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Falls represent a significant cause of injury among the elderly population. Extensive research has been devoted to the utilization of wearable IMU sensors in conjunction with machine learning techniques for fall detection. To address the challenge of acquiring costly training data, this paper presents a novel method that generates a substantial volume of synthetic IMU data with minimal actual fall experiments. First, unmarked 3D motion capture technology is employed to reconstruct human movements. Subsequently, utilizing the biomechanical simulation platform Opensim and forward kinematic methods, an ample amount of training data from various body segments can be custom generated. Synthetic IMU data was then used to train a machine learning model, achieving testing accuracies of 91.99% and 86.62% on two distinct datasets of actual fall-related IMU data. Building upon the simulation framework, this paper further optimized the single IMU attachment position and multiple IMU combinations on fall detection. The proposed method simplifies fall detection data acquisition experiments, provides novel venue for generating low cost synthetic data in scenario where acquiring data for machine learning is challenging and paves the way for customizing machine learning configurations.

Summary: Piezoelectric atomization is becoming mainstream in the field of inhalation therapy due to its significant advantages. With the rapid development of high-viscosity gene therapy drugs, the demand for piezoelectric atomization devices is increasing. However, conventional piezoelectric atomizers with a single-dimensional energy supply are unable to provide the energy required to atomize high-viscosity liquids. To address this problem, our team has designed a flow tube internal cavitation atomizer (FTICA). This study focuses on dissecting the atomization mechanism of FTICA. In contrast to the widely supported capillary wave hypothesis, our study provides evidence in favor of the cavitation hypothesis, proving that cavitation is the key to atomizing high-viscosity liquids with FTICA. In order to prove that the cavitation is the key to atomizing in the structure of FTICA, the performance of atomization is experimented after changing the cavitation conditions by heating and stirring of the liquids.

BackgroundMany studies have investigated the efficacy of acupuncture in treating depression, but the mechanism of acupuncture for depression is still controversial and there is a lack of meta-analysis of mechanisms. Consequently, we investigated acupuncture’s efficacy and mechanism of depression.MethodsWe searched the Cochrane Library, PubMed, EMBASE, Web of Science. The SYRCLE Risk of Bias Tool was used to assess bias risk. Meta-analysis was performed using Stata 15.0 for indicators of depression mechanisms, body weight and behavioral tests.ResultsA total of 22 studies with 497 animals with depressive-like behaviors were included. Meta-analysis showed that acupuncture significantly increased BDNF [SMD = 2.40, 95% CI (1.33, 3.46); I2 = 86.6%], 5-HT [SMD = 2.28, 95% CI (1.08, 3.47); I2 = 87.7%] compared to the control group (p

BackgroundMore than half of cancer patients develop severe chemotherapy-induced peripheral neuropathy (CIPN), resulting in low quality of life, negative effects on function, and challenges in treatment compliance. Most recent studies have shown that exercise therapy has a positive impact on reducing CIPN symptoms and can also improve quality of life, balance, and activity levels. The aim of this meta-analysis was to evaluate the effect of exercise therapy on the efficacy of CIPN.MethodsComputerized search of Embase, Web of Science, CNKI, Wan Fang Data, VIP, CBM for RCTs on exercise therapy for CIPN from database creation to November 2022, without language restriction. The Cochrane Handbook 5.3 risk of bias assessment tool was used to evaluate the quality of the included studies. Then Revman 5.3 software was used to evaluate the quality of the included studies. The heterogeneity of the research results is tested by I2, continuous variables were presented as weighted mean difference or standard mean difference, and confidence intervals were set at 95%. Stata15.0 was utilized to conduct a meta-analysis.ResultsA total of 15 RCTs with 1,124 patients were included. Meta-analysis showed that the test group was superior to the control group in terms of total symptom score (SMD: −0.62; 95% Cl: −0.99, −0.24), numbness, tingling, quality of life score (total score, physical, function), pain, balance, and neurotoxicity function assessment (FACT/GOG-NTX) questionnaire (p

