Polypropylene-based aerogels with high surface area have been developed for the first time. By chemical crosslinking of polypropylene with oligomeric capped-end amino compounds, followed by dissolution, thermally induced phase separation, and the supercritical CO₂ drying process or freeze-drying method, the aerogels exhibit high specific surface areas up to 200 m²/g. Moreover, the silica-cage multi-amino compound was utilized in a similar vein for forming hybrid polypropylene aerogels. According to the SEM, the developed polypropylene-based aerogels exhibit highly porous morphology with micro-nanoscale structural features that can be controlled by processing conditions. Our simple and inexpensive synthetic strategy results in a low-cost, chemically resistant, and highly porous material that can be tailored according to end-use applications. Full article

In this paper, the initiation of the fracture of a segment caused by the pressure of the jack and other factors during shield construction is discussed. Based on the Rots model in the finite element software Diana 10.4 , the fracture width is solved. Combined with in situ measurements, the mechanisms of concrete fracturing of a segment under external loads, such as the jack thrust deflection angle and uneven jack thrust caused by the changes in the segment due to the upward buoyancy and shield attitude, are studied; additionally, the occurrence conditions and engineering control measures for segment fracture are summarized. The results show that when the attitudes of the shield and segment are identical, the total thrust of the shield is recommended not to exceed 21,000 kN, and is strictly limited to 24,000 kN. When the attitude inclination angle between the shield machine and the segment is less than 1°, the impact on the segment quality is small. When the inclination angle reaches 2°, the total thrust of the shield is recommended not to exceed 16,000 kN, and is strictly limited to 18,000 kN. When the inclination reaches 3°, a fracture is easily produced. When the total thrust is 19,000 kN, it is recommended that the loading increase or decrease in the left and right four grippers should not exceed 20%, and they are prohibited to exceed 30%. The fracture width increases exponentially with the increase in misalignment between adjacent segment rings. These research results provide a theoretical basis for jack pressure control during shield construction. Full article

This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Toward that goal, we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in the proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant SpikerBox, which is a device that measures changes in the voltage differentials of plants between roots and leaves. Using machine learning, we found that the voltage differentials over time of the plant predict if (a) eurythmy has been performed, and (b) which kind of eurythmy gestures has been performed. We also find that the signals are different based on the species of the plant. In other words, the perception of a salad, tomato, or basil might differ just as perception of different species of animals differ. This opens new ways of studying plant ecosystems while also paving the way to use plants as biosensors for analyzing human movement. Full article

This study aims to develop a fault detection system designed specifically for wind turbine gearboxes. It proposes a hybrid fault diagnosis algorithm that combines scatter plot analysis with the visual geometric group (VGG) technique to identify various fault types, including gear rust, chipping, wear, and aging. To capture vibration signals, a three-axis vibration sensor was integrated with a NI-9234 DAQ card. Digital signal processing techniques were employed to actively filter out noise from the captured signals. Gaussian white noise was incorporated into the training data to enhance the noise resistance of the network model, which was then utilized for scatter plot generation. The VGG technique was subsequently applied to identify faults. The testing data were collected at two different speeds, with 1500 samples taken at each speed, totaling 3000 samples. For both training and testing, 400 samples of each fault type were employed for training, while 200 samples were allocated for testing. The test results demonstrated an overall identification accuracy of 97.7% for both the no-fault gearbox and the four-fault states, underscoring the effectiveness of the proposed methodology. Full article

The supercritical hydrothermal synthesis of nanomaterials has gained significant attention due to its straightforward operation and the excellent performance of the resulting products. In this study, the supercritical hydrothermal method was used with Zn(CH₃COO)₂·2H₂O as the precursor and deionized water and ethanol as the solvent. Nano-ZnO was synthesized under different reaction temperatures (300~500 °C), reaction times (5~15 min), reaction pressures (22~30 MPa), precursor concentrations (0.1~0.5 mol/L), and ratios of precursor to organic solvent (C₂H₅OH) (2:1~1:4). The effects of synthesis conditions on the morphology and size of ZnO were studied. It was found that properly increasing hydrothermal temperature and pressure and extending the hydrothermal time are conducive to the more regular morphology and smaller size of ZnO particles, which is mainly achieved through the change of reaction conditions affecting the hydrothermal reaction rate. Moreover, the addition of ethanol makes the morphology of nano-zno more regular and significantly inhibits the agglomeration phenomenon. In addition to the change in physical properties of the solvent, this may also be related to the chemical bond established between ethanol and ZnO. The results show that the optimum synthesis conditions of ZnO are 450 °C, 26 MPa, 0.3 mol/L, 10 min, and the molar ratio of precursor to ethanol is 1:3. Full article

