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In this study, high-pressure torsion (HPT) processing is applied to the as-cast Al0.5CoCrFeNi high-entropy alloy (HEA) for 1, 3, and 5 turns. Microstructural observations reveal a significant refinement of the second phase after HPT processing. This refinement effect is influenced by the number of processing turns and the distance of the processing position from the center. As the number of processing turns or the distance of the processing position from the center increases, the fragmentation effect on the second phase becomes more pronounced. The hardness of the alloy is greatly enhanced after HPT processing, but there is an upper limit to this enhancement. After increasing the number of processing turns to 5, the increase in hardness at the edge becomes less significant, while the overall hardness becomes more uniform. Additionally, the strength of the processed alloy is significantly enhanced, while its ductility undergoes a noticeable decrease. With an increase in the number of processing turns, the second phase is further refined, resulting in improvement of strength and ductility. [ABSTRACT FROM AUTHOR]

The prediction of the ablation rate of silicone rubber-based composites is of great significance to accelerate the development of flexible thermal protection materials. Herein, a method which combines uniform design experimentation, active learning, and virtual sample generation was proposed to establish a prediction model of the mass ablation rate based on a small dataset. Briefly, a small number of sample points were collected using uniform design experimentation, which were marked to construct the initial dataset and primitive model. Then, data points were acquired from the sample pool and iterated using various integrated algorithms through active learning to update the above dataset and model. Finally, a large number of virtual samples were generated based on the optimal model, and a further optimized prediction model was achieved. The results showed that after introducing 300 virtual samples, the average percentage error of the gradient boosting decision tree (GBDT) prediction model on the test set decreased to 3.1%, which demonstrates the effectiveness of the proposed method in building prediction models based on a small dataset. [ABSTRACT FROM AUTHOR]

As LiDAR technology progressively advances, the capability of radar in detecting targets has become increasingly vital across diverse domains, including industrial, military, and automotive sectors. Frequency-modulated continuous-wave (FMCW) LiDAR in particular has garnered substantial interest due to its efficient direct velocity measurement and excellent anti-interference characteristics. It is widely recognized for its significant potential within radar technology. This study begins by elucidating the operational mechanism of FMCW LiDAR and delves into its basic principles. It discuss, in depth, the influence of various parameters on FMCW LiDAR's performance and reviews the latest progress in the field. This paper proposes that future studies should focus on the synergistic optimization of key parameters to promote the miniaturization, weight reduction, cost-effectiveness, and longevity of FMCW LiDAR systems. This approach aims at the comprehensive development of FMCW LiDAR, striving for significant improvements in system performance. By optimizing these key parameters, the goal is to promote FMCW LiDAR technology, ensuring more reliable and accurate applications in automated driving and environmental sensing. [ABSTRACT FROM AUTHOR]

With the continuous development of artificial intelligence, effectively solving the problem of data islands under the premise of protecting user data privacy has become a top priority. Federal learning is an effective solution to the two significant dilemmas of data islands and data privacy protection. However, there are still some security problems in federal learning. Therefore, this study simulates the data distribution in a hardware-based trusted execution environment in the real world through two processing methods: independent identically distributed and non-independent identically distributed methods. The basic model uses ResNet164 and innovatively introduces a greedy hierarchical training strategy to gradually train and aggregate complex models to ensure that the training of each layer is optimized under the premise of protecting privacy. The experimental results show that under the condition of an IID data distribution, the final accuracy of the greedy hierarchical model reaches 86.72%, which is close to the accuracy of the unpruned model at 89.60%. In contrast, under the non-IID condition, the model's performance decreases. Overall, the TEE-based hierarchical federated learning method shows reasonable practicability and effectiveness in a resource-constrained environment. Through this study, the advantages of the greedy hierarchical federated learning model with regard to enhancing data privacy protection, optimizing resource utilization, and improving model training efficiency are further verified, providing new ideas and methods for solving the data island and data privacy protection problems. [ABSTRACT FROM AUTHOR]

To address the issue of reduced positioning and anti-swing accuracy of bridge cranes under disturbed conditions within a prescribed time, a positioning and anti-swing control algorithm, based on a prescribed-time disturbance observer, is proposed. Unlike existing research, the novel disturbance observer is designed to accurately estimate disturbances within a prescribed time, ensuring precise disturbance compensation. This allows for high-precision positioning and anti-swing of bridge cranes under disturbed conditions within a prescribed time. Firstly, a prescribed-time disturbance observer is designed to ensure accurate disturbance estimation. Secondly, a new prescribed-time sliding mode surface and a prescribed-time reaching law with a recursive structure are designed to ensure that the system state converges accurately within the prescribed time. Finally, theoretical analysis and simulation verify that the proposed control algorithm achieves the control objective of high-precision positioning and anti-swing of bridge cranes under disturbed conditions within a prescribed time. [ABSTRACT FROM AUTHOR]

Monitoring human activities, such as walking, falling, and jumping, provides valuable information for personalized health assistants. Existing solutions require the user to carry/wear certain smart devices to capture motion/audio data, use a high-definition camera to record video data, or deploy dedicated devices to collect wireless data. However, none of these solutions are widely adopted for reasons such as discomfort, privacy, and overheads. Therefore, an effective solution to provide non-intrusive, secure, and low-cost human activity monitoring is needed. In this study, we developed a contactless human activity monitoring system that utilizes channel state information (CSI) of the existing ubiquitous WiFi signals. Specifically, we deployed a low-cost commercial off-the-shelf (COTS) router as a transmitter and reused a desktop equipped with an Intel WiFi Link 5300 NIC as a receiver, allowing us to obtain CSI data that recorded human activities. To remove the outliers and ambient noise existing in raw CSI signals, an integrated filter consisting of Hampel, wavelet, and moving average filters was designed. Then, a new metric based on kurtosis and standard deviation was designed to obtain an optimal set of subcarriers that is sensitive to all target activities from the candidate 30 subcarriers. Finally, we selected a group of features, including time- and frequency-domain features, and trained a classification model to recognize different indoor human activities. Our experimental results demonstrate that the proposed system can achieve a mean accuracy of above 93%, even in the face of a long sensing distance. [ABSTRACT FROM AUTHOR]

Nicotinamide adenine dinucleotide (NAD+) is an essential metabolite that plays a crucial role in diverse biological processes, including energy metabolism, gene expression, DNA repair, and mitochondrial function. An aberrant NAD+ level mediates the development of cardiovascular dysfunction and diseases. Both in vivo and in vitro studies have demonstrated that nicotinamide mononucleotide (NMN), as a NAD+ precursor, alleviates the development of cardiovascular diseases such as heart failure, atherosclerosis, and myocardial ischemia/reperfusion injury. Importantly, NMN has suggested pharmacological activities mostly through its involvement in NAD+ biosynthesis. Several clinical studies have been conducted to investigate the efficacy and safety of NMN supplementation, indicating its potential role in cardiovascular protection without significant adverse effects. In this review, we systematically summarize the impact of NMN as a nutraceutical and potential therapeutic drug on cardiovascular diseases and emphasize the correlation between NMN supplementation and cardiovascular protection. [ABSTRACT FROM AUTHOR]

