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The conversion of CO2 into high-value fuels and chemicals has garnered research interest worldwide. The conversion and utilization of CO2 has become one of the most urgent tasks for society. In this context, using solar energy to convert CO2 into high-value fuels such as CH4 and CH3OH has extremely high potential application value. Herein, the research progress and results of applying various photocatalysts in photocatalytic CO2 reduction with various novel catalysts were reviewed. Furthermore, strategies for improving photocatalytic performance were reviewed. Finally, improving the catalytic mechanism of catalysts and designing novel high-activity, high-stability catalysts through comprehensive exploration of the reaction mechanism were suggested to meet the future requirements of industrial production.

Scorpions, an ancient group of venomous invertebrates, have existed for over 430 million years. Their toxins, important for predation and defense, exhibit a variety of biological and pharmacological activities. Research on scorpion toxins has spanned decades. Notably, the toxin genes of Mesobuthus martensii (Scorpiones: Buthidae), a well-known Chinese herbal medicine, have been described at genomic and proteomic levels. However, previous studies primarily focused on the toxin genes expressed in the venom glands, overlooking their expression in multiple tissues. This study analyzed transcriptomes from 14 tissues of M. martensii. Gene annotation revealed 83 toxin and toxin-like genes, including those affecting sodium, potassium, calcium, and chloride ion channels. Approximately 70% of toxin genes were highly expressed in the vesicle; additionally, some exhibited low or no expression in the vesicle while showing high expression in other tissues. Beyond the vesicle, high expression levels of toxin genes were observed in metasoma segments II-V, blood, lateral eyes, chelicerae, legs, pedipalp chelae, femurs, and patellae. This expression pattern suggests that toxin genes are recruited from multiple tissues and may help prevent intraspecific harm during courtship and competition for prey. These findings inspire further research into the evolutionary recruitment process of scorpion toxins.

Photocatalytic technology could utilize solar energy to reduce CO2 into high-value-added fossil fuels, providing promising solutions for global energy and environmental issues. Metal-organic frameworks (MOFs) are a class of crystalline porous solids with high porosity and flexible structure. MOF-based photocatalysts have excellent CO2 capture ability, photochemical and structural characteristics and have shown infinite development potential in CO2 reduction. However, in practical large-scale applications, MOF-based photocatalysts still have some urgent problems to be solved, such as high composite rate of photogenerated carriers, limited response range to visible spectrum, poor photocatalytic activity and weak reduction ability. This paper introduces series of MOF-based photocatalysts, including pure MOF materials, compounds, and derivatives, were reviewed based on recent reports. Emphasis was placed on the modification strategy of photocatalysts, the photocatalytic reaction's key physical and chemical parameters, and the mechanism of synergistic improvement of chemical fuel yield. Ultimately and most importantly, the future development trends and prospects of MOF-based catalysts for photocatalytic CO2 reduction were discussed.

Heterogeneous CO2 hydrogenation catalytic reactions, as the strategies for CO2 emission reduction and green carbon resource recycling, play important roles in alleviating global warming and energy shortages. Among these strategies, photothermal CO2 hydrogenation technology has become one of the hot catalytic technologies by virtue of the synergistic advantages of thermal catalysis and photocatalysis. And it has attracted more and more researchers’ attentions. Various kinds of effective photothermal catalysts have been gradually discovered, and nickel-based catalysts have been widely studied for their advantages of low cost, high catalytic activity, abundant reserves and thermal stability. In this review, the applications of nickel-based catalysts in photothermal CO2 hydrogenation are summarized. Finally, through a good understanding of the above applications, future modification strategies and design directions of nickel-based catalysts for improving their photothermal CO2 hydrogenation activities are proposed.

This work presents a recyclable liquid-metal/NdFeB composite magnet (LM magnet) with a reconfigurable shape and polarity. Taking advantage of the low-temperature phase-transition property of LMs, we can re-orient the NdFeB particles and reshape the LM magnets, to produce complex magnetization profiles or complex structured magnets. The manufacturing of LM magnets was realized at low temperatures by using a template and paired permanent magnets. The microscopic morphology and elemental composition of the LM magnet were analyzed by scanning electron microscopy and energy dispersive spectroscopy, respectively. The magnetic properties and phase-transition properties of the LM magnet were analyzed using a vibrating sample magnetometer (VSM) and a differential scanning calorimeter. The experimental results verified that the LM magnet can be recycled, reconfigured, and welded. The configurable magnetization profile with a resolution of up to 800 µm demonstrates that manufacturing of complex magnetic poles can be achieved through this technique. Finally, three application cases show the application prospects of the LM magnet in robots and sensors.