Fracture expansion in rock masses can be observed by monitoring the break of contacts between the bounding particles via the discrete element method. The latter’s realization in this study via the PFC^2D program tracked the evolution process of the zonal disintegration in an exemplary roadway-surrounding rock affected by mining. Besides, the damage evolution pattern in a high-stress soft rock roadway was simulated by the FLAC^2D program using a strain-softening plastic model, revealing the effects of rock mass strength, stress state, and anchor support on the zonal disintegration of the roadway. Numerical simulation results show that in a roadway with high-level stress, the obvious fractures spread from the roadway surface to the depth of the surrounding rock along a series of geometric planes and cut the surrounding rock into rock mass blocks. Under high crustal stress, conjugate shear fractures occur near the roadway surfaces and form a closed-loop fractured zone after intersecting the conjugate fracture faces. The closed fractured zone becomes a free face, from which conjugate shear fractures develop, forming new closed fractured zones in the deep surrounding rock. By repeatedly generating the closed fracture zones, a fracture network appears in the roadway-surrounding rock. The development of zonal disintegration of roadway-surrounding rock mainly depends on the rock mass strength and its stress state. Zonal disintegration only occurs when the crustal stress of the roadway-surrounding rock exceeds its strength. When the horizontal stress is low and the vertical stress exceeds the rock mass strength, zonal disintegration only occurs on two sides of the roadway. When the vertical stress is low and the horizontal stress exceeds the rock’s mass strength, it only appears on the roof and floor. When the values of cohesion, internal friction angle, and tensile strength are reduced in the same proportion, cohesion has the greatest impact on the expansion of the zonal disintegration zone, followed by the internal friction angle, while the tensile strength effect is the least. In anchor-supported roadways undergoing zonal disintegration processes, the intact zone blocks slide relatively along the fracture surface during the process of loosening and deformation of the surrounding rock, making the anchor rods susceptible to tensile, shear, and bending actions. Full article

This research presents an approach to the hybrid energy harvesting paradigm (HEHP) based on suspended energy harvest. It uses a harvesting vibration absorber (HVA) with an SC/NMC-lithium battery hybrid energy storage paradigm (SCB-HESP) equipped regenerative braking system (SCB-HESP-RBS) for electric vehicles 2 tons in gross weight (MEVs) driven by a 30 kW permanent magnet synchronous motor (PMSM). During regenerative braking, the ANN mechanism controls the RBS to adjust the switching waveform of the three-phase power inverter, and the braking energy transfers to the energy storage device. Additionally, a supercapacitor (SC) equipped with HVA can absorb energy from vehicle vibrations and convert it into electrical energy. The energy-harvesting efficiency of MEV based on SCB-HESP-RBS using HVA suspended energy harvesting enhances the efficiency maximum to 50.58% and 15.36% in comparison to MEV with only-HVA and SCB-HESP-RBS, respectively. Further, the MEV with SCB-HESP-RBS using HVA has a driving distance of up to 247.34 km (22.5 cycles) when compared with SCB-HESP-RBS (214.40 km, 19.5 cycles) and only-HVA (164.25 km, 15 cycles). Full article

Background: School meals represent a significant supply of nutrients for children in Brazil, especially those in conditions of social vulnerability. Objectives: This study aimed to assess the levels of arsenic (As), cadmium (Cd), and lead (Pb) in meals served in public elementary schools in four municipalities in the state of Bahia, Brazil, and assess the risk posed to children’s health. Methods: Ninety-six samples were collected from 16 schools, freeze-dried, and subjected to microwave-assisted digestion. The As, Cd, and Pb levels were determined by graphite furnace atomic absorption spectrometry. The risk assessment was based on calculating each element’s hazard quotient (HQ). Results: None of the samples reached or exceeded the tolerable levels for the elements analyzed. Pb was the metal that obtained the most significant result, reaching maximum levels of 39–157 µg·kg⁻¹. Conclusions: No element exceeded the PTWI proposed by JECFA; thus, the toxic metal content in school meals poses a negligible risk to children’s health. Full article

