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Plant height is an important agronomic characteristic of rice (Oryza sativa L.). Map-based cloning analyses of a natural semi-dwarf rice mutant with inwardly curled leaves found in the field revealed that the defects were due to a mutation of a SHAQKYF-class MYB family transcription factor, OsKANADI1 (OsKAN1). OsKAN1 directly bound to the OsYABBY5 (OsYAB5) promoter to repress its expression and interacted with OsYAB5 to form a functional OsKAN1-OsYAB5 complex. GIBERELLIN 2-OXIDASE6 (OsGA2ox6), encoding an enzyme in the gibberellin (GA) catabolic pathway, was activated by OsYAB5. Furthermore, the OsKAN1-OsYAB5 complex suppressed the inhibitory effect of OsKAN1 toward OsYAB5 and inhibited OsYAB5-induced OsGA2ox6 expression. The proOsKAN1:OsYAB5 transgenic plants were taller than wild-type plants, whereas oskan1 proOsKAN1:OsYAB5 plants exhibited a severe dwarf phenotype due to the absence of the OsKAN1-OsYAB5 complex. The OsKAN1-OsYAB5 complex modulated OsGA2ox6 expression, thereby regulating the levels of bioactive gibberellins and, consequently, plant height. This study elucidated the mechanism underlying the effect of the OsKAN1-OsYAB5-OsGA2ox6 regulatory pathway on plant height at different positions in rice stems and provided insights on stem development and candidate genes for the aerial architecture improvement of crop plants., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society of Plant Biologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Regarding the difficulty of extracting the acquired fault signal features of bearings from a strong background noise vibration signal, coupled with the fact that one-dimensional (1D) signals provide limited fault information, an optimal time frequency fusion symmetric dot pattern (SDP) bearing fault feature enhancement and diagnosis method is proposed. Firstly, the vibration signals are transformed into two-dimensional (2D) features by the time frequency fusion algorithm SDP, which can multi-scale analyze the fluctuations of signals at minor scales, as well as enhance bearing fault features. Secondly, the bat algorithm is employed to optimize the SDP parameters adaptively. It can effectively improve the distinctions between various types of faults. Finally, the fault diagnosis model can be constructed by a deep convolutional neural network (DCNN). To validate the effectiveness of the proposed method, Case Western Reserve University's (CWRU) bearing fault dataset and bearing fault dataset laboratory experimental platform were used. The experimental results illustrate that the fault diagnosis accuracy of the proposed method is 100%, which proves the feasibility and effectiveness of the proposed method. By comparing with other 2D transformer methods, the experimental results illustrate that the proposed method achieves the highest accuracy in bearing fault diagnosis. It validated the superiority of the proposed methodology.

In view of the frequent failures occurring in rolling bearings, the strong background noise present in signals, weak features, and difficulties associated with extracting fault characteristics, a method of enhancing and diagnosing rolling bearing faults based on coarse-grained lattice features (CGLFs) is proposed. First, the vibrational signals of bearings are subjected to adaptive filtering to eliminate background noise. Second, frequency-domain transformation is performed, and a coarse-grained approach is used to continuously segment the spectrum. Within each segment, amplitude-enhancement operations are executed, transforming the data into a CGLF graph that enhances fault characteristics. This graph is then fed into a Swin Transformer-based pattern-recognition network. Third and finally, a high-precision fault diagnosis model is constructed using fully connected layers and Softmax, enabling the diagnosis of bearing faults. The fault recognition accuracy reaches 98.30% and 98.50% with public datasets and laboratory data, respectively, thereby validating the feasibility and effectiveness of the proposed method. This research offers an efficient and feasible fault diagnosis approach for rolling bearings.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder with pathological features of β-amyloid (Aβ) and hyperphosphorylated tau protein accumulation in the brain, often accompanied by cognitive decline. So far, our understanding of the extent and role of adaptive immune responses in AD has been quite limited. T cells, as essential members of the adaptive immune system, exhibit quantitative and functional abnormalities in the brains of AD patients. Dysfunction of the blood-brain barrier (BBB) in AD is considered one of the factors leading to T cell infiltration. Moreover, the degree of neuronal loss in AD is correlated with the quantity of T cells. We first describe the differentiation and subset functions of peripheral T cells in AD patients and provide an overview of the key findings related to BBB dysfunction and how T cells infiltrate the brain parenchyma through the BBB. Furthermore, we emphasize the risk factors associated with AD, including Aβ, Tau protein, microglial cells, apolipoprotein E (ApoE), and neuroinflammation. We discuss their regulation of T cell activation and proliferation, as well as the connection between T cells, neurodegeneration, and cognitive decline. Understanding the innate immune response is crucial for providing comprehensive personalized therapeutic strategies for AD., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Accurately detecting the depolarization QRS complex in the ventricles is a fundamental requirement for cardiovascular disease detection using electrocardiography (ECG). In contrast to traditional signal enhancement algorithms, emerging neural network approaches have shown promise for QRS detection because of their generalizability on complex data. However, the inevitable noise present during ECG recording leads to a decrease in the performance of neural networks. To enhance the robustness and performance of neural network-based QRS detectors, we propose a simulated degeneration unit (SDU)-assisted convolutional neural network (CNN). An SDU simulates the physical degeneration process of interfering optical pulses, which can effectively suppress in-band noise. Through comprehensive performance evaluations on three open-source databases, the SDU-enhanced CNN-based approach demonstrated better performance in detecting QRS complexes than other recently reported QRS detectors. Furthermore, real-world noise injection tests indicate that the optimal noise robustness boundary for the CNN equipped with SDU is 167-300% higher than that for the CNN without SDU., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