The problem of the decomposition of an exponential sum sequence is a classical mathematical problem.Prony method and Prony-Kung method are the classical methods to solve this problem.The Prony-Kung method was analyzed in detail from the point of view of support subspace.On this basis, it was proved theoretically that the problem of the decomposition of an exponential sum sequence could be reduced to the determination of the support subspace, and a Lloyd-like algorithm with low-complexity for searching the support subspace for contaminated exponential sum sequence by noised was proposed.Finally, the above algorithm was applied to estimate millimeter wave channel, and the simulation results show the effectiveness of this application.

Abstract Accurate acquisition of channel state information (CSI) is crucial but difficult in frequency division duplex (FDD) massive multiple‐input multiple‐output (MIMO) systems. To improve the estimation accuracy and to minimize the training consumption, an adaptive training‐feedback scheme based on spatial reciprocity in FDD is proposed. The main idea of this scheme is to construct a reference frame that matches the channel structure. Reference vectors with the strongest correlation with the uplink channel are selected to design the pilots. The pilots can adapt to channel changes in this proposed scheme, which lead to substantial reduction of the training and feedback overheads. The simulation results show that under full feedback, the throughput of proposed adaptive training‐feedback scheme can approach to the optimal performance, and under finite‐bit feedback, the channel utilization is also significantly high with less training.

Many transcriptomic pathways in the liver show circadian rhythms, which have been reported to be disrupted in aged mice. Here the authors report that the expression of transcription factor Egr-1 decreases and its rhythm is shifted with age in the liver of male mice, and that deletion of Egr-1 results in increased liver fat accumulation.

Constructive defect engineering has emerged as a prominent method for enhancing the performance of photocatalysts. The mechanisms of the influence of defect types, concentrations, and distributions on the efficiency, selectivity, and stability of CO2 reduction were revealed for this paper by analyzing the effects of different types of defects (e.g., metallic defects, non-metallic defects, and composite defects) on the performance of photocatalysts. There are three fundamental steps in defect engineering techniques to promote photocatalysis, namely, light absorption, charge transfer and separation, and surface-catalyzed reactions. Defect engineering has demonstrated significant potential in recent studies, particularly in enhancing the light-harvesting, charge separation, and adsorption properties of semiconductor photocatalysts for reducing processes like carbon dioxide reduction. Furthermore, this paper discusses the optimization method used in defect modulation strategy to offer theoretical guidance and an experimental foundation for designing and preparing efficient and stable photocatalysts.

The global burden of obesity, diabetes, and hypertension is high and increasing. Several systematic reviews suggest yoga, an ancient mind–body discipline from the Indian subcontinent, is safe and can be beneficial for preventing and managing obesity, diabetes, and hypertension. Several qualitative studies have been conducted to explore barriers and facilitators to yoga practice among people at high risk of or with obesity, diabetes, or hypertension and providers who delivered yoga to these people. However, no systematic review on this topic has been conducted to date, and this systematic review will aim to synthesize such barriers and facilitators to yoga practice. We will follow the JBI guideline on systematic reviews of qualitative evidence. For published studies, we will search the following electronic databases from inception dates: MEDLINE, EMBASE, CINAHL Plus, APA PsycInfo, AMED, and Web of Science. For gray literature, we will search EthOS and ProQuest Dissertations and Theses. Screening of studies, methodological quality assessment, and data extraction will be performed independently by two reviewers. Any disagreements between reviewers will be resolved through discussion or by involving a third reviewer. Initially, a narrative synthesis will be conducted. Study findings from the included studies will be pooled using the meta-aggregation approach, where possible. Systematic Review Registration Number: PROSPERO (CRD42020220640).

Abstract The operating state of bearing affects the performance of rotating machinery; thus, how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical. In this study, the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error. Then XGBoost is used to recognise the faults from the obtained features, and artificial bee colony algorithm (ABC) is introduced to optimise the parameters of XGBoost. Moreover, for improving the performance of intelligent algorithm, an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed. The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%, which is much higher than the ones corresponding to traditional extraction strategies. And with the help of improved ABC algorithm, the performance of XGBoost classifier could be optimised; the accuracy could be improved from 97.02% to 98.60% compared with the traditional classification strategy.

The beamforming technology is one of the core technologies in 5G and it can concentrate the transmitted energy in a specified area, thereby improving the performance of the system.But it is because of this feature that the energy is concentrated，any changes of the channel may cause to keep the receiver outside the specified area, and therefore, the performance of robustness is reduced.The concept of the beam width of a beamforming is one of the indicators to measure this tradeoff.Existing definition of beam widths are based on antennas, not on beamforming.Interms of beamforming, the definition of beam width was given and studied with the help of the concept in Grassmannian manifolds.On this basis, the method of beam width was discussed.It is found that the beam width of beamforming is closely related to the number of the transmitting antennas.The more the number of antennas, the more concentrated the energy, and the weaker robustness is.

