The aim of the study was to evaluate the polyphenol profile of Castanea sativa leaf methanolic extract and further evaluate its biological activities in vitro. After purification with an RP-18 resin, the extract was assessed for its polyphenol profile by UPLC-PDA-MS/MS, as well as for the antioxidant potential (ABTS, CUPRAC, ChA, ROS scavenging methods), anticancer, antiobesity, antidiabetic and antimicrobial potential. Eighteen polyphenols were identified and the dominant compounds were chestatin followed by quercetin 3-O-glucoside. The total phenolic content of the extract showed a value of 1426.55 mg/100 g d.w. The obtained preparation showed the ability to scavenge O₂^•− (0.067 mg/mL) and OH^• (0.207 mg/mL) radicals and had a stronger anti-obesity than anti-diabetic effect. Additionally, this extract exhibited a strong anticancer activity against the Caco-2 line (153.54 µg/mL), with anti-migratory and anti-proliferative activity. In turn, among the tested strains, the highest activity was demonstrated against Staphylococcus aureus. Moreover, the effects demonstrated were strongly dependent on the content of polyphenols. In conclusion, C. sativa is a promising source of natural antioxidant, antibacterial, antiobesity, antidiabetic and chemopreventive compounds for food-pharma industry; however, further experimental studies are needed to validate its pharmacological properties. Full article

This paper proposes a novel approach to audio game design by introducing the concept of speech puzzles (spuzzles) to describe the utilisation of recorded voice for the creation of audio puzzles in ways that challenge players’ different listening modes. In the fields of audio games and audio-interactive applications, speech serves instructive, descriptive, narrative, and in some cases—in the form of hints or quizzes—gameplay purposes by addressing users through language. The suggested approach of spuzzles extends this potential by including, besides encoded meaning, the acoustic properties of sound, thus engaging the user’s causal and reduced listening modes in parallel with the semantic listening mode. An audio game consisting of four inherently different spuzzles was designed as proof of concept and tested by seven third-year students of Audiovisual Arts, who elaborated on their experience through a focus group semi-structured discussion. Despite their difficulty, the spuzzles were well accepted by most of the participants (5/7), whereas all participants agreed on their acoustic richness, need for concentration, and independence from pre-existing musical knowledge. Therefore, the authors suggest that the proposed design approach could serve as a paradigm for future research in the design of complex audio-based game mechanics. Full article

The absence of analytical expressions in current codes for evaluating the critical moment for lateral–torsional buckling of cold-formed beams with omega-shaped sections presents a fundamental challenge when assessing their resistance to global buckling. In response to this challenge, a comparative study was conducted to explore various approaches for calculating the critical moment. This involved both analytical and numerical analyses, using different methods available in codes and computational tools. The analytical analysis followed the Effective Width Method, employing the expression proposed in ENV 1993-1-1:1992, which is commonly used for evaluating the critical lateral–torsional moment of hot-rolled profiles. Numerical analyses were then performed using the ABAQUS v6.13, GBTUL v2.0, and CUFSM v5.05 software packages. The ABAQUS model, validated with results obtained from an experimental campaign, serves as the reference model. Upon assessing the bending moment resistances according to European, Brazilian, and American standards, consistency was found among these standards. However, it became evident that using the analytical expression proposed for hot-rolled profiles is inadequate for evaluating the critical lateral–torsional moment of CFS omega-shaped profiles. Conversely, the agreement between the ABAQUS, GBTUL, and CUFSM results suggests their utility as reliable tools for estimating the elastic critical lateral–torsional buckling moment. Full article

Complex fluid–solid systems generally exist in process engineering. The cognition of complex flow systems depends on numerical and experimental methods. The computational fluid dynamics–discrete phase method simulation based on coarsening technology has potential application prospects in industrial-scale equipment. This review outlines the computational fluid dynamics–discrete phase method and its application in several typical types of process engineering. In the process research, more attention is paid to the dense condition and multiphase flow. Furthermore, the CFD-DPM and its extension method for comprehensive hydrodynamics modeling are introduced. Subsequently, the current challenges and future trends of the computational fluid dynamics–discrete phase method are proposed. Full article