To improve the quality of modern life in the current society, low-power, highly sensitive, and reliable healthcare technology is necessary to monitor human health in real-time. In this study, we fabricated partially suspended monolayer graphene surface acoustic wave gas sensors (G-SAWs) with a love-mode wave to effectively detect ppt-level acetone gas molecules at room temperature. The sputtered SiO 2 thin film on the surface of a black 36°YX-LiTaO 3 (B-LT) substrate acted as a guiding layer, effectively reducing the noise and insertion loss. The G-SAWs exhibited enhanced gas response towards acetone gas molecules (800 ppt) in a real-time atmosphere. The high sensitivity of the G-SAW sensor can be attributed to the elasticity and surface roughness of the SiO 2 film. In addition, the G-SAW sensor exhibited rapid response and recovery at room temperature. This study provides a potential strategy for diagnosing different stages of diabetes in the human body., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this study., (This journal is © The Royal Society of Chemistry.)

Epilepsy is a chronic disorder that leads to transient neurological dysfunction and is clinically diagnosed primarily by electroencephalography. Several intelligent systems have been proposed to automatically detect seizures, among which deep convolutional neural networks (CNNs) have shown better performance than traditional machine-learning algorithms. Owing to artifacts and noise, the raw electroencephalogram (EEG) must be preprocessed to improve the signal-to-noise ratio prior to being fed into the CNN classifier. However, because of the spectrum overlapping of uncontrollable noise with EEG, traditional filters cause information loss in EEG; thus, the potential of classifiers cannot be fully exploited. In this study, we propose a stochastic resonance-effect-based EEG preprocessing module composed of three asymmetrical overdamped bistable systems in parallel. By setting different asymmetries for the three parallel units, the inherent noise can be transferred to the different spectral components of the EEG through the asymmetric stochastic resonance effect. In this process, the proposed preprocessing module not only avoids the loss of information of EEG but also provides a CNN with high-quality EEG of diversified frequency information to enhance its performance. By combining the proposed preprocessing module with a residual neural network, we developed an intelligent diagnostic system for predicting seizure onset. The developed system achieved an average sensitivity of 98.96% on the CHB-MIT dataset and 95.45% on the Siena dataset, with a false prediction rate of 0.048/h and 0.033/h, respectively. In addition, a comparative analysis demonstrated the superiority of the developed diagnostic system with the proposed preprocessing module over other existing methods.

Reservoir computing is a brain heuristic computing paradigm that can complete training at a high speed. The learning performance of a reservoir computing system relies on its nonlinearity and short-term memory ability. As physical implementation, spintronic reservoir computing has attracted considerable attention because of its low power consumption and small size. However, few studies have focused on developing the short-term memory ability of the material itself in spintronics reservoir computing. Among various magnetic materials, spin glass is known to exhibit slow magnetic relaxation that has the potential to offer the short-term memory capability. In this research, we have quantitatively investigated the short-term memory capability of spin cluster glass based on the prevalent benchmark. The results reveal that the magnetization relaxation of Co, Si-substituted Lu 3 Fe 5 O 12 with spin glass behavior can provide higher short-term memory capacity than ferrimagnetic material without substitution. Therefore, materials with spin glass behavior can be considered as potential candidates for constructing next-generation spintronic reservoir computing with better performance., (© 2023. The Author(s).)

