Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning.

The elevator door system plays a crucial role in ensuring elevator safety. Fault prediction is an invaluable tool for accident prevention. By analyzing the sound signals generated during operation, such as component wear and tear, the fault of the system can be accurately determined. This study proposes a GNN-LSTM-BDANN deep learning model to account for variations in elevator operating environments and sound signal acquisition methods. The proposed model utilizes the historical sound data from other elevators to predict the remaining useful life (RUL) of the target elevator door system. Firstly, the opening and closing sounds of other elevators is collected, followed by the extraction of relevant sound signal characteristics including A-weighted sound pressure level, loudness, sharpness, and roughness. These features are then transformed into graph data with geometric structure representation. Subsequently, the Graph Neural Networks (GNN) and long short-term memory networks (LSTM) are employed to extract deeper features from the data. Finally, transfer learning based on the improved Bhattacharyya Distance domain adversarial neural network (BDANN) is utilized to transfer knowledge learned from historical sound data of other elevators to predict RUL for the target elevator door system effectively. Experimental results demonstrate that the proposed method can successfully predict potential failure timeframes for different elevator door systems.

High Triglyceride-Glucose Index Is Associated with Poor Prognosis in Patients with Acute Pancreatitis.

BACKGROUND: Acute pancreatitis (AP) is a common gastrointestinal disease worldwide. Severe acute pancreatitis (SAP) is characterized as persistent organ failure with a mortality rate as high as 20-30%. Early assessment of the severity and screening out possible SAP is of great significance. Given that there is still a lack of both convenient and practical tools for evaluating SAP, we conducted this study to explore the association between TyG index and acute pancreatitis prognosis. METHODS: A total of 353 in-patients diagnosed with acute pancreatitis in the Second Hospital of Shandong University were retrospectively enrolled from January 2018 to November 2021 in this study. According to the Atlanta Classification, they were divided into two groups based on the AP severity. Demographic information and clinical materials were retrospectively collected. The TyG index calculation formula is as follows: ln [fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2]. Statistical analyses were performed using SPSS software (IBM version 22.0) and Medcalc software. Multivariable logistic regression analyses were used to investigate independent predictors for SAP. ROC curve was plotted to assess the predictive ability and cutoffs of TyG index. RESULTS: A total of 353 AP patients were respectively enrolled in this study, of which 47 suffered from SAP. Compared with the non-SAP group, TyG index was significantly higher in the SAP group (10.44 ± 1.55 vs 9.33 ± 1.44, P < 0.001). Multivariate logistic regression analysis showed that TyG index was an independent risk factor for SAP (OR 1.835, 95% CI 1.380-2.442 P < 0.001), with a cutoff of 8.76 for non-HTG/AAP and 11.81 for HTG/AAP by ROC curve. TyG index of patients who suffered from SIRS, OF, APFC, and ANC was higher than those without (P < 0.05). CONCLUSIONS: The triglyceride-glucose index is an independent risk factor for SAP. High TyG index is closely related to SAP and AP-related complications.

Floral morphology and phenology of Sassafras tzumu (Lauraceae).

BACKGROUND: Sassafras has been considered to belong to trib. Laureae of Lauraceae and has been assumed to have unisexual flowers. However, recent molecular phylogenetic studies have consistently suggested that Sassafras does not belong to the trib. Laureae but to Cinnamomeae and that it is nested within Cinnamomum. A recent morphological study revealed that one of the Asian species, S. randaiense, possesses bisexual flowers that are plesiomorphic in the family Lauraceae. As reports on the flower structure of the second Asian species, S. tzumu, have been contradictory, we wanted to ascertain if it has bisexual flowers or not. If the flowers were bisexual, could earlier reports that they were unisexual have been based on dichogamous flowering? RESULTS: In this study, we investigated two populations of S. tzumu. We found that this species has determinate botryoid racemes, and possesses bisexual flowers. Among the three extant species, S. tzumu is more similar to its sister species S. randaiense but markedly different from the American S. albidum: the two Asian species possess bisexual flowers while the American species has unisexual flowers. The bisexual flower of S. tzumu is protogynous, and shows two phenological phases typical of Lauraceae: 1) in a flower, the pistil functions first, the stigma is fresh and white, stamens of the outer two whorls are spreading, anthers do not open, and the staminodes secrete nectar at this stage; 2) in the second phase, the stigma becomes brown, staminodes are withered, stamens of the third whorl stand up and surround the pistil, glands of the third whorl of stamens secrete nectar, and the anthers open and release pollen. CONCLUSIONS: The similarity of racemose inflorescences between Sassafras and some members of Laureae were caused by parallel evolution; the racemose inflorescence of ancestral Sassafras originated from the thyrsoid-cymose inflorescence in Cinnamomum. The Asian species S. tzumu and S. randaiense possess bisexual flowers with two phenological phases, the American S. albidum evolved unisexual flowers independently from other clades with unisexual flowers in the Lauraceae, i.e., the Laureae, Alseodaphnopsis in the Perseeae and the unisexual clade in the Ocotea complex of the Cinnamomeae.

Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things.

With the surge of Internet of Things (IoT) applications using unmanned aerial vehicles (UAVs), there is a huge demand for an excellent complexity/power efficiency trade-off and channel fading resistance at the physical layer. In this paper, we consider the blind equalization of short-continuous-phase-modulated (CPM) burst for UAV-aided IoT. To solve the problems of the high complexity and poor convergence of short-burst CPM blind equalization, a novel turbo blind equalization algorithm is proposed based on establishing a new expectation-maximization Viterbi (EMV) algorithm and turbo scheme. Firstly, a low complexity blind equalization algorithm is obtained by applying the soft-output Lazy Viterbi algorithm within the EM algorithm iteration. Furthermore, a set of initializers that achieves a high global convergence probability is designed by the blind channel-acquisition (BCA) method. Meanwhile, a soft information iterative process is used to improve the system performance. Finally, the convergence, bit error rate, and real-time performance of iterative detection can be further improved effectively by using improved exchange methods of extrinsic information and the stopping criterion. The analysis and simulation results show that the proposed algorithm achieves a good blind equalization performance and low complexity.

Massive MIMO-Based Distributed Signal Detection in Multi-Antenna Wireless Sensor Networks.

For massive multiple-input multiple-output (MIMO) distributed wireless sensor networks, this paper investigates the role of multi-antenna sensors in improving network perception performance. First, we construct a distributed multi-antenna sensor network based on massive MIMO. By using the anti-fading characteristics of multi-antennas, it is better to achieve accurate detection than the single-antenna sensor network. Based on this, we derive a closed-loop expression for the detection probability of the best detector. Then, we consider the case that the sensor power resources are limited, and thus we want to use finite power to achieve higher detection probability. For this reason, the power was optimized by the alternating direction method of multipliers (ADMM). Moreover, we also prove that only statistical channel state is needed in large-scale antenna scenarios, which avoid the huge overhead of channel state information. Finally, according to the simulation results, the multi-antenna sensor network has better detection performance than the single-antenna sensor network which demonstrates the improved performance of the proposed schemes and also validates the theoretical findings.

In Situ Raman Study of CO Electrooxidation on Pt(hkl) Single-Crystal Surfaces in Acidic Solution.

The adsorption and electrooxidation of CO molecules at well-defined Pt(hkl) single-crystal electrode surfaces is a key step towards addressing catalyst poisoning mechanisms in fuel cells. Herein, we employed in situ electrochemical shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) coupled with theoretical calculation to investigate CO electrooxidation on Pt(hkl) surfaces in acidic solution. We obtained the Raman signal of top- and bridge-site adsorbed CO* molecules on Pt(111) and Pt(100). In contrast, on Pt(110) surfaces only top-site adsorbed CO* was detected during the entire electrooxidation process. Direct spectroscopic evidence for OH* and COOH* species forming on Pt(100) and Pt(111) surfaces was afforded and confirmed subsequently via isotope substitution experiments and DFT calculations. In summary, the formation and adsorption of OH* and COOH* species plays a vital role in expediting the electrooxidation process, which relates with the pre-oxidation peak of CO electrooxidation. This work deepens knowledge of the CO electrooxidation process and provides new perspectives for the design of anti-poisoning and highly effective catalysts.

Multilevel Structure of Wheat Starch and Its Relationship to Noodle Eating Qualities.

Although the processing and eating qualities of noodles are largely related to the quality and quantity of wheat protein (gluten), the importance of starch, a major ingredient of wheat flour, is often overlooked. Recent developments on the multilevel structural model of starch have brought new insights into the role of starch for better processing and noodle eating qualities. With critical analysis and discussion, this review outlines the comprehensive relationships between the multilevel (molecular, crystalline, and granular) starch structure, noodle eating qualities, and related physicochemical properties. Further, the major and minor structural features of wheat starch and their contributions toward noodle quality are summarized and presented as a schematic diagram, which shows the effects of starch structure on cooked noodles. These features provide new insights for the scientific community, as well as industry, into the role of starch, along with gluten, on the quality of noodles.