Abstract To solve the problem of nonlinear characteristics neglecting and fault mechanism analysis lacking in fault diagnosis research, a new method of fault mechanism analysis and diagnosis based on nonlinear spectrum is proposed. Firstly, based on the Permanent Magnet Synchronous Motor (PMSM) model of robot, the first 4-order spectrums based on nonlinear output frequency response function (NOFRF) in different states are obtained by batch calculation method. Secondly, the high-frequency spectrum distribution rule of NOFRF spectrum in different states are analyzed. Finally, in the closed-loop simulation environment of robot, the identification method based on data-driven is adopted for NOFRF spectrum calculation to verify power loss fault of PMSM. Meanwhile, the fault diagnosis experiment is also carried out. The experimental results indicate that the key characteristics distribution rule of NOFRF spectrums in the real environment is consistent with the theoretical analysis results, and compared with the traditional fault feature extraction methods by output signal, the diagnosis with fault feature of NOFRF spectrum for industrial robot closed-loop drive system has the highest accuracy, which verifies the validity of NOFRF spectrum as the fault feature.

Abstract Neonatal heart undergoes metabolic conversion and cell cycle arrest preparing for the increased workload during adulthood. Herein, we report that neonatal ketone body elevation is a critical regulatory factor for postnatal heart development. Through multiomics screening, we found that the expression of 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2), the rate-limiting enzyme of ketogenesis, was transiently induced by colostrum in the neonatal heart. Hmgcs2 knockout caused mitochondrial maturation defects. Meanwhile, postnatal heart development was compromised and cardiomyocytes reacquired proliferation capacity in Hmgcs2 knockout mice. Consequently, over 40% of newborn Hmgcs2 knockout mice died before weaning. The heart function of surviving Hmgcs2 knockout mice was also impaired, which could be rescued by ketone body supplementation during the suckling stage. Mechanistically, ketone body deficiency inhibited β-hydroxybutyrylation but enhanced acetylation of mitochondrial proteins, which might be responsible for the inhibition of the enzyme activity in mitochondria. These observations suggest that ketone body is critical for postnatal heart development through regulating mitochondrial maturation and metabolic reprogramming.

Aiming at improving the operating efficiency of air freight station, the problem of optimizing the sequence of inbound/outbound tasks meanwhile scheduling the actions of elevating transfer vehicles (ETVs) is discussed in this paper. First of all, the scheduling model in airport container storage area, which considers not only the influence of picking sequence, optimal ETVs routing without collision, but also the assignment of input and output ports, is established. Then artificial bee colony (ABC) is proposed to solve the above scheduling issue. For further balancing the abilities of exploration and exploitation, improved multi-dimensional search (IMABC) algorithm is proposed where more dimensions will be covered, and the best dimension of the current optimal solution is used to guide the evolutionary direction in the following exploitation processes. Numerical experiments show that the proposed method can generate optimal solution for the complex scheduling problem, and the proposed IMABC outperforms original ABC and other improved algorithms.

Abstract Recently, adaptive filtering algorithms have attracted much more attention in the field of signal processing. By studying the shortcoming of the traditional real‐valued fixed step size adaptive filtering algorithm, this paper proposed the novel approach to adaptive filtering with variable step size based on Sigmoid function and geometric algebra (GA). First, the proposed approach to adaptive filtering with variable step size based on geometric algebra represents the multi‐dimensional signal as a GA multi‐vector for the vectorization process. Second, the proposed approach to adaptive filtering with variable step size based on geometric algebra solves the contradiction between the steady‐state error and the convergence rate by establishing a non‐linear function relationship between the step size and the error signal. Finally, the experimental results demonstrate that the proposed approach to adaptive filtering with variable step size based on geometric algebra achieves better performance than that of the existing adaptive filtering algorithms.

In this study, 3MgO-B2O3-xwt%BaCu(B2O5) (BCB)-ywt%H3BO3 (2 ≤ x ≤ 8, 0 ≤ y ≤ 20) ceramics were sintered at the optimum temperature to form Mg3B2O6 and MgO phases. The effects of H3BO3 and BCB on the product characteristics, phase transition, microstructure, and microwave dielectric properties of 3MgO-B2O3 ceramics were investigated. The intensities of diffraction peaks of two phases varied with changing the x and y values. After sintering at 950°C, the ceramics with x = 6 and y = 15 achieved the excellent microwave properties with a εr of 6.72, Q × f of 83,205 GHz and τf of – 65.05 ppm/°C. Besides, the ceramics with x = 8 and y = 5 sintered at 925°C also achieved good microwave dielectric properties with a εr of 6.64, Q × f of 78,173 GHz and τf of – 57.27 ppm/°C. The sintering temperatures of above both ceramics are well lower than the melting point of Ag, showing promising applications in low temperature cofired ceramic devices. In particular, these two ceramics can be used as the potential candidate materials for microwave ceramics for 5 G technology, provided that τf can be further optimized.

