Anomaly detection and segmentation aim at distinguishing abnormal images from normal images and further localizing the anomalous regions. Feature reconstruction based method has become one of the mainstream methods for this task. This kind of method has two assumptions: (1) The features extracted by neural network is a good representation of the image. (2) The autoencoder solely trained on the features of normal images cannot reconstruct the features of anomalous regions well. But these two assumptions are hard to meet. In this paper, we propose a new anomaly segmentation method based on feature reconstruction. Our approach mainly consists of two parts: (1) We use a pretrained vision transformer (ViT) to extract the features of the input image. (2) We design a self-attention autoencoder to reconstruct the features. We regard that the self-attention operation which has a global receptive field is beneficial to the methods based on feature reconstruction both in feature extraction and reconstruction. The experiments show that our method outperforms the state-of-the-art approaches for anomaly segmentation on the MVTec dataset. It is both effective and time-efficient.

In Taiwan, remote healthcare was initially limited to telemedicine services, and the systematic development of remote care was relatively delayed. It was not until 2007 that the Department of Health of the Executive Yuan first initiated the 'Remote Care Pilot Program,' followed by the 'Remote Care Service Improvement and Quality Enhancement Plan' in the subsequent year. In 2010, Taiwan officially launched the 'Remote Health Care Services Development Plan' and established a dedicated project office, marking the comprehensive start-up of remote care services. This stage saw significant contributions from artificial intelligence aids and ICT remote transmission technologies, greatly promoting the application of smart healthcare in home care. This study aims to explore the strategic gaps in the application of smart healthcare in home remote care. By analyzing gaps in 'legal regulations,' 'economic factors,' 'user behavior habits,' 'policy and culture,' and 'environment and technology,' this study employs a mixed-methods approach. Particularly, it uses the Analytic Hierarchy Process (AHP) to construct a weighted ranking of these strategic gaps. Through in-depth interviews with doctors and smart healthcare experts and the collection of 16 valid AHP questionnaire samples, which cover both industry and academia with a balanced distribution, the survey results are ensured to be representative and reliable. The findings indicate that 'legal regulations' are considered the most critical strategic gap that needs to be addressed first, followed by 'user behavior habits.' This suggests that when designing smart healthcare applications, greater consideration needs to be given to user acceptance and habits. By systematically analyzing and addressing these strategic gaps, this study not only provides solutions to issues in the application of smart healthcare in home remote care but also offers valuable references for future research and practice, aiming to make home remote care services more comprehensive and efficient.

Excitation of the acoustic field leading to the Blaha effect affects the plasticity of the material significantly in ultrasonic vibration-assisted forming. In a micro-forming field, the effects are more significant in the deformation in surface of materials [1]-[3], in which reduction of the surface roughness based on the increasing of plastic deformation of surface asperity was effective [4]. On the other hand, the effect on deformation behavior of the bulk region indicted reduction in the yield stress of materials, and not only acoustic effect [5], but also impact effect is found to generate a large amount of dislocation and produce plastic deformation [6][7]. However, the effect on the bulk is more significant as that on the surface. Differences in the effect on the surface and the bulk are not clarified. In this study, the mechanism of the deformation in the surface of the material with ultrasonic vibration assistance is investigated and compared with that in the bulk. Forging tests using a newly developed ultrasonic vibrator were carried out on pure Cu foils with various process conditions. The longitudinal vibration frequency of the ultrasonic transducer is 60∓2kHz, and the vibration amplitude is in an adjustable range of 0~10μm. Forging test was carried out at different initial stress, specimen size and amplitude. The difference in acoustic softening and impact effects on the surface and the bulk was discussed.

With the development of big data analysis, blockchain and other technologies, the supply chain of enterprises is transforming to lean and intelligent. As an important link in the enterprise supply chain, the intelligent transformation of procurement plays an important role in the improvement of the supply chain efficiency, therefore, the construction of a common method supporting the intelligent upgrade of the enterprise procurement business has become a key concern for enterprise managers. Based on the balanced scorecard theory and the supply chain maturity model, this study combines the actual situation of procurement management in Chinese energy enterprises and constructs a procurement benchmarking system that balances the development direction of the industry and the actual needs of enterprises. Meanwhile, based on the grounded theory, three major themes of the intelligent procurement system (digital business module, procurement synergy mechanism and procurement ecosystem) are extracted to provide a methodological reference for the construction of intelligent procurement systems of energy enterprises. The study concludes with a case study of China National Energy Group Materials Company to demonstrate the application of the intelligent procurement system built in this paper, with a view to providing methodological reference for the intelligent procurement management in energy enterprises.

Jade waste is a normal byproduct that makes up much more than the amount of jade extracted. Therefore, recycling jade waste is worth investigating from the point of view of energy conservation. Moreover, it is an environment-friendly material, which is desirable for use in building materials. In this study, Xiuyan jade waste was repurposed as antibacterial additives for building coatings. The powder waste was activated by milling and subsequent annealing. The antibacterial properties of the treated waste were mostly related to the annealing temperatures. Based on the investigations of the phase change and the release of metal ions of a series of samples and their antibacterial activities, the antibacterial mechanism of the treated samples was explored experimentally. The most applicable sample for coatings was finally chosen by considering its pH values and its antibacterial abilities. Antibacterial testing showed that the addition of treated jade waste could enhance the bacterial inhibition rate of building coatings from 60% to 99.9%.

The fast and flexible characteristics of badminton determine the necessity of its lunge, and fast lunge will have an important impact on the results of the game in actual combat. In lunge evaluation, the contact time to the ground is a key indicator. This article selects two middle-aged male badminton amateurs with similar body shape and age， but different skill levels as the research objects to carry out comparative experiments. The microseismic equipment is used to record the floor vibration which is caused by the running of badminton amateurs in sports. The microseismic signals of lunge are processed and analyzed by MATLAB software. And we evaluate the pro and cons of various time to touch the ground from amateurs with different skill levels. The research found that 1. Microseismic technology can detect the contact time to the ground. 2. High-level badminton amateurs have obvious advantages in the footwork contact time. Microseismic technology has a certain feasibility as a tool for evaluating the footwork contact time of badminton.

Passive Global Navigation Satellite System (GNSS)-based Synthetic Aperture Radar (SAR), known as GNSS-SAR, is a new passive radar imaging system. However, compared with conventional SAR, range resolution of GNSS-SAR is significantly lower. To improve range resolution of GNSS-SAR is an interested topic for investigation. In this paper, a novel range compression algorithm for enhancing range resolutions of GNSS-SAR is proposed. In the proposed scheme, at first, range compression is conducted by correlating the received reflected GNSS signal of intermediate frequency (IF) with the synchronized direct baseband GNSS signal in range domain. Then spectrum equalization is applied to the compressed results to suppress side lobes. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR can be obtained by the proposed range compression algorithm, compared to the conventional range compression algorithm.

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

This paper investigates the leader–follower formation tracking control of underactuated surface vessels (USVs) with input saturation. Each vessel is subject to the uncertainties induced by model uncertainties and environmental disturbances. At first, an event-trigged extended state observer (ETESO) is used to recover the velocity, yaw rate and the uncertainties. Then, an estimator is used to estimate velocity of the leader. An event-trigged controller (ETC) is constructed based on the estimator, the observer and extra variables. Specifically, extra variables are used to solve the underactuated problem and input saturation. Stability analysis of control system is given to prove all signals are bounded. Simulations demonstrate that the ETESO can estimate accurately the uncertainties, the velocity and yaw rate; the ETC can largely reduce the action times of actuator.

Ni-rich layered cathodes are regarded as a potential candidate for high energy-density lithium-ion batteries, but their high sensitivity to air during storage and poor thermal stability are a vital challenge for large-scale applications. In this paper, an effective solid-solution strategy was proposed to improve the surface and structural stability of Ni-rich layered cathodes by introducing Li2MnO3. The structural analysis results indicate that the formation of Li2CO3 inert layers on Ni-rich layered cathodes during storage in air is responsible for the increased electrode interfacial impedance, thereby resulting in the severe deterioration of electrochemical performance. The introduction of Li2MnO3 can reduce the surface reactivity of Ni-rich cathode materials, playing a certain suppression effect on the formation of surface Li2CO3 layer and the deterioration of electrochemical performances. Additionally, the thermal analysis results show that the heat release of Ni-rich cathodes strongly depends on the charge of states, and Li2MnO3 can suppress oxygen release and significantly improves the thermal stability of Ni-rich layered cathodes. This work provide a method to improve the storage performance and thermal stability of Ni-rich cathode materials.

This paper empirically analyzes the relationship between digital inclusive finance and rural income, and demonstrates that digital inclusive finance plays a significant role in promoting rural income, based on the 2012-2018 panel data of 17 cities in Henan Province of China. Further dimensional research unveils that all of its breadth of coverage, depth of use and degree of digitization present significant positive correlation with rural income, and a robustness test was conducted through the sub-sample method. In addition, according to regression analysis on the influencing factors of digital inclusive finance through the Tobit model, the level of economic development, the Internet popularity rate and the urbanization rate have a positive effect on the development of digital inclusive finance in Henan, while the income gap between urban and rural areas and the degree of opening up have a negative effect. Finally, predicated on the analysis of the restrictive factors of Henan’s digital inclusive finance in increasing rural income, the paper puts forward specific policy proposals based on the native state quo.

Creep is a fundamental property naturally exists in some types of rock, which is significant for the long-term stability of roadways during the mining process. In this paper, the long-term strength of coal and rock were obtained via laboratory experiments, and a Cvisc elasto-viscoplastic model was adopted and introduced in FLAC3D, based on the 3101 transport roadway in Hongqinghe Coal Mine, to investigate the influence of creep on the stability of deep-high stress roadway. The test results show that the long-term strength of 3-1 coal and sandy mudstone, respectively, is 18.65 MPa, 39.95 MPa. The plastic zone, the deformation, and the damage of roadway surrounding rock behave an obvious increase after being excavated for 720d as the creep model was chosen. The plastic zone failure with models shear-p (1090.7 m3), shear-n (381.7 m3), tension-n (98.4 m3), and tension-p (30.8 m3), the damage value has an obvious increment of increased 21.2% (0.053), the deformation increases in the order is two-sidewalls (1978 mm), roof (907 mm), floor (101 mm). The creep of the roadway can be divided into three stages: the accelerating stage, the decaying stage, and the stable stage. The creep speed of each stage is greatly affected by the presence or absence of anchor spray support: the creep speed of the bare roadway roof, roadway side, and floor stability, respectively, is 1.01, 1.02, and 0.12 mm/d; After anchor spray support, the creep velocity, correspondingly, decreased to 0.69, 0.37, and 0.12 mm/d, and the amount of surrounding rock damage decreased from 0.302 to 0.243. This indicates that the anchor spray support can significantly reduce the creep effect of the roadway. The Cvisc creep model was verified reliable and can provide guidance for deep high-stress coal roadway support.

Control charts have been widely used for monitoring process quality in manufacturing and play an important role in triggering a signal in time when detecting a change in process quality. Many control charts in literature assume that the in-control distribution of the univariate or multivariate process data is continuous and not categorical. This research develops two exponentially weighted moving average (EWMA) proportion control charts for monitoring a process with multinomial proportions when considering both large and small sample sizes. For a large sample size , the charting statistic depends on the well-known Pearson χ2 statistic, and the control limit of the EWMA proportion chart is determined by an asymptotical chi-square distribution. For a small sample size, we derive the exact mean and variance of the Pearson χ2 statistic. Hence, the exact EWMA proportion chart is determined. The proportion chart can also be applied to monitor the distribution-free continuous multivariate process as long as each categorical proportion associated with specification limits of each quality variable is known or estimated. Lastly, we investigate the detection performance of the proposed EWMA proportion chart by numerical analyses. Real data analysis demonstrates the beneficial application of the proposed EWMA proportion charts.