Glycyrrhiza inflata Bat. produces a lot of licorice waste after water extraction, which also retains abundant total flavonoids (TFs) and licochalcone A. However, licorice residue is often wasted due to the lack of good utilization of resources in practical applications. This study first screened the optimal membrane pore size and resin type and then explored the mechanism and conditions of the adsorption of TFs on the resin. Then, different combinations and sequences of membrane and macroporous resin (MR) methods were investigated. It was found that using the membrane method for initial purification, followed by the MR method for further purification, yielded the best purification results. Next, response surface methodology was utilized to investigate the resin’s dynamic desorption conditions for TFs. Finally, the TF purity increased from 32.9% to 78.2% (2.38-fold) after purification by a combined membrane–MR process; the purity of licochalcone A increased from 11.63 mg·g⁻¹ to 22.70 mg·g⁻¹ (1.95-fold). This study verified the feasibility of enriching TFs and licochalcone A from licorice residue using a membrane–MR coupling method. In addition, a quality-control method was established using a fingerprinting method on the basis of ultrahigh-performance liquid chromatography (UPLC) to ensure the stability of the enrichment process. Full article

Bisphenol A (BPA), representing a class of organic pollutants, finds extensive applications in the pharmaceutical industry. However, its widespread use poses a significant hazard to both ecosystem integrity and human health. Advanced oxidation processes (AOPs) based on peroxymonosulfate (PMS) via heterogeneous catalysts are frequently proposed for treating persistent pollutants. In this study, the degradation performance of BPA in an oxidation system of PMS activated by transition metal sites anchored nitrogen-doped carbonaceous substrate (M-N-C) materials was investigated. As heterogeneous catalysts targeting the activation of peroxymonosulfate (PMS), M-N-C materials emerge as promising contenders poised to overcome the limitations encountered with traditional carbon materials, which often exhibit insufficient activity in the PMS activation process. Nevertheless, the amalgamation of metal sites during the synthesis process presents a formidable challenge to the structural design of M-N-C. Herein, employing ZIF-8 as the precursor of carbonaceous support, metal ions can readily penetrate the cage structure of the substrate, and the N-rich linkers serve as effective ligands for anchoring metal cations, thereby overcoming the awkward limitation. The research results of this study indicate BPA in water matrix can be effectively removed in the M-N-C/PMS system, in which the obtained nitrogen-rich ZIF-8-derived Cu-N-C presented excellent activity and stability on the PMS activation, as well as the outstanding resistance towards the variation of environmental factors. Moreover, the biological toxicity of BPA and its degradation intermediates were investigated via the Toxicity Estimation Software Tool (T.E.S.T.) based on the ECOSAR system. Full article

Nitric oxide (NO) is a key diffusible messenger in the mammalian brain. It has been proposed that NO may diffuse in retrograde into presynaptic terminals, contributing to the induction of hippocampal long-term potentiation (LTP). Here, we present novel evidence that NO is selectively required for the synaptic potentiation of the interhemispheric projection in the anterior cingulate cortex (ACC). Unilateral low-frequency stimulation (LFS) induced a short-term synaptic potentiation on the contralateral ACC through the corpus callosum (CC). The use of the antagonists of the NMDA receptor (NMDAR), or the inhibitor of the L-type voltage-dependent Ca²⁺ channels (L-VDCCs), blocked the induction of this ACC-ACC potentiation. In addition, the inhibitor of NO synthase, or inhibitors for its downstream signaling pathway, also blocked this ACC-ACC potentiation. However, the application of the NOS inhibitor blocked neither the local electric stimulation-induced LTP nor the stimulation-induced recruitment of silent responses. Our results present strong evidence for the pathway-selective roles of NO in the LTP of the ACC. Full article