Abstract Anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) surgery are common. Laboratory-based biomechanical assessment can evaluate ACL injury risk and rehabilitation progress after ACLR; however, lab-based measurements are expensive and inaccessible to most people. Portable sensors such as wearables and cameras can be deployed during sporting activities, in clinics, and in patient homes. Although many portable sensing approaches have demonstrated promising results during various assessments related to ACL injury, they have not yet been widely adopted as tools for out-of-lab assessment. The purpose of this review is to summarize research on out-of-lab portable sensing applied to ACL and ACLR and offer our perspectives on new opportunities for future research and development. We identified 49 original research articles on out-of-lab ACL-related assessment; the most common sensing modalities were inertial measurement units, depth cameras, and RGB cameras. The studies combined portable sensors with direct feature extraction, physics-based modeling, or machine learning to estimate a range of biomechanical parameters (e.g., knee kinematics and kinetics) during jump-landing tasks, cutting, squats, and gait. Many of the reviewed studies depict proof-of-concept methods for potential future clinical applications including ACL injury risk screening, injury prevention training, and rehabilitation assessment. By synthesizing these results, we describe important opportunities that exist for clinical validation of existing approaches, using sophisticated modeling techniques, standardization of data collection, and creation of large benchmark datasets. If successful, these advances will enable widespread use of portable-sensing approaches to identify ACL injury risk factors, mitigate high-risk movements prior to injury, and optimize rehabilitation paradigms.

Abstract Manufacturing resources allocation (MRA) is important area, and a significant challenge is encountered when considering high value, customized, complex structure and long lifespan of complex product system (CoPS). The relationship between uncertainty factors (i.e., inputs and outputs) of processes in CoPS’s manufacturing, operation and maintenance needs comprehensive trade-offs in the preliminary MRA stage. Meanwhile, the CoPS’s MRA schemes are contradictory from a customer’s perspective with different emphasis on operating cost related to operation and maintenance stage. These problems are unavailable in traditional expressions for model and objective function. In this paper, a new variant of MRA multi-criteria decision-making (MCDM) model of CoPS (MRA&CoPS) is developed to evaluate MRA schemes with considering CoPS’s lifecycle. Meanwhile, considering characteristics of CoPS and customer-involved MRA process, the three-layer criteria cumulative model is established. In the proposed method, intuitionistic fuzzy sets (IFSs) based subjective–objective hybrid fuzzy method is presented to deal with uncertainty of evaluation criteria. The weights of criteria are determined by the proposed intuitionistic fuzzy information entropy (IFIE). The hybrid IFIE-TOPSIS method is proposed to obtain the optimum MRA scheme by ranking results. An example of CoPS’s MRA in a case enterprise is addressed to verify the rationality and validity of the proposed method. The results show that the proposed method is more preferable and robust in MCDM problem of MRA&CoPS.

Through facilitating DNA homologous recombination repair, PPIP5K2 has been proven to be essential for improving colorectal cancer survival in our previous research. However, its function in the tumorigenesis of NSCLC, the most common cancer and the primary cause of cancer-related death globally, is still unknown. Here, we initially discovered that PPIP5K2 had significant effects on proliferation of NSCLC cells through loss- and gain-of-function assays in vitro and in vivo. Moreover, PPIP5K2 is capable of regulating NSCLC cells metastasis in an EMT-dependent manner. In terms of mechanism exploration, we found that PPIP5K2 knockdown can significantly inhibit the phosphorylation of AKT/mTOR signaling pathway, whereas the overexpression of PPIP5K2 resulted in converse effects. By employing AKT signaling related agonists or antagonists, we further demonstrated that PPIP5K2 regulates NSCLC tumorigenesis partly via the AKT/mTOR pathway. In conclusion, PPIP5K2 plays a key oncogenic role in NSCLC by the activation of the AKT/mTOR signaling axis. It is anticipated that targeting PPIP5K2 might emerge as a viable therapeutic approach for NSCLC patients.

The seasonal variations in carbon (C), nitrogen (N), and phosphorus (P) at the organ level of Pinus yunnanenis during different season are poorly understood. In this study, the C, N, P, and their stoichiometric ratios in various organs of P. yunnanensis during the four seasons are discussed. The middle and young aged P. yunnanensis forests in central Yunnan province, China were chosen, and the contents of C, N, and P in fine roots (