Unmanned Aerial Vehicles (UAVs) severing as the relay is an effective technology method to extend the coverage. It can also alleviate the congestion and increase the throughput, especially applied in UAV networks. However, since the energy of UAVs is limited and the resources in UAV networks are scarce, how to optimize the network delay performance under these constraints should be well investigated. Besides, the relationship among different resources, e.g. power and bandwidth, is coupled which makes the optimization more complex. This article investigates the problem of joint power and bandwidth allocation in UAV backhaul networks, which considers both the delay performance and the resource utilization efficiency. Considering the heterogeneous locations characteristics of different UAVs, we formulate the optimization problem as a Stackelberg game. The relay UAV acts as the leader and extended UAVs act as followers. Their utility functions take both the delay durance and the resource consumption into account. To capture the competitive relationship among followers, the sub-game is proved to be an exact potential game and exists Nash equilibriums (NE). The Stackelberg Equilibrium (SE) is proved afterwards. We utilize a hierarchical learning algorithm (HLA) to find out the best resource allocation strategies, which also reduces the computational complexity. Simulation results demonstrate the effectiveness of the proposed method.

The acquisition of vegetation coverage information is crucial for crop field management, and utilizing visible light spectrum vegetation indices to extract vegetation coverage information is a commonly used method. However, most visible light spectrum vegetation indices do not fully consider the relationships between the red, green, and blue bands during their construction, making it difficult to ensure accurate extraction of coverage information throughout the crop's entire growth cycle. To rapidly and accurately obtain potato vegetation coverage information, drones were used in this study to obtain high-resolution digital orthoimages of potato growth stages. Based on the differences in the grayscale values of potato plants, soil, shadows, and drip irrigation belts, this study presents a combination index of blue and green bands (BGCI) and a combination index of red and green bands (RGCI). The vegetation index intersection method was used with 10 vegetation information indices to extract vegetation coverage, and the differences in extraction accuracy were compared with those of the maximum entropy method and bimodal histogram method. Based on the high-precision fraction vegetation coverage (FVC) extraction results, the Pearson correlation coefficient method and random forest feature selection were used to screen 10 vegetation and 24 texture features, and the top 6 vegetation indices most strongly correlated with the FVC were selected for potato growth stage FVC estimation and accuracy verification. A high-precision potato vegetation coverage estimation model was successfully established. This study revealed that during the potato tuber formation and expansion stages, the BGCI combined with the vegetation index intersection method achieved the highest vegetation coverage extraction accuracy, with overall accuracies of 99.61% and 98.84%, respectively. The RGCI combined with the vegetation index intersection method achieved the highest accuracy, 98.63%, during the maturation stage. For the potato vegetation coverage estimation models, the model based on the BGCI achieved the highest estimation accuracy (R2=0.9116, RMSE=5.7903), and the RGCI also achieved good accuracy in terms of vegetation coverage estimation (R2=0.8987, RMSE=5.8633). In the generality verification of the models, the R2 values of the FVC estimation models based on the BGCI and RGCI were both greater than 0.94. A potato vegetation coverage estimation model was constructed based on two new vegetation information indices, demonstrating good accuracy and universality.

The influence of the Fe2O3 addition amount on the dephosphorization of hot metal at 1623 K with the slag of the low basicity (CaO/SiO2) of about 1.5 was investigated by using high-temperature laboratorial experiments. With increasing the Fe2O3 addition amount, the contents of [C], [Si], [Mn] and [P] in hot metal at the end of dephosphorization decrease, and the corresponding removal ratios increase. The P2O5 content in slag increases, and the CaO and SiO2 contents in slag decrease. The phosphorus mainly exists in the form of the nCa2SiO4-Ca3(PO4)2 solid solution in the phosphorus-rich phase and the value of coefficient n decreases from 20 to 1 with increasing the Fe2O3 addition amount from 5 g to 30 g. With increasing the Fe2O3 addition amount, the oxygen potential and activity at the interface between the slag and hot metal increase. When the oxygen potential and the oxygen activity at the interface are greater than 0.72×10-12 and 7.1×10-3, respectively, the dephosphorization ratio begins to increase rapidly. With increasing the Fe2O3 addition amount to 30 g, the ratio of the Fe2O3 addition amount to theoretical calculation consumption is around 175%, and the dephosphorization ratio reaches the highest value of 83.3%.

The high level of noise in helicopter cabins considerably compromises the comfort and safety of the pilot and passengers. To verify the feasibility and effectiveness of microperforated panel composite sound absorption structures for noise suppression in helicopter cabins, simulation and experimental studies were conducted on a model of a light helicopter cabin. First, three microperforated composite sound absorption structures for the helicopter cabin wall panel were designed. Then, a finite element model of the main gear/body acoustic vibration coupling was established to obtain the target frequencies of the microperforated composite sound absorption structures; the acoustic effect was verified by simulation. Finally, a model helicopter cabin equipped with the three microperforated composite sound absorption structures was built, and a cabin noise test was performed. The test results showed that the combined microperforated panel acoustic structure and microperforated panel–porous material composite structure realized an overall cabin sound pressure level attenuation of 8–10 dB, on average, in the wide frequency range of 500–2000 Hz, with an amplitude of more than 20 dB. The microperforated panel–acoustic supermaterial composite structure achieved low-frequency sound absorption in the frequency range of 300–450 Hz. The sound absorption effect reached 50%, and it also exhibited good noise reduction effects in the middle- and high-frequency bands.

In offshore drilling, accidents such as gas invasion, overflow, and kicks are inevitable, and they can escalate into blowouts and other catastrophic events that result in casualties and substantial economic losses. Therefore, maintaining drilling safety requires accurate monitoring of gas invasion and overflow. The majority of overflow monitoring methods currently utilized at drilling sites are threshold-based. However, monitoring parameters acquired during actual drilling operations frequently contain noise signals, making it difficult for threshold-based methods to strike a balance between improving accuracy and minimizing false positives. In this paper, Pattern Recognition-based Kick Detection (PRKD) is proposed as a novel method for diagnosing overflow in offshore drilling. This method utilizes the overflow evolution process by integrating multi-phase flow calculations, data filtering theory, pattern recognition theory, the Bayesian framework, and other theoretical models. The PRKD effectively detects and monitors gas intrusion and overflow based on single parameters by analyzing the shape and wave characteristics of the curves. The case analysis demonstrates that the proposed method for monitoring drilling overflow achieves high precision while maintaining a low false positive rate. By combining advanced computational techniques with pattern recognition algorithms, the PRKD improves the accuracy and reliability of kick detection, enabling proactive responses to potential risks, protecting the environment and human lives, and optimizing drilling operations.

Correlation between the category richness (CR) and morphological diversity (MD) of some communities at a local scale was found pendent, however, examination of a whole category using a large dataset are lacking. In this study, 1119 jewel species from around the world representing all existing subfamilies and 33.78% of Buprestidae genera were selected as a test group. A geometric morphometric analysis on the contour of homologous traits: pronotum and elytra was conducted to quantify morphological diversity. Correlations between MD and CR among subfamilies were found to be consistently positive with the exceptions of a pronotum genus-level test on the subfamily category. The correlation was also found to be higher at the genus-level than it on the species-level, in both pronotum and elytron measurements. Based on our analyses the hypothesis of positive correlations was expected in the genus-level test of jewel beetles but rejected in species-level test. The inconsistent correlation between morphological diversity and species richness revealed convergent morphological variation of pronotum under the similar functional diversity in Buprestidae. In addition, our test revealed variable correlation between MD and CR based on different groups and characters, which might be caused by morphological changes under coevolution with different ecological factors.

The present paper reports an alternative solution of the measurement problem in quantum theory. The measurement problem can be characterized as that the U-procedure verses the R-procedure. The R-procedure is tested by the Yes/No measurement originally proposed by von Neuman and discussed in details by Penrose. We propose a novel stochastic sampling method to tackle the measurement paradox. Each testing sample produces a pair of Yes-number c and No-number d, which in turn generates a sample phase with respect to the exponential form of (c+id). All the sample phases forms a group G_R. Taking G_R as the sampling potential, combined with the dynamic phase group G_U，we establish a unified model of U-procedure and R-procedure for the evolution of wavefunction. Based on the Born rule, the probability of a sample is given by the squared magnitude of （c+id）. Some metaproperties of the new U-R model, such as natural transformation, consistency, and completeness, are presented. The present work provides a new picture of quantum mechanics and alike. The measurement problem widely exists in dynamic analysis, higher cognition, and large data modeling. Thus, the work reported here has a wide range of applications. Keywords: The measurement problem; U-R procedures; the Yes/No type measurement; stochastic sampling; sample phase; Born probability.

The continental shale layers are more complex in matrix pore-microfracture interactions and connectivity characteristics under the influence of tuff devitrification, which limits the explicit understanding of the coupling relationship between wettability and pore structure, as well as hydrocarbon flow behavior. Therefore, the influence of tuffaceous components on the reservoir formation mechanism and oil-rich effect of the continental shale formation system is a critical issue to be solved. The Upper Triassic Yanchang Formation tuffaceous shale in Ordos Basin and the Middle Permian Jingjingzigou Formation tuffaceous shale in the southern margin of Junggar Basin is taken as the research objects, using whole-rock mineral composition analysis, FE-SEM observation and digital image processing and extraction, as well as wettability angle determination and spontaneous imbibition experiments to illustrate the influence of the difference of devitrification extent on reservoir pore connectivity and interfacial wettability in two different environments of freshwater and saline lacustrine basins, and summarize the reservoir formation differential pattern under the influence of devitrification. The results show that the saline lacustrine is significantly modified by devitrification. The pore plane porosity of the devitrified tuffaceous shales in the saline lacustrine is higher than that of the tuffaceous shales in the freshwater; the oleophilic of the saline lacustrine tuffaceous shales is better than that of the tuffaceous shales in the freshwater, moreover, the pore connectivity of the saline lacustrine tuffaceous shales is better than that of the tuffaceous shales in the freshwater. Wettability and connectivity control the storage performance of shale oil in the tuffaceous shales formation system. The more oleophilic the reservoir wettability is, the better the pore connectivity is, and the better the reservoir of tuffaceous shale formation system is. According to the different extents of devitrification, two different reservoir formation models of freshwater and saline lacustrine are established, which are of great theoretical and practical significance to enrich the theory of shale oil enrichment and reservoir formation in the continental shale formation system and to guide the exploration of continental shale oil benefits.

The research on calcium leaching of 3D printed cement pastes (3DPC) exposed in aggressive water is limited, and the mechanism underlying its effect needs to be better understood. The performance anisotropy of degraded 3DPC due to calcium leaching is investigated in this paper. Samples with aggregate micro fines (AMF) and fly ash (FA) were prepared. A 6 mol/L NH₄Cl solution was used in the accelerated experiment. At specific leaching durations, performances were tested on the samples in different leaching directions. Results show that the performances of 3DPC in aggressive water have a significant anisotropic behavior. The evolution of pore defects and hydration products mainly governs the performance anisotropy of degraded 3DPC. The remaining hydration products of the surface of 3DPC leached in the Z direction are less than the other two directions because calcium ions leached in the Z direction are difficult to be compensated through weak interfaces between layers.