Recurrent Neural Network for Computing Outer Inverse.

Two linear recurrent neural networks for generating outer inverses with prescribed range and null space are defined. Each of the proposed recurrent neural networks is based on the matrix-valued differential equation, a generalization of dynamic equations proposed earlier for the nonsingular matrix inversion, the Moore-Penrose inversion, as well as the Drazin inversion, under the condition of zero initial state. The application of the first approach is conditioned by the properties of the spectrum of a certain matrix; the second approach eliminates this drawback, though at the cost of increasing the number of matrix operations. The cases corresponding to the most common generalized inverses are defined. The conditions that ensure stability of the proposed neural network are presented. Illustrative examples present the results of numerical simulations.

Complex Neural Network Models for Time-Varying Drazin Inverse.

Two complex Zhang neural network (ZNN) models for computing the Drazin inverse of arbitrary time-varying complex square matrix are presented. The design of these neural networks is based on corresponding matrix-valued error functions arising from the limit representations of the Drazin inverse. Two types of activation functions, appropriate for handling complex matrices, are exploited to develop each of these networks. Theoretical results of convergence analysis are presented to show the desirable properties of the proposed complex-valued ZNN models. Numerical results further demonstrate the effectiveness of the proposed models.

Recurrent Neural Network Approach Based on the Integral Representation of the Drazin Inverse.

In this letter, we present the dynamical equation and corresponding artificial recurrent neural network for computing the Drazin inverse for arbitrary square real matrix, without any restriction on its eigenvalues. Conditions that ensure the stability of the defined recurrent neural network as well as its convergence toward the Drazin inverse are considered. Several illustrative examples present the results of computer simulations.

Inverse kinematics solution and control method of 6-degree-of-freedom manipulator based on deep reinforcement learning.

The advent of Industry 4.0 has significantly promoted the field of intelligent manufacturing, which is facilitated by the development of new technologies are emerging. Robot technology and robot intelligence methods have rapidly developed and been widely applied. Manipulators are widely used in industry, and their control is a crucial research topic. The inverse kinematics solution of manipulators is an important part of manipulator control, which calculates the joint angles required for the end effector to reach a desired position and posture. Traditional inverse kinematics solution algorithms often face the problem of insufficient generalization, and iterative methods have challenges such as large computation and long solution time. This paper proposes a reinforcement learning-based inverse kinematics solution algorithm, called the MAPPO-IK algorithm. The algorithm trains the manipulator agent using the MAPPO algorithm and calculates the difference between the end effector state of the manipulator and the target posture in real-time by designing a reward mechanism, while considering Gaussian distance and cosine distance. Through experimental comparative analysis, the feasibility, computational efficiency, and superiority of this reinforcement learning algorithm are verified. Compared with traditional inverse kinematics solution algorithms, this method has good generalization and supports real-time computation, and the obtained result is a unique solution. Reinforcement learning algorithms have better adaptability to complex environments and can handle different sudden situations in different environments. This algorithm also has the advantages of path planning, intelligent obstacle avoidance, and other advantages in dynamically processing complex environmental scenes.

Prevalence and factors associated with dynapenia among middle-aged and elderly people in rural southern China.

To estimate the prevalence of dynapenia and examine potential risk factors for dynapenia using a sample of rural middle-aged and elderly Chinese. A cross-sectional study of 253 Chinese adults aged 50 years and older was conducted from June to August in 2022 in Nanjing. A questionnaire was used to collect data on all socioeconomic variables. Body weight, height, body fat percentage, grip strength, waist circumference, calf circumference, and gait speed were measured. The prevalence of dynapenia was 69.6 %, 62.3 % in men and 72.7 % in women respectively. Binary logistic regressions indicated significant associations between dynapenia and age (odds ratio [OR] = 2.59; 95 % confidence interval [CI] 1.63, 4.12; p < 0.001), educational level (OR = 0.55; 95 % CI 0.38, 0.80; p = 0.002). Dynapenia was prevalent among rural middle-aged and elderly people in southern China. Age and lower education level were both associated with dynapenia. Nutrition and physical activity should be strongly recommended as important strategies to maintain and improve muscle strength.