Spin waves (SWs), an ultra-low power magnetic excitation in ferro or antiferromagnetic media, have tremendous potential as transport less data carriers for post-CMOS technology using their wave interference properties. The concept of magnon interference originates from optical interference, resulting in a historical taboo of maintaining an identical wavevector for magnon interference-based devices. This makes the attainment of on-chip design reconfigurability challenging owing to the difficulty in phase tuning via external fields. Breaking the taboo, this study explores a novel technique to systematically control magnon interference using asymmetric wavevectors from two different SW modes (magnetostatic surface SWs and backward volume magnetostatic SWs) in a microstructured yttrium iron garnet crossbar. Using this system, we demonstrate phase reconfigurability in the interference pattern by modulating the thermal landscape, modifying the dispersion of the interfering SW modes. Thus, we manifest that such a tunable interference can be used to implement reconfigurable logic gates operating between the XNOR and XOR modes by using symmetric and asymmetric interference, respectively., (© 2023. The Author(s).)

Adipose-derived stem cells (ADSCs) are employed as a promising alternative in treating cartilage injury. Regulating the inflammatory "fingerprint" of ADSCs to improve their anti-inflammatory properties could enhance therapy efficiency. Herein, a novel injectable decorin/gellan gum hydrogel combined with ADSCs encapsulation for arthritis cartilage treatment is proposed. Decorin/gellan gum hydrogel was prepared according to the previous manufacturing protocol. The liquid-solid form transition of gellan gum hydrogel is perfectly suitable for intra-articular injection. Decorin-enriched matrix showing an immunomodulatory ability to enhance ADSCs anti-inflammatory phenotype under inflammation microenvironment by regulating autophagy signaling. This decorin/gellan gum/ADSCs hydrogel efficiently reverses interleukin-1β-induced cellular injury in chondrocytes. Through a mono-iodoacetate-induced arthritis mice model, the synergistic therapeutic effect of this ADSCs-loaded hydrogel, including inflammation attenuation and cartilage protection, is demonstrated. These results make the decorin/gellan gum hydrogel laden with ADSCs an ideal candidate for treating inflammatory joint disorders.

Magnonics, an emerging research field that uses the quanta of spin waves as data carriers, has a potential to dominate the post-CMOS era owing to its intrinsic property of ultra-low power operation. Spin waves can be manipulated by a wide range of parameters; thus, they are suitable for sensing applications in a wide range of physical fields. In this study, we designed a highly sensitive, simple structure, and ultra-low power magnetic sensor using a simple CoFeB/Y 3 Fe 5 O 12 bilayer structure. We demonstrated that the CoFeB/Y 3 Fe 5 O 12 bilayer structure can create a sharp rejection band in its spin-wave transmission spectra. The lowest point of this strong rejection band allows the detection of a small frequency shift owing to the external magnetic field variation. Experimental observations revealed that such a bilayer magnetic sensor exhibits 20 MHz frequency shifts upon the application of an external magnetic field of 0.5 mT. Considering the lowest full width half maximum, which is about 2 MHz, a sensitivity of 10 -2 mT order can be experimentally achieved. Furthermore, the higher sensitivity in the order of 10 -6  T (µT) has been demonstrated using the sharp edge of the rejection band of the CoFeB/Y 3 Fe 5 O 12 bilayer device. A Y-shaped spin waves interference device with two input arms consisting of CoFeB/Y 3 Fe 5 O 12 and Y 3 Fe 5 O 12 has been theoretically investigated. We proposed that such a structure can demonstrate a magnetic sensitivity in the range of [Formula: see text] T (nT) at room temperature. The sensitivity of the sensor can be further enhanced by tuning the width of the CoFeB metal stripe., (© 2022. The Author(s).)