This paper addresses the challenge of mitigating low-frequency flow noise signals in autonomous underwater vehicles through the optimization of a placoid-scale skin. Drawing inspiration from the bio-inspired surface features of cylindrical shell structures, an enhanced design of placoid-scale skin is developed using 3D printing technology. This improved structure effectively reduced boundary layer vortices and wake intensity, thereby contributing to the suppression of low-frequency flow noise signals. Experimental results demonstrate that the notable reduction in low-frequency flow noise within the frequency range of 0–500 Hz, with average noise reduction of approximately 5 dB observed at 150 Hz. This reduction is validated by a combination of numerical simulations and experimental testing, confirming the efficacy of the optimized placoid-scale skin in attenuating the low-frequency flow noise associated with uniformly advancing turbulent boundary layers underwater. Full article

The main objective of this study was to evaluate the reliability of Garmin Vivofit^® Jr. physical activity (PA) wristbands during daily life physical activities. Six wristbands were randomly selected from a stock of twenty-four. The wristbands were worn by a single four-year-old participant, with three on the right wrist area and three on the left wrist area. To assess device reliability under laboratory conditions on a treadmill (Powerjog, model JM200, SportEngineering Ltd., Birmingham, UK), the participant wore the six wristbands while performing five work conditions: sitting and standing (30 times per minute, controlled by a metronome), walking at 3 km/h, walking at 4 km/h, running at 5 km/h, and running at 6 km/h. Throughout the six minutes, variables related to physical activity provided by the device, step volume, and minutes of physical activity were recorded using the specific application of the wristband (Garmin International Inc., Olathe, KS, USA). The intraclass correlation coefficients (ICCs) were high for all six wristbands with each other, for both the number of steps taken (ICC = 0.991–0.998) and the number of minutes of PA (ICC = 0.892–0.977). The critical alpha value of the Cusum test was highest at.050 for all wristband associations. In conclusion, good reliability was found among the six wristbands, which could be adopted for field-based research to quantify physical activities. Full article

This paper proposes a reinforcement learning method using a deep residual shrinkage network based on multi-reward priority experience playback for high-frequency and high-dimensional continuous vibration control. Firstly, we keep the underlying equipment unchanged and construct a vibration system simulator using FIR filters to ensure the complete fidelity of the physical model. Then, by interacting with the simulator using our proposed algorithm, we identify the optimal control strategy, which is directly applied to real-world scenarios in the form of a neural network. A multi-reward mechanism is proposed to assist the lightweight network to find a near-optimal control strategy, and a priority experience playback mechanism is used to prioritize the data to accelerate the convergence speed of the neural network and improve the data utilization efficiency. At the same time, the deep residual shrinkage network is introduced to realize adaptive denoising and lightweightness of the neural network. The experimental results indicate that under narrowband white-noise excitation ranging from 0 to 100 Hz, the DDPG algorithm achieved a vibration reduction effect of 12.728 dB, while our algorithm achieved a vibration reduction effect of 20.240 dB. Meanwhile, the network parameters were reduced by more than 7.5 times. Full article

The mineralization of soil organic carbon (SOC) is a critical process in the soil carbon cycle. This study aimed to investigate the vertical distribution characteristics and mineralization dynamics of SOC in soils and their aggregates across different steppe types in the Loess Plateau (LP). Soil profiles from three steppe types under varying precipitation gradients were selected: meadow steppe (MS), typical steppe (TS), and desert steppe (DS). A 60-day controlled laboratory incubation study was conducted for carbon mineralization and the influence of climatic and soil properties on SOC mineralization was analyzed. The results showed that the SOC content and cumulative mineralization (C_M) in 1–2 mm aggregates were higher than in other particle sizes; SOC content and C_M followed the order MS > TS > DS and both decreased significantly with increasing soil depth. Correlation analysis revealed that precipitation significantly affected aggregate mineralization (p < 0.001) and that mineralization in the 1–2 mm aggregates was more closely related to mean annual precipitation (MAP), SOC, and water-soluble organic carbon (SWOC). Precipitation primarily controlled SOC mineralization in the 0–50 cm soil layer, while SOC mineralization in the 50–100 cm layer was influenced by soil-related carbon content. Structural Equation Modeling indicated that precipitation influences the mineralization of organic carbon in topsoil indirectly through its direct impact on SOC. In the context of global warming, the SOC turnover rate in high-precipitation areas (MS) was faster than in low-precipitation areas (TS, DS), necessitating greater attention to soil carbon dynamics in these regions. Full article