Piezoelectric pumps play an important role in modern medical technology. To improve the flow rate of valveless piezoelectric pumps with flow tube structures and promote the miniaturization and integration of their designs, a cardioid flow tube valveless piezoelectric pump (CFTVPP) is proposed in this study. The symmetric dual-bend tube design of CFTVPP holds great potential in applications such as fluid mixing and heat dissipation systems. The structure and working principle of the CFTVPP are analyzed, and flow resistance and velocity equations are established. Furthermore, the flow characteristics of the cardioid flow tube (CFT) are investigated through computational fluid dynamics, and the output performance of valveless piezoelectric pumps with different bend radii is studied. Experimental results demonstrate that CFTVPP exhibits the pumping effect, with a maximum vibration amplitude of 182.5 μm (at 22 Hz, 100 V) and a maximum output flow rate of 5.69 mL/min (at 25 Hz, 100 V). The results indicate that a smaller bend radius of the converging bend leads to a higher output flow rate, while the performance of valveless piezoelectric pumps with different diverging bends shows insignificant differences. The CFTVPP offers advantages such as a high output flow rate, low cost, small size for easy integration, and ease of manufacturing.

Piezoelectric atomization has been applied in the field of respiratory medicine delivery and chemistry. However, the wider application of this technique is limited by the viscosity of the liquid. High-viscosity liquid atomization has great potential for applications in aerospace, medicine, solid-state batteries and engines, but the actual development of atomization is behind expectations. In this study, instead of the traditional model of single-dimensional vibration as a power supply, we propose a novel atomization mechanism that uses two coupled vibrations to induce micro-amplitude elliptical motion of the particles on the surface of the liquid carrier, which produces a similar effect as localized traveling waves to push the liquid forward and induce cavitation to achieve atomization. To achieve this, a flow tube internal cavitation atomizer (FTICA) consisting of a vibration source, a connecting block and a liquid carrier is designed. The prototype can atomize liquids with dynamic viscosities up to 175 cP at room temperature with a driving frequency of 507 kHz and a voltage of 85 V. The maximum atomization rate in the experiment is 56.35 mg/min, and the average atomized particle diameter is 10 µm. Vibration models for the three parts of the proposed FTICA are established, and the vibration characteristics and atomization mechanism of the prototype were verified using the vibration displacement measurement experiment and the spectroscopic experiment. This study offers new possibilities for transpulmonary inhalation therapy, engine fuel supply, solid-state battery processing and other areas where high-viscosity microparticle atomization is needed.

Data-driven mechanical fault diagnosis has been successfully developed in recent years, and the task of training and testing data from the same distribution has been well-solved. However, for some large machines with complex mechanical structures, such as reciprocating pumps, it is often not possible to obtain data from specific sensor locations. When the sensor position is changed, the distribution of the features of the signal data also changes and the fault diagnosis problem becomes more complicated. In this paper, a cross-sensor transfer diagnosis method is proposed, which utilizes the sharing of information collected by sensors between different locations of the machine to complete a more accurate and comprehensive fault diagnosis. To enhance the model’s perception ability towards the critical part of the fault signal, the local attention mechanism is embedded into the proposed method. Finally, the proposed method is validated by applying it to experimentally acquired vibration signal data of reciprocating pumps. Excellent performance is demonstrated in terms of fault diagnosis accuracy and sensor generalization capability. The transferability of practical industrial faults among different sensors is confirmed.

Dynamic mesh atomizers have been widely used in various fields because of their compact structure, low energy consumption, and low production costs. The finite element method is an important technique to analyze the factors affecting the atomization performance of dynamic mesh atomizers. However, at present, there is a lack of decisive solutions to the basic problems of boundary setting in terms of the simulation and vibration displacement characteristics of atomizers under different vibration modes. In this paper, two errors were found in the Vibration Analysis of a Piezoelectric Ultrasonic Atomizer to Control Atomization Rate paper written by Esteban Guerra-Bravo et al. in 2021. First, in the finite element analysis, the boundary condition of the atomizing sheet was set to be fixed, which is inconsistent with the actual support situation and seriously affects the vibration of the atomizing sheet. Second, in the simulation result, from the first mode to the third mode, the growth rate of the maximum displacement at the center of the atomizing sheet was as high as 77.12%, even up to 221.05%, which is inconsistent with the existing vibration theory. In view of these errors, in this paper, the working principle of dynamic mesh atomizers is analyzed and the vibration equation of the atomizing sheet under peripheral simple support is derived. Through comparison with the literature, it was proven that the boundary setting and vibration displacement of the atomizing sheet in the original paper are unreasonable. By measuring the atomizing rate of the atomizing sheet under different boundary conditions, it was proven that the peripheral freedom of the atomizing sheet should be greater than or equal to 1, namely, peripheral freedom or peripheral simply supported. The vibration displacement theory was used for the simulation, and the relationship between the vibration displacement and resonant frequency of the atomizing sheet under peripheral simple support was measured. It was found that with the increase in the resonance frequency, the maximum displacement of vibration modes with only nodal circles was larger than that of the other vibration modes, and the maximum displacement increased slightly with the increase in the number of nodal circles by about 0.98%.