The process of image fusion is the process of enriching an image and improving the image’s quality, so as to facilitate the subsequent image processing and analysis. With the increasing importance of image fusion technology, the fusion of infrared and visible images has received extensive attention. In today’s deep learning environment, deep learning is widely used in the field of image fusion. However, in some applications, it is not possible to obtain a large amount of training data. Because some special organs of snakes can receive and process infrared information and visible information, the fusion method of infrared and visible light to simulate the visual mechanism of snakes came into being. Therefore, this paper takes into account the perspective of visual bionics to achieve image fusion; such methods do not need to obtain a significant amount of training data. However, most of the fusion methods for simulating snakes face the problem of unclear details, so this paper combines this method with a pulse coupled neural network (PCNN). By studying two receptive field models of retinal nerve cells, six dual-mode cell imaging mechanisms of rattlesnakes and their mathematical models and the PCNN model, an improved fusion method of infrared and visible images was proposed. For the proposed fusion method, eleven groups of source images were used, and three non-reference image quality evaluation indexes were compared with seven other fusion methods. The experimental results show that the improved algorithm proposed in this paper is better overall than the comparison method for the three evaluation indexes. Full article

Background: Survival rates of cancer patients have increased globally and across age groups. Challenges arising from craniofacial growth-development disturbances and dental abnormalities might warrant modifications to standard orthodontic pathways of care. Objective: The aim of this study was to systematically summarize and critically assess the available literature regarding the characteristics of orthodontic treatment in cancer survivors. Materials and Methods: A systematic search was conducted in seven databases for studies on malignant tumor survivors having undergone orthodontic intervention with fixed appliances following cancer treatment up to August 2023. The outcomes of interest included quantitative data regarding various characteristics of orthodontic treatment and the post-treatment period. The risk of bias was assessed individually with the Newcastle-Ottawa scale. Results: Out of 347 records, 4 cohort studies were eventually included in the qualitative synthesis. Leukemia was the most common malignancy type, with treatment involving mainly chemotherapy and/or radiotherapy. The duration of orthodontic treatment in cancer survivors varied. Occlusal results, quality of life, and satisfaction were comparable to healthy peers. However, in some survivors’ groups, treatment was shorter and the final results were compromised. Root resorption and oral mucositis were reported among the treated cancer survivors. Reduced occlusal outcome stability during the retention period was also reported. Conclusions: Overall, the duration of orthodontic treatment varied among cancer survivors. The occlusal results achieved were similar to those of their healthy peers, though potentially less stable. Patient-reported outcomes did not differ significantly between cancer survivors and healthy individuals treated orthodontically. Full article

: Effective decarbonization of the power generation sector requires a multi-pronged approach, including the implementation of CO₂ capture and storage (CCS) technologies. The Graz cycle features oxy-combustion CO₂ capture in a power production scheme which can result in higher thermal efficiencies than that of a combined cycle. However, the auxiliary consumption required by the air separation unit to provide pure O₂ results in a significant energy penalty relative to an unabated plant. In order to mitigate this penalty, the present study explores the possibility of chemical looping combustion (CLC) as an alternative means to supply oxygen for conversion of the fuel. For a midscale power plant, despite reducing the levelized cost of electricity (LCOE) by approximately 12.6% at a CO₂ tax of EUR 100/ton and a natural gas price of EUR 6.5/GJ and eliminating the energy penalty of CCS relative to an unabated combined cycle, the cost reductions of CLC in the Graz cycle were not compelling relative to commercially available post-combustion CO₂ capture with amines. Although the central assumptions yielded a 3% lower cost for the Graz-CLC cycle, an uncertainty quantification study revealed an 85.3% overlap in the interquartile LCOE range with that of the amine benchmark, indicating that the potential economic benefit is small compared to the uncertainty of the assessment. Thus, this study indicates that the potential of CLC in gas-fired power production is limited, even when considering highly efficient advanced configurations like the Graz cycle. Full article