In gynecologic surgery, a uterine manipulator is one of the instruments used to perform the laparoscopy. Throughout the past decade, a number of robotic technology applications used for uterine manipulation during surgery have been designed with the aim of increasing the efficiency, improving the precision, and reducing the workload of medical assistants. Although the RCM (Remote Center of Motion) mechanism is one of the key features in a Minimally Invasive Surgical (MIS) robot, the preliminary result in this study, in which the RCM mechanism was applied in a uterine manipulation robot, proved that this may cause unpleasant sensations such as irritation or harm to the nearby area during such manipulation. Therefore, a design of a non-RCM 2-DoF (Degree of Freedom) Robotic Uterine Manipulation System, in cooperation with an existing, reusable and tiltable-tip uterine manipulator, for laparoscopic gynecologic surgery has been proposed and evaluated via a mathematical model along with numerical analysis, a 3D uterus model, and a 1:1 uterus manikin model in order to demonstrate the use of the essential functions. According to the experimental results, the maximum load of 500 g has been handled well by the prototype, with the movement ranges of ±150° in the roll panel and ±90° in the pitch panel (0∼90° for anteversion and 0∼−90° for retroversion, if needed, which can be achieved by rotating the instrument to the other side). Furthermore, to verify this new design prior to its use on patients, and also in consideration of the ethics of human experimentation, through extensive testing on five donated soft-tissue cadavers, the proposed robot received positive feedback from all five surgeons performing the experiments and could offer effective uterine manipulation at the angular velocity of 4 °/s (0.67 RPM) with steady delineation of the vaginal fornices to create necessary motions in the pitch and roll panels of 30∼80° and ±15°, respectively, providing efficient visualization of the uterus. These features make this robot a valuable addition to the surgical instruments available to gynecologic surgeons. Full article

Long-term preventive maintenance planning using finite annual budgets is vital for maintaining the service performance of airport runway composite pavements. Using the pavement condition index (PCI) to quantify composite pavement performance, this study investigated the PCI deterioration tendencies of middle runways, terminal runways, and taxiways and developed prediction models related to structural thickness and air traffic. Performance jump (PJ) and deterioration rate reduction (DRR) were used to measure maintenance benefits. Based on 112 composite pavement sections in the Long-term Pavement Performance Program, this study analyzed the influences of five typical preventive maintenance technologies on PJ, DRR, and PCI deterioration rates. The logarithmic regression relationship between PJ and PCI was obtained. For sections treated with crack sealing and crack filling, the DRR was nearly 0. For sections treated with fog seal, thin HMA overlay, and hot-mix recycled AC, the DRR was 0.2, 0.7, and 0.8, respectively. To solve the multi-objective maintenance problem, this study proposed a decision-making optimization method based on dynamic programming, and the solution algorithm was optimized, which was applied in a five-year maintenance plan. Considering different PCI deterioration tendencies of airport regions, as well as PJ, DRR, and costs of maintenance technologies, the preventive maintenance decision-making optimization method meets performance and financial requirements sufficiently. Full article

Shale is composed of a rock matrix and bedding planes with a layered structure, resulting in significant anisotropy in its mechanical properties. In order to study the anisotropic mechanical properties of shale, the shale samples were prepared in different orientations with respect to the bedding planes, and the composition and microstructure of shale were first analyzed by X-ray diffractometer (XRD) and scanning electron microscope (SEM), and then the uniaxial and triaxial compression experiment on shale samples with five different bedding angles (the angle between the loading direction and the normal direction of the bedding planes, 0°, 30°, 45°, 60°, and 90°) were conducted under five confining pressures (0, 10, 20, 30, and 40 MPa), respectively; meanwhile, the acoustic emission (AE) test was carried out in the uniaxial test. The results indicate that the mechanical properties and parameters of shale have obvious anisotropy, and the AE characteristics of shale samples with different bedding angles are significantly different during uniaxial loading. Furthermore, the compressive strength and elastic modulus of the shale samples first decrease and then increase with the increase in the bedding angle under different confining pressures. Moreover, according to the anisotropic grade of compressive strength, the shale has moderate anisotropy. In addition, the failure mode of the shale samples is also anisotropic, and varies with the bedding angle and confining pressure. Full article