In a density-dependent single-species population growth model, a simple method is proposed to explicitly and directly derive the analytic expressions of reliable regions for local and global asymptotic stability. Specifically, first, a reliable region ΛLAS is explicitly represented by solving the fixed point and utilizing the asymptotic stability criterion, over which the fixed point is locally asymptotically stable. Then, two types of auxiliary Liapunov functions are constructed, where the variation of the Liapunov function is decomposed into the product of two functions and is always negative at the non-equilibrium state. Finally, based on the Liapunov stability theorem, a closed-form expression of reliable region ΛGAS is obtained, where the fixed point is globally asymptotically stable in the sense that all the solutions tend to fixed point. Numerical results show that our analytic expressions of reliable regions are accurate for both local and global asymptotic stability.

The roles of Rho GTPases in various types of cancer have been extensively studied, but the research of Rho guanine nucleotide exchange factors (GEFs) in cancer is not comprehensive. Rho guanine nucleotide exchange factor 6 (ARHGEF6) is an important member of the Rho GEFs family involved in cytoskeletal rearrangement, and it has not been investigated in acute myeloid leukemia (AML). Our research showed that the expression of ARHGEF6 was mainly higher in AML cell lines, meanwhile, was highest in the samples from patients with AML compared to other cancer types. High ARHGEF6 expression in AML was associated with a good prognosis. ARHGEF6low cases showed significantly higher overall survival (OS) after autologous or allogeneic HSCT (auto/allo-HSCT). High expression of ARHGEF6 downregulates the negative regulation of myeloid differentiation process and upregulates G protein-coupled receptor signaling pathway-related processes, among which HOXA9, HOXB6, and TRH have significant differential expression and prognostic impact in AML. Therefore, ARHGEF6 can become a prognostic marker in AML; ARHGEF6low patients can gain from auto/allo-HSCT.

Due to the rapid increase in cargoes and postal transport volumes in smart transportation systems, an efficient automated multidimensional terminal with autonomous elevating transfer vehicles (ETVs) should be established, and an effective cooperative scheduling strategy for vehicles needs to be designed for improving cargo handling efficiency. In this paper, as one of the most effective artificial intelligence technologies, the artificial bee colony algorithm (ABC), which possesses a strong global optimization ability and fewer parameters, is firstly introduced to simultaneously manage the autonomous ETVs and assign the corresponding entrances and exits. Moreover, as ABC has the disadvantage of slow convergence rate, a novel full-dimensional search strategy with parallelization (PfdABC) and a random multidimensional search strategy (RmdABC) are incorporated in the framework of ABC to increase the convergence speed. After being evaluated on benchmark functions, it is applied to solve the combinatorial optimization problem with multiple tasks and multiple entrances and exits in the terminal. The simulations show that the proposed algorithms can achieve a much more desired performance than the traditional artificial bee colony algorithm in terms of balancing the exploitation and exploration abilities, especially when dealing with the cooperative control and scheduling problems.

Multi-modal medical image fusion can reduce information redundancy, increase the understandability of images and provide medical staff with more detailed pathological information. However, most of traditional methods usually treat the channels of multi-modal medical images as three independent grayscale images which ignore the correlation between the color channels and lead to color distortion, attenuation and other bad effects in the reconstructed image. In this paper, we propose a multi-modal medical image fusion algorithm with geometric algebra based sparse representation (GA-SR). Firstly, the multi-modal medical image is represented as a multi-vector, and the GA-SR model is introduced for multi-modal medical image fusion to avoid losing the correlation of channels. Secondly, the orthogonal matching pursuit algorithm based on geometric algebra (GAOMP) is introduced to obtain the sparse coefficient matrix. The K-means clustering singular value decomposition algorithm based on geometric algebra (K-GASVD) is introduced to obtain the geometric algebra dictionary, and update the sparse coefficient matrix and dictionary. Finally, we obtain the fused image by linear combination of the geometric algebra dictionary and the coefficient matrix. The experimental results demonstrate that the proposed algorithm outperforms existing methods in subjective and objective quality evaluation, and shows its effectiveness for multi-modal medical image fusion.