Catalytic performance decline is a general issue when shaping fine powder into macroscale catalysts (e.g., beads, fiber, pellets). To address this challenge, a phenolic resin-assisted strategy was proposed to prepare porous Co/N carbon beads (ZACBs) at millimeter scale via the phase inversion method followed by confined pyrolysis. Specially, p-aminophenol–formaldehyde (AF) resin-coated zeolitic imidazolate framework (ZIF-67) nanoparticles were introduced to polyacrylonitrile (PAN) solution before pyrolysis. The thermosetting of the coated AF improved the interface compatibility between the ZIF-67 and PAN matrix, inhibiting the shrinkage of ZIF-67 particles, thus significantly improving the void structure of ZIF-67 and the dispersion of active species. The obtained ZACBs exhibited a 99.9% removal rate of tetracycline (TC) within 120 min, with a rate constant of 0.069 min−1 (2.3 times of ZIF-67/PAN carbon beads). The quenching experiments and electron paramagnetic resonance (EPR) tests showed that radicals dominated the reaction. This work provides new insight into the fabrication of high-performance MOF catalysts with outstanding recycling properties, which may promote the use of MOF powder in more practical applications. [ABSTRACT FROM AUTHOR]

Conflicting clinical trial results on omega-3 highly unsaturated fatty acids (n-3 HUFA) have prompted uncertainty about their cardioprotective effects. While the VITAL trial found no overall cardiovascular benefit from n-3 HUFA supplementation, its substantial African American (AfAm) enrollment provided a unique opportunity to explore racial differences in response to n-3 HUFA supplementation. The current observational study aimed to simulate randomized clinical trial (RCT) conditions by matching 3766 AfAm and 15,553 non-Hispanic White (NHW) individuals from the VITAL trial utilizing propensity score matching to address the limitations related to differences in confounding variables between the two groups. Within matched groups (3766 AfAm and 3766 NHW), n-3 HUFA supplementation's impact on myocardial infarction (MI), stroke, and cardiovascular disease (CVD) mortality was assessed. A weighted decision tree analysis revealed belonging to the n-3 supplementation group as the most significant predictor of MI among AfAm but not NHW. Further logistic regression using the LASSO method and bootstrap estimation of standard errors indicated n-3 supplementation significantly lowered MI risk in AfAm (OR 0.17, 95% CI [0.048, 0.60]), with no such effect in NHW. This study underscores the critical need for future RCT to explore racial disparities in MI risk associated with n-3 HUFA supplementation and highlights potential causal differences between supplementation health outcomes in AfAm versus NHW populations. [ABSTRACT FROM AUTHOR]

This study utilized Fe, Co, Ni elemental powders alongside GH3230 pre-alloyed powder as raw materials, employing high-throughput additive manufacturing based on laser powder bed fusion in situ to alloying technology to fabricate the bulk samples library for GH3230 superalloy efficiently. A quantitative identification algorithm for detecting crack and hole defects in additive manufacturing samples was developed. The primary focus was to analyze the composition variations in specimens at varying Fe, Co, and Ni elemental compositions and their impact on crack formation. Experimental results demonstrated that increased laser power improved element distribution uniformity but it proved to be not significantly effective in reducing crack defects. Moreover, augmented Fe and Co alloying content could not eliminate these defects. However, elevated Ni content led to a decrease in the alloy's solidification cracking index and carbide reduction in solidification products. Notably, a significant reduction in cracks was observed when the Ni content of the alloy reached 63 wt.%, and these defects were nearly eliminated at 67 wt.% Ni content. [ABSTRACT FROM AUTHOR]

Shallow slope instability poses a significant ecological threat, often leading to severe environmental degradation. While vegetation, particularly woody plants, is commonly employed in slope stabilization, herbaceous vegetation offers distinct and underexplored advantages. This paper reviews the role of herbaceous plants in enhancing slope stability, analyzing their mechanical and ecological mechanisms. Through an extensive review of the literature, this review challenges the prevailing view that woody vegetation is superior for slope stabilization, finding that herbaceous plants can be equally or more effective under certain conditions. The key findings include the identification of specific root parameters and species that contribute to soil reinforcement and erosion control. The review highlights the need for further research on optimizing plant species selection and management practices to maximize the slope stabilization effects. These insights have practical implications for ecological slope engineering, offering guidance on integrating herbaceous vegetation into sustainable land management strategies. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect has evolved into one of the key environmental problems affecting the urban ecological environment and sustainable development. Based on 52 Urban Thermal Heat spots (UTHSs) with significant differences between land use structure and urban green infrastructure (UGI) spatial layout within the influence range of UHI in Shanghai, Landsat-8/9 satellite images were used to construct a high-dimensional dataset reflecting the impact of built environment components on urban thermal environment. Descriptive statistical analysis was used to analyze the spatial difference qualitatively. Using the stepwise regression (SWR) model and partial least square regression (PLSR) model, the complex response relationship between UGI's structure/spatial pattern differentiation and urban thermal environment in three spatial stratification ranges of UTHSs was quantitatively analyzed. Overall, the statistical explanatory power of the PLSR model is much better than the stepwise regression model. The PLSR model points out that moderately increasing the average building height, class area (CA), percentage of landscape (PLAND), landscape shape index (LSI), and largest patch index (LPI) play a positive role in inhibiting the growth of land surface temperature (LST), and the cooling effect of index weights decreases in order. However, the interaction effects of the box-cox transformed indices with underlines, e.g., CA × Cohesion × AI × LPI and PLAND × CA × Cohesion × AI × LPI, exert relatively small weight on the cooling effect. According to the results, suggestions such as optimization of the UGI structure and urban construction layout were proposed, which can effectively mitigate the UHI effect. [ABSTRACT FROM AUTHOR]

In this paper, targeting the problem that it is difficult to deal with the time-varying sideslip angle of an underactuated unmanned surface vehicle (USV), a line–of–sight (LOS) guidance law based on an improved extended state observer (ESO) is proposed. A reduced-order ESO is introduced into the identification of the sideslip angle caused by the environmental disturbance, which ensures a fast and accurate estimation of the sideslip angle. This enables the USV to follow the reference path with high precision, despite external disturbances from wind, waves, and currents. These unknown disturbances are modeled as drift, which the modified ESO-based LOS guidance law compensates for using the ESO. In the guidance subsystem incorporating the reduced-order state observer, the observer estimation and track errors are proved uniformly ultimately bounded. Simulation and experimental results are presented to validate the effectiveness of the proposed method. The simulation and comparison results demonstrate that the proposed ELOS guidance can help a USV track different types of paths quickly and smoothly. Additionally, the experimental results confirm the feasibility of the method. [ABSTRACT FROM AUTHOR]

The decision-making performance of autonomous vehicles tends to be unstable at unsignalized intersections, making it difficult for them to make optimal decisions. We propose a decision-making model based on the Variable-Noise Multi-Agent Deep Deterministic Policy Gradient (VN-MADDPG) algorithm to address these issues. The variable-noise mechanism reduces noise dynamically, enabling the agent to utilize the learned policy more effectively to complete tasks. This significantly improves the stability of the decision-making model in making optimal decisions. The importance sampling module addresses the inconsistency between outdated experience in the replay buffer and current environmental features. This enhances the model's learning efficiency and improves the robustness of the decision-making model. Experimental results on the CARLA simulation platform show that the success rate of decision making at unsignalized intersections by autonomous vehicles has significantly increased, and the pass time has been reduced. The decision-making model based on the VN-MADDPG algorithm demonstrates stable and excellent decision-making performance. [ABSTRACT FROM AUTHOR]