Abstract：Ballast pick-up velocity is a crucial parameter in the design of a slurry system in excavating devices, it influences transportation stability, safety, and economy of the system. In this study, a vertical pipe hydraulic experiment with different ballast sizes under water condition was conducted to investigate the ballast pick-up velocity in the shield slurry system. Thereafter, the coupling method of computational fluid dynamics and the discrete element method (CFD-DEM) was employed to establish a numerical model of the ballast pick-up velocity in the shield slurry system, and its applicability was verified by comparing the experiment results. Additionally, the ballast pick-up process under actual working conditions was studied, and the influence of ballast size and concentration on pick-up velocity was investigated. Overall, the primary findings of the study are as follows: 1) The numerical method can be applied to predict both the single ballast and ballast group pick-up velocity. 2) The pick-up process of the ballast group can be divided into four stages. 3) The conveying velocity of the ballast should be higher than the pick-up velocity of the single ballast to facilitate smooth discharge. 4) For a certain increase in ballast concentration, the pick-up velocity tends to be stable.

Fall prediction using machine learning has become one of the most fruitful and socially relevant applications of computer vision in gerontological research. Since its inception in the early 2000s, this subfield has proliferated into a robust body of research underpinned by various machine learning algorithms (including neural networks, support vector machines, and decision trees) as well as statistical modeling approaches (Markov chains, Gaussian mixture models, and hidden Markov models). Furthermore, some advancements have been translated into commercial and clinical practice, with companies in various stages of development capitalizing on the aging population to develop new commercially available products. Yet despite the marvel of modern machine learning-enabled fall prediction, little research has been conducted to shed light on the security and privacy concerns that such systems pose for older adults. The present study employs an interdisciplinary lens in examining privacy issues associated with machine learning fall prediction and exploring the implications of these models in elderly care and the Internet of Medical Things (IoMT). Ultimately, a justice-informed set of best practices rooted in social geroscience is suggested to help fall prediction researchers and companies continue to advance the field while preserving elderly privacy and autonomy.

Now when the whole world is still under COVID-19 pandemic, many schools have transferred the teaching from physical classroom to online platforms. It is highly important for schools and online learning platforms to investigate the feedback to get valuable insights about online teaching process so that both platforms and teachers are able to learn which aspect they can improve to achieve better teaching performance. But handling reviews expressed by students would be a pretty laborious work if they were handled manually as well as it is unrealistic to handle large-scale feedback from e-learning platform. In order to address this problem, both machine learning algorithms and deep learning models are used in recent research to automatically process students' review getting the opinion, sentiment and attitudes expressed by the students. Such studies may play a crucial role in improving various interactive online learning platforms by incorporating automatic analysis of feedback. Therefore, we conduct an overview study of sentiment analysis in educational field presented in recent research, to help people grasp an overall understanding of the sentiment analysis research. Besides, according to the literature review, we identify three future directions that researchers can focus on in automatically feedback processing: high-level entity extraction, multi-lingual sentiment analysis, and handling of figurative language.

The interfacial polymerization (IP) of piperazine (PIP) and trimesoyl chloride (TMC) has been extensively utilized to synthesize nanofiltration (NF) membranes. However, it is still a huge challenge to monitor the IP reaction, because of the fast reaction rate and the formed ultra-thin film. Herein, two effective strategies were applied to reduce the IP reaction rate: (1) the introduction of hydrophilic interlayers between the porous substrate and the formed polyamide layer, and (2) the addition of macromolecular additives in the aqueous solution of PIP. As a result, in situ Fourier transform infrared (FT-IR) spectroscopy was firstly used to monitor the IP reaction of PIP/TMC with hydrophilic interlayers or macromolecular additives in the aqueous solution of PIP. Moreover, the formed polyamide layer growth on the substrate was studied in a real-time manner. The in situ FTIR experimental results confirmed that the IP reaction rates were effectively suppressed and that the formed polyamide thickness was reduced from 138 ± 24 nm to 46 ± 2 nm according to TEM observation. Furthermore, an optimized NF membrane with excellent performance was consequently obtained, which included boosted water permeation of about 141–238 (L/m²·h·MPa) and superior salt rejection of Na₂SO₄ > 98.4%.

We are currently investigating the ultrasound imaging of a sensor consisting of a randomized encoding mask attached to a single lead zirconate titanate (PZT) oscillator for use in a puncture microscope. The proposed model was performed using a finite element method (FEM) simulator. To increase the number of measurements required by a single element system, the transducer was rotated at different angles. The image was constructed by solving a linear equation of the image model. In a previous study, the image resolution was improved by extracting phase information. In this study, we propose a strategy by integrating the weighted frequency subbands compound and a super resolution method (SCM) to improve the resolution in range and lateral resolution. We also evaluated the image performance by applying different methods to improve the image quality. The results show that better image resolution and speckle suppression were obtained by applying the proposed method.

The anodic polarization response of magnesium alloy AZ31 was characterized during exposure to aerated 0.1M NaCl solutions with millimolar additions of NaVO₃, Na₃PO₄, Na₂HPO₄, NaF and various pairings to assess their ability to inhibit corrosion kinetics and retard localized corrosion. Each of the candidate inhibitors reduced the corrosion rate of the alloy to some degree. A Na₃PO₄ - NaVO₃ pair produced a powerful inhibiting response decreasing the corrosion rate to about 10^-7 A/cm², which was two orders of magnitude lower than the uninhibited control case. A Bliss Independence assessment indicated that this inhibitor pair acted synergistically. A Na₂HPO₄ - NaVO₃ pair reduced the corrosion rate to 10^-6 A/cm² but was not assessed to be acting synergistically. The NaVO₃ - NaF pair did not reduce the corrosion rate significantly compared to the control case and was an antagonistic pairing. SEM imaging showed film formation due to exposure, which appears to be the origins of the observed inhibition. The resistance to localized corrosion was assessed as the difference in the breakdown potential and the corrosion potential with larger values indicating a lower probability of localized corrosion during free corrosion exposures. Effects of the inhibitors on this characteristic were mixed, but each of the inhibitor pairs yielded potential differences in excess of 100mV. A conceptual conversion coating process based on a mixture of vanadate and phosphate compounds were demonstrated. A fluoride-bearing formulation produced coatings whose total impedance was increased by a factor or 2 compared to an uncoated control. A fluoride-free formulation produced coatings whose corrosion resistance was increased by more than a factor of 3.

Xinjiang, as a climate-sensitive region in northwest China, holds significant importance in studying extreme weather events for agricultural production and socioeconomic development. Based on data from 52 meteorological stations and 23 extreme climate indicators in Xinjiang from 1960 to 2021, the 5a moving average, linear trend estimation, climate tendency rate, and GIS spatial analysis methods are used to analyze the distribution and change characteristics of extreme climate. The results revealed that from 1960 to 2021, TXx, TNx, TXn, TNn, FD, ID, TN10p, TX10p, and WSDI showed a significant upward trend, while SU, TR, TN90p, TX90p, and CSDI showed a significant downward trend. What is more, the range of changes in the extreme temperature indexes from 1990 to 2021 is higher than that of 1960 to 1989. The high-value areas of extreme indexes are primarily concentrated in the northern part, while the high-value areas of cold event indexes are mainly distributed in the southern part. The upward/downward trends all accounting for over 80.00% of the entire region. The Rx1day, Rx5day, PRCPTOT, R95p, R99p, R10, R20, and R25 showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indexes from 1990 to 2021 was lower than that from 1960 to 1989. Moreover, their high-value areas and high climate tendency areas are mainly concentrated in the northern and western parts of Xinjiang, with upward trends all accounting for over 71.00% of the entire region. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.

The insufficient toughness of concrete can lead to the occurrence of crack expansion, ultimately resulting in a decline in the physical and mechanical properties such as bearing capacity and deformation characteristics of structural components. In severe cases, this may lead to brittle failure, where the entire structure and system fail suddenly without significant deformation after being stressed. Additionally, during the process of shear and bending failure of reinforced concrete beams, the cracks generated on the side of the beam are mostly I-II composite cracks. Considering the material nonlinearity and geometric irregularity of reinforced concrete, it is of great significance to study the dynamic evolution law of crack expansion in reinforced concrete members. Based on experimental data, this paper aims to utilize the nonlinear finite element simulation software Abaqus to conduct finite element simulations of crack expansion in simple supported reinforced concrete beams. By accurately controlling the constitutive curves of concrete and reinforcement materials, the design of seismic performance of structural members can be enhanced, thereby making essential contributions to the preliminary analysis of actual crack control engineering. The nonlinear finite element results indicate that the joint cracks of the frame with the floor slab primarily manifest as flexural cracks, with the concrete damage degree of the floor slab being higher than that of the beam. This finding underscores the importance of considering the behavior of concrete and reinforcement materials in designing for seismic performance and crack control in actual engineering applications.

Diamond is a material with excellent performances which attracts the attention from researchers for decades. Pt (111), owing to its catalytic activity on diamond synthesis, is regarded to be a candidate for diamond hetero-epitaxity, which can enhance nucleation density. Molten surface at diamond growth temperature can also improve mobility and aggregation capability of primitive nuclei. Generally, (100)-oriented is welcomed for the achivement of high quality and large size diamond, since the formation of defects and twins are prevented. First-principle calculations and experimental researches were carried out for the study of transformation of orientation. The transformation from {111} to {100}-oriented diamond has been observed on Pt (111) substrate, which can be promoted by the increase of carbon source concentration and substrate temperature. The process is energetic favorable, which may provides a way towards large-scale (100) diamond films.

Blood image intensity has been used to detect erythrocyte sedimentation rate (ESR). However, it does not give an information on biophysical properties of blood sample under continuous ESR. In this study, three physical properties, including, τ₀, ESR_τ, and AI_I, are suggested to quantify mechanical variations of blood under continuous ESR. To demonstrate the proposed method, blood sample is loaded into a driving syringe. The blood flow rate is set in a periodic on–off pattern. Blood sample is then supplied into a capillary chip, and microscopic blood images are captured at specific intervals. Blood shear stress is quantified from the interface of blood stream in the coflowing channel. Both τ₀ and ESR_τ are then obtained by analyzing the blood shear stress. Simultaneously, the AI_I is evaluated by analyzing the image intensity of blood flow. According to the experimental results, the τ₀ exhibits consistent trends with respect to hematocrit as well as diluent. The ESR_τ and AI_I showed a reciprocal relationship each other. Three suggested properties represented substantial differences for suspended blood samples (i.e., hardened red blood cells, different concentration of dextran solution and fibrinogen). In conclusion, the present method can detect variations of blood sample under continuous ESR effectively.

A conjugate compound 5, (E)-6-hydroxy-2-oxo-2H-chromen-7-yl 3-(4-hydroxy-3-methoxyphenyl)acrylate, of 6,7-hydroxycoumarin (esculetin) 3 and (E)-3-(4-hydroxy-3-methoxyphenyl)- acrylic acid (ferulic acid) 1 was prepared in 61% yield by the esterification reaction of a protected ferulic acid 2a with esculetin 3 in the presence of triethylamine in dichloromethane for 3 h, followed by deprotection using 3M HCl. The structure of compound 5 was confirmed by 1H, 13C NMR spectroscopy, mass-spectrometry and elemental analysis.