Alteration of the hydrogen-bond (H-bond) network by trehalose is acknowledged as a bioprotective agent. However, most studies exploring the hydration superiority of the trehalose structure are limited structure are limited by the computational cost or a narrow-range spectrum. In the present study, the structural and dynamical behaviors of the H-bond network of trehalose and maltose solutions were observed and compared with a broadband dielectric spectrum (100 MHz-18 THz) to investigate the influence of the trehalose structure on the bioprotective function. From the relaxation time, the reorientation cooperativity, resonant frequency, and damping constant of water-water vibration, the symmetric structure of trehalose allowed a more significant H-bond strengthening effect and homogeneous aqueous environment. In contrast, the difference in the hydration number between trehalose and maltose was negligible. Thus, the enhanced H-bond strengthening effect and homogeneous aqueous environment owing to the symmetric structure are the essential factors that contribute to the remarkable bioprotective effect of trehalose.

Circular RNAs (circRNAs) are closed back-splicing products of precursor mRNA in eukaryotes. Compared with linear mRNAs, circRNAs have a special structure and stable expression. A large number of studies have provided different regulatory mechanisms of circRNAs in tumors. Challenges exist in understanding the control of circRNAs because of their sequence overlap with linear mRNA. Here, we survey the most recent progress regarding the regulation of circRNA biogenesis by RNA-binding proteins, one of the vital functional proteins. Furthermore, substantial circRNAs exert compelling biological roles by acting as protein sponges, by being translated themselves or regulating posttranslational modifications of proteins. This review will help further explore more types of functional proteins that interact with circRNA in cancer and reveal other unknown mechanisms of circRNA regulation., (© 2021 Wiley Periodicals LLC.)

Urban green spaces (UGSs) improve the quality of life of urban inhabitants. With the acceleration of urbanization and changes in traffic networks, it remains unclear whether changes in the distribution of UGSs can satisfy the needs of all inhabitants and offer equal services to inhabitants from different socioeconomic backgrounds. This study addresses this issue by analyzing dynamic changes in UGS accessibility in 2012, 2016, and 2020 for inhabitants of the central urban area of Fuzhou in China at the community level. The study introduces multiple transportation modes for an accessibility estimation based on a framework using the two-step floating catchment area method and examines the dynamic changes in community deprivation of UGS accessibility using Kernel regularized least squares, a machine learning algorithm. The results demonstrate that spatial disparities of UGS accessibility exist among the multi-transport modes and vary with time. Communities with high accessibility to UGSs by walking are scattered around the urban area; for accessibility by cycling, the high accessibility regions expand and surround the regions with low accessibility in the core urban areas, forming a semi-enclosed spatial pattern. However, the core urban spatial orientation of UGS accessibility by public transit demonstrates a reverse trend to the above two modes. The spatial pattern of UGS accessibility also varies over time, and the growth rate of accessibility slightly declined during the study period. Furthermore, the increase in UGS accessibility tended to slow from 2016-2020 compared with 2012-2016, and the trend toward equality was also erratic. The degree of deprivation for communities first weakened and was then aggravated, corresponding to the slowdown in the growth rate of accessibility, leading to the persistence existence of social inequality. Moreover, significant deprivation mainly exists among less educated people or those using the cycling and integrated travel modes. Although public transport is developing, deprived communities, such as communities with large proportion of older people, have experienced a decline in access to UGSs by public transport. Based on these findings, the study proposes a policy framework for the balanced distribution of UGSs as part of urbanization., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Cheng, Liao and Zhu.)

Colorectal cancer (CRC) is one of the most common cancers worldwide. Recent studies have shown that long non-coding RNAs (lncRNAs) are involved in tumorigenesis and the development of CRC. By constructing a differential lncRNA expression profile, we screened gene chips and found that DNAJC3-AS1 was highly expressed in CRC tissues and was associated with poor prognosis in patients with CRC. Further, we proved through assays such as wound healing, colony formation, and Cell Counting Kit-8 (CCK8) that interfering with DNAJC3-AS1 could reduce the proliferation, migration, and invasion of CRC cells. Mechanically, we found that DNAJC3-AS1 regulates fatty acid synthase to promote the progression of CRC via the epidermal growth factor receptor/phosphatidylinositol 3-kinase/protein kinase B/nuclear factor κB signaling pathway. Therefore, DNAJC3-AS1 may be a new target for the diagnosis and therapy of CRC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Tang, Tang, Tang, Huang, Liao, Zhou, Zhou, Su, Chen, Jiang, Hu, Zhou, Liao, Zeng, Xiong, Chen and Nie.)