Protein disulfide isomerases (PDIs) and PDI-like proteins catalyze the oxidation and reduction in protein disulfide bonds, inhibit aggregation of misfolded proteins, and participate in isomerization and abiotic stress responses. The wild type 'duli' pear (Pyrus betulaefolia) is an important rootstock commonly used for commercial pear tree grafting in northern China. In this study, we identified 24 PDI genes, named PbPDIs, from the genome of 'duli' pear. With 12 homologous gene pairs, these 24 PbPDIs distribute on 12 of its 17 chromosomes. Phylogenetic analysis placed the 24 PbPDIs into four clades and eleven groups. Collinearity analysis of the PDIs between P. betulaefolia, Arabidopsis thaliana, and Oryza sativa revealed that the PbPDIs of 'duli' pear show a strong collinear relationship with those from Arabidopsis, a dicot; but a weak collinear relationship with those from rice, a monocot. Quantitative RT-PCR analysis showed that most of the PbPDIs were upregulated by salt stress. Identification and expression analysis of 'duli' pear PbPDIs under salt stress conditions could provide useful information for further research in order to generate salt-resistant rootstock for pear grafting in the future. [ABSTRACT FROM AUTHOR]

During the sintering process in iron production, wheel swing is a sign of sintering machine trolley axle faults, which may lead to the wheel falling off and affect the production operation of the sintering machine system in serious cases. To solve this problem, this paper proposes a fault detection and localization method based on the You Only Look Once version 9 (YOLOv9) object detection algorithm and frame difference method for detecting sintering machine trolley wheel swing. The wheel images transmitted from the camera were sent to a trolley wheel and side panel number detection model that was trained on YOLOv9 for recognition. The wheel recognition boxes of the previous and subsequent frames were fused into the wheel region of interest. In the wheel region of interest, the difference operation was carried out. The result of the difference operation was compared with the preset threshold to determine whether the trolley wheel swings. When a wheel swing fault occurs, the image of the side plate at the time of the fault is collected, and the number on the side plate is identified so as to accurately locate the faulty trolley and to assist the field personnel in troubleshooting the fault. The experimental results show that this method can detect wheel swing faults in the industrial field, and the detection accuracy of wheel swing faults was 93.33%. The trolley side plate numbers' average precision was 99.2% in fault localization. Utilizing the aforementioned method to construct a system for detecting wheel swing can provide technical support for fault detection of the trolley axle on the sintering machine. [ABSTRACT FROM AUTHOR]

Al-Mg-Sc-Zr alloy processed via laser powder bed fusion (LPBF) is poised for significant application in aerospace, where its high-temperature capabilities are paramount for the safety and longevity of engineered structures. This study offers a systematic examination of the alloy's high-temperature tensile properties in relation to its microstructure and precipitate phases, utilizing experimental approaches. The LPBF-processed Al-Mg-Sc-Zr alloy features a bimodal microstructure, with columnar grains in the melt pool's interior and equiaxed grains along its boundary, conferring exceptional properties. The application of well-calibrated processing parameters has yielded an alloy with an impressive relative density of 99.8%, nearly fully dense. Following a thermal treatment of 350 °C for 4 h, the specimens were subjected to tensile tests at both room and elevated temperatures. The data reveal that the specimens exhibit a tensile strength of 560.6 MPa and an elongation of 11.1% at room temperature. A predictable decline in tensile strength with rising temperature is observed: at 100 °C, 150 °C, 200 °C, and 250 °C; the respective strengths and elongations are 435.1 MPa and 25.8%, 269.4 MPa and 20.1%, 102.8 MPa and 47.9%, 54.0 MPa and 72.2%. These findings underpin the technical rationale for employing LPBF-processed Al-Mg-Sc-Zr alloy in aerospace applications. [ABSTRACT FROM AUTHOR]

The prediction of the containerized freight index has important economic and social significance. Previous research has mostly applied sub-predictors directly for integration, which cannot be optimized for different datasets. To fill this research gap and improve prediction accuracy, this study innovatively proposes a new prediction model based on adaptive model selection and multi-objective ensemble to predict the containerized freight index. The proposed model comprises the following four modules: adaptive data preprocessing, model library, adaptive model selection, and multi-objective ensemble. Specifically, an adaptive data preprocessing module is established based on a novel modal decomposition technology that can effectively reduce the impact of perturbations in historical data on the prediction model. Second, a new model library is constructed to predict the subseries, consisting of four basic predictors. Then, the adaptive model selection module is established based on Lasso feature selection to choose valid predictors for subseries. For the subseries, different predictors can produce different effects; thus, to obtain better prediction results, the weights of each predictor must be reconsidered. Therefore, a multi-objective artificial vulture optimization algorithm is introduced into the multi-objective ensemble module, which can effectively improve the accuracy and stability of the prediction model. In addition, an important discovery is that the proposed model can acquire different models, adaptively varying with different extracted data features in various datasets, and it is common for multiple models or no model to be selected for the subseries.The proposed model demonstrates superior forecasting performance in the real freight market, achieving average MAE, RMSE, MAPE, IA, and TIC values of 9.55567, 11.29675, 0.44222%, 0.99787, and 0.00268, respectively, across four datasets. These results indicate that the proposed model has excellent predictive ability and robustness. [ABSTRACT FROM AUTHOR]

As of 31 October 2023, there have been 771,795,258 confirmed cases of COVID-19 globally. Developing simple, portable, and reliable testing devices has become increasingly important. This paper presents a point-of-care testing (POCT) device for COVID-19 based on the dual-excitation fluorescence RT-LAMP method, which is derived from the principles of RT-LAMP-based COVID-19 detection kits available in the market. The key design solutions of the device were simulated and modeled. Key performance metrics such as detection repeatability and linearity were validated. Comparative experiments with the RT-qPCR detection method were conducted to verify the accuracy and reliability of the device. Additionally, the device's detection sensitivity and accuracy were assessed. Experimental results show that the repeatability coefficient of variation (CV) value is ≤0.09%; the linearity R2 for the FAM channel is 0.9977 and that for the HEX channel is 0.9899; it exhibits good anti-interference performance, with negligible cross-channel interference; the temperature stability is ±0.062 °C, the temperature accuracy is less than 0.2 °C, and there is no significant temperature overshoot during the heating process. Compared with the real-time quantitative PCR (RT-qPCR) instrument, the positive agreement rate is 100% and the negative agreement rate is 95.0%. This research provides a foundational basis for the development of equipment for the prevention of infectious diseases and clinical diagnostics. [ABSTRACT FROM AUTHOR]

Obesity-related ciliopathies, as a group of ciliopathies including Alström Syndrome and Bardet–Biedl Syndrome, exhibit distinct genetic and phenotypic variability. The understanding of these diseases is highly significant for understanding the functions of primary cilia in the human body, particularly regarding the relationship between obesity and primary cilia. The diagnosis of these diseases primarily relies on clinical presentation and genetic testing. However, there is a significant lack of research on biomarkers to elucidate the variability in clinical manifestations, disease progression, prognosis, and treatment responses. Through an extensive literature review, the paper focuses on obesity-related ciliopathies, reviewing the advancements in the field and highlighting the potential roles of biomarkers in the clinical presentation, diagnosis, and prognosis of these diseases. [ABSTRACT FROM AUTHOR]

