characteristics, forming the basis for developing a smart Industry 4.0 component that incorporates both actuator and sensor function. The design process examines the main challenges, advantages, disadvantages, and working principles to define parameters that impact the actuator’s behaviour and performance. The new piezoelectric actuator system features three piezoelectric stack actuators in series, enabling simultaneous actuation and sensing by applying and measuring the electrical voltage at each piezo element. The experimental setup and test methodology are explained in detail, revealing that the new design, combined with an appropriate open-loop or closed-loop control method, offers superior actuator stroke control, high stroke resolution, and a high-dynamic step response. This paper proposes a concept of a smart piezo actuator system focused on I4.0 and an actuator administration shell, integrated with 5G and RFID technology, which will allow automatic plug-and-play functionality and efficient interconnection, communication, and data transfer between the hydraulic valve and the piezoelectric actuator system.

The current advancements in the field of machine learning can have an important application in agriculture and global food security. Machine learning has considerable potential in establishing knowledge-based farming systems. One of the main challenges of data-driven agriculture is to minimize food waste and establish more sustainable farming systems. The prediction of the right harvest time is one of the ways to obtain the mentioned goals. This paper describes multiple machine learning algorithms that are used to predict peach firmness. By accurately predicting peach firmness based on various peach measurement data, a more precise harvest time can be obtained. The evaluation of nature-inspired metaheuristic optimization algorithms in enhancing machine learning model accuracy is the primary objective of this paper. The possibility of improving the peach firmness prediction accuracy of regression tree models using various metaheuristic optimization techniques implemented in GA and metaheuristicOpt R packages is studied. The RMSE on test data of the default regression tree model is 1.722285, while the regression tree model optimized using the gray wolf optimization algorithm scored the lowest RMSE of 1.570924. The obtained results show that it is possible to improve the peach firmness prediction accuracy of the regression tree model by 8.8% using the described method.

Smoothed particle hydrodynamics (SPH) is a state-of-the-art numerical simulation method in fluid mechanics. It is a novel approach for modeling and comprehending complex fluid behaviors. In contrast to traditional grid-dependent techniques like finite element and finite difference methods, SPH utilizes a meshless, purely Lagrangian approach, offering significant advantages in fluid simulations. By leveraging a set of arbitrarily distributed particles to represent the continuous fluid medium, SPH enables the precise estimation of partial differential equations. This grid-free methodology effectively addresses many challenges associated with conventional methods, providing a more adaptable and efficient solution framework. SPH’s versatility is evident across a broad spectrum of applications, ranging from advanced computational fluid dynamics (CFD) to complex computational solid mechanics (CSM), and proves effective across various scales—from micro to macro and even astronomical phenomena. Although SPH excels in tackling problems involving multiple degrees of freedom, complex boundaries, and large discontinuous deformations, it is still in its developmental phase and has not yet been widely adopted. As such, a thorough understanding and systematic analysis of SPH’s foundational theories are critical. This paper offers a comprehensive review of the defining characteristics and theoretical foundations of the SPH method, supported by practical examples derived from the Navier–Stokes (N-S) equations. It also provides a critical examination of successful SPH applications across various fields. Additionally, the paper presents case studies of SPH’s application in tunnel and underground engineering based on practical engineering experiences and long-term on-site monitoring, highlighting SPH’s alignment with real-world conditions. The theory and application of SPH have thus emerged as highly dynamic and rapidly evolving research areas. The detailed theoretical analysis and case studies presented in this paper offer valuable insights and practical guidance for scholars and practitioners alike.

Sitting posture recognition systems have gained significant attention due to their potential applications in various domains, including healthcare, ergonomics, and human-computer interaction. This paper presents a comprehensive literature review and analysis of existing sitting posture recognition systems. Through an extensive examination of relevant research articles and conference papers, we identify and analyze the underlying technologies, methodologies, datasets, performance metrics, and applications associated with these systems. The review encompasses both traditional methods, such as vision-based approaches and sensor-based techniques, as well as emerging technologies such as machine learning and deep learning algorithms. Additionally, we examine the challenges, constraints, and future trends in the field of sitting posture recognition systems. Researchers, practitioners, and policymakers who want to comprehend the most recent developments and latest trends in sitting posture recognition technology will find great value in this study.