The sources of dark matter and its spatial distribution is not in the typical standard model. The study thought to induct the physical causalities with empirical evidence on the theory of black hole and white hole juxtapose. The research applied a mixed method of data analysis and observational cosmology. With the cosmological observations on the traces of white holes, the research finds that the accretion phenomenon of black holes is largely contributed by its thermonuclear binding with the white hole. The antimatter saturated plasma of cold fusion and hot fission are key to the phenomenon of black hole and white hole juxtapose. The force between the black hole and the white hole is stronger than the strong force, and sheds new directions on cosmological and gravitation studies. The research concludes that black holes are white hole entropy. The higher order of the universe in matter forms can exist beyond the observed force. The thermal arrow of time is critical to future general relativity dependent instrumentation discoveries.

Ocean surface waves have been utilized as fundamental information in various fields of oceanic research. In this paper, we suggest a simulation and modelling technique for generating an ocean surface wave using an inverse Fast Fourier Transform (iFFT), and we subsequently verify the wave’s accuracy. The conventional method, linear superposition, requires recursive calculation because of the double summation and the time variable; to circumvent this issue, the new algorithm is presented. The Joint North Sea Wave Project (JONSWAP) spectrum is utilized for the ocean surface wave simulation example, and the parameters are the significant wave height (HS) and the zero-crossing wave period (TZ). A coordinate transform for the wavenumber domain was used to apply the inverse FFT algorithm. To verify the accuracy of the simulation result, the relative error between the input condition and the analysis result was calculated. The result for TZ is below 4% relative error, and the maximum relative error for HS is 7%. To avoid the Nyquist frequency for wave-field analysis and simulation, the minimum grid size was calculated by twice applying the maximum wavenumber.

This paper proposes a forecast-centric adaptive learning model that engages with the past studies on the order book and high-frequency data, with applications to hypothesis testing. In line with the past literature, we produce brackets of summaries of statistics from the high-frequency bid and ask data in the CSI 300 Index Futures market and aim to forecast the one-step-ahead prices. Traditional time series issues, e.g. ARIMA order selection, stationarity, together with potential financial applications are covered in the exploratory data analysis, which pave paths to the adaptive learning model. By designing and running the learning model, we found it to perform well compared to the top fixed models, and some could improve the forecasting accuracy by being more stable and resilient to non-stationarity. Applications to hypothesis testing are shown with a rolling window, and further potential applications to finance and statistics are outlined.

A practical method is presented to estimate rare earth elements (REE) concentrations in magmatic vapour phase (MVP) in equilibrium with water-saturated granitic melts based on empirical fluid-melt partition coefficients of REE (k_P^REE). The values of k_P^REEcan be calculated from a set of new polynomial equations linking to the chlorine molality (m_Cl^v) of the MVP associated with granitic melts, which are established via a statistical analysis on the existing experimental dataset. These equations may be applied to the entire pressure range (0.1 to 10.0 kb) within the continental crust, suggesting that light REEs behave differently in magmatic fluids, i.e. either being fluid compatible with higher m_Cl^v or fluid incompatible with lower m_Cl^v values. In contrast, heavy REEs are exclusively fluid incompatible and partition favourably into granitic melts. Consequently, magmatic fluids tend to be rich in LREE relative to HREE, leading to REE fractionation during the evolution of magmatic hydrothermal systems. Maximum k_P^REEvalue for each element is predicted and presented in a REE distribution diagram constrained by the threshold of m_Cl^v. REE contents of the granitic melt is approximated by whole-rock analysis, so that REE concentrations in the associated MVP would be estimated from the value of k_P^REE given chemical equilibrium retains. Two examples are provided respectively, to show the use of this method as a REE tracer to fingerprint the source of ore-fluids responsible for the Lake George intrusion-related Au-Sb deposit in New Brunswick (Canada), and for the Bakircay Cu-Au (-Mo) porphyry systems in northern Turkey.

In this paper, I investigate Hyers-Ulam-Rassias stabilities of the general quintic functional equation and the general sextic functional equation.

Background and Aims: Of all primary liver tumors, primary hepatic angiosarcoma (PHA) is a rare and aggressive malignant vascular tumor. The standard therapeutic care for hepatic angiosarcoma remains unclear. This study compared the survival outcomes of aggressive treatment (resection and liver transplant) and nonaggressive treatment (chemotherapy, transarterial chemoembolization [TACE], and conservative treatments) for patients with PHA and analyzed the prognostic factors influencing survival. Materials and Methods: Data of patients diagnosed as having PHA at our facility were retrospectively reviewed. The primary outcome was survival time. The secondary outcome was calculated baseline characteristics. Results: We included a total of 19 patients, who were divided into 2 treatment groups: aggressive (8 patients had undergone resection or transplants) and nonaggressive (11 patients had undergone TACE, chemotherapy, or conservative treatment). The mean survival time was 233.1 ± 189.7 days in the aggressive treatment group and 146.5 ± 115.8 days in the nonaggressive treatment group. A Kaplan-Meier plot revealed no significant difference in survival time between the 2 treatment groups (P = .3256). Conclusions: The survival time of patients receiving aggressive treatment was longer than that of those receiving nonaggressive treatment. The long term survival time in some selective cases of aggressive treatment will be achieved. Thought a difference was not significant between the groups. Because the number of patients was limited, more cases are required to confirm these findings.

In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., Ozone, PM_2.5 and Sulfur Dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exists some works applying machine learning to air quality prediction, most of the prior studies are restricted to small scale data and simply train standard regression models (linear or non-linear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration based on meteorological data of previous days by formulating the prediction of 24 hours as a multi-task learning problem. It enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other, and compare with several typical regularizations for multi-task learning including standard Frobenius norm regularization, nuclear norm regularization, ℓ_2,1 norm regularization. Our experiments show the proposed formulations and regularization achieve better performance than existing standard regression models and existing regularizations.

The present paper deals with a class of even entire functions of order $\rho =1$ and genus $\vartheta =0$ of the polynomials form, \[ \sum\limits_{m=0}^\infty {\frac{\left( {-1} \right)^m\Phi ^{\left( {2m} \right)}\left( 0 \right)}{\Gamma \left( {2m+1} \right)}x^{2m}} =\Phi \left( 0 \right)\prod\limits_{k=1}^\infty {\left( {1-\frac{x}{\ell _k }} \right)} , \] where $\Phi\left( 0 \right)\ne 0$, real numbers $x$, nonnegative integers $m$, and $\ell _k \ne 0$ are all of the nonzero roots with $\sum\limits_{k=1}^\infty {1/\left| {\ell _k } \right|} <\infty $ and natural numbers $k$. We provide an efficient criterion for the polynomials with only real zeros. We also prove that the conjecture of Jensen is our special case.

In this paper we address some variants for the products of Hadamard and Patterson. We prove that all zeros of the Riemann $\Xi$--function are real. We also prove that the Riemann hypothesis is true. The equivalence theorems associated with the Riemann zeta--function are obtained in detail.

In this article we propose the integral, series and product representations for the Ramanujan zeta-function. We suggest a variant for the Conrey-Ghosh product for the entire Ramanujan zeta-function. We present some variants for the product for the Ramanujan $\Xi$-function. We prove that all of its zeros are real. Along the way we obtain the truth of the Riemann-Hardy conjecture.

This paper addresses a variant of the product for the Dirichlet $L$--functions. We propose a completely detailed proof for the truth of the generalized Riemann conjecture for the Dirichlet $L$--functions, which states that the real part of the nontrivial zeros is $1/2$. The Wang and Hardy--Littlewood theorems are also discussed with removing the need for it. The results are applicable to show the truth of the Goldbach's conjecture.

It is due to Littlewood that the truth of the Riemann theorem implies that of the Lindel\"{o}f conjecture. This paper aims to use the idea of Littlewood to prove the Lindel\"{o}f conjecture for the Riemann zeta function. The Lindel\"{o}f $\mu $ function at the critical line is zero, with use of the Riemann theorem for the entire Riemann zeta function, proved based on the work of Heath-Brown. Our result is given to show that the Lindel\"{o}f conjecture, connected with the proof of the moment conjecture, is true.

College students play an important role in higher education and future social life. Research has a significant impact on college students' self-efficacy. Taking college students in Chengdu, China as an example, this study adopts the fuzzy-set qualitative comparative analysis (fsQCA) method, comprehensively incorporating various influencing factors such as demographic characteristics, physical exercise behavior, and life satisfaction. From the perspective of physical exercise behavior and life satisfaction, an asymmetric causal model that affects college students' high self-efficacy is constructed. The research results support the main principles of complexity theory, confirming the heterogeneity and complexity of the antecedent conditions for high self-efficacy among college students. Nineteen causal condition combinations that can promote high self-efficacy among college students are obtained. This study conducts exploratory research on the introduction of complexity theory analysis into the study of college students' physical exercise, life satisfaction, and self-efficacy. It also provides a decision-making reference path for improving the life satisfaction, physical exercise behavior, and self-efficacy of college students.

To improve the measurement accuracy of three-dimensional rotation angle of the spherical joint, a novel approach is proposed in this study, which combines the magnetic detection by Hall sensor and surface feature identification by eddy current sensor. Firstly, a permanent magnet is embedded in the ball head of spherical joint, and Hall sensors are set and distributed in the ball socket to measure the variation of magnetic flux density when spherical joint rotating, which are related to 3D rotation angle. In order to further improve measurement accuracy and robustness, we also set grooves on the ball head and use eddy current sensors to synchronously identify the rotation angle of the ball head. After the combination of two signals is adopted, a measurement model is established using the RBF neural network by training, and realized real-time measurement of the 3D rotation angle of spherical joint. The feasibility and superiority of this method are validated through experiments. The experimental results indicated that the measurement accuracy is promoted substantially compared to the preliminary measurement scheme based on spherical coding, the average measurement error of single axis is reduced by 9'9". The root mean square errors for the measurement of 3D rotation angles in this proposed method are as follows: the pitch angle α has an error of 1'8", the yaw angle β has an error of 2'15", and the roll angle γ has an error of 29'6".

Cervical cancer is the most common female reproductive tract tumor and is currently the only cancer that can be prevented by vaccination. Early detection and treatment of cervical cancer can be achieved through regular screening for human papillomavirus (HPV). Therefore, in this paper, an electrochemical sensor was designed to detect HPV 16 using a dual signal amplification technique. APTES modified glassy carbon electrode to improve stability. Combining gold nanoparticles and chain amplification reactions for signal amplification purposes. The limit of detection (LOD) was 1.731×10-16 mol/L, and the detection range of this electrochemical sensor was increased in comparison to previous research, with a linear response range from 1.0×10-13 mol/L to 1.0×10-5 mol/L (R2 = 0.99232) for the target detector.In addition, the sensor showed equally good recovery in sophisticated human serum samples. It shows that it has good interference resistance and the designed biosensor can be a powerful and reliable tool for clinical disease diagnosis.