Optical vortex lattices (OVLs) with diverse modes show potential for a wide range of applications, such as high-capacity optical communications, optical tweezers, and optical measurements. However, vortices in typical regulated OVLs often exhibit irregular shapes, such as being narrow and elongated. The resulting increase in asymmetry negatively impacts the efficiency of particle trapping. Additionally, the vortex radii expand with an increase in topological charge (TC), limiting the TC value of the vortices and hindering their ability to fully utilize orbital angular momentum (OAM). Herein, we propose an alternative approach to custom OVLs using off-axis techniques combined with amplitude modulation. Amplitude modulation enables the precise generation of an OVL with perfect vortex properties, known as a perfect off-axis OVL. Further, the number of vortices in the perfect off-axis OVL, the off-axis distances, and the TC can be freely modulated while maintaining a circular mode. This unique OVL will promote new applications, such as the complex manipulation of multi-particle systems and optical communication based on OAM. [ABSTRACT FROM AUTHOR]

Although the beneficial role of enterprises in the management of construction and demolition waste (CDW) should not be overlooked, existing relevant studies have neglected to address the specific effects of the behavioral decisions of leaders and employees within enterprises in CDW recycling projects. This study creatively introduces psychological contract theory into the field of CDW management and aims to reveal the mechanism of recycling participation behaviors between leaders and employees in CDW recycling projects. Using the Stackelberg game method, a model consisting of leaders and employees in the construction enterprise was constructed to analyze the optimal decision-making of the enterprise under two cases of whether or not the employees contributed additional effort. The conclusions of this study are as follows: (1) The profits of construction enterprise leaders are always positively correlated with the degree of employee additional effort. Unlike the case of leaders, the effect of additional effort on employee profits hinges on the coefficient of employee additional effort. When the coefficient of employee additional effort is below the threshold, excessive additional effort can negatively impact their own profits. When the additional effort coefficient exceeds the threshold, the employee profits are positively related to the degree of additional effort. (2) Similar to the change in employee profits, the change in the total profit of the construction enterprise with the degree of additional effort is influenced by the additional effort coefficient. However, as the additional effort coefficient increases, the total profit of the enterprise shows a significant increase before employee profits. This study enriches the theoretical study of psychological contracts and provides guidance for decision-making between leaders and employees in the management of CDW recycling projects. [ABSTRACT FROM AUTHOR]

Cymbidium faberi Rolfe is one of the traditional Chinese orchids with important ornamental value, and the cultivation of Cymbidium faberi Rolfe mutant strains with different appearances is essential to increase its economic value. However, at present, their acquisition largely relies on natural mutation. The objectives of this research were to mutagenize Cymbidium faberi Rolfe protocorm-like bodies (PLBs) and shoots in vitro using sodium azide (NaN3) and to screen and evaluate mutants in the mutagenized seedlings using morphological characteristics. Cymbidium faberi Rolfe PLBs and shoots were used as mutagenic materials. Mutations were induced by the addition of 0.0 (control), 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 mg·L−1 and 0.0 (control), 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, and 10.0 mg·L−1 NaN3 to a shoot-growth-inducing medium. The mortality rates of the PLBs and shoots increased with an increase in the NaN3 concentration. At 14 d of co-cultivation, the PLBs and shoots were most efficiently mutagenized with 1.5 mg·L−1 and 4.0 mg·L−1 NaN3, respectively. After the explants were cultured for 3 months, changes in leaf and flower morphology were observed in some mutants: shorter and thicker leaves, shorter node length, reduced height, and mid-translucent leaves compared with controls. Some Cymbidium faberi Rolfe bloomed prematurely, with single flowers with large, thick petal sepals and small inflorescences. Colors included light green throughout, with some exhibiting purple stamens. This suggests that NaN3 can effectively mutagenize Cymbidium faberi Rolfe PLBs and shoots to satisfy people's demand for this plant's ornamental properties while increasing its economic value. [ABSTRACT FROM AUTHOR]

TC4 ELI alloy is widely used in the marine, medicine, and aviation fields. The failure performance of TC4 ELI alloy is significantly different from that of other metal materials, such as steels. In this paper, a modified Mohr–Coulomb criterion is calibrated based on several kinds of specimens under different stress states and a 3D geometric representation of a modified Mohr–Coulomb fracture locus for TC4 ELI is obtained based on these parameters. The effectiveness of the modified M-C criterion is studied by a ring-stiffened cylinder made of TC4 ELI. The ultimate strength of the cylinder obtained in the simulation with the modified M-C criterion is close to that obtained in an external pressure experiment, which shows that the modified M-C criterion is suitable for predicting failure in pressure hulls made of titanium alloy used in the deep-sea field. [ABSTRACT FROM AUTHOR]

Learning English as a second language (ESL) has garnered significant attention from researchers and practitioners over the past few decades, with numerous ESL learning outcomes examined in the literature. However, self-sustained learning (SSL), a crucial factor in promoting students' sustained learning and development within a sustainable society, has long been overlooked. To deepen the understanding of SSL, especially in the context of ESL in China, this study examined the direct and indirect effects of intercultural communicative skills, language mindset, and positive L2 self on sustained English learning among 1238 Chinese college students through descriptive statistics and a Structural Equation Modeling (SEM) mediation analysis. The results indicated that Chinese college students exhibited a strong language mindset and positive L2 self. They also scored high in intercultural communicative skills and long-term self-sustained English learning. SEM analysis showed that, after controlling for students' demographic characteristics, both intercultural communicative skills and language mindset positively predicted positive L2 self and self-sustained English learning. Moreover, intercultural communicative skills and language mindset had significant and positive indirect effects on self-sustained English learning through positive L2 self, underscoring the significant mediating role of positive L2 self in the relationships between intercultural communicative skills, language mindset, and self-sustained English learning. These findings suggest that, to promote self-sustained learning among English learners, instructors should enhance students' intercultural communicative skills, foster a growth language mindset, and cultivate positive language learning beliefs. [ABSTRACT FROM AUTHOR]

The use of industrial solid waste to capture and fix CO2 is a promising technology for CO2 sequestration. A thermogravimetric analyzer and CO2 cement hydration mixing device were used to study the effects of mixing method, curing system, temperature, CO2 concentration and other factors on the carbon fixation amount and performance of the circulating fluidized bed fly ash cement-based material system. The results showed that the carbon fixation and early strength of the cementitious materials could be improved by adding CO2 in the stirring process and making CO2 directly participate in the process reaction. The cementing materials samples prepared with CO2 were cured in a standard curing box for 2 days and a carbon atmosphere for 1 day, the carbon fixation amount of the cementing material was increased by 33% and the compressive strength of the cementing material was also improved. This is because under the combined action of carbon mixing and carbon curing, the prepared binding materials produced more Ca(OH)2 in the early stage, and it reacts with the introduced CO2 to form CaCO3. The strength of the calcium carbonate crystals is higher than the strength of the earlier stage of cement, and at the same time, the samples would solidify more CO2. Considering the carbon fixation amount, sample performance and solid waste utilization rate, the best conditions for the cementing materials are as follows: the content of the circulating fluidized bed fly ash (CFA) was 35%, the concentration of carbon curing was 30%, the curing temperature was 40 ℃, the water-binder ratio was 0.4, and the carbon fixation amount of the cementing material could reach about 20%. The use of CFBFA to solidify and storge CO2 is not only a new way to utilize high value-added fly ash resources, but also beneficial for reducing industrial carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