This paper uses Long Short-Term Memory (LSTM) networks to predict the stock prices of the Yuanta Taiwan Top 50 ETF (ETF50). To improve the accuracy of the model’s predictions, a calibration procedure called “Short-Term Bias Compensation” (STBC) is proposed to adjust the predicted stock prices. In STBC, the daily prediction error is calculated to estimate the short-term bias (STB) in prediction. Then, the predicted price of its next day will be corrected if this STB has exceeded a certain threshold. In this paper, we apply Genetic Algorithms (GAs) to optimize the parameters used in STBC for providing more confidence in its estimation. Based on these predicted stock prices, we propose a Genetic Fuzzy System (GFS) to determine the trading strategy, with trading points for buying and selling stocks. In GFS, various technical indicators are used to establish the fuzzy rules of the trading strategy, and GAs are used to evolve the best parameters for these fuzzy rules. Our experiments cover over 17 years of data (from 2003 to 2020) for ETF50 to consider black swan events such as the 2020 COVID-19 pandemic, the 2018 US–China trade war, and the 2011 US debt crisis. The first 90% of the data is used as training data, and the last 10% is used as testing data. We use 12 technical indicators of these data as the input of LSTM. The predicted values of LSTM are corrected using STBC and compared to the uncorrected prices. We use Mean Square Error (MSE) to evaluate the prediction accuracy. The results show that STBC can nearly reduce 90% of the prediction error (where MSE drops from 11.5758 to 1.2687). By using GFS with STBC to determine trading points, we achieve a return rate of 32.0%.

Endometrial cancer is reported to be one of the most prevalent cancers of the female reproductive organs worldwide, with increasing incidence and mortality rates over the past decade. Early diagnosis is critical for effective treatment. Recently, there has been a growing focus on the role of nutrition and micronutrient and macronutrient status in patients with gynecologic cancers, including endometrial cancer. In the following paper, we have conducted an in-depth narrative literature review with the aim of evaluating the results of metallomic studies specifically concerning the micro- and macronutrient status of patients with endometrial cancer. The main objective of the paper was to analyze the results regarding the nutritional status of endometrial cancer patients and describe the role of chosen elements in the onset and progression of endometrial carcinogenesis. Further, we have focused on the evaluation of the usage of the described elements in the potential treatment of the abovementioned cancer, as well as the possible prevention of cancer considering proper supplementation of chosen elements in healthy individuals. Calcium supplementation has been proposed to reduce the risk of endometrial cancer, although some studies offer conflicting evidence. Deficiencies in phosphorus, selenium, and zinc have been inversely associated with endometrial cancer risk, suggesting they may play a protective role, whereas excessive levels of iron, copper, and cadmium have been positively correlated with increased risk. However, the molecular mechanisms by which these elements affect endometrial carcinogenesis are not fully understood, and current findings are often contradictory. Further research is needed to clarify these relationships and to evaluate the potential of nutritional interventions for the prevention and treatment of endometrial cancer.

Many skin diseases begin with inflammatory changes on a molecular level. To develop a more thorough understanding of skin pathology and to identify new targets for therapeutic advancements, molecular mechanisms of inflammation in the context of skin disease should be studied. Current research efforts to better understand skin disease have focused on examining the role of molecular processes at several stages of the inflammatory response such as the dysregulation of innate immunity sensors, disruption of both transcriptional and post-transcriptional regulation, and crosstalk between immune and neuronal processes (neuro-immune crosstalk). This review seeks to summarize recent developments in our understanding of inflammatory processes in skin disease and to highlight opportunities for therapeutic advancements. With a focus on publications within the past 5 years (2019–2024), the databases PubMed and EBSCOhost were used to search for peer-reviewed papers regarding inflammatory molecular mechanisms and skin disease. Several themes of research interest regarding inflammatory processes in skin disease were determined through extensive review and were included based on their relative representation in current research and their focus on therapeutic potential. Several skin diseases such as psoriasis, atopic dermatitis, hidradenitis suppurativa, and scleroderma were described in the paper to demonstrate the widespread influence of inflammation in skin disease.