Dynamic Study on Water State and Water Migration during Gluten-Starch Model Dough Development under Different Gluten Protein Contents.

Mixing is crucial for dough quality. The gluten content influences water migration in dough development and properties, leading to quality changes in dough-based products. Understanding how the gluten protein content influences water migration during dough development is necessary for dough processing. A compound flour with different gluten protein contents (GPCs, 10-26%, w/w) was used to study the dough farinograph parameters and water migration during dough development. According to the farinograph test of the gluten-starch model dough, the GPC increases the water absorption and the strength of the dough. Water migration was determined via low-field nuclear magnetic resonance (LF-NMR). With the increase in GPC, the gluten protein increases the binding ability of strongly bound water and promotes the transformation of weakly bound water. However, inappropriate GPC (10% and 26%, w/w) results in the release of free water, which is caused by damage to the gluten network according to the microstructure result. Moreover, the changes in proteins' secondary structures are related to the migration of weakly bound water. Therefore, weakly bound water plays an important role in dough development. Overall, these results provide a theoretical basis for the optimization of dough processing.

Stretching single atom contacts at multiple subatomic step-length.

This work describes jump-to-contact STM-break junction experiments leading to novel statistical distribution of last-step length associated with conductance of a single atom contact. Last-step length histograms are observed with up to five for Fe and three for Cu peaks at integral multiples close to 0.075 nm, a subatomic distance. A model is proposed in terms of gliding from a fcc hollow-site to a hcp hollow-site of adjacent atomic planes at 1/3 regular layer spacing along with tip stretching to account for the multiple subatomic step-length behavior.

Temperature-Immune, Wide-Range Flexible Robust Pressure Sensors for Harsh Environments.

Flexible pressure sensors possess vast potential for various applications such as new energy batteries, aerospace engines, and rescue robots owing to their exceptional flexibility and adaptability. However, the existing sensors face significant challenges in maintaining long-term reliability and environmental resilience when operating in harsh environments with variable temperatures and high pressures (∼MPa), mainly due to possible mechanical mismatch and structural instability. Here, we propose a composite scheme for a flexible piezoresistive pressure sensor to improve its robustness by utilizing material design of near-zero temperature coefficient of resistance (TCR), radial gradient pressure-dividing microstructure, and flexible interface bonding process. The sensing layer comprising multiwalled carbon nanotubes (MWCNTs), graphite (GP), and thermoplastic polyurethane (TPU) was optimized to achieve a near-zero temperature coefficient of resistance over a temperature range of 25-70 °C, while the radial gradient microstructure layout based on pressure division increases the range of pressure up to 2 MPa. Furthermore, a flexible interface bonding process introduces a self-soluble transition layer by direct-writing TPU bonding solution at the bonding interface, which enables the sensor to achieve signal fluctuations as low as 0.6% and a high interface strength of up to 1200 kPa. Moreover, it has been further validated for its capability of monitoring the physiological signals of athletes as well as the long-term reliable environmental resilience of the expansion pressure of the power cell. This work demonstrates that the proposed scheme sheds new light on the design of robust pressure sensors for harsh environments.

Identifying changes in soybean protein properties during high-moisture extrusion processing using dead-stop operation.

To provide additional evidence and better understand the high-moisture texturing extrusion process of soybean protein isolate (SPI), the physico-chemical changes of SPI during extrusion were investigated. SPI in the extruder feeding zone, barrel zones 1 (80 °C) to 4 (135 °C), and the cooling die (80 °C) were obtained from a dead-stop operation. The lowest values associated with the denaturation enthalpy and the extractable protein occurred in zone 3 (150 °C). The minimum level of the protein subunit content was identified in zone 4. The highest value of the average protein molar mass occurred in zone 3. The ß-sheet ratio in the protein increased, and the unordered ratio decreased after extrusion. The surface hydrophobicity of the protein decreased with water injection in zone 1; however, it increased in zone 2 (110 °C). Overall, SPI undergoes swelling, denaturation, aggregation, and depolymerization due to water injection, heating, and shearing during high-moisture extrusion.

MRI-Based Optimization Design of the Pre-Spinal Route of Contralateral C7 Nerve Transfer for Spastic Arm Paralysis.