Plants have evolved intricate signaling pathways, which operate as networks governed by feedback to deal with stressors. Nevertheless, the sophisticated molecular mechanisms underlying these routes still need to be comprehended, and experimental validation poses significant challenges and expenses. Consequently, computational hypothesis evaluation gains prominence in understanding plant signaling dynamics. Biosensors are genetically modified to emit light when exposed to a particular hormone, such as abscisic acid (ABA), enabling quantification. We developed computational models to simulate the relationship between ABA concentrations and bioluminescent sensors utilizing the Hill equation and ordinary differential equations (ODEs), aiding better hypothesis development regarding plant signaling. Based on simulation results, the luminescence intensity was recorded for a concentration of 47.646 RLUs for 1.5 μmol, given the specified parameters and model assumptions. This method enhances our understanding of plant signaling pathways at the cellular level, offering significant benefits to the scientific community in a cost-effective manner. The alignment of these computational predictions with experimental results emphasizes the robustness of our approach, providing a cost-effective means to validate mathematical models empirically. The research intended to correlate the bioluminescence of biosensors with plant signaling and its mathematical models for quantified detection of specific plant hormone ABA. [ABSTRACT FROM AUTHOR]

Currently, there are limitations in the research on the use of carbon fiber geogrids to prevent low-temperature cracking in asphalt pavements. This study aims to comparatively investigate the effects of carbon fiber-based geogrid type and dense-graded asphalt concrete mixture (AC) surface combined body (SCB) type on the low-temperature cracking resistance of reinforced asphalt pavement through low-temperature bending damage tests. Two geogrid types were prepared: a carbon fiber geogrid (CCF) and a glass/carbon fiber composite qualified geogrid (GCF). Two SCB types were studied: AC-13/AC-20 and AC-20/AC-25. The results show that the improvement in the flexural tensile strength of CCF is similar to that for GCF. Moreover, under reinforced conditions, the improvement in the low-temperature cracking resistance of AC-20/AC-25 is better than that for AC-13/AC-20 by 16.26–24.57%. Based on the analysis, the reasonable ratio range of the aperture sizes to the major particle sizes in the dense gradation can achieve a more effective interlocking effect. This can improve the low-temperature cracking resistance of carbon fiber-based geogrid-reinforced samples. Then, increasing the bending absorption energy is a key way of improving the low-temperature cracking resistance of carbon fiber-based geogrid reinforcements. Eventually, the fracture type of carbon fiber-based geogrid-reinforced samples is a mixed plastic–brittle fracture. These results can provide a reference for the road failure analysis of geogrid-reinforced asphalt pavement. [ABSTRACT FROM AUTHOR]

The unique geographic environment, diverse ecosystems, and complex landforms of the Qinghai–Tibet Plateau make accurate land cover classification a significant challenge in plateau earth sciences. Given advancements in machine learning and satellite remote sensing technology, this study investigates whether emerging ensemble learning classifiers and submeter-level stereoscopic images can significantly improve land cover classification accuracy in the complex terrain of the Qinghai–Tibet Plateau. This study utilizes multitemporal submeter-level GF-7 stereoscopic images to evaluate the accuracy of 11 typical ensemble learning classifiers (representing bagging, boosting, stacking, and voting strategies) and 3 classification datasets (single-temporal, multitemporal, and feature-optimized datasets) for land cover classification in the loess hilly area of the Eastern Qinghai–Tibet Plateau. The results indicate that compared to traditional single strong classifiers (such as CART, SVM, and MLPC), ensemble learning classifiers can improve land cover classification accuracy by 5% to 9%. The classification accuracy differences among the 11 ensemble learning classifiers are generally within 1% to 3%, with HistGBoost, LightGBM, and AdaBoost-DT achieving a classification accuracy comparable to CNNs, with the highest overall classification accuracy (OA) exceeding 93.3%. All ensemble learning classifiers achieved better classification accuracy using multitemporal datasets, with the classification accuracy differences among the three classification datasets generally within 1% to 3%. Feature selection and feature importance evaluation show that spectral bands (e.g., the summer near-infrared (NIR-S) band), topographic factors (e.g., the digital elevation model (DEM)), and spectral indices (e.g., the summer resident ratio index (RRI-S)) significantly contribute to the accuracy of each ensemble learning classifier. Using feature-optimized datasets, ensemble classifiers can improve classification efficiency. This study preliminarily confirms that GF-7 images are suitable for land cover classification in complex terrains and that using ensemble learning classifiers and multitemporal datasets can improve classification accuracy. [ABSTRACT FROM AUTHOR]

There are fewer studies on the width of coal pillar retaining under a thick, hard roof. This paper takes the thick limestone roof in the 10110 working face of Jinniu Coal Mine as the background, taking the reasonable coal pillar width and its stability control technology as research objectives. Taking the theoretical analysis and calculation, numerical simulation to study the stress distribution along goaf under different parameters of the roof cutting, the stress distribution of the roadway, and displacement of the surrounding rock under different coal pillar widths, finally examined through on-site industrial experiments. The results show that (1) the vertical stress along goaf shows a gradual decrease with the increase of the roof cutting height and angle; after considering the cost and the difficulty, the optimal height and angle are chosen to be 21 m and 15°; (2) the vertical peak stress of coal pillar decreases with the increase of the width, coal pillar is gradually transformed from the crushed state to the elastic state, the displacement of the roadway also decreases with the increase of the width of the pillar, and the width of the coal pillar is chosen to be 8.0 m after comprehensive analysis; (3) during the roadway excavation and working face mining, the deformation of the surrounding rock is in a reasonable range, and the anchors and bolts are in a good state of stress, which indicates that retaining 8 m coal pillar is a success. This paper also provides theoretical references and implications for coal pillar retaining in similar geological mining conditions. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease (NAFLD) is now recognized as the most prevalent liver disease globally. Pea albumin (PA) has demonstrated positive impacts on reducing obesity and improving glucose metabolism. In this research, a mouse model of NAFLD induced by a high-fat diet (HFD) was employed to examine the impact of PA on NAFLD and explore its potential mechanisms. The findings revealed that mice subjected to a HFD developed pronounced fatty liver alterations. The intervention with PA significantly lowered serum TC by 26.81%, TG by 43.55%, and LDL-C by 57.79%. It also elevated HDL-C levels by 1.2 fold and reduced serum ALT by 37.94% and AST by 31.21% in mice fed a HFD. These changes contributed to the reduction in hepatic steatosis and lipid accumulation. Additionally, PA improved insulin resistance and inhibited hepatic oxidative stress and inflammatory responses. Mechanistic studies revealed that PA alleviated lipid accumulation in HFD-induced NAFLD by activating the phosphorylation of AMPKα and ACC, inhibiting the expression of SREBF1 and FASN to reduce hepatic lipogenesis, and increasing the expression of ATGL, PPARα, and PPARγ to promote lipolysis and fatty acid oxidation. These results indicate that PA could serve as a dietary supplement for alleviating NAFLD, offering a theoretical foundation for the rational intake of PA in NAFLD intervention. [ABSTRACT FROM AUTHOR]

The oxidation of benzylic alcohols is an important transformation in modern organic synthesis. A plethora of photoredox protocols have been developed to achieve the aerobic oxidation of alcohols into carbonyls. Recently, several groups described that ultraviolet (UV) or purple light can initiate the aerobic oxidation of benzylic alcohols in the absence of an external catalyst, and depicted different mechanisms involving the photoinduction of •O2− as a critical reactive oxygen species (ROS). However, based on comprehensive mechanistic investigations, including control experiments, radical quenching experiments, EPR studies, UV–vis spectroscopy, kinetics studies, and density functional theory calculations (DFT), we elucidate here that HOO•, which is released via the H2O2 elimination of α-hydroxyl peroxyl radicals [ArCR(OH)OO•], serves as the real chain carrier for the autocatalytic photooxidation of benzylic alcohols. The mechanistic ambiguities depicted in the precedent literature are clarified, in terms of the crucial ROS and its evolution, the rate-limiting step, and the primary radical cascade. This work highlights the necessity of stricter mechanistic analyses on UV-driven oxidative reactions that involve aldehydes' (or ketones) generation. [ABSTRACT FROM AUTHOR]