Rapid antimicrobial susceptibility testing (AST) is urgently needed to slow down the emergence of antibiotic-resistant bacteria and treat infections with correct antibiotics. Stimulated Raman scattering (SRS) microscopy is a technique that enables rapid chemical-bond imaging with sub-cellular resolution. It can obtain the AST results with a single bacterium resolution. Although the SRS imaging assay is relatively fast, taking less than 2 h, the calculation of single-cell metabolism inactivation concentration (SC-MIC) is performed manually and takes a long time. The bottleneck tasks that hinder the SC-MIC throughput include bacterial segmentation and intensity thresholding. To address these issues, we devised a hybrid algorithm to segment single bacteria from SRS images with automatic thresholding. Our proposed method comprises a U-Net convolutional neural network (CNN), DropBlock, and secondary segmentation post-processing. Our results show that SC-MIC calculation can be accomplished within 1 min and more accurate segmentation results using deep learning-based bacterial segmentation method, which is essential for its clinical applications.

Introduction: Many Ayurvedic medicines have the potential for managing type 2 diabetes mellitus (T2DM), with previous systematic reviews demonstrating effectiveness and safety for specific Ayurvedic medicines. However, many of the reviews need updating and none provide a comprehensive summary of all the Ayurvedic medicines evaluated for managing T2DM.Objective: The objective of this systematic review was to evaluate and synthesize evidence on the effectiveness and safety of Ayurvedic medicines for managing T2DM.Inclusion criteria: Published and unpublished RCTs assessing the effectiveness and safety of Ayurvedic medicines for managing T2DM in adults.Methods: The JBI systematic review methodology was followed. A comprehensive search of sources (including 18 electronic databases) from inception to 16 January 2021 was made. No language restrictions were applied. Data synthesis was conducted using narrative synthesis and random effects meta-analyses, where appropriate. Pooled results are reported as mean differences (MD) with 95% confidence intervals (CI).Results: Out of 32,519 records identified from the searches, 219 articles were included in the systematic review representing 199 RCTs (21,191 participants) of 98 Ayurvedic medicines. Overall, in the studies reviewed the methodology was not adequately reported, resulting in poorer methodological quality scoring. Glycated hemoglobin (HbA1c) was reduced using Aegle marmelos (L.) Corrêa (MD -1.6%; 95% CI −3 to −0.3), Boswellia serrata Roxb. (−0.5; −0.7 to −0.4), Gynostemma pentaphyllum (Thunb.) Makino (−1; −1.5 to −0.6), Momordica charantia L. (−0.3; −0.4 to −0.1), Nigella sativa L. (−0.4; −0.6 to −0.1), Plantago ovata Forssk. (−0.9; −1.4 to −0.3), Tinospora cordifolia (Willd.) Hook.f. and Thomson (−0.5; −0.6 to −0.5), Trigonella foenum-graecum L. (−0.6; −0.9 to −0.4), and Urtica dioica L. (−1.3; −2.4 to −0.2) compared to control. Similarly, fasting blood glucose (FBG) was reduced by 4–56 mg/dl for a range of Ayurvedic medicines. Very few studies assessed health-related quality of life (HRQoL). Adverse events were not reported in many studies, and if reported, these were mostly none to mild and predominately related to the gastrointestinal tract.Conclusion: The current evidence suggests the benefit of a range of Ayurvedic medicines in improving glycemic control in T2DM patients. Given the limitations of the available evidence and to strengthen the evidence base, high-quality RCTs should be conducted and reported.

Automatic and accurate onset time picking of the acoustic emission (AE) signals is of great significance for the location of acoustic events. However, the onset times are difficult to be picked accurately due to the presence of noise in signal records. To overcome this problem, an improved method including three consecutive processes is proposed in this study. First, the windowing Lempel-Ziv (WLZ) complexity method is applied to determine an approximate onset time. Then, the time series selected from the start time to the vicinity of the approximate onset time is coarsened at different scales with the application of multi-scale (MS) theory. Finally, Akaike information criterion (AIC) method is utilized to pick up the accurate onset time. Feasibility and performance of the proposed improved method are tested and compared with the other automatic onset time picking methods, such as AIC, short and long time average ratio (STA/LTA), modified energy ratio (MER), AIC with low pass filter and STA/LTA with low pass filter, using the same broken load experiment and 100 rock fracture signals with various signal-to-noise ratios (SNRs). The results show that the accuracy of automatic picks provided by the improved method are far beyond the other methods mentioned. Meanwhile, both the accuracy stability and SNR adaptability of the improved method for rock fracture signals are better than other methods, which proves that the improved method has the excellent performance to pick up onset times of AE signals.

Anomalous trajectory detection plays a significant role in fraud detection and adverse events monitoring for ride-hailing services. The spatial and temporal dynamics of road networks and the sparsity of trajectories make anomalous trajectory detection a challenging task. Most existing methods are based on density and isolation approaches, which ignore geographical information. Motivated by these challenges and shortcomings, we propose a novel method, which considers geospatial constraints of the trajectories and avoids sparsity issues. In our method, the geographical information and topological constraints of trajectories are embedded into structured vector space. Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are used to model common trajectory features. Our method could identify anomalous trajectories and determine which parts are responsible for anomalies by using these features. Experiments on two real-world datasets have been conducted, and results demonstrate the effectiveness and feasibility of the proposed method.