The rail vehicle industry wants to produce vehicles with higher speeds, to maintain and increase its market share. However, when the speed of the vehicle increases, it may have an undesirable effect on ride comfort, in terms of ride dynamics. Recent developments towards lighter and faster vehicles make the problem of ride comfort at higher speeds increasingly important. Focusing on the behavior of flexible rather than rigid body behavior should not be neglected when designing long and light car bodies. There are several approaches to incorporate body flexibility in multibody simulations and they have some superiorities and weaknesses. In this study, an efficient and accurate vertical dynamic model for the ride comfort analysis is developed and implemented in a commercial object-oriented modeling (OOM) software Dymola (2015 FD01) which uses the open-source code Modelica. This model includes car body flexibility with the assembling of a rigid body approach. The developed model is compared to a three-dimensional vehicle model in the commercial Vampire software (Pro V5.50) at different velocities. For the vertical ride comfort analysis, the ISO 2631-1 standard was used for both the developed model and the three-dimensional model. The results are presented as acceleration history and $a^{wrms}$—weighted r.m.s (root mean square) of accelerations—as required by the standard. The developed model has shown its feasibility in terms of its efficiency and accuracy for the vertical ride comfort analysis. The accuracy of the model is evidenced by the fact that the car body vibration level at high speeds shows minor differences compared to the results of the Vampire, which is a validated commercial software in the area of rail vehicle dynamics. The approach involving the assembly of rigid bodies is applied for the first time for high-speed trains in dynamical modelling, with flexible car bodies for ride comfort analysis. Furthermore, it can be used for parametrical studies focusing on ride comfort, thereby offering a quite beneficial framework for addressing the challenges of ride comfort analysis in high-speed rail vehicles. Improvements for and analyses of other aspects are also possible, since the optimization and other useful libraries are readily available in Dymola/Modelica. Full article

One of the most pressing issues in the implementation of construction projects is the extension of planned deadlines, significantly impacting project costs. This situation often arises due to inaccurate estimation of construction durations, which rely on normative values without accounting for factors hindering construction progress. Consequently, this article aims to develop an innovative approach for assessing construction durations, considering specific risk factors and their influence on construction activities. Given the difficulty of determining risk factors and their effects during the design phase using classical probability theory, characterized by unknown probability distributions, it is highlighted that this scenario represents planning and implementation under conditions of non-statistical uncertainty. Therefore, the article proposes an approach utilizing elements of fuzzy set theory, particularly fuzzy rules and linguistic variables, to determine delays in individual construction tasks. The proposed approach involves estimating extensions of construction timelines based on a specified probability level of occurrence for risk events and their impact. Additionally, the article provides a theoretical description of the proposed approach and practical calculation examples, demonstrating that the authors’ approach significantly enhances the accuracy of construction timeline forecasts, providing more reliable data for project planning and management. Full article

The issue of the transport of goods is well-known, yet, in practice, there are often cases of damaged shipments due to improper storage and inappropriately chosen transport technology. Many cases are due to ignorance of the basic characteristics of the cargo and, consequently, its transport characteristics. Vehicle dynamics is crucial to the design of proper cargo securing; therefore, this article provides the values of longitudinal and lateral acceleration of a 16.5 m semi-trailer vehicle combination for test routes of length of 10,827 km on highways and other roads in Slovakia, Austria, and Germany from the monitoring of goods. The horizontal acceleration of 0.2 g is considered as the minimum stability of the load unit that should withstand transport. A load unit with a stability from 0.2 g to 0.3 g could be considered as the weakest load unit. The test results show that even the weakest load units such as these can be damaged in transports, as semi-trailer vehicle combinations still reach longitudinal ax1000 and lateral ay1000 accelerations between 0.2 g and 0.3 g relatively frequently. Acceleration events higher than 0.3 g occur very rarely, at 1.4 event/1000 km for roads, but only 0.1 event/1000 km for highways from our test transports. We have demonstrated through our research that it is necessary for the load units to have a minimum stability of 0.2 g. We can conclude that load units with a stability of less than 0.2 g are completely unacceptable for transport without additional securing because we obtained 70.3 acceleration events per 1000 km in the interval from 0.1 g to 0.2 g on highways but 1148.1 events per 1000 km on other roads. There is a big difference between the number of acceleration events per 1000 km on roads and highways for all acceleration intervals, which means that there is a substantially lower probability of damaging the weak load units on highways than on other roads. Full article