The exterior orientation parameters (EOPs) provided by the self-developed position and orientation system (POS) of the first Chinese airborne three-line scanner mapping system, AMS-3000, are impacted by jitter, resulting in waveform distortions in rectified images. This study introduces a Gaussian Markov EOP refinement method enhanced by cubic spline interpolation to mitigate stochastic jitter errors. Our method first projects tri-view images onto a mean elevation plane using POS-provided EOPs to generate Level 1 images for dense matching. Matched points are then back-projected to the original Level 0 images for the bundle adjustment based on the Gaussian Markov model. Finally, cubic spline interpolation is employed to obtain EOPs for lines without observations. Experimental comparisons with the piecewise polynomial model (PPM) and Lagrange interpolation model (LIM) demonstrate that our method outperformed these models in terms of geo-referencing accuracy, EOP refinement metric, and visual performance. Specifically, the line fitting accuracies of four linear features on Level 1 images were evaluated to assess EOP refinement performance. The refinement performance of our method showed improvements of 50%, 45.1%, 29.9%, and 44.6% over the LIM, and 12.9%, 69.2%, 69.6%, and 49.3% over the PPM. Additionally, our method exhibited the best visual performance on these linear features. [ABSTRACT FROM AUTHOR]

Lutein is an oxygenated fat-soluble carotenoid and a functional compound with proven health benefits for the human body. Nevertheless, the poor water solubility and low oral bioavailability of lutein greatly limit its application. To address this, we developed an effective approach to enhance the water solubility of lutein through co-amorphous formulation. Specifically, the lutein-sucralose co-amorphous mixture was prepared at a molar ratio of 1:1 using ethanol and water as solvents by employing the solvent evaporation method, followed by solid-state characterization and dissolution testing conducted to assess the properties of the formulation. The X-ray diffraction pattern with an amorphous halo and the differential scanning calorimetry thermogram with no sharp melting peaks confirmed the formation of a binary co-amorphous system. Changes in peak shape, position, and intensity observed in the Fourier transform infrared spectroscopy spectrum revealed intermolecular interactions between lutein and sucralose molecules, while molecular dynamics simulations identified interaction sites between their hydroxyl groups. Additionally, dissolution testing demonstrated better dissolution performance of lutein in the co-amorphous form compared to pure lutein and physical mixture counterparts. Our findings present a novel strategy for improving the water solubility of lutein to make better use of it. [ABSTRACT FROM AUTHOR]

Phosphate plays a crucial role in microbial proliferation, and the regulation of the phosphate concentration can modulate the fermentation efficiency. In this study, based on Lambert–Beer's Law and the selective absorption characteristics of substances under light, a dual-light-type photoelectric colorimetric device for phosphate determination was designed. The device's main components, such as the excitation light path and incubation stations, were modeled and simulated. The primary performance of the instrument was verified, and comparative experiments with a UV-1780 spectrophotometer were conducted to validate its performance. The experimental results demonstrate that this device exhibits a high degree of linearity with an R2 value of 0.9956 and a repeatability of ≤1.72%. The average temperature rise rate at the incubation stations was measured at 0.44 °C/s, with a temperature uniformity ≤ ±0.1 °C (temperature set at 37.3 °C). Consistently observed trends in the measurement of 23 CHO cell suspensions using the UV-1780 spectrophotometer further validated the accuracy and reliability of the device's detection results. [ABSTRACT FROM AUTHOR]

Protein tyrosine kinases (PTKs) and protein tyrosine phosphatases (PTPs) regulate the level of tyrosine phosphorylation in proteins. PTKs are key enzymes that catalyze the transfer of an ATP phosphoric acid to a tyrosine residue on target protein substrates. Protein tyrosine phosphatases (PTPs) are responsible for the dephosphorylation of tyrosine residues and play a role in countering PTK overactivity. As widespread oncogenes, PTKs were once considered to be promising targets for therapy. However, tyrosine kinase inhibitors (TKIs) now face a number of challenges, including drug resistance and toxic side effects. Treatment strategies now need to be developed from a new perspective. In this review, we assess the current state of TKIs and highlight the role of PTPs in cancer and other diseases. With the advances of allosteric inhibition and the development of multiple alternative proprietary drug strategies, the reputation of PTPs as "undruggable" targets has been overturned, and they are now considered viable therapeutic targets. We also discuss the strategies and prospects of PTP-targeted therapy, as well as its future development. [ABSTRACT FROM AUTHOR]

The cultivation of Cunninghamia lanceolata (Lamb.) Hook. (Chinese fir) plays a crucial role in enhancing ecological security through water resource preservation and carbon sequestration in China. The biotic and abiotic environmental conditions vary across different stages of plantation, thereby influencing soil nutrient levels and microbial dynamics. However, the interconnection between the soil nutrient cycle and microbial communities within Chinese fir plantations throughout their entire life cycle remains inadequately understood. In this study, conducted across various management stages of Chinese fir plantations in China (including the juvenile stage, first thinning stage, second thinning stage, mature stage, and over-mature stage), we examined the associations among soil organic carbon, soil physicochemical properties, soil enzyme activities, and soil microbial dynamics. Our results revealed that forest management practices significantly modify soil physicochemical properties and enzyme activities across all management stages. Specifically, the concentrations of total soil carbon, soil organic carbon, and soil microbial biomass carbon were notably higher in the over-mature stage compared to other management stages. At the genus level, the five highest contributors belonged to Subgroup2, AD3, Xanthobacteraceae, Elsterales, and Acidobacteriales for the bacterial community. For the fungal community at the genus level, the five highest contributors belonged to Ascomycota, Serendipita, Saitozyma, Mortieralla, and Venturiales. Moreover, anthropogenic thinning activities during the management phase substantially altered both stand and soil environments, as well as the structural characteristics of soil microbial communities. Soil cellulase, soil electrical conductivity (EC), and soil available phosphorus (AP) emerged as key factors influencing the relative abundance of major fungal communities, whereas soil total nitrogen, EC, and AP were identified as critical factors affecting the relative abundance of major bacterial phyla. More microbiological groups increased significantly in the juvenile stage and over-mature stage. Our findings elucidate the intricate relationships between the soil nutrient cycle and soil microbiological dynamics across various forest management stages within Chinese fir plantations. [ABSTRACT FROM AUTHOR]

Forest fires require rapid and precise early smoke detection to minimize damage. This study focuses on employing smoke recognition methods for early warning systems in forest fire detection, identifying smoke as the primary indicator. A significant hurdle lies in the absence of a large-scale dataset for real-world early forest fire smoke detection. Early smoke videos present characteristics such as smoke plumes being small, slow-moving, and/or semi-transparent in color, and include images where there is background interference, posing critical challenges for practical recognition algorithms. To address these issues, this paper introduces a real-world early smoke monitoring video dataset as a foundational resource. The proposed 4D attention-based motion target enhancement network includes an important frame sorting module which adaptively selects essential frame sequences to improve the detection of slow-moving smoke targets. Additionally, a 4D attention-based motion target enhancement module is introduced to mitigate interference from smoke-like objects and enhance recognition of light smoke during the initial stages. Moreover, a high-resolution multi-scale fusion module is presented, incorporating a small target recognition layer to enhance the network's ability to detect small smoke targets. This research represents a significant advancement in early smoke detection for forest fire surveillance, with practical implications for enhancing fire management. [ABSTRACT FROM AUTHOR]