The phenomenon of seat occupancy in university libraries is a prevalent issue. However, existing solutions, such as software-based seat reservations and sensors-based occupancy detection, have proven to be inadequate in effectively addressing this problem. In this study, we propose a novel approach: a serial dual-channel object detection model based on Faster RCNN. Furthermore, we develop a user-friendly web interface and mobile APP to create a computer vision-based platform for library seat occupancy detection. To construct our dataset, we combine real-world data collection with UE5 virtual reality. The results of our tests also demonstrate that the utilization of personalized virtual dataset significantly enhances the performance of the convolutional neural network (CNN) in dedicated scenarios. The serial dual-channel detection model comprises three essential steps. Firstly, we employ Faster RCNN algorithm to determine whether a seat is occupied by an individual. Subsequently, we utilize an object classification algorithm based on transfer learning, to classify and identify images of unoccupied seats. This eliminates the need for manual judgment regarding whether a person is suspected of occupying a seat. Lastly, the web interface and APP provide seat information to librarians and students respectively, enabling comprehensive services. By leveraging deep learning methodologies, this research effectively addresses the issue of seat occupancy in library systems. It significantly enhances the accuracy of seat occupancy recognition, reduces the computational resources required for training CNNs, and greatly improves the efficiency of library seat management.

Improper lifting measures of long-span spatial steel structures during construction process may delay the construction period and even cause safety accidents. Few studies on long-span spatial steel structures considered time-varying mechanical characteristic during construction process. In order to achieve safe and efficient installation in a long-span spatial steel structure, this research presents a time-varying mechanical analysis of the synchronous and asynchronous integral lifting and a single and interlaced point’s asynchronous integral lifting analysis method of a long-span spatial steel structure. The results showed that in the case of asynchronous lifting of single point, the displacement influence on other members is related to the distance. The closer the distance from the lifting point, the greater the influence. In asynchronous integral lifting, the lifting point with large lifting force is first installed to the specified position, and the lifting point with small lifting force is installed to the specified position.

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

The current methods for lightning risk warnings that are based on atmospheric electric field (AEF) data have a tendency to rely on single features, which results in low robustness and efficiency. Additionally, there is a lack of research on cancelling warning signals, contributing to the high false alarm rate (FAR) of these methods. To overcome these limitations, this study proposes a lightning risk warning method that incorporates enhanced empirical Wavelet transform-Adaptive Savitzky Gorey filter (EEWT-ASG) and one-dimensional morphology, using time-frequency domain features obtained through the Wavelet transform (WT). The proposed method achieved a probability of detection (POD) of 77.11%, miss alarm rate (MAR) of 22.89%, FAR of 40.19%, and critical success index (CSI) of 0.51, as evaluated on 83 lightning processes. This method can issue a warning signal up to 22 minutes in advance for lightning processes.

β-cyclodextrin-based star polymers have attracted much interest because of their unique structures and potential biomedical and biological applications. Herein, we synthesized well-defined folic acid (FA)-conjugated and disulfide bond-linked star polymer ((FA-Dex-SS)-βCD-(PCL)14) acted as theranostic nanoparticles for tumor-targeted magnetic resonance imaging (MRI) and chemotherapy. Theranostic nanoparticles were obtained by loading doxorubicin (DOX) and superparamagnetic iron oxide particles (SPIO) were loaded into the star polymer nanoparticles to obtain ((FA-Dex-SS)-βCD-(PCL)14@DOX/SPIO) theranostic nanoparticles. In vitro drug release studies showed that approximately 100% of the DOX was released from disulfide bond-linked theranostic nanoparticles within 24 h under a reducing environment in the presence of 10.0 mM GSH. DOX and SPIO could be delivered into HepG2 cells efficiently, owing to folate receptor-mediated endocytosis process of the nanoparticles and GSH triggered disulfide-bonds cleaving.Moreover, (FA-Dex-SS)-βCD-(PCL)14@DOX/SPIO showed strong MRI contrast enhancement properties. In conclusion, folate-decorated reduction-sensitive star polymeric nanoparticles are a potential theranostic nanoparticle candidate for tumor-targeted MRI and chemotherapy.

It is proved that there exist two odd prime numbers, p_i and p_j , such that p_i + p_j = 2r + 2(r ⩾ 2), where p_n denote the n-th odd prime number, 1 ⩽ i, j ⩽ n. By the theory of generating functions, we prove that there exist two positive integers, h_i and h_j , such that h_i + h_j = r(r ⩾ 2), where h_n = (p_n − 1)/2，(p_n ⩾ 3).Then we complete the proof.

The moisture content of maize seed is an important indicator for evaluating seed quality and a fundamental item in grain testing. The experiment used direct drying method to measure the moisture content of 80 different varieties of maize samples, and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1100~2498nm. By comparing seven spectral preprocessing methods, the PLSR model established after Normalize pretreatment had the best effect. The characteristic wavelengths were selected by Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS) and Uninformative Variable Elimination (UVE). Twelve prediction models were established for full band spectra and characteristic wavelengths based on PLSR, PCR and SVM. By comparing the performance of the prediction models, it was found the Normalize-SPA-PLSR algorithm was optimized. The values of RC2 and RP2 in the model were higher, which were 0.9936 and 0.9933, respectively, while the values of RMSEP and RMSECV were lower, which were 0.0357 and 0.0380, respectively. The Normalize-SPA-PLSR model was used as a visual prediction model for moisture content of maize seed, the 20 maize varieties in the prediction set were visualized to obtain visualized color images of moisture content. The color differences between different moisture content images were significant. The result indicated that hyperspectral imaging could accurately, rapidly, and non-destructive predict moisture content of maize seed, which provided technical support for moisture content detection in the process of maize harvest, storage and processing.

With the advancement of next generation sequencing in past several years, a rising number of non-coding RNAs have been found as new actors to regulate gene expression. Non-coding RNAs not only play important roles in carcinogenesis, but also affect the clinical treatment strategies. They have been proved to be deeply correlated with chemoresistance in several cancers. Ovarian cancer is the leading cause of death in gynecological malignancy, with low 5-year survival rate. Most patients are identified when they have late-stage disease. This review mainly makes a compilation of the most relevant research literature in this field with the purpose of shedding light on the relation between ncRNAs and chemoresistance in ovarian cancer.

Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in underwater confined environments, researchers have studied different underwater positioning methods and selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms, and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have also developed some bio-inspired autonomous positioning methods. This article introduces the applicable robot positioning methods and sensors in underwater confined environments and discusses the research directions of robot positioning methods in such environments.

This work focuses on addressing the dynamic positioning and trajectory tracking problem for a 4 degree-of-freedom (DOF) unmanned underwater vehicle (UUV) in the presence of nonlinear dynamics, parametric uncertainties, system constraints, and time-varying external disturbances. To tackle this problem, a disturbance observer-based control (DOBC) scheme is proposed. The scheme is structured around the model predictive control (MPC) method integrated with an extended active observer (EAOB). Compared to the conventional disturbance observer, the EAOB has the unique ability to handle both external disturbances and system/measurement noises simultaneously. The EAOB leverages a combination of sensor measurements and a system dynamic model to estimate disturbances in real-time, which allows continuous estimation and compensation of time-varying disturbances back to the controller. The proposed disturbance observer-based MPC (DOBMPC) is implemented by feeding the estimated disturbances back into the MPC’s prediction model, which forms a robust adaptive controller with a parameter-varying model. The proposed control strategy is validated through simulations in a Gazebo and Robot Operating System (ROS) environment. The results show that it can effectively reject unpredictable disturbances and improve the UUV’s control performance.

Electrochemical Impedance Spectroscopy (EIS) is a powerful non-invasive technique used to characterize Lithium-ion cells. Its application allows to identify and track the evolutions of cell degradation processes within a short testing time. This review collects all the available works which have used EIS spectra either to characterize Li-ion cell degradation or to develop Electric Circuit Models (ECMs). The objectives of this work are: (i) to highlight the influence of different aging test conditions on the EIS spectra; (ii) to find the correlations between EIS spectra variations and the underlying degradation mechanisms and (iii) to list the available options to formulate ECMs from EIS spectra of aged cells. After an exhaustive analysis of the state-of-the-art, a critical review is presented to discuss the existing links between degradation mechanisms and the most reliable solutions to model them.

This study investigates the spatial and temporal dynamics of Paomo restaurants in Xi'an's central urban area from 2012 to 2023, leveraging Point of Interest (POI) data through a series of sophisticated analytical techniques. Methods including nearest neighbor distance, kernel density estimation, and band set statistical analysis were employed to thoroughly examine the evolutionary patterns and key drivers influencing the spatial distribution of these eateries. Our results highlight a notable trend of expansion from the central city to the periphery, characterized by significant spatial agglomeration. Initially concentrated in the city center, the distribution of Paomo restaurants has progressively shifted towards multiple distinct cores, reflecting the dual influence of urban policy initiatives and the dynamics of market competition. Furthermore, the research identifies several pivotal factors shaping this spatial distribution, particularly transportation access, residential quality of life, and the availability of commercial services. These findings demonstrate the profound effects of urban spatial reconfiguration and socioeconomic development on the specialized catering industry. The insights gleaned from this analysis offer valuable guidance for urban planners and policymakers, providing strategic recommendations for the spatial planning of specialty catering services to optimize urban layouts and foster sustainable economic progress.

Based on a modified dice-and-fill technique, a PIN-PMN-PT single crystal 1-3 composite with the kerf of 12 μm and pitch of 50 μm was prepared. The as-made piezoelectric composite material behaved with high piezoelectric constant (d33 = 1500 pC/N), high electromechanical coefficient (kt = 0.81), and low acoustic impedance (16.2 Mrayls). Using lithography and flexible circuit method, a 48-element phased array was successfully fabricated from such a piezoelectric composite. The array element was measured to have a central frequency of 20 MHz and a fractional bandwidth of approximately 77% at −6 dB. Of particular significance was that this PIN-PMN-PT single crystal 1-3 composite-based phased array exhibits a superior insertion loss compared with PMN-PT single crystal and PZT-5H-based 20 MHz phased arrays. The focusing and steering capabilities of the obtained phased array were demonstrated theoretically and experimentally. These promising results indicate that the PIN-PMN-PT single crystal 1-3 composite-based high frequency phased array is a good candidate for ultrasound imaging applications.

Abstract: The promotion and development of healthy cities are vital for enhancing human habitats and fostering sustainable economic growth. Based on the core database of Web of Science and the knowledge graph software, this paper makes a quantitative analysis of the literature related to attention recovery abroad. It is found that in recent years, the research on attention recovery has developed rapidly, the number of related literature has been increasing, and the research content presents the characteristics of interdisciplinary integration. By further analyzing the characteristics of research literature, research context, and knowledge basis, this paper summarizes the empirical research based on the existing quantitative analysis, reviews the research field based on the mechanism of attention recovery, and analyzes the development process and trend based on the research basis of attention recovery. Due to the change of modern life style, human health problems are becoming more and more prominent. Attention restoration design provides a new research idea and method to balance the relationship between humans and the urban environment.

We propose a novel plasmon nano-optical antenna that consists of three vertically superimposed discs with different materials. Two symmetrical nanometer-scale Yagi antenna elements with gold-material are embedded into the surface of the middle disc with silicon dioxide. Based on plasmon theory and Yagi antenna principle, we explore the properties of the far-field radiation and near-field transmission of the antenna in near infrared band. It is found that the pattern of the xoy plane in the antenna can be adjusted in arbitrary direction. The far-field pattern and near-field transmission spectrum can be reconfigured by changing the angle a of the Yagi antenna elements in the middle disc. Moreover, we also discuss the influences of the widths and lengths of the elements on the near-field transmission spectrum when a is fixed at 0 degree.