Improvement of the food value of rice straw is urgently required in rice crop growing areas to mitigate pollution caused by rice straw burning and enhance the supply of high-quality forages for ruminants. The aims of the present study were to compare the effects of fresh corn Stover and rice straw co-fermented with probiotics and enzymes on rumen fermentation and establish the feasibility of increasing the rice straw content in ruminant diets and, by extension, reducing air pollution caused by burning rice straw. Twenty Simmental hybrid beef cattle were randomly allotted to two groups with ten cattle per group. They were fed diets based either on rice straw co-fermented with probiotics and enzymes or fresh corn Stover for 90 days. Rumen fluid was sampled with an esophageal tube vacuum pump device from each animal on the mornings of days 30, 60, and 90. Bacterial diversity was evaluated by sequencing the V4-V5 region of the 16S rRNA gene. Metabolomes were analyzed by gas chromatography/time-of-flight mass spectrometry (GC-TOF/MS). Compared to cattle fed fresh corn Stover, those fed rice straw co-fermented with probiotics and enzymes had higher (P < 0.05) levels of acetic acid and propionate in rumen liquid at d 60 and d 90 respectively, higher (P < 0.05) abundances of the phyla Bacteroidetes and Fibrobacteres and the genera Ruminococcus, Saccharofermentans, Pseudobutyrivibrio, Treponema, Lachnoclostridium, and Ruminobacter, and higher (P < 0.05) concentrations of metabolites involved in metabolisms of amino acid, carbohydrate, and cofactors and vitamins. Relative to fresh corn Stover, rice straw co-fermented with probiotics and enzymes resulted in higher VFA concentrations, numbers of complex carbohydrate-decomposing and H 2 -utilizing bacteria, and feed energy conversion efficiency in the rumen.

It is universally acknowledged that long non-coding RNAs (lncRNAs) involved in tumorigenesis in human cancers. However, the function and mechanism of many lncRNAs in colorectal cancer (CRC) remain unclear. By analyzing the two sets of CRC-related gene microarrays data, downloaded from the Gene Expression Omnibus (GEO) database and the lncRNA expression in a set of RNA sequencing data, we found that lncRNA SLCO4A1-AS1 was significantly upregulated in CRC tissues. We then collected CRC tissue samples and verified that SLCO4A1-AS1 is highly expressed in CRC tissues. Furthermore, SLCO4A1-AS1 was also upregulated in the CRC cell line. In situ hybridization results showed that high expression of SLCO4A1-AS1 was associated with poor prognosis in patients with CRC. Next, we found that SLCO4A1-AS1 promoted CRC cell proliferation, migration, and invasion. Results of western blotting assays show that its mechanism may relate to the epidermal growth factor receptor (EGFR)/mitogen-activated protein kinase (MAPK) pathway. Therefore, SLCO4A1-AS1 may be a potential biomarker for CRC prognosis and a new target for colorectal cancer therapy., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Detecting unresolved targets is very important for radars in their target tracking phase. For wideband radars, the unresolved target detection algorithm should be fast and adaptive to different bandwidths. To meet the requirements, a detection algorithm for wideband monopulse radars is proposed, which can detect unresolved targets for each range profile sampling points. The algorithm introduces the Gaussian mixture model and uses a priori information to achieve high performance while keeping a low computational load, adaptive to different bandwidths. A comparison between the proposed algorithm and the latest unresolved target detection algorithm Joint Multiple Bin Processing Generalized Likelihood Ratio Test (JMBP GLRT) is carried out by simulation. On Rayleigh distributed echoes, the detection probability of the proposed algorithm is at most 0.5456 higher than the JMBP GLRT for different signal-to-noise ratios (SNRs), while the computation time of the proposed algorithm is no more than two 10,000ths of the JMBP GLRT computation time. On bimodal distributed echoes, the detection probability of the proposed algorithm is at most 0.7933 higher than the JMBP GLRT for different angular separations of two unresolved targets, while the computation time of the proposed algorithm is no more than one 10,000th of the JMBP GLRT computation time. To evaluate the performance of the proposed algorithm in a real wideband radar, an experiment on field test measured data was carried out, in which the proposed algorithm was compared with Blair GLRT. The results show that the proposed algorithm produces a higher detection probability and lower false alarm rate, and completes detections on a range profile within 0.22 ms., Competing Interests: The authors declare no conflict of interest.