This paper proposes a new type of cogeneration system coupled by Organic Rankine Cycle (ORC), Absorption Heat Pump (AHP), and Compression Heat Pump (CHP). The new system can meet the needs of different scenarios. Simulations were conducted to analyze the effect of different factors on the parameters and performance indexes of the new system and compared with the system of ORC, AHP, CHP. The results showed that the factors of flue gas temperature at evaporator1 outlet and CHP have the greatest effect on the parameters and performance indexes of the Organic Absorption-Compression Coupling Heat Power (OACCHP) system, and the effect of AHP on the parameters and performance indexes of the OACCHP system can be ignored. In the given range of flue gas temperature at evaporator1 outlet, the heat production of total and net work is reduced by 1.17% and 33.33%, respectively. In a given range of CHP evaporation temperature, the heat production of the total is reduced by 5.74%. In the given range of the outlet temperature of the gas cooler, the heat production of the total is increased by 10.58%. In a given working condition, compared with the single ORC system, the single CHP system, and the single AHP system, the thermal efficiency, COP, and energy earning rate of the OACCHP system are increased by up to 755.49%, 59.8%, 5.8%, respectively. Finally, the optimal operating conditions for different scenarios were determined. [ABSTRACT FROM AUTHOR]

The utilization of resources plays a crucial role in mitigating the environmental pollution issue that improper disposal of construction and demolition waste (CDW) causes. However, the slow growth of the recycled building materials market limits the development of CDW resource utilization. Green value co-creation among remanufacturers, construction enterprises, and the public in CDW resource utilization projects is an effective way to address the issue. This study, based on the theory of value co-creation, uses the evolutionary game method to construct an evolutionary game model for CDW resource utilization projects. The main conclusions are as follows: (1) When the degree of green value co-creation is 0.1 or 0.5, the remanufacturer, the construction enterprise, and the public cannot maintain a state of green value co-creation; when the degree of green value co-creation is 0.9, the remanufacturer, the construction enterprise, and the public in the CDW resource utilization project finally reach a stable state of green value co-creation. (2) When the degree of green value co-creation is 0.5, enhancing the green value co-creation willingness of the remanufacturer or the public can lead other CDW resource utilization project stakeholders to participate in green value co-creation. This study contributes to the promotion of stakeholder cooperation in CDW resource utilization projects, thus providing implications for the promotion of CDW resources. [ABSTRACT FROM AUTHOR]

The non-equilibrium solidification process in the additive manufacturing of titanium alloy leads to special microstructures, and the resulting changes in corrosion behavior are worthy of attention. In this paper, the microstructure and electrochemical corrosion behavior of Ti6Al4V alloys prepared using laser powder bed melting (LPBF) and casting are systematically compared. The results show that the LPBF-processed Ti6Al4V alloy is composed of dominant acicular α′ martensite within columnar prior β phase, and less β disperses have also been discovered, which is significantly different from the α + β dual-phase structure of cast Ti6Al4V alloy. Compared to the as-cast Ti6Al4V alloy, LPBF-processed Ti6Al4V alloy has a thinner and unstable passive film, and exhibits slightly poorer corrosion resistance, which is mainly related to its higher porosity, a large amount of acicular α′ martensite and less β phase compared to as-cast Ti6Al4V alloy. This result proves that suitable methods should be taken to control the relative density and phase composition of LPBF-processed Ti6Al4V alloys before application. [ABSTRACT FROM AUTHOR]

Maritime UAV path planning is a key link in realizing the intelligence of maritime emergency transportation, providing key support for fast and flexible maritime accident disposal and emergency material supply. However, most of the current UAV path planning methods are designed for land environments and lack the ability to cope with complex marine environments. In order to achieve effective path planning for UAV in marine environments, this paper proposes a Directional Drive-Rotation Invariant Quadratic Interpolation White Shark Optimization algorithm (DD-RQIWSO). First, the directional guidance of speed is realized through a directional update strategy based on the fitness value ordering, which improves the speed of individuals approaching the optimal solution. Second, a rotation-invariant update mechanism based on hyperspheres is added to overcome the tracking pause phenomenon in WSO. In addition, the quadratic interpolation strategy is added to enhance the utilization of local information by the algorithm. Then, a wind simulation environment based on the Lamb–Oseen vortex model was constructed to better simulate the real scenario. Finally, DD-RQIWSO was subjected to a series of tests in 2D and 3D scenarios, respectively. The results show that DD-RQIWSO is able to realize path planning under wind environments more accurately and stably. [ABSTRACT FROM AUTHOR]

The distribution of ocean sound speed profiles (SSPs) profoundly influences the design of underwater acoustic communication and positioning systems. Conventional methods for measuring sound speed by instruments entail high time costs, while sound speed inversion methods offer rapid estimation of SSPs. However, these methods heavily rely on sonar observational data and lack the capacity to swiftly estimate SSPs in arbitrary oceanic regions, particularly in scenarios with few-shot data. Precisely estimating non-cooperative maritime SSPs under such conditions poses a significant challenge. To explore temporal distribution patterns of sound speed and achieve precise SSP predictions with limited data, we propose a hierarchical long short-term memory transfer learning (H-LSTM-TL) framework. The core idea involves pre-training the base model on extensive public datasets, transferring the acquired knowledge to task models, and fine-tuning the task model on few-shot data to predict future SSPs. Through H-LSTM-TL, it accelerates model convergence, enhances sensitivity to few-shot input data, alleviates overfitting issues, and notably improves the accuracy of SSP predictions. Experimental results demonstrate that the H-LSTM-TL model exhibits strong generalization capabilities in few-shot data scenarios, effectively reducing overfitting problems and proving its applicability for rapid prediction of SSPs. [ABSTRACT FROM AUTHOR]

Timber is the most widely used material for furniture in view of its characteristics of light mass, high strength, easy processing, coloring, and decorative appearance. However, the flammability of wood has been frequently associated with increased fire intensity and the rapid spread of fire in buildings. In this paper, the combustion performance of six kinds of common furniture timber was investigated based on thermogravimetric analysis at 25–500 °C, cone calorimetry with 50 kW/m2 thermal radiation intensity, and flame spread experiments with 3 kW/m2 thermal radiation intensity. The ignition, weight loss, thermogenesis, smoke, and flame spread characteristics of these timbers were obtained. Subsequently, a comprehensive index system including thermal stability, heat release ability, smoke production capacity, and flame spreading speed was constructed to evaluate the combustion performance of the selected timbers. In addition, a grey correlation method relying on the game theory to assign weight was proposed for the quantitative analysis of the relevant evaluation indexes. As a result, the combustion performance of the six kinds of timber, which was defined as a specific value from poor to good, was as follows: pine (0.8696) > Chinese fir (0.8568) > Oriented Strandboard (OSB) (0.8425) > density board (0.8122) > plywood (0.8087) > elm (0.7909). Timber with poor combustion performance contributes to the reduction in fire risk in buildings. Our suggestions are of great significance for selecting furniture timber from the perspective of the prevention and control of building fires. [ABSTRACT FROM AUTHOR]