Abstract: NFinFET transistors with fin channel length 90nm and a planar MOSFET transistor with channel length 180nm and 90nm are presented with characteristic curves at various gate biases. A finalized algorithm with kink effects is effectively responsible for addressing the field effect transistors. The algorithm includes the modified conventional current-voltage formula and a nonlinear heat-associated kink solution which is simplified as a Gaussian form. Three parameters in modified model includes kN (related with channel width, channel length, and gate oxide capacitor, and proportional to the mobility of carriers)Vth(Threshold Voltage), and (the inverse of Early Voltage). Those parameters are determined to minimize the discrepancies between the measured data and the fitting values, but leave kinks located at around (VGS－Vth), which are deliberately eliminated by the Gaussian form because of the agitation of thermal kink effects. The whole fitting is made to be as close as the as-measured IDS-VDS. In the mean time, those determined parameters are physically meaningful after the analysis has been done.

Layered 2D graphene oxide (GO) films are integrated with silicon nitride (SiN) waveguides to experimentally demonstrate an enhanced Kerr nonlinearity via four-wave mixing (FWM). Owing to the strong light–matter interaction between the SiN waveguides and the highly nonlinear GO films, the FWM performance of the hybrid waveguides is significantly improved. SiN waveguides with both uniformly coated and patterned GO films are fabricated based on a transfer-free, layer-by-layer GO coating method together with standard photolithography and lift-off processes, yielding precise control of the film thickness, placement and coating length. Detailed FWM measurements are carried out for the fabricated devices with different numbers of GO layers and at different pump powers. By optimizing the trade-off between the nonlinearity and loss, we obtain a significant improvement in the FWM conversion efficiency of ≈7.3 dB for a uniformly coated device with 1 layer of GO and ≈9.1 dB for a patterned device with 5 layers of GO. We also obtain a significant increase in FWM bandwidth for the patterned devices. A detailed analysis of the influence of pattern length and position on the FWM performance is performed. Based on the FWM measurements, the dependence of GO’s third-order nonlinearity on layer number and pump power is also extracted, revealing interesting physical insights about the 2D layered GO films. Finally, we obtain an enhancement in the effective nonlinear parameter of the hybrid waveguides by over a factor of 100. These results verify the enhanced nonlinear optical performance of SiN waveguides achievable by incorporating 2D layered GO films.

This paper proposes an interpretable geometry solution based on the formal language set of text and diagram. Geometry problems are solved by machines, which still poses challenges in natural language processing and computer vision. Significant progress promotes existing methods in the extraction of geometric formal languages. However, the neglect of the graph structure information in the formal language and the lack of further refinement of the extracted language set can lead to the poor effect of the theorem prediction and affect the accuracy in problem solving. In this paper, the formal language graph is constructed by the extracted formal language set and used to theorem prediction by graph convolutional network. So as to better extract the relationship set of diagram elements, an improved diagram parser was proposed. The test results indicate that the improved method has good results in solving the problem with interpretability geometry.

The Laser Thermal-Joule Heating Composite Process was studied by orthogonal tests based on an analysis of fabrication parameters such as the laser power, wire feeding speed, and electric current. Temperature profiles and the geometric morphology of deposited layers under different process parameters were analyzed, and the overlaps between the layers and the substrate were observed. Results show that when the temperature at the bottom layer of the additive manufacturing is higher than the melting point of the substrate, and the highest temperature at the top layer does not exceed the over-firing temperature, good morphology and close bonding with the substrate can be obtained. Finally, appropriate process parameters were identified and verified to print multiple layers continuously.

This paper presents a steady-state and transient heat conduction analysis framework using the polygonal scaled boundary finite element method (PSBFEM) with polygon/quadtree meshes. The PSBFEM is implemented with commercial finite element code Abaqus by the User Element Subroutine (UEL) feature. The detailed implementation of the framework, defining the UEL element, and solving the stiffness/mass matrix by the eigenvalue decomposition are presented. Several benchmark problems from heat conduction are solved to validate the proposed implementation. Results show that the PSBFEM is reliable and accurate for solving heat conduction problems. Not only can the proposed implementation help engineering practitioners analyze the heat conduction problem using polygonal mesh in Abaqus, but it also provides a reference for developing the UEL to solve other problems using the scaled boundary finite element method.

Epoxy asphalt (EA) concrete is widely used in constructing long-span steel bridge pavements (SBDPs). This study aims to derive a fatigue damage evolution law, conducting an experimental investigation of SBDP. First, a general theoretical form of the fatigue damage evolution law of materials is established based on the thermal motion of atoms. Then, fatigue experiments demonstrate that this evolution law well represents the known damage–life relationships of SBDP. Taking into account the experimental relationships between damage and fatigue life under symmetrical cyclic loadings with different overload amplitudes and temperature variations, a detailed damage evolution law is deduced. Finally, the role of damage accumulation is discussed on the basis of the proposed damage evolution law for the extreme situation of heavy overload and severe environments. The results show that both heavy loading and falling temperatures increase the fatigue damage of SBDP considerably; therefore, SBDP should avoid heavy loading combined with winter temperatures. EA shows a fatigue life two to three times longer than that of modified matrix asphalt (SMA) or guss asphalt (GA). For the same thickness, EA pavement is demonstrated to be more suitable for an anti-fatigue design of large-span SBDP under high traffic flows and low temperatures.

The forming of large rivers are the integral consequences of the deep earth process and the surface. In contrast to the hot topics for rivers related to orogenic domains, rift-related large rivers are largely ignored especially in deep time studies. The Cenozoic East Asia margin provides very good opportunity to observe this kind of rivers. It has been believed that basin-and-swell physiography dominated the East Asia margin and impeded the forming of large rives in the early Cenozoic. In this paper, we combined provenance analysis of East China Sea Basin, where is a crucial place to trace the river evolution in East Asia margin, and regional geologic constraints to reveal drainage reorganizations. Detrital zircon U-Pb ages from the Early Eocene sediments of the East China Sea Basin are firstly reported. Our results together with literature data demonstrate that regional provenance changes occurred at the middle Eocene from one singe age peak at ~110 Ma of proximal sources to multiple age spectrum derived from far inland. Source to sink analysis indicated that the North China Block and Korea Peninsular provided the most detritus. Sedimentation and tectonic features of rift basins in the potential source areas indicated that rivers flowed into Bohai Basin and Jianghan Basin cannot provide terrigenous clasts for the lower reaches in the Eocene. Contrastingly, the dominantly fluvial sediments across the Subei-South Yellow Sea Basin suggested external river system and a bypassing region since the middle Eocene, coinciding with provenance change in ECSB. All these demonstrated that a large river (East Asia River) established in east Asia margin in the middle Eocene and flowed southwestward approximately 1500km to the sea in southern ECSB. This river might last to the middle Miocene. The deep earth processes driven by Izanagi-Pacific ridge subduction resulted in the overfilled stage of Subei-South Yellow Sea Basin and the post-rift subsidence in west depression of ECSB, and thus facilitated the initiation of the EAR. Our finding shed new light on the evolving landscape in East Asia and showed how subduction of deep earth process controlled the initiation of rift-related large rivers.

p53 is among the most frequently mutated tumor suppressor genes given its prevalence in >50% of all human cancers, including high grade serous endometrial cancers and ovarian cancers. In addition to loss of tumor suppression function, many mutated p53 (Mutp53) proteins acquire gain-of-function (GOF) activities as oncogenes to promote cancer progression, which manifest through aberrant expression of p53. As we have come to see, statins induce CHIP-mediated degradation of mutp53 by blocking the interaction between mutp53 and DNAJA1. Therefore, targeting critical downstream pathways of mutp53 provides an alternative strategy for treating cancers expressing mutp53. In this review, we summarize recent advances with Wee1 inhibitors and mevalonate pathway inhibitors, particularly statins, regarding their use in gynecological cancers with p53 mutations.

This systematic literature review meticulously explores the complexities of technological innovation and entrepreneurship, with a particular focus on the strategies and management practices involved in new product development from an interdisciplinary standpoint. By conducting an extensive analysis of 323 key articles from 11 top-tier journals published between 2020 and 2023, the review delineates the current research trajectories within these fields, underscoring significant scholarly interest and development. The study particularly emphasizes the emergence of artificial intelligence-generated content technology since 2022 and critically assesses its influence on innovation processes and product development practices among both nascent entrepreneurs and well-established corporations. This emergent technology's role in shaping the competitive landscape and its potential to drive significant shifts in market dynamics are thoroughly examined.

This paper reviews the multiple roles of machine learning in improving the resilience of power grids, especially in applying new energy storage technologies. Energy storage technologies, such as compressed air energy storage, flywheel energy storage, and superconducting coil energy storage, significantly improve the power grid's ability to respond to load fluctuations and emergencies through intelligent control and optimisation of machine learning algorithms. This not only helps to optimise energy dispatch and improve the operational efficiency and flexibility of the grid but also significantly improves the stability and reliability of the grid so that it is better able to meet future challenges and needs.

Serine/threonine kinase (AKT) signaling regulates diverse cellular processes and is one of the most important aberrant cell survival mechanisms associated with tumorigenesis, metastasis, and chemoresistance. Targeting AKT has become an effective therapeutic strategy for the treatment of many cancers. AKT3 (PKBγ), the least studied isoform of the AKT family, has emerged as a major contributor to malignancy. AKT3 is frequently overexpressed in human cancers, and many regulatory oncogenic or tumor suppressor small non-coding RNAs (ncRNAs), including microRNAs (miRNAs), have recently been identified to be involved in regulating AKT3 expression. Therefore, a better understanding of regulatory miRNA/AKT3 networks may reveal novel biomarkers for diagnosis of patients with cancer and provide invaluable information for developing more effective therapeutic strategies. The aim of this review is to summarize current research progress in the isoform-specific functions of AKT3 in human cancers and the roles of dysregulated miRNA/AKT3 in specific types of human cancers.

Analyzing network robustness against random failures or malicious attacks is a critical research issue in network science as it helps to enhance the robustness of beneficial networks or efficiently disintegrate harmful networks. Most studies commonly neglect the impact of the attack success rate (ASR) and assume that attacks on the network will always be successful. However, in real-world scenarios, an attack may not always succeed. This paper proposes a novel robustness measure called Robustness-ASR (RASR), which utilizes mathematical expectations to assess network robustness when considering the ASR of each node. To efficiently compute the RASR for large-scale networks, a parallel algorithm named PRQMC is presented, which leverages randomized quasi-Monte Carlo integration to approximate the RASR with a faster convergence rate. Additionally, a new attack strategy named HBnnsAGP is introduced to better assess the lower bound of network RASR. Finally, the experimental results on 6 representative real-world complex networks demonstrate the effectiveness of the proposed methods compared with the state-of-the-art baselines.

This study examined infants' neural responses to emotional prosody in natural speech. A multi-feature oddball paradigm was used with 34 3~11 month-old infants. Results showed distinct early (100-200 ms) and late (300-500 ms) mismatch responses to different emotional prosodies. Older infants had more negative early responses with happy and angry prosodies evoking stronger responses compared to sad prosody while younger infants showed a clearer distinction between angry and sad prosodies. In the late time window, angry prosody elicited more negative responses than sad prosody with younger infants showing more distinct responses between these two prosodies. Males exhibited stronger early mismatch responses than females. These findings call for further research on the implications for socio-emotional and language development.