Tumor therapies have entered the immunotherapy era. Immune checkpoint inhibitors have achieved tremendous success, with some patients achieving long-term tumor control. Tumors, on the other hand, can still accomplish immune evasion, which is aided by immune checkpoints. The majority of immune checkpoints are membrane glycoproteins, and abnormal tumor glycosylation may alter how the immune system perceives tumors, affecting the body’s anti-tumor immunity. Furthermore, RNA can also be glycosylated, and GlycoRNA is important to the immune system. Glycosylation has emerged as a new hallmark of tumors, with glycosylation being considered a potential therapeutic approach. The glycosylation modification of immune checkpoints and the most recent advances in glycosylation-targeted immunotherapy are discussed in this review.

To reliably evaluate the practical performance and to undertake optimal control of PV systems, a precise PV cell parameter extraction–based accurate modeling of PV cells is extremely crucial. However, its inherent high nonlinear and multimodal characteristics usually hinder conventional optimization methods to obtain a fast and satisfactory performance. Besides, insufficient current–voltage (I–V) data provided by manufacturers cannot guarantee high accuracy and flexibility of PV cell parameter extraction under various operation scenarios. Hence, this article proposes a novel parameter extraction strategy by data prediction–based meta-heuristic algorithm (DPMhA). An extreme learning machine (ELM) is adopted to predict output I–V data from measured data, which can provide a more reliable fitness function to meta-heuristic algorithms (MhAs). Consequently, MhAs can undertake a more stable search for optimal solution through extended I–V data; thus, PV cell parameters can be obtained with high accuracy and convergence rate. Its effectiveness is validated via three typical PV cell models, that is, single diode model (SDM), double diode model (DDM), and three diode model (TDM). Last, comprehensive case studies illustrate that the DPMhA can considerably enhance the accuracy and effectiveness compared with those without data prediction.

This paper investigates the hybrid-driven mechanism problem for Markov jump system, where both channel quantization (BCQ) and network-induced delay based on uncertain network are considered. Firstly, comparing with the traditional event-triggered scheme, a hybrid-driven mechanism is employed in networked control systems (NCSs) for the finite capacity of communication bandwidth resources and system performance in equilibrium. Then, the quantization technology is applied in the communication channel from sensor-to-controller and controller-to-actuator. The application of BCQ is for further investigation that mitigate data packet transmission rate. Thirdly, Markov jump system is modeled for the hybrid-driven mechanism and network-induced delay. By constructing the Lyapunov–Krasovskii function, a sufficient condition is derived as the stability criterion, and the controller is designed in which the nonlinear term is rewritten for simplifying the calculation. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed approach.

This paper concentrates on the event-triggered H∞ filter design for the discrete-time Markovian jump neural networks under random missing measurements and cyber attacks. Considering that the controlled system and the filtering can exchange information over a shared communication network which is vulnerable to the cyber attacks and has limited bandwidth, the event-triggered mechanism is proposed to relieve the communication burden of data transmission. A variable conforming to Bernoulli distribution is exploited to describe the stochastic phenomenon since the missing measurements occur with random probability. Furthermore, seeing that the communication networks are vulnerable to external malicious attacks, the transferred information via the shared communication network may be changed by the injected false information from the attackers. Based on the above consideration, sufficient conditions for the filtering error system to maintain asymptotically stable are provided with predefined H∞ performance. In the end, three numerical examples are given to verify the proposed theoretical results.

In an inertial navigation system, especially in a pedestrian dead-reckoning system, gyroscope bias can demonstrably reduce positioning accuracy. A novel gyroscope bias estimation algorithm is proposed, which estimates the bias of a gyroscope under any set of angle observations. Moreover, a method for obtaining Euler angles using map corridor information is proposed. The heading information obtained from a map is used to estimate the bias, and the estimated bias is used to correct the trajectories. Experimental results show that it is feasible for the algorithm to estimate the bias of the gyroscope.