Rotor imbalance is considered to be one of the main mechanical faults of rotating machinery; which may result in bearing damage and even catastrophic system failure. Recent progress in the Internet of Things (IoT) has promoted the application of novel sensing and computing techniques in the industry, and it is promising to employ novel IoT techniques for imbalance detection to avoid potential failures. Existing sensing techniques suffer from the impact of bearing structure dynamics, loss of accuracy during their lifetime, and security risks introduced by the sensor cabling and supports, which may, in turn, interfere with the machine operations due to inappropriate design and installation. This investigation provides an on-shaft machine wearable vibration sensing technique for effectively monitoring the running state of rotors while minimizing the interference with their operations. In this work, key investigations include the following: (1) theoretical modeling and an analysis of rotor imbalance, and its measurement with an on-shaft micro-electromechanical system (MEMS) accelerometer; (2) the development of a wirelessly powered, cordless on-shaft vibration measurement (OSVM) sensor for unobtrusive sensing of the vibration of rotating shafts; (3) the in-sensor computing design for optimizing the distribution of computing resources and decreasing data transmission. The tests and evaluation of the proposed techniques were conducted with a rotor test rig to demonstrate their feasibility. The presented investigation is a typical example of applying new sensing and computing paradigms to improve the flexibility and convenience of applications, which is a good reference for related investigations and practices. [ABSTRACT FROM AUTHOR]

Coastal areas often experience high population density and intense human activity owing to the considerable value of the ocean. Therefore, devices for monitoring marine disasters are crucial for ensuring the safety of human life. Herein, we develop hemispherical spring origami (SO) triboelectric nanogenerators (TENGs) (HSO-TENGs) for self-powered ocean wave monitoring. Optimization is performed using two approaches. First, swing machine experiments are conducted to investigate the monitoring performance of the HSO-TENGs regarding wave height and period with satisfactory accuracy. To increase power generation and monitoring accuracy, the internal inertia and centroid of gravity of the HSO-TENGs are optimized with respect to the structural parameters (i.e., magnet weight, hammer height, and external swing arm length). Second, numerical simulations are performed using the smoothed-particle hydrodynamics (SPH) method to determine the most suitable fixed condition for the HSO-TENGs for sensing wave changes. Subsequently, wave tank experiments are conducted on the HSO-TENGs to determine their ability to sense wave height, period, frequency, and direction. Tests related to supplying other sensors are also conducted. Eventually, the ability of the HSO-TENGs to monitor wave direction and spreading parameters is investigated in a numerical SPH circular wave tank. The results prove that the optimized HSO-TENGs can achieve powering and sensing through the same device. [ABSTRACT FROM AUTHOR]

To further enhance the crashworthiness of subway vehicle anti-climb energy-absorbing devices, this paper proposes a novel collapsible structure, which is embedded with honeycomb aluminum blocks and consists of an inner and outer double-layer square tube. An impact finite element model of the subway vehicle's anti-climb energy-absorbing device was established using LS-DYNA (v.17.2). The effects of the connecting diaphragm thickness, the inner and outer tube thicknesses of the thin-walled tubes, and the yield strength of the honeycomb aluminum on the energy absorption and impact force of the anti-climb energy-absorbing device were investigated. The results indicate that changes in the inner and outer tube thicknesses of the thin-walled tubes significantly affect the energy absorption and impact force of the anti-climb energy-absorbing device. However, changes in the connecting diaphragm thickness only increase the maximum energy absorption by up to 7.5% but have a significant impact on the maximum peak force. It was also found that the difference in energy absorption due to changes in the yield strength of the honeycomb aluminum is only 3.6%, suggesting that the yield strength of the honeycomb aluminum and the connecting diaphragm thickness have a limited influence on the crashworthiness design of the anti-climb energy-absorbing device. Furthermore, a multi-objective surrogate model characterizing the specific energy absorption and maximum peak force was established for the aforementioned four influential parameters using the response surface method. The model was then optimized using a genetic algorithm, resulting in optimal parameters that increased the energy absorption and specific energy absorption by 27.3% and 13.8%, respectively, compared to the original values, leading to a significant improvement in crashworthiness. The findings provide valuable references for and insights into the crashworthiness design and optimization of subway vehicles' anti-climb energy-absorbing devices. [ABSTRACT FROM AUTHOR]

High-entropy alloys (HEAs) have garnered significant attention for their exceptional properties, with eutectic high-entropy alloys (EHEAs) emerging as particularly notable due to their incorporation of eutectic structures comprising soft and hard phases. This study investigated the influence of shear strain on the microstructural refinement and mechanical properties of AlCoCrFeNi2.1 EHEAs, which were subjected to high-pressure torsion (HPT) at room temperature under a pressure of 6 GPa across 0.5 to 3 turns, compared to the initial material. After HPT treatment, significant grain refinement occurred due to strong shear strain, evidenced by the absence of B2 phase peaks in X-ray diffraction (XRD) analysis. Microhardness increased substantially post-HPT, reaching a saturation point at approximately 575 HV after three turns, significantly higher than that of the original sample. Moreover, the ultimate tensile strength of HPT-treated specimens reached around 1900 MPa after three revolutions, compared to approximately 1100 MPa for the as-cast alloy, with a mixed fracture mode maintained. This investigation underscores the efficacy of HPT in enhancing the mechanical properties of AlCoCrFeNi2.1 EHEAs through microstructural refinement induced by shear deformation, offering insights into the design and optimization of advanced HEAs for various engineering applications. [ABSTRACT FROM AUTHOR]

Graphene superlattices have simple and controllable electronic band structures, which can also be electrostatically tuned. They have been widely studied for band engineering and strong correlated physics, and have led to the discovery of a variety of exciting phenomena. To experimentally study the physics of graphene superlattices in a systematic way, it is desirable to control the structure parameters, which barely exist at the moment, onsite. Here, a tunable superlattice with graphene and a deformable gating structure is demonstrated. The period and duty cycle of the nano-gating, and furthermore of the superlattice potential, can be tuned through altering the shape of the gating structure with piezo-actuators, offering a tunable band structure. The tuning of the electronic band structures of both a two-dimensional and a one-dimensional superlattice is demonstrated with numerical simulations, offering a new approach for tunable electronic and photonic devices. [ABSTRACT FROM AUTHOR]

The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions. However, existing methods have shortcomings in dealing with sample imbalance and effective feature extraction. To address these issues, this study proposes an Ethereum phishing scam detection method based on DA-HGNN (Data Augmentation Method and Hybrid Graph Neural Network Model), validated by real Ethereum datasets to prove its effectiveness. Initially, basic node features consisting of 11 attributes were designed. This study applied a sliding window sampling method based on node transactions for data augmentation. Since phishing nodes often initiate numerous transactions, the augmented samples tended to balance. Subsequently, the Temporal Features Extraction Module employed Conv1D (One-Dimensional Convolutional neural network) and GRU-MHA (GRU-Multi-Head Attention) models to uncover intrinsic relationships between features from the time sequences and to mine adequate local features, culminating in the extraction of temporal features. The GAE (Graph Autoencoder) concept was then leveraged, with SAGEConv (Graph SAGE Convolution) as the encoder. In the SAGEConv reconstruction module, by reconstructing the relationships between transaction graph nodes, the structural features of the nodes were learned, obtaining reconstructed node embedding representations. Ultimately, phishing fraud nodes were further identified by integrating temporal features, basic features, and embedding representations. A real Ethereum dataset was collected for evaluation, and the DA-HGNN model achieved an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 0.994, a Recall of 0.995, and an F1-score of 0.994, outperforming existing methods and baseline models. [ABSTRACT FROM AUTHOR]