This paper aims to enhance the Graphical Set-based model (GSB) for ranking and classification tasks by incorporating node and word embeddings. The model integrates a textual graph representation with a set-based model for information retrieval. Initially, each document in a collection is transformed into a graph representation. The proposed enhancement involves augmenting the edges of these graphs with embeddings, which can be pretrained or generated using Word2Vec and GloVe models. Additionally, an alternative aspect of our proposed model consists of the Node2Vec embedding technique, which is applied to a graph created at the collection level through the extension of the set-based model, providing edges based on the graph’s structural information. Core decomposition is utilized as a method for pruning the graph. As a byproduct of our information retrieval model, we explore text classification techniques based on our approach. Node2Vec embeddings are generated by our graphs and are applied in order to represent the different documents in our collections that have undergone various preprocessing methods. We compare the graph-based embeddings with the Doc2Vec and Word2Vec representations to elaborate on whether our approach can be implemented on topic classification problems. For that reason, we then train popular classifiers on the document embeddings obtained from each model. Full article

There are many opportunities to increase the efficiency of photovoltaic cells. These include solutions such as tracking mechanisms, hybrid systems or dye concentrators. Importantly, their implementation can reduce the number of silicon cells in installations, leading to reduced environmental impact. The principle of a dye concentrator is to focus sunlight onto the surface of PV modules, increasing electricity production. In this study, the potential for increased PV cell efficiency is investigated using a selected dye concentrator—tinted and luminescent acrylic glass (polymethylmethacrylate, PMMA) in yellow and red colors. The experiment included multiple measurement calibrations, such as the temperature of the silicon cell under test and the irradiation, as well as different variants of PV systems consisting of a silicon cell and different types of PMMA. Overall, the results show an increase in PV cell performance and the dependence of the increase on the type of PMMA used. The most favorable of the PV systems tested appeared to be the combination of a PV cell with a red luminescent PV, for which an average efficiency improvement of 1.21% was obtained. Full article

This study deals with a method for anomaly detection in seawater temperature data using machine learning methods with oversampling techniques. Data were acquired from 2017 to 2023 using a Conductivity–Temperature–Depth (CTD) system in the Pacific Ocean, Indian Ocean, and Sea of Korea. The seawater temperature data consist of 1414 profiles including 1218 normal and 196 abnormal profiles. This dataset has an imbalance problem in which the amount of abnormal data is insufficient compared to that of normal data. Therefore, we generated abnormal data with oversampling techniques using duplication, uniform random variable, Synthetic Minority Oversampling Technique (SMOTE), and autoencoder (AE) techniques for the balance of data class, and trained Interquartile Range (IQR)-based, one-class support vector machine (OCSVM), and Multi-Layer Perceptron (MLP) models with a balanced dataset for anomaly detection. In the experimental results, the F1 score of the MLP showed the best performance at 0.882 in the combination of learning data, consisting of 30% of the minor data generated by SMOTE. This result is a 71.4%-point improvement over the F1 score of the IQR-based model, which is the baseline of this study, and is 1.3%-point better than the best-performing model among the models without oversampling data. Full article

The exploration of the relationship between process parameters and grain evolution during the thermal deformation of rare-earth magnesium alloys using simulation software has significant implications for enhancing research and development efficiency and advancing the large-scale engineering application of high-performance rare-earth magnesium alloys. Through single-pass hot compression experiments, this study obtained high-temperature flow stress curves for rare-earth magnesium alloys, analyzing the variation patterns of these curves and the softening mechanism of the materials. Drawing on physical metallurgical theories, such as the evolution of dislocation density during dynamic recrystallization, recrystallization nucleation, and grain growth, the authors of this paper establish a cellular automaton model to simulate the dynamic recrystallization process by tracking the sole internal variable—the evolution of dislocation density within cells. This model was developed through the secondary development of the DEFORM-3D finite element software. The results indicate that the model established in this study accurately simulates the evolution process of grain growth during heat treatment and the dynamic recrystallization microstructure during the thermal deformation of rare-earth magnesium alloys. The simulated results align well with relevant theories and metallographic experimental results, enabling the simulation of the dynamic recrystallization microstructure and grain size prediction during the deformation process of rare-earth magnesium alloys. Full article