Objectives Children in China have low levels of physical activity. We developed a school-based behaviour change intervention to increase their physical activity levels. The study aimed to determine the feasibility of undertaking a cluster randomised controlled trial (RCT) in the future. This future cluster RCT will evaluate the effectiveness of the intervention.Design Feasibility cluster non-RCT design.Setting Two public schools (one intervention and one control) in Yangzhou, China.Participants Children aged 10–12 years and their parents.Intervention The 16-week school-based behaviour change intervention to increase physical activity levels consisted of three components (a) health education (physical education), (b) family involvement and (c) school environment support.Outcomes measures We estimated important parameters that are needed to design the future cluster RCT, such as SD of the primary outcome (ie, 7-day steps in children), intracluster correlation coefficient (ICC), recruitment of child–parent dyads, follow-up of children, completion of and time needed for data collection among children and intervention attendance.Results Sixty-four children and their parents participated in the study (32 per study group). The SD of the primary outcome was 34 519 steps. The ICC was 0.03. The recruitment and follow-up rates were 100%. The completion of data collection was 100% (except for the 7-day steps at baseline—one child lost the step log in the intervention group and two children lost their pedometer in the control group). The time needed to complete the self-reported questionnaire by children was around 15 min per study group, and the measurement of their anthropometric parameters took around 40 min per study group. The intervention attendance was 100%.Conclusions Based on the promising recruitment, follow-up, completion of and time needed for data collection and intervention attendance, it would be feasible to undertake the future cluster RCT in China.Trial registration number ChiCTR1900026865.

Abstract Cache placement is an integral part of information centric networking (ICN) for optimizing the performance of network. The majority of the existing research on cache placement explores fixed or semi fixed network. The present study proposed a cache placement/update policy in a mobile ad-hoc network (MANET) environment, where the consumers and providers of information are constantly moving. On the basis of request probability and transition probability analysis, a novel approach called cache rebalancing is projected to deal with the cache placement/update problem between different regions. Besides, this study also assesses the performance of the proposed approach in comparison with current caching scheme in large-scale real urban mobility traces, demonstrating that our approach are advantageous in hit ratio, hop count, and other performance indexes over previous strategy. Moreover, the cache steady state of our approach is also obtained to demonstrate the request probability in any region.

In this paper, an improved automatic method for picking arrival times of acoustic emission (AE) signals, has been proposed to overcome the interference of spike noise and channel crosstalk. First, median filter is applied to eliminate spike noise presented in signals. Second, the preliminary arrival time is obtained by the AIC picker. Then a judgment whether the signal is disturbed by channel crosstalk is conducted. If the signal is triggered normally, the preliminary arrival time is regarded as the final arrival time of the AE signal. If the signal is triggered in advance, the signal series is selected from the preliminary arrival time to the time corresponding to the maximum of absolute values of signal series. At last the AIC picker is used again to pick the arrival time of selected series and the second AIC arrival time is interpreted as the final arrival time. To validate the performance of the improved automatic method, AIC picker, AIC picker applied after the median filter (MF-AIC) are also used simultaneously to pick the arrival times of AE signals and perform event location. The results show that the improved automatic method has better waveform adaptability and location accuracies than other two methods.

Understanding passenger behaviors is of great importance in intelligent transportation and infrastructure planning. However, the passenger trajectories are actually complex temporal data, which consist of rich spatial and temporal information. What's more, the observed passenger trajectories may be a mixture of different types of passengers with various travelling purposes. These difficulties make the prediction of passenger behaviors a challenging work. To address these problems, this paper improves the existing passenger behavior prediction methods from the following two aspects: 1) Encoding the travelling sequence with personalized semantic sensing, and 2) constructing multi-pattern prediction models to capture multiple travelling purposes and dynamics. Along this line, this paper provides a novel passenger behavior prediction model, namely, Semantic and multi-Pattern Long Short-Term Memory (SP-LSTM) model. Particularly, 1) a translation unit is designed, which is able to encode an observed travelling sequence into a structured sequence with consideration of individual travelling semantics; 2) a multi-pattern learning schematic is proposed, which first identifies the travelling patterns of passengers and then handles different patterns with different learning modules; 3) a unified learning framework is provided to integrate the semantic sensing module and multi-pattern learning module together, and present the final prediction results. To evaluate the proposed method, this paper conducts experiments on real-world passenger travelling data. Results demonstrate the superiority of SP-LSTM over both classical and the state-of-the-art methods.