This article studies the migration and settlement laws of backflow proppants after fracturing tight sandstone. This paper proposes a fitting method based on a multi-task learning network to address the issue of interference from multiple physical parameters during the transport and settlement processes of proppants. This method can effectively handle multi-dimensional interference factors and fit the mapping logic of multiple engineering parameters to transport patterns through the continuous correction of multi-layer networks. We first introduce the characteristics of tight sandstone reservoirs and their important value in mining, as well as the status of current research on the migration and settlement laws of proppants at home and abroad. Based on this, we then deeply analyze the sedimentation rate model of proppants in tight sandstone backflow and the equilibrium height of proppants under multiple factors of interference while considering the distribution characteristics of proppants. In order to more accurately simulate the transport and settlement laws of proppants, this paper introduces a multi-task learning network. This network can comprehensively consider multi-dimensional parameters, learn the inherent laws of data through training, and achieve accurate fitting of the transport and settlement laws of proppants. This study trained and tested the model using actual production data, and the results showed that the proposed model can fit the input–output relationship well, thus effectively supporting the study of proppant transport and settlement laws.

Healthcare is one of the industries that seeks to deliver medical services to patients on time. One of the issues it currently grapples with is real-time patient data exchange between various healthcare organizations. This challenge was solved by both centralized and decentralized cloud computing architecture solutions. In this paper, we review the current state of these two cloud computing architectures in the health sector with regard to the effect on the efficiency of Health Information Exchange (HIE) systems. Our study seeks to determine the relevance of these cloud computing approaches in assisting healthcare facilities in the decision-making process to adopt HIE systems. This paper considers the system performance, patient data privacy, and cost and identifies research directions in each of the architectures. This study shows that there are some benefits in both cloud architectures, but there are also some drawbacks. The prominent characteristic of centralized cloud computing is that all data and information are stored together at one location, known as a single data center. This offers many services, such as integration, effectiveness, simplicity, and rapid information access. However, it entails providing data privacy and confidentiality aspects because it will face the hazard of a single point of failure. On the other hand, decentralized cloud computing is built to safeguard data privacy and security whereby data are distributed to several nodes as a way of forming mini-data centers. This increases the system’s ability to cope with a node failure. Thus, continuity and less latency are achieved. Nevertheless, it poses integration issues because managing data from several sites could be a problem, and the costs of operating several data centers are higher and complex. This paper also pays attention to the differences in aspects like efficiency, capacity, and cost. This paper assists healthcare organizations in determining the most suitable cloud architecture strategy for deploying secure and effective HIE systems.

In this study, we developed a cost-effective and rapid method for fabricating force-sensing resistor (FSR) sensors as an alternative to commercial force sensors. Our aim was to achieve performance characteristics comparable to existing commercial products while significantly reducing costs and fabrication time. We analyzed the material composition of two widely used commercial force sensors: Interlink FSR-402 and Flexiforce A201-1. Based on this analysis, we selected 4B and 9B pencils, which contain high concentrations of graphite, and silicone sealant to replicate these material properties. The fabrication process involved creating piezoresistive sheets by shading A4 copy paper with 4B and 9B pencils to form a uniform layer of graphite. Additionally, we prepared a mixture of 9B pencil lead powder and silicone sealant, ensuring a consistent application on the paper substrate. Measurement results indicated that the force sensor fabricated using a mixture of 9B pencil powder and silicone sealant exhibited electrical and mechanical characteristics closely resembling those of commercial sensors. Load tests revealed that the hand-made sensors provided a proportional voltage output in response to increasing and decreasing loads, similar to commercial FSR sensors. These results suggest that our fabrication method can produce reliable and accurate FSR sensors suitable for various applications, including wearable technology, robotics, and force-sensing interfaces. Overall, this study demonstrates the potential for creating cost-effective and high-performance FSR sensors using readily available materials and simple fabrication techniques.

A digital twin is a new trend in the development of the current smart water conservancy industry. The main research content of intelligent water conservancy is clarified. This paper first summarizes and combs the relevant system architecture of smart water conservancy, and puts forward a smart water conservancy framework based on digital twins, highlighting the characteristics of virtual and real interaction, and symbiosis of the water conservancy twin platform. Secondly, the status quo of intelligent water conservancy “sky, air, ground and water” integrated monitoring technology, big data and artificial intelligence, model platform technology, knowledge graph and security technology is analyzed. From the perspective of application, the research progress of each technology in water security, water resources and hydraulic engineering is reviewed. Although the construction of smart water conservancy has made remarkable progress, it still faces many challenges such as data governance, technology integration and innovation, and standardization. In view of these challenges, this paper puts forward a series of countermeasures, and looks forward to the future development direction of intelligent water conservancy.