Few studies have considered whether hidden (asymptomatic) plant pathogen infection alters ecological interactions at the higher trophic levels, even though such infection still affects plant physiology. We explored this question in two field experiments, where two varieties of lettuce (Little Gem, Tom Thumb) infected with Botrytis cinerea were either (1) naturally colonised by aphids or (2) placed in the field with an established aphid colony. We then recorded plant traits and the numbers and species of aphids, their predators, parasitoids and hyperparasitoids. Infection significantly affected plant quality. In the first experiment, symptomatically infected plants had the fewest aphids and natural enemies of aphids. The diversity and abundance of aphids did not differ between asymptomatically infected and uninfected Little Gem plants, but infection affected the aphid assemblage for Tom Thumb plants. Aphids on asymptomatically infected plants were less attractive to predators and parasitoids than those on uninfected plants, while hyperparasitoids were not affected. In the second experiment, when we excluded natural enemies, aphid numbers were lower on asymptomatically and symptomatically infected plants, but when aphid natural enemies were present, this difference was removed, most likely because aphids on uninfected plants attracted more insect natural enemies. This suggests that hidden pathogen infection may have important consequences for multitrophic interactions. Full article

Unmanned Aerial Vehicle (UAV) infrared detection has problems such as weak and small targets, complex backgrounds, and poor real-time detection performance. It is difficult for general target detection algorithms to achieve the requirements of a high detection rate, low missed detection rate, and high real-time performance. In order to solve these problems, this paper proposes an improved small target detection method based on Picodet. First, to address the problem of poor real-time performance, an improved lightweight LCNet network was introduced as the backbone network for feature extraction. Secondly, in order to solve the problems of high false detection rate and missed detection rate due to weak targets, the Squeeze-and-Excitation module was added and the feature pyramid structure was improved. Experimental results obtained on the HIT-UAV public dataset show that the improved detection model’s real-time frame rate increased by 31 fps and the average accuracy (MAP) increased by 7%, which proves the effectiveness of this method for UAV infrared small target detection. Full article

The widespread use of encrypted traffic poses challenges to network management and network security. Traditional machine learning-based methods for encrypted traffic classification no longer meet the demands of management and security. The application of deep learning technology in encrypted traffic classification significantly improves the accuracy of models. This study focuses primarily on encrypted traffic classification in the fields of network analysis and network security. To address the shortcomings of existing deep learning-based encrypted traffic classification methods in terms of computational memory consumption and interpretability, we introduce a Parameter-Efficient Fine-Tuning method for efficiently tuning the parameters of an encrypted traffic classification model. Experimentation is conducted on various classification scenarios, including Tor traffic service classification and malicious traffic classification, using multiple public datasets. Fair comparisons are made with state-of-the-art deep learning model architectures. The results indicate that the proposed method significantly reduces the scale of fine-tuning parameters and computational resource usage while achieving performance comparable to that of the existing best models. Furthermore, we interpret the learning mechanism of encrypted traffic representation in the pre-training model by analyzing the parameters and structure of the model. This comparison validates the hypothesis that the model exhibits hierarchical structure, clear organization, and distinct features. Full article

Creating a walkable environment is an essential step toward the 2030 Sustainable Development Goals. Nevertheless, not all people can enjoy a walkable environment, and neighborhoods with different socioeconomic status are found to vary greatly with walkability. Former studies have typically unraveled the relationship between neighborhood deprivation and walkability from a temporally static perspective and the produced estimations to a point-in-time snapshot were believed to incorporate great uncertainties. The ways in which neighborhood walkability changes over time in association with deprivation remain unclear. Using the case of the Hangzhou metropolitan area, we first measured the neighborhood walkability from 2016 to 2018 by calculating a set of revised walk scores. Further, we applied a machine learning algorithm, the kernel-based regularized least squares regression in particular, to unravel how neighborhood walkability changes in relation to deprivation over time. The results not only capture the nonlinearity in the relationship between neighborhood deprivation and walkability over time, but also highlight the marginal effects of each neighborhood deprivation indicator. Additionally, comparisons of the outputs between the machine learning algorithm and OLS regression illustrated that the machine learning approach did tell a different story and should contribute to remedying the contradictory conclusions in earlier studies. This paper is believed to renew the understanding of social inequalities in walkability by bringing the significance of temporal dynamics and structural interdependences to the fore. Full article