In order to gain insights into the potential and behavior of training-based MIMO systems, the relationship of joint decoding scheme and separate decoding scheme is considered, and the equivalence between these two decoding schemes is proved. For the considered joint decoding scheme, receiver decodes out data by joint processing of received signals of both training symbols and data symbols in the maximum likelihood (ML) sense. We refer it as the joint ML-decoder. By contrast, in the considered separate decoding scheme, receiver first estimates channels in the minimum mean-square-errors (MMSE) sense, and then, based on the estimated channel information and the received signals of data symbols, the receiver decodes out data in the ML sense. We refer it as the separate MMSE-ML decoder. Notice that this separate MMSE-ML decoder is different from the decoder appeared in the most of existing works, where a kind of mismatched ML decoding is used after the phase of channel estimation. Although the above-mentioned decoding schemes have different decoding procedures, we prove that the joint ML-decoder and the separate MMSE-ML decoder are equivalent, while they outperform the mismatched ML decoder. With this equivalence, it is implied that the MMSE channel estimator is optimal when the overall system performance is considered, and the separate MMSE-ML decoding scheme can achieve the same performance with that of joint ML decoding scheme. Furthermore, this equivalence also provides us another way to analyze the system performance of the considered separate or joint decoding scheme. Namely, the results obtained from the considered joint decoding scheme can be directly applied to the considered separate decoding scheme, and vice versa.

Regular physical activity has a range of benefits for children's health, academic achievement, and behavioral development, yet they face barriers to participation. The aim of the study was to systematically develop an intervention for improving Chinese children's physical activity participation, using the Behavior Change Wheel (BCW) and Theoretical Domains Framework (TDF). The BCW and TDF were used to (i) understand the behavior (through literature review), (ii) identify intervention options (through the TDF-intervention function mapping table), (iii) select content and implementation options [through behavior change technique (BCT) taxonomy and literature review], and (iv) finalize the intervention content (through expert consultation, patient and public involvement and engagement, and piloting). A systematic iterative process was followed to design the intervention by following the steps recommended by the BCW. This systematic process identified 10 relevant TDF domains to encourage engagement in physical activity among Chinese children: knowledge, memory, attention and decision processes, social influences, environmental context and resources, beliefs about capabilities, beliefs about consequences, social/professional role and identity, emotions, and physical skills. It resulted in the selection of seven intervention functions (education, persuasion, environmental restricting, modeling, enablement, training, and incentivization) and 21 BCTs in the program, delivered over a period of 16 weeks. The BCW and TDF allowed an in-depth consideration of the physical activity behavior among Chinese children and provided a systematic framework for developing the intervention. A feasibility study is now being undertaken to determine its acceptability and utility.

Introduction: Many Indians are at high-risk of type-2 diabetes mellitus (T2DM). Yoga is an ancient Indian mind-body discipline, that has been associated with improved glucose levels and can help to prevent T2DM. The study aimed to systematically develop a Yoga program for T2DM prevention (YOGA-DP) among high-risk people in India using a complex intervention development approach.Materials and Methods: As part of the intervention, we developed a booklet and a high-definition video for participants and a manual for YOGA-DP instructors. A systematic iterative process was followed to develop the intervention and included five steps: (i) a systematic review of the literature to generate a list of Yogic practices that improves blood glucose levels among adults at high-risk of or with T2DM, (ii) validation of identified Yogic practices by Yoga experts, (iii) development of the intervention, (iv) consultation with Yoga, exercise, physical activity, diet, behavior change, and/or diabetes experts about the intervention, and (v) pretest the intervention among Yoga practitioners and lay people (those at risk of T2DM and had not practiced Yoga before) in India.Results: YOGA-DP is a structured lifestyle education and exercise program, provided over a period of 24 weeks. The exercise part is based on Yoga and includes Shithilikarana Vyayama (loosening exercises), Surya Namaskar (sun salutation exercises), Asana (Yogic poses), Pranayama (breathing practices), and Dhyana (meditation) and relaxation practices. Once participants complete the program, they are strongly encouraged to maintain a healthy lifestyle in the long-term.Conclusions: We systematically developed a novel Yoga program for T2DM prevention (YOGA-DP) among high-risk people in India. A multi-center feasibility randomized controlled trial is in progress in India.

For a MIMO downlink system with finite-bit feedback,combined with the Golden code and the precoding technology,a new precoding design criterion and its related codeword selection method,transmissionschemes and corresponding low complexity decoding methods were proposed.In fact,based on fully utilizing the characteristics that the Golden code could overcome the channel depth fading,a double precoding matrix was constructed as transmission signals by using the selected precoding codeword,so that the decoding complexity could be reduced to linear.In the meantime,a joint ML and MMSE decoding method was proposed for the traditional transmission scheme,which could dynamically adjust the decoding complexity based on a threshold.The numerical simulation results confirm the superiority of the proposed schemes and methods.