In order to meet the diversified rehabilitation needs of people, it is necessary to design a product for active rehabilitation of patients. Existing rehabilitation chairs use intelligent massage, which can cause problems such as large massage area, inability to local massage, large chair size, and inability to meet the continuous use of the damaged parts. In this paper, modular design method and multi-layer evaluation method are used to solve the problems related to rehabilitation chairs. The authors use the questionnaire survey method and the functional technology matrix method to determine the functional requirements of the rehabilitation chair, and then use the multilevel evaluation methods, including the AHP method, entropy weight method main and grey correlation analysis, to optimize the functional solutions of the rehabilitation chair, and finally obtain a chair for the rehabilitation of patients with upper and lower limb disorders. Problems such as generalisation of rehabilitation scope and non-durable use of components were solved, and the purpose of active exercise was achieved. This study verifies that the use of multilevel decision evaluation method can effectively improve the efficiency of programme decision-making, and provides theoretical and practical basis for the design of similar products.

A (2,6)-fullerene F is a 2-connected cubic planar graph whose faces are only 2-length and 6-length. Furthermore, it consists of exactly three 2-length faces by Euler's formula. The (2,6)-fullerene comes from Do\v{s}li\'{c}'s (k,6)-fullerene, a 2-connected 3-regular plane graph with only k-length faces and hexagons. Do\v{s}li\'{c} showed that the (k,6)-fullerenes only exist for k = 2, 3, 4 or 5, and some of the structural properties of (k,6)-fullerene for k = 3, 4 or 5 are studied. In this paper, we study the properties of (2,6)-fullerene. We obtain that the edge-connectivity of (2,6)-fullerenes is 2. Every (2,6)-fullerene is 1-extendable, but not 2-extendable. F is said to be k- resonant (k>0) if the deleting of any i (0=< i<= k) disjoint even faces of F results in a graph with at least one perfect matching. We have that every (2,6)-fullerene is 1-resonant. An edge set S of F is called an anti-Kekule set if F-S is connected and has no perfect matchings, where F-S denotes the subgraph obtained by deleting all edges in S from F. The anti-Kekule number of F, denoted by ak(F), is the cardinality of a smallest anti-Kekule set of F. We have that every (2,6)-fullerene F with |V(F)|>6 has anti-Kekule number 4. Further we mainly prove that there exists a (2,6)-fullerene F having f_{F} hexagonal faces, where f_{F} is related to the two parameters n, m.

. The impedance change of an induction coil surrounding a metal tube adapter is investigated by the truncated region eigenfunction expansion (TREE) method. Conventional TREE method is inapplicable to this problem as a consequence of the numerical overflow of eigenfunctions of the air-metal multi-subdomain regions. The difficulty is surmounted by a normalization procedure for the numerical eigenfunctions obtained from the finite element method (FEM). Efficient algorithm is devised by the Clenshaw-Curtis quadrature rule for integrals involving the numerical eigenfunctions. Numerical results of the TREE and FEM simulation coincide very well in all cases, and the efficiency of the proposed method is also confirmed.

Intergenerational integration has given rise to a novel aging paradigm known as all-age communities, which is garnering international attention. In China, the aging population and the implementation of the three-child policy have resulted in increased demand for retirement and childcare services among residents in older neighborhoods. Consequently, there is a pressing need to retrofit these older neighborhoods to accommodate all-age living arrangements given the high demand they generate. Therefore, this study undertakes research interviews with residents and constructs an exploratory theoretical model rooted in established theory. To assess the significance of our model, we employ Smart PLS 3.0 based on 297 empirical data points. Our findings indicate that anxiety has a significant negative effect on payment behavior; objective perception, willingness to pay, and government assistance exert significant positive effects on payment behavior. By comprehensively analyzing the mechanisms underlying residents’ payment behavior, this study provides valuable insights to the government for promoting the aging process within communities and formulating effective transformation policies.

This study was aimed to identify perception, knowledge, attitude and nursing practice toward use of physical restraints among clinical nurses, and further examined the factors associated with nursing practice toward use of physical restraints. The research participants were 180 nurses from a urban general hospital located in Korea. Data were collected using self-report questionnaires and analyzed using t-test, ANOVA, Pearson correlation coefficients, and multiple regression. There were significant negative relationships attitudes towards the use of physical restraints with knowledge (r = -.32, p &lt; .001) and nursing practice (r = -.12, p = .107). Knowledge showed a positive correlation with nursing practice (r = .28, p &lt; .001). Factors affecting nursing practice of clinical nurses were identified as knowledge (β= .23), education experiences (Yes) (β= .18), and work unit (ICU) (β= .43). The explanation power of this regression model was 22% and it was statistically significant (F=7.45, p&lt;.001). The findings from this study suggest that knowledge, education experiences and work unit were the strongest predictor on nursing practice toward use of physical restraints. Thus, we propose that developing and applying evidence-based educational intervention programs by work unit to reduce the inappropriate use of physical restraints in hospitals are required.

The divination function of China's Yijing has led to its circulation for thousands of years. In our exploration of Yijing's characteristics using big data, we have discovered variations in results between the coin toss method and the ancient yarrow-stalk method of divination, known as "the number for the great expansion method of divination(大衍之数)". The yarrow-stalk method serves as the fundamental method of divination in Yijing and continues to hold significance in studying the essential characteristics of Yijing. Despite the complexity of yarrow calculations, advancements in computer technology and big data have simplified its application. By employing the yarrow-stalk method, we simulated changes in the 64 hexagrams, calculated probabilities and proportions of hexagram alterations, and derived fundamental characteristics and patterns of hexagrams. Additionally, we constructed the spatial representation of lines and hexagrams. Through a binary system rearrangement, we created a 64x64 matrix illustrating hexagram transformations. Subsequently, we generated 100 million random hexagrams and analyzed line and hexagram changes accordingly. Our findings indicate the following:(1) Big data analysis reveals evident asymmetry in the hexagrams obtained through the yarrow-stalk method, with a triangular fractal characteristic forming the background.(2) Each of the 64 hexagrams exhibits a distinct probability distribution when transforming into other hexagrams, which can be categorized into five types.(3)The occurrence probabilities of Laoyang, Laoyin, Shaoyang, and Shaoyin are 18.61%, 6.387%, 31.38%, and 43.62% respectively. The probabilities of Yin and Yang occurrences are nearly equal, each close to 50%. However, the probability of Laoyang is approximately three times higher than that of Laoyin.(4) Visualized and analyzed the characteristics of hexagram changes greater than 100000 times using 3D statistical maps and Sankey diagram.These results demonstrate that the yarrow-stalk method effectively unveils the characteristics and underlying patterns of the 64 hexagrams. This study provides a novel approach for scientifically exploring the internal laws governing the 64 hexagrams in Yijing.

This study is a two-phase hybrid study on the behavioral activities of undergraduate students during the COVID-19 pandemic lockdown. A questionnaire survey of students during the whole school period and a semistructured interviews with 13 undergraduates of different majors were conducted. First, we found that compared with primary and secondary school students, undergraduates showed many problems with self-time management. Second, the behavior of undergraduates at home may result from multiple factors, such as past experience, professional identity and behavioral habits. When secondary school provided a relatively relaxed learning environment or secondary school teachers placed more emphasis on continuous effort, and when students had a strong sense of the social value of their undergraduate major, students worked harder and achieved more during the pandemic. Otherwise, it was easier for students to slack off and be passive during the pandemic. Altogether, this study deepens our understanding of undergraduates' self-management of free time, and the differences in the values underlying different behaviors and the formation process of various behaviors.

This study examines the unique circumstances surrounding state-owned equity participation in non-state-controlled enterprises in China. Specifically, this study examines the impact of state-owned equity participation on the environmental, social, and governance (ESG) performance of non-state-controlled enterprises. Focusing on A-share listed firms on the Shanghai and Shenzhen Stock Exchanges and using data from 2013 to 2021, empirical testing shows that state-owned equity participation can significantly improve the ESG performance of non-state-controlled enterprises, with this conclusion remaining reliable after a series of robustness tests. Top management incentives is a mediating mechanism for state-owned equity participation in enhancing ESG performance. This study also finds that when state-owned equity participates in large enterprises or companies with a high degree of digital transformation, the effect on ESG performance is greater than that of small to medium-sized enterprises (SMEs) or enterprises with a low level of digital transformation. The findings of this study add to the current body of the existing field of research on the factors influencing corporate ESG performance and the impact of state-owned equity on corporate non-financial performance.

This study aims to optimize the subway platform passenger flow guidance method using Anylogic simulation software. The objective of the study is to minimize overcrowding within subway carriages, enhance transportation capacity, and augment service levels by guiding passengers on the platform to board trains in a distributed manner. The study achieves a balanced flow of passengers in each carriage by taking into consideration the number of passengers in each carriage, the number of passengers at the station, and the expected number of passengers disembarking from the trains. The standard deviation of balance is used to optimize the subway platform passenger flow guidance method. We simulated Guangzhou Metro Zhujiang New Town Line 5 platform to compare different guidance methods and evaluate their effects on the uneven distribution of passengers with passenger flows ranging from 120K to 600K people per day. Our results show that guidance method 2, relative to manual guidance, improves by 38.5% under a daily passenger flow of 240K people on Zhujiang New Town Line 5. Guidance method 3 improves by 25.7% relative to guidance method 2 and by 54.4% relative to manual guidance.

Purpose: Emotional voice conveys important social cues that demand listeners’ attention and timely processing. This event-related potential study investigated the feasibility of a multi-feature oddball paradigm to examine adult listeners’ neural responses to detecting emotional prosody changes in non-repeating naturally spoken words. Method: Thirty-three adult listeners completed the experiment by passively listening to the words in neutral and three alternating emotions while watching a silent movie. Previous research documented pre-attentive change-detection electrophysiological responses (e.g., MMN, P3a) to emotions carried by fixed syllables or words. Given that the MMN and P3a have also been shown to reflect extraction of abstract regularities over repetitive acoustic patterns, the current study employed a multi-feature oddball paradigm to compare listeners’ MMN and P3a to emotional prosody change from neutral to angry, happy, and sad emotions delivered with hundreds of non-repeating words in a single recording session. Results: Both MMN and P3a were successfully elicited by the emotional prosodic change over the varying linguistic context. Angry prosody elicited the strongest MMN compared to happy and sad prosodies. Happy prosody elicited the strongest P3a in the centro-frontal electrodes, and angry prosody elicited the smallest P3a. Conclusions: The results demonstrated that listeners were able to extract the acoustic patterns for each emotional prosody category over constantly changing spoken words. The findings confirm the feasibility of the multi-feature oddball paradigm in investigating emotional speech processing beyond simple acoustic change detection, which may potentially be applied to pediatric and clinical populations.

Background: It is necessary to apply a behavior-based safety (BBS) program to prevent at-risk behavior. An effective BBS program requires the implementation of not only behavioral definitions and a customized critical behavior checklist (CBC) but also observations of behavior, coupled with customized interventions at power plants. Method: In this study, a customized CBC and behavioral definition were developed through a review of five different sites that previously used a CBC. The rules of observation, flow, and target were established to initiate the observations. Customized interventions were selected to increase safe behaviors. CBC scoring was used to evaluate observed safe behaviors for three years. Recognized safe behaviors were evaluated with a questionnaire that included four items each for conformity and participation behaviors and were then analyzed through a factor analysis and a t-test. The questionnaires were conducted three months before and after the implementation of the BBS program. Results: The customized CBC, behavioral definition, and interventions were effective, such that observed safe behaviors and the levels of workers’ recognized safe behaviors increased. Conclusion: The application of the BBS program was found to increase the observed and recognized safe behaviors. Therefore, the program applied to this site can help increase safe behaviors at other identical or similar sites, as well as prevent an accident, which also corresponds with the results of prior studies.