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. By leveraging machine learning, this study used a backpropagation (BP) neural network to optimize commercial spaces in Weinan City’s central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development that emphasizes aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small- and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning. Full article

Sulfur hexafluoride (SF₆) is a typical fluorine gas with excellent insulation and arc extinguishing properties that has been widely used in large-scale power equipment. The detection of SF₆ gas in high-power electrical equipment is a necessary measure to ensure the reliability and safety of power grid operation. A failure of SF₆ insulated electrical equipment, such as discharging or overheating conditions, can cause SF₆ gas decomposition, resulting in various decomposition products. The decomposed gases inside the equipment decrease the insulating properties and are toxic. The leakage of SF₆ can also decrease the insulating properties. Therefore, it is crucial to monitor the leakage of SF₆ decomposed gases from electrical equipment. Quantitative testing of decomposition products allows us to assess the insulation state of the equipment, identify internal faults, and maintain the equipment. This review comprehensively introduces the decomposition formation mechanism of SF₆ gas and the current detection technology of decomposition products from the aspects of principle and structure, materials, test effect, and practicability. Finally, the development trends of SF₆ and decomposition gas detection technology for the reliability and safety of power grid operation are prospected. Full article

Multi-sensor fusion is pivotal in augmenting the robustness and precision of simultaneous localization and mapping (SLAM) systems. The LiDAR–visual–inertial approach has been empirically shown to adeptly amalgamate the benefits of these sensors for SLAM across various scenarios. Furthermore, methods of panoptic segmentation have been introduced to deliver pixel-level semantic and instance segmentation data in a single instance. This paper delves deeper into these methodologies, introducing PSMD-SLAM, a novel panoptic segmentation assisted multi-sensor fusion SLAM approach tailored for dynamic environments. Our approach employs both probability propagation-based and PCA-based clustering techniques, supplemented by panoptic segmentation. This is utilized for dynamic object detection and the removal of visual and LiDAR data, respectively. Furthermore, we introduce a module designed for the robust real-time estimation of the 6D pose of dynamic objects. We test our approach on a publicly available dataset and show that PSMD-SLAM outperforms other SLAM algorithms in terms of accuracy and robustness, especially in dynamic environments. Full article

In the screening phase of a systematic review, screening prioritization via active learning effectively reduces the workload. However, the PRISMA guidelines are not sufficient for reporting the screening phase in a reproducible manner. Text screening with active learning is an iterative process, but the labeling decisions and the training of the active learning model can happen independently of each other in time. Therefore, it is not trivial to store the data from both events so that one can still know which iteration of the model was used for each labeling decision. Moreover, many iterations of the active learning model will be trained throughout the screening process, producing an enormous amount of data (think of many gigabytes or even terabytes of data), and machine learning models are continually becoming larger. This article clarifies the steps in an active learning-aided screening process and what data is produced at every step. We consider what reproducibility means in this context and we show that there is tension between the desire to be reproducible and the amount of data that is stored. Finally, we present the RDAL Checklist (Reproducibility and Data storage for Active Learning-Aided Systematic Reviews Checklist), which helps users and creators of active learning software make their screening process reproducible. Full article