A hybrid precoding algorithm for millimeter wave (mmWave) MIMO systems without knowledge of channel state information at the transmitter was proposed based on the limited feedback.Specifically, the joint optimization problem of analog precoding and digital precoding was firstly separated into two independent optimization problems, which correspond to the analog precoding and digital precoding respectively.Based on this analog precoding codebook, the best precoding matrix was selected by analog precoding in the designed analog precoding codebook.Secondly, according to the obtained analog precoding matrix, the digital precoding matrix was obtained through the least square method, and then, the codeword closest to it in the random vector quantization codebook was selected as the feedback digital precoding matrix.Finally, the receiver feeds the index values of the analog and digital precoding matrices back to the transmitter.Simulation results show that the proposed algorithm can achieve better balance between complexity and performance.

For a massive multiple-input multiple-output system in the multi-cell scenarios, it generally suffers from pilot contamination due to the fact that the adjacent cells may use the same pilot sequences. In order to reduce the impact of pilot contamination on the system performance, a novel iterative channel estimation method is proposed. Specifically, in the proposed method, besides the pilot sequences, the data symbols decoded with high reliability are selected to enhance the channel estimation. Furthermore, the selection criteria are derived to determine which data symbols are reliably decoded. The simulation results show that the proposed method can effectively improve the channel estimation accuracy and the error performance of the considered systems.

The high-speed mobile transportations,such as high-speed rails,pilot-less airplanes,are mostly in the open areas.Due to the existence of line of sight (LOS) propagations,the Rayleigh fading model can not describe the channels very well in these environments.The Rice fading channel model is constituted of a LOS component and a multiple-path component,which can characterize the channels more accurately.Based on the Rice model,the improved channel estimation methods and corresponding decoding methods were proposed based on the superimposed training sequences for massive multiple input multiple output (MIMO) system.The improved channel estimation methods were divided into two cases:the LOS component was known to the receiver and the other was unknown.The numerical simulation results show that the superiority of the proposed methods.

A spectrum sensing algorithm using double eigenvalue limiting（DEL）distributions was presented. The difference between the maximum and the minimum eigenvalue was exploited as the test statistic. The double eigenvalue were used to estimate a threshold based on the double eigenvalue limiting distributions to heighten an accuracy of the threshold. Compared with the algorithm of single eigenvalue, the theoretical analyses demonstrate that the threshold estimated by DEL was less than its threshold and the performance of DEL was improved. In addition, simulations results show the proposed algorithm needs neither the prior acknowledge of the signal transmitted from primary user, nor the noise power in advance, and gains better performance against the short of sampling number than the difference between the maximum and the minimum eigenvalue（DMM）algorithm.

Rock stability has long been a hot topic during underground energy exploitation, but the failure process of rock materials under earthquake effects is extremely complicated, and the failure mechanism still remains unclear. In order to investigate the fatigue damage and failure behavior of rocks under aftershock effects considering the post-mainshock damage states, a series of laboratory tests were conducted on marble specimens subjected to stepwise cyclic loading. Four levels of peak stress (i.e., 10, 30, 50, and 70 MPa) were applied in the first cycle, to simulate mainshock damage. The results indicate that, with the increase of initial cycle amplitude, mainshock damage has a significant effect on deformation behavior, dissipated energy, P-wave velocity, and AE characteristics of tested specimens during aftershock process. The increasing amplitude of initial cycle enhances irreversible deformation and weakens the resistance to deformation, which accelerates the expansion of specimen volume and results in the reduction of bearing capacity. Furthermore, the increasing amplitude of initial cycle obviously changes the failure morphologies and intensifies the final macro-fracture scale of tested specimens, which are verified by acoustic emission AF-RA value and b-value, respectively.

Efficient dissemination of messages is a vital solution to the vehicular communications and applications. Geocast, delivering messages to a few specific areas, has become an important issue with the accelerating development of the location-based services in vehicular networks. Different from the conventional geocast schemes focusing on the static target areas, in this paper, we propose a content-based mobile tendency geocast (CMTG) routing protocol for urban vehicular networks from the delay tolerant networks (DTNs) perspective to better deal with the high mobility and transient connectivity issues. The target roads of dissemination depend on the preferred tendency and path of message, based on the determination to the message's expected propagating roads. And vehicle decides whether to relay the messages carried to other encountered vehicles by calculating the mobile tendency to the source location of message. The robustness and adaptability of protocol are also proved theoretically. Extensive performance comparisons with other protocols are conducted to show the advantages of CMTG.