High-precision and rapid detection of apparent defects in subway tunnel linings is crucial for ensuring the structural integrity of tunnels and the safety of train operations. However, current methods often do not adequately account for the spatial characteristics of these defects and perform poorly in detecting and extracting small-scale defects, which limits the accuracy of detection and geometric parameter extraction. To address these challenges, this paper proposes an efficient algorithm for detecting and extracting apparent defects in subway tunnels. Firstly, YOLOv8 was selected as the foundational architecture due to its comprehensive performance. The coordinate attention module and Bottleneck Transformer 3 were then integrated into the model’s backbone to enhance the focus on defect-prone areas and improve the learning of feature relationships between defects and other infrastructure. Subsequently, a high-resolution detection layer was added to the model’s head to further improve sensitivity to subtle defects. Additionally, a low-quality crack dataset was created using an open access dataset, and transfer learning combined with Real-ESRGAN was employed to enhance the detail and resolution of fine cracks. The results of the field experiments demonstrate that the proposed model significantly improves detection accuracy in high-incidence areas and for small-scale defects, achieving a mean average precision (mAP) of 87% in detecting cracks, leakage, exfoliation, and related infrastructure defects. Furthermore, the crack enhancement techniques substantially improve the representation of fine-crack details, increasing feature extraction accuracy by a factor of four. The findings of this paper could provide crucial technical support for the automated operation and maintenance of metro tunnels.

As large-scale renewable energy sources are increasingly integrated into AC grids via voltage source converters (VSCs), the transient synchronization stability of phase-locked loop (PLL)-based VSCs during fault ride-through is gaining more attention. Most existing studies assume that the positive and negative sequence separation and current control dynamics are much faster than the PLL dynamics, thereby neglecting their impact on the transient synchronization stability of the system. However, when the PLL bandwidth is relatively large, ignoring the positive and negative sequence separation and current control dynamics may result in incorrect stability assessments. To address this issue, this paper first considers the multiple control loop coupling, including positive and negative sequence separation, current control, and PLL, to construct a full-order nonlinear mathematical model of the VSC grid-connected system under asymmetric fault conditions. Based on this, the phase trajectory method is employed to analyze the transient synchronization stability of the system. Additionally, this full-order mathematical model is used to determine the PLL bandwidth boundary beyond which the effects of positive and negative sequence separation and current control dynamics cannot be neglected. Finally, PSCAD/EMTDC simulation results validate the effectiveness of the theoretical analysis presented in this paper.

Drone area coverage primarily involves using unmanned aerial vehicles (UAVs) for extensive monitoring, surveying, communication, and other tasks over specific regions. The significance and value of this technology are multifaceted. Firstly, UAVs can rapidly and efficiently reach remote or inaccessible areas to perform tasks such as terrain mapping, disaster monitoring, or search and rescue, significantly enhancing response speed and execution efficiency. Secondly, drone area coverage in agricultural monitoring, forestry conservation, and urban planning offers high-precision data support, aiding scientists and decision-makers in making more accurate judgments and decisions. Additionally, drones can serve as temporary communication base stations in areas with poor communication, ensuring the transfer of crucial information. Drone area coverage technology is vital in improving work efficiency, reducing costs, and strengthening decision support. This paper aims to solve the optimization problem of multi-UAV area coverage flight path planning to enhance system efficiency and task execution capability. For multi-center optimization problems, a region decomposition method based on the Voronoi graph is designed, transforming the multi-UAV area coverage issue into the single-UAV area coverage problem, greatly simplifying the complexity and computational process. For the single-UAV area coverage problem and its corresponding area, this paper contrives a convolutional neural network with the channel and spatial attention mechanism (CSAM) to enhance feature fusion capability, enabling the model to focus on core features for solving single-UAV path selection and ultimately generating the optimal path. Simulation results demonstrate that the proposed method achieves excellent performance.

This paper aims to present an approach to address the atom rearrangement problem by developing Convolutional Neural Network (CNN) models. These models predict the coordinates for atom movements while ensuring collision-free transitions and filling all vacancies in the target array. The process begins with designing a cost function for the assignment problem that incorporates constraints to prevent collisions and guarantee vacancy filling. We then build and train CNN models using datasets for three different grid sizes: 10×10, 13×13, and 21×21. Our models achieve high accuracy in predicting atom positions, with individual position accuracies of 99.63%, 98.93%, and 97.24%, respectively, for the aforementioned grid sizes. Despite limitations in training larger models due to hardware constraints, our approach demonstrates significant improvements in speed and accuracy. The final section of the paper presents detailed accuracy results and calculation times for each model, highlighting the potential of CNN-based methods in optimizing atom rearrangement processes.