Purpose: The prespinal route of contralateral cervical 7 nerve transfer developed by Prof. Wendong Xu helps realize the direct anastomosis of the bilateral cervical 7 nerves. However, 20% of operations still require a nerve graft, which leads to an unfavorable prognosis. This study aims to explore the optimized prespinal route with MRI to further improve the prognosis. Methods: The current study enrolled 30 patients who suffered from central spastic paralysis of an upper limb and who underwent contralateral cervical 7 nerve transfer via Prof. Xu's prespinal route through the anterior edge of the contralateral longus colli. MRI images were used to analyze the route length, vertebral artery exposure, and contralateral cervical 7 nerve included angle. Three prespinal routes were virtually designed and analyzed. The selected optimal route was applied to another 50 patients with central spastic paralysis of an upper limb for contralateral cervical 7 nerve transfer. Results: By the interventions on the 30 patients, the middle and posterior routes were shorter than the anterior route in length, but with no statistical difference between the two routes. Of 30 contralateral vertebral arteries, 26 were located at the posterior medial edge of the longus colli. The average included angles of the anterior, middle, and posterior routes were 108.02 ± 7.89°, 95.51 ± 6.52°, and 72.48 ± 4.65°, respectively. According to these data, the middle route was optimally applied to 50 patients, in whom the rate of nerve transplantation was only 4%, and no serious complications such as vertebral artery or brachial plexus injury occurred. Conclusion: The low rate of nerve transplantation in 50 patients and the absence of any serious complications in these cases suggests that the middle route is the optimal one.

Reaction kinetics in food extrusion: methods and results.

Extrusion cooking is a highly efficient food processing technology. During the extrusion process, there are many desirable and undesirable reactions which will determine final product quality. While being heated and sheared simultaneously, food raw materials experience a non-isothermal process and their residence time in the extruder is distributed. All these factors contribute to the difficulties in determining the kinetic parameters for those reactions. Therefore, this paper attempts to review the reaction kinetics in food extrusion. First of all, the kinetic models for the reactions are outlined. After elucidating how to determine reaction time in an extruder, the methodological approaches for determining the reaction order, rate constant, and activation energy of a reaction under isothermal or non-isothermal conditions with or without residence time distribution (RTD) are presented. Then, different models relating the rate constant to its various impact factors, with especially focusing on shear stress, are reviewed. Subsequently, how shear stress is estimated in an extruder, is illustrated. In the last part of this paper, the reported data of rate constant, reaction order, and activation energy for the reactions occurring during food extrusion are summarized, with detailed impacts of temperature, moisture content, shear stress, and determination method on these kinetic parameters. Finally, future research needs are suggested.

Phase-modulated waveform design for extended target detection in the presence of clutter.

The problem to be addressed in this paper is a phase-modulated waveform design for the detection of extended targets contaminated by signal-dependent noise (clutter) and additive noise in practical radar systems. An optimal waveform design method that leads to the energy spectral density (ESD) of signal under the maximum signal-to-clutter-and-noise ratio (SCNR) criterion is introduced first. In order to make full use of the transmission power, a novel phase-iterative algorithm is then proposed for designing the phase-modulated waveform with a constant envelope, whose ESD matches the optimal one. This method is proven to be able to achieve a small SCNR loss by minimizing the mean-square spectral distance between the optimal waveform and the designed waveform. The results of extensive simulations demonstrate that our approach provides less than 1 dB SCNR loss when the signal duration is greater than 1 µs, and outperforms the stationary phase method and other phase-modulated waveform design methods.

[Application of near infrared spectral fingerprint technique in lamb meat origin traceability].

Near infrared spectra of 99 lamb meat samples from three pasturing areas and two farming areas of China were scanned and analyzed to seek a cheap, rapid and effective method for lamb meat origin traceability. Two chemometric methods including linear discriminant analysis based on principal component analysis (PCA+LDA) and partial least squares discriminant analysis (PLS-DA) were used to develop the discriminate models. It was showed that there were significantly differences among the lamb meat samples from five regions based on NIR spectra after second derivative (Savitzky-Golay, 9 point) and multiplicative scattering correction (MSC) transformation in the whole wavelength. The discrimination of two models was best for classification of pasturing area and farming area, with both correctly classified by 100%. The correct classification rate of samples from five different regions using PCA+LDA model was 91.2%, higher than using PLS-DA model (76.7%). These results demonstrate that near infrared reflectance spectroscopy (NIRS) combined with chemometric analysis can be used as an effective method to classify lamb meat according to its geographical origin.