To precisely diagnose the rotating machinery structural faults, especially structural faults under low rotating speeds, a novel scheme based on combination of empirical mode decomposition (EMD), sample entropy, and deep belief network (DBN) is proposed in this paper. EMD can decompose a signal into several intrinsic mode functions (IMFs) with different signal-to-noise ratios (SNRs) and sample entropy is performed to extract the signals that carry fault information with high SNR. The extracted fault signal is reconstructed into a new vibration signal that will carry abundant fault information. DBN has strong feature extraction and classification performance. It is suitably performed to build the diagnosis model based on the reconstructed signal. The effectiveness of the proposed method is validated by structural faults signal and the comparative experiments (BPNN, CNN, time-domain signal only, frequency-domain signal only). The results show that the diagnosis accuracy of the proposed method is between 99% and 100%, the BPNN is less than 25%, and the CNN is between 70% and 95%, which means the verified, proposed method has a superior performance to diagnose the structural fault.

Singular value decomposition (SVD) is an effective method used in bearing fault diagnosis. Ideally two important problems should be solved in any diagnosis: one is how to decide the dimension embedding of the trajectory matrix (TM); the other is how to select the singular value (SV) representing the intrinsic information of the bearing condition. In order to solve such problems, this study proposed an effective method to find the optimal TM and SV and perform fault signal filtering based on false nearest neighbors (FNN) and statistical information criteria. First of all, the embedded dimension of the trajectory matrix is determined with the FNN according to the chaos theory. Then the trajectory matrix is subjected to SVD, which is helpful to acquire all the combinations of SV and decomposed signals. According to the similarities of the signal changed back and signal in normal state based on statistical information criteria, the SV representing fault signal can be obtained. The spectrum envelope demodulation method can be used to perform effective analysis on the fault. The effectiveness of the proposed method is verified with simulation signals and low-speed bearing fault signals, and compared with the published SVD-based method and Fast Kurtogram diagnosis method.

As the essential element of a triboelectric nanogenerator (TENG), friction layers play key roles that determine the device performance, which can be enhanced by material selection and surface modification. In this work, we have embedded aligned carbon nanotubes (CNTs) on the polydimethylsiloxane (PDMS) surface as the effective dielectric layer to donate electrons. This layer not only increases the electron generation for the output, but also shows notable stretchability. The length and the properties of the aligned CNTs can be controlled precisely. Using the 40 μm CNT as an example, the fabricated CNT-PDMS TENG shows an output voltage of 150 V and a current density of 60 mA m -2 , which are 250% and 300% enhancement compared to the TENG using directly doped PDMS/multiwall carbon nanotubes, respectively. The maximum power density of this TENG reaches 4.62 W m -2 at an external load of 30 MΩ. The TENG has demonstrated superior stability during cyclic measurement of over 12 000 cycles. Besides, the aligned CNT-PDMS film shows superhydrophobicity (154°) and good sheet resistance of 280 Ω sq -1 . This stretchable aligned CNT-PDMS film can be universally utilized as a positive triboelectric layer pairing with polymeric materials such as polyethylene terephthalate, polyimide, PDMS and polytetrafluoroethylene for TENGs. This work provides an effective method of structure design for flexible and stretchable nanogenerators.

The total compression of municipal solid waste (MSW) consists of primary, secondary, and decomposition compressions. It is usually difficult to distinguish between the three parts of compressions. In this study, the odeometer test was used to distinguish between the primary and secondary compressions to determine the primary and secondary compression coefficient. In addition, the ending time of the primary compressions were proposed based on municipal solid waste compression tests in a degradation-inhibited condition by adding vinegar. The amount of the secondary compression occurring in the primary compression stage has a relatively high percentage to either the total compression or the total secondary compression. The relationship between the degradation ratio and time was obtained from the tests independently. Furthermore, a combined compression calculation method of municipal solid waste for all three parts of compressions including considering organics degradation is proposed based on a one-dimensional compression method. The relationship between the methane generation potential L0 of LandGEM model and degradation compression index was also discussed in the paper. A special column compression apparatus system, which can be used to simulate the whole compression process of municipal solid waste in China, was designed. According to the results obtained from 197-day column compression test, the new combined calculation method for municipal solid waste compression was analyzed. The degradation compression is the main part of the compression of MSW in the medium test period., (Copyright © 2015 Elsevier Ltd. All rights reserved.)