We propose a new ensemble classification algorithm, named Super Random Subspace Ensemble (Super RaSE), to tackle the sparse classification problem. The proposed algorithm is motivated by the Random Subspace Ensemble algorithm (RaSE). The RaSE method was shown to be a flexible framework that can be coupled with any existing base classification. However, the success of RaSE largely depends on the proper choice of the base classifier, which is unfortunately unknown to us. In this work, we show that Super RaSE avoids the need to choose a base classifier by randomly sampling a collection of classifiers together with the subspace. As a result, Super RaSE is more flexible and robust than RaSE. In addition to the vanilla Super RaSE, we also develop the iterative Super RaSE, which adaptively changes the base classifier distribution as well as the subspace distribution. We show the Super RaSE algorithm and its iterative version perform competitively for a wide range of simulated datasets and two real data examples. The new Super RaSE algorithm and its iterative version are implemented in a new version of the R package RaSEn.

Paraphrase generation is an important yet challenging task in NLP. Neural network-based approaches have achieved remarkable success in sequence-to-sequence(seq2seq) learning. Previous paraphrase generation work generally ignores syntactic information regardless of its availability, with the assumption that neural nets could learn such linguistic knowledge implicitly. In this work we make an endeavor to probe into the efficacy of explicit syntactic information for the task of paraphrase generation. Syntactic information can appear in the form of dependency trees which could be easily acquired from off-the-shelf syntactic parsers. Such tree structures could be conveniently encoded via graph convolutional networks(GCNs) to obtain more meaningful sentence representations, which could improve generated paraphrases. Through extensive experiments on four paraphrase datasets with different sizes and genres, we demonstrate the utility of syntactic information in neural paraphrase generation under the framework of seq2seq modeling. Specifically, our GCN-enhanced models consistently outperform their syntax-agnostic counterparts in multiple evaluation metrics.

The flow behavior of the administrated fluid matrices demands careful assessments for stability as consumed by individuals with dysphagia. In the present study, we incorporate tapioca starch (TS), hydroxypropyl distarch phosphate (HDP), and xanthan gum (XG) as thickeners into different nectars (300±20 mPa.s) undergoing thermal processing and evaluated their stability. The thickened nectars presented better water holding and oil binding capacities at 25 ℃ than 4 ℃, and the nectars with TS provided the best results for both capacities as well as the highest solubility index and swelling power (p<0.05). All prepared nectars appeared to be shear-thinning fluids with yield stress closely fitting the power law and Casson models. XG-contained nectars presented a higher yield stress and consistency index. Matrices thickened by HDP exhibited a higher viscoelastic property compared to those thickened by TS during thermal processing. TS nectars presented viscous behavior, whereas HDP and XG nectars presented elastic behavior at 80 ℃ processing. The 3-min thermal processing HDP-nectars remained stable and met dysphagia-friendly requirements under 4 ℃ storage for 28 days regardless of the types of fluid bases (distilled water, sport drink, or orange juice). The employed thickeners present adequate physicochemical properties to be potentially utilized for producing dysphagia-friendly formulations.

Abstract In order to reduce the failure accidents of conventional packers, a kind of sealing structure of retrievable packers is designed, which can provide reference for optimizing the sealing performance and pressure capacity of packers. And theoretical calculation and experimental research on its sealing performance and pressure capacity are carries out through the finite element analysis on the contact with large deformation based on Abaqus and experimental. The results show that when the rubber cylinder of retrievable packers in the sealing process, the upper packer is subjected to greater stress and mainly plays a sealing role. In order to improve the sealing performance of packer, the height of upper and lower packer should be preferentially selected about 60 mm, the height of middle rubber cylinders should be about 50 mm, and the thickness of packer should be about 21 mm. The newly designed rubber cylinders can meet 150 ℃. Sealing and pressure-bearing function under the condition of 10000 Psi. The rubber cylinder of newly designed can satisfy the function of sealing and pressure-bearing under 140 ℃ and 10000 Psi. The whole experiment shows that the capacity of bearing pressure and sealing is stable, which can better meet the requirements of field use.

The advantageous location, port clusters, strong economic strength, developed financial system, rational and orderly urban division of labor and modern industrial system of Guangdong-Hong Kong-Macao greater bay area provide sustainable driving force for innovation activities in this region. This paper selected the Gini-coefficient, first degree index and concentration index to measure the spatial pattern characteristics of innovation output in Guangdong-Hong Kong-Macao greater bay area. The results show that the innovation output presented a spatial pattern of center-periphery in the study region with Shenzhen and Guangzhou as the dual centers and engines of innovation and Dongguan and Foshan as the main innovative areas. Further empirical analysis of the impact of various factors on innovation output in the study region found that R&D expenditure, the number of R&D personnel, the level of economic development and industrial structure all have significant promoting effects on innovation output. Accordingly, this paper put forward countermeasures and suggestions to promote the innovative development of Guangdong-Hong Kong-Macao greater bay area and build a world-class scientific and technological innovation bay area.

Various sudden shifts in financial market conditions over the past decades have demonstrated the significant impact of market structural breaks on firms' credit behavior. To characterize such effect quantitatively, we develop a continuous-time modulated Markov model for firms' credit rating transitions with the possibility of market structural breaks. The model takes a semi-parametric multiplicative regression form, in which the effects of firms' observable covariates and macroeconomic variables are represented parametrically and nonparametrically, respectively, and the frailty effects of unobserved firm-specific and market-wide variables are incorporated via the integration form of the model assumption. We further develop a mixtured-estimating-equation approach to make inference on the effect of market variations, baseline intensities of all firms' credit rating transitions, and rating transition intensities for each individual firm. We then use the developed model and inference procedure to analyze the monthly credit rating of U.S. firms from January 1986 to December 2012, and study the effect of market structural breaks on firms' credit rating transitions.

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrated that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result obtained was compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

Landslide is a sliding movement of rock mass, debris and soil along the slope under the action of gravity. Small Baseline Subset (SBAS) is an established method for the investigation and monitoring of landslide moments. This study focuses on monitoring the long-temporal displacement of mountainous terrain in Danba County, Sichuan Province via SBAS technique, based on 31 scenes of L-band ALOS/PALSAR data from Feb. 2007 to Oct. 2010.The results show that the largest velocity rates in LOS direction are ±120 mm/yr and maximum accumulated displacement is up to -300, which indicates fast movement of the mountainous terrain during the observation time. These results get good consistency against the results of previous study. This demonstrates the strong potential of SBAS technique for monitoring the landslides geohazard.

In the process of use and manufacture, carbon fiber foam sandwich structures were often damaged by low-energy impact, resulting in performance degradation. Therefore, it was necessary to study the damage caused by low-speed impact of composite sandwich structures. In this paper, based on Hashin failure criterion, an equivalent finite element model of carbon fiber foam sandwich panel under low velocity impact was established. The model was used to simulate the damage of foam sandwich panel with [±45°/±45°/ (core) /±45°/±45°] ply structure under the impact energy of 10.58J, 21.17J, 31.75J and 42.34J. The simulation results of impact damage depth were compared with the experimental results. The error was less than 10%, which proved the rationality of the impact equivalent model. The model was used to predict and analyze the damage of foam sandwich panels with [±45°/ (core) /±45°], [±45°/ (0°, 90°) / (core) /±45°] and [±45°/ (0°, 90°) (core) / (0°, 90°) /±45°] ply structures under 21.17J impact energy. By comparing and analyzing the damage situation, impact force response time and impact velocity response time, the low energy impact resistance was analyzed. The results showed that increasing the number of ply structure of [±45°] can reduce the impact damage degree and improve the bearing capacity of sandwich panels.

With the rapid development of artificial intelligence, machine learning is gradually becoming popular in predictions in all walks of life. In meteorology, It is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.

Recently, the difficulties in recycling and environmental pollution caused by metal-based electrode materials have limited the further development of batteries. Therefore, biomass carbon materials, with their environmental friendliness and stable performance, are the key to solving this problem. Many advances have been achieved by researchers for the application of biomass porous carbon materials in batteries. Nevertheless, the same biomass carbon materials in batteries face problems such as low utilization, low yield, and a cumbersome preparation process. This paper thus aims to summarize the recent progress of biomass carbon materials in the field of batteries and provide a detailed description of the battery performance of biomass carbon materials from three perspectives: synthesis, structure and application of carbon materials. We believe that biomass carbon composites are in line with future development and have a broad application prospect. Finally, we will give our in-depth prospect on developing biomass carbon materials.

ALDH+ H1975 lung adenocarcinoma stem cells (LSCs) are a rare cell population identified in lung adenocarcinoma (LUAD). LSCs can self-renew, drive tumor initiation, growth, metastasis, and recurrence, and are also the predominant cause of poor prognosis due to their intrinsic resistance to drugs and chemotherapy. Consequently, LSCs are a promising target for LUAD therapy. Noncoding RNAs (ncRNAs), including microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), exert many significant regulatory functions in the pathogenesis of human cancers, showing the necessity for a comprehensive understanding of the mechanisms that underlie lung carcinogenesis. Nonetheless, research on many known transcripts and messenger RNAs (mRNAs) has already generated new information. Unknown biomarkers in ncRNAs and systematic and comprehensive interrelation with unknown ncRNAs and mRNAs may provide further insights into the biology of LUAD. Herein, a set of novel ncRNAs that include miRNAs, lncRNAs, circRNAs, were identified and differentially expressed patterns of ncRNAs and mRNAs in LSCs and ALDH- H1975 LUAD tumor cells (LTCs) were obtained using stringent bioinformatics pipelines. Through a meta-analysis of the identified landscapes, novel competitive endogenous RNA (ceRNA) networks were constructed to reveal the potential molecular mechanisms that regulate the hallmarks of LSCs and LTCs. This study presents a summary of novel ncRNAs and the fundamental roles of differentially expressed ncRNAs implicated in the activity of LSCs and LTCs. In addition, the study also provides a comprehensive resource for the future identification of diagnostic, therapeutic, and prognostic biomarkers in LUAD.

In accordance with problems such as difficulty in obtaining aerodynamic parameters of a quad-rotor model, the change of model parameters with external interference affects the control performances, an aerodynamic parameter estimation method and an adaptive attitude control method based on LADRC are designed. Firstly, the motion model, dynamics model and control distribution model of quad-rotor are established by using the aerodynamic and Newtonian Euler equations. Secondly, the identification tool CIFER is used to identify the aerodynamic parameters with large uncertainties in frequency domain and a more accurate attitude model of the quad-rotor is obtained. Then an adaptive attitude decoupling controller based on LADRC is designed to solve the problem of poor anti-interference ability of the quad-rotor, so that the control parameter b0 can be automatically adjusted to identify the change of the moment of inertia in real time. Finally, a semi-physical simulation platform is used for simulation verification. The results show that the adaptive LADRC attitude controller designed can effectively estimate and compensate the system's internal and external disturbances, and the tracking speed of the controller is faster and the precision is higher which can effectively improve system's anti-interference and robustness.