Black soldier fly larvae (BSFL) (Hermetia illucens) reared on food waste streams are considered a sustainable source of protein in feed livestock diets. Recently, portable near-infrared spectroscopy (NIR) instruments have been assessed to monitor the consistency and quality of food waste streams used to feed black soldier fly larvae. During the application of NIR spectroscopy, sample presentation (e.g., drying, processing, particle size) plays an important role in the accuracy of the models developed (quantitative or qualitative analysis). The objective of this study was to evaluate the effect of sample presentation (number of larvae used during the scanning of BSFL) on the accuracy of classification models developed to trace the food waste stream (e.g., supermarket of childcare) used to feed the larvae. BSFL samples were sourced from two waste streams and scanned as half, 1, 2, or 3 larvae using an NIR portable instrument (MicroNIR, Viavi, Milpitas, California, USA). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data and to classify the samples according to the waste stream. The main differences in the NIR spectra of the BSFL samples associated with the number of larvae scanned were observed around 1200 nm, mainly associated with the C-H overtones (lipids). The classification results showed that high classification rates (>93%) were obtained regardless of the number of larvae scanned, ranging from 93% (using 0.5 larvae) to 100% (using 1, 2, or 3 larvae samples). Overall, the number of larvae scanned had minimal to no effect on the accuracy of the LDA classification models. The present study demonstrated that a portable NIR instrument can be suitable for an initial rapid classification or determination of the origin of the waste stream used to feed the BSFL. Full article

Low Earth orbit (LEO) satellite networks are characterized by rapid topological changes, numerous network nodes and varying states of node resource constraints, which have resulted in traditional routing algorithms no longer being suitable for LEO satellite network routing. Therefore, this paper proposes an inductive learning architecture based on Graph Sample and Aggregate (GraphSAGE), which can significantly reduce the number of topology nodes to be trained, thereby reducing the computational complexity of the nodes. Then deep reinforcement learning (DRL) is employed for the continuous learning optimization of routing algorithms, and its generalization is improved by selecting GraphSAGE to construct the DRL agent. In the proposed graph neural-network-based routing optimization algorithm for LEO satellite networks, each Deep Q-Network (DQN) agent independently generates the hidden states of the nodes through the GraphSAGE model and uses them as inputs to the DRL model to make routing decisions. After a simulation and comparison, the proposed algorithm not only improves the overall network throughput, but also reduces the average end-to-end delay. The average throughput of the proposed algorithm increases by 29.47% and 18.42% compared to that of Dijkstra and the DQN, respectively. The average end-to-end delay is reduced by 39.76% and 15.29%, respectively, and can also adapt to changing topologies. Full article

Obtaining accurate basic parameters for long hole blasting is challenging, and the resulting vibration damage significantly impacts key surface facilities. Predicting ground vibration velocity accurately and mitigating the harmful effects of blasting are crucial aspects of controlled blasting technology. This study focuses on the prediction of ground vibration velocity induced by underground long hole blasting tests. Utilizing the fitting equation based on the US Bureau of Mines (USBM) formula as a baseline for predicting peak particle velocity, two machine learning models suitable for small sample data, Support Vector Regression (SVR) machine and Random Forest (RF), were employed. The models were optimized using the particle swarm optimization algorithm (PSO) to predict peak particle velocity with multiple parameters specific to long hole blasting. Mean absolute error (MAE), mean Squared error (MSE), and coefficient of determination (R²) were used to assess the model predictions. Compared with the fitting equation based on the USBM model, both the Support Vector Regression (SVR) and Random Forest (RF) models accurately and effectively predict peak particle velocity, enhancing prediction accuracy and efficiency. The SVR model exhibited slightly superior predictive performance compared to the RF model. Full article

On-road driving studies are essential for comprehending real-world driver behavior. This study investigates the use of eye-tracking (ET) technology in research on driver behavior and attention during Controlled Driving Studies (CDS). One significant challenge in these studies is accurately detecting when drivers divert their attention from crucial driving tasks. To tackle this issue, we present an improved method for analyzing raw gaze data, using a new algorithm for identifying ID tags called Binarized Area of Interest Tracking (BAIT). This technique improves the detection of incidents where the driver’s eyes are off the road through binarizing frames under different conditions and iteratively recognizing markers. It represents a significant improvement over traditional methods. The study shows that BAIT performs better than other software in identifying a driver’s focus on the windscreen and dashboard with higher accuracy. This study highlights the potential of our method to enhance the analysis of driver attention in real-world conditions, paving the way for future developments for application in naturalistic driving studies. Full article