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Abstract With the popularity of the media cloud computing industry, individuals and organizations outsource image computation and storage to the media cloud server to reduce the storage burden. Media images usually contain a large amount of private information. To prevent disclosure of privacy of the image owners, media images are encrypted before uploading to the server. However, this operation will greatly limit the utilization of the image for the user, such as content-based image retrieval. We propose an efficient similarity query algorithm with access control based on Bkd-tree in this paper, in which a searchable encryption scheme is designed for similarity image retrieval, and the encrypted image is used to extract image features by a pre-trained CNN model. The Bkd-tree is utilized to generate an index tree for the image features to speed up retrieval and make it faster than linear indexing. Finally, the security performances of the proposed scheme is analyzed and the performance of this scheme is evaluated by experiments. The results show that the security of the image content and image features can be ensured, and it has a shorter retrieval time and higher retrieval efficiency.

Abstract Background The induction of lipid and astaxanthin accumulation in microalgae is often achieved through abiotic stress. However, this approach usually leads to oxidative stress, which results in relatively low growth rate. Phytohormones, as important small molecule signaling substances, not only affect the growth and metabolism of microalgae but also influence the intracellular reactive oxygen species level. This study aimed to screen phytohormones that could promote the fatty acids and astaxanthin yield of heterotrophic Chromochloris zofingiensis without causing oxidative damage, and further investigate the underlying mechanisms. Results In the present study, among all the selected phytohormones, the addition of exogenous salicylic acid (SA) could effectively promote cell growth along with the yield of total fatty acids (TFA) and astaxanthin in heterotrophic C. zofingiensis. Notably, the highest yields of TFA and astaxanthin were achieved at 100 μM SA, 43% and 97.2% higher compared with the control, respectively. Interestingly, the intracellular reactive oxygen species (ROS) levels, which are usually increased with elevated TFA content under abiotic stresses, were significantly decreased by SA treatment. Comparative transcriptome analysis unveiled significant alterations in overall carbon metabolism by SA. Specifically, the upregulation of fatty acid synthesis pathway, upregulation of β-carotene-4-ketolase (BKT) in carotenoid synthesis aligned with biochemical findings. Weighted gene co-expression network analysis highlighted ABC transporters and GTF2B-like transcription factor as potential key regulators. Conclusion This study found that salicylic acid can serve as an effective regulator to promote the celling growth and accumulation of fatty acids and astaxanthin in heterotrophic C. zofingiensis without ROS elevation, which provides a promising approach for heterotrophic production of TFA and astaxanthin without growth inhibition. Graphical Abstract

Subtropical Moso bamboo (Phyllostachys edulis) forests have become hot spots for nitrogen (N) deposition in China. However, the effect of long-term N deposition on the structure of soil microbial communities in different horizons of these forests remains unclear. To investigate the influence of extended N deposition on soil bacteria and fungi, a simulated N deposition field study was conducted in Fujian Province with four N deposition levels (0, 20, 40 and 80 kg N hm−2 a−1). The absolute abundances, diversity, community structure, and potential functions of soil bacteria and fungi were investigated after 10 years of simulated N deposition. The results indicated that there was no significant effect of N deposition on the abundance or diversity of soil microbes in either the organic or mineral horizons of Moso bamboo forests. However, under rates of 40 and 80 kg N hm−2 a−1, long-term N deposition altered the structure of the soil bacterial community in the organic horizon. Specifically, there was a 27.75∼36.40 % increase in the relative abundance of Actinobacteria, while that of Acidobacteria decreased by 24.40∼27.52 %. Redundancy analysis (RDA) demonstrated that soil total carbon (TC) was the key variable influencing the bacterial community structure in the organic horizon. Analysis of potential microbial functions showed that in the organic horizon, the rates of chemoheterotrophy significantly decreased under 80 kg N hm−2 a−1. However, N deposition did not noticeably impact the fungal community structure in either soil horizon. Our study suggested that under 10 years of N deposition, only the bacterial community composition and function in the organic horizon was affected, indicating that soil bacteria are more sensitive to N deposition than fungi in subtropical Moso bamboo forests. Therefore, further research is necessary to fully understand the impact of N deposition on fungal community composition in the soil organic horizon and on the microbial community in the mineral horizon of subtropical bamboo forests. This study provides valuable insights for informing future environmental management and conservation strategies.

In the background of rapid development of digital technology, exploring the relationship between residents' digital literacy (DL) and household carbon emissions (HCEs) is of great practical significance for China to achieve the carbon peak carbon neutral goal. Based on the three-period panel data of the China Family Panel Survey (CFPS) 2016-2020, we categorized digital literacy into digital learning, digital socialization, and digital consumption, and explored the impact of digital literacy on HCEs and its mechanisms. We find that digital literacy significantly and positively affects HCEs, while household life cycle, government governance, and ecological risk perception have a negative moderating effect on digital literacy affecting HCEs respectively. We also find that DL has the largest impact coefficient on HCEs from developmental consumption compared to survival and enjoyment consumption, and that DL has a more significant impact on HCEs from the consumption of young and middle-aged residents compared to older residents. These findings suggest that the dissemination of information and knowledge about carbon emissions through digital technologies, such as Internet platforms, can strengthen residents' awareness of environmental protection and thus promote their low-carbon consumption behavior.

Abstract The indiscriminate distribution of a large number of unlicensed images through the Internet seriously harms the benefits of image owners, which prompts the study of image copyright protection solutions. Existing blockchain‐free solutions are limited to using centralized servers to store copyright information, leaving data vulnerable to being lost or tampered with by external attacks. Although the blockchain‐based image copyright protection solutions are capable of addressing the above problem, improvements are needed in choosing a suitable blockchain platform and supporting integrated copyright life cycle management. To this end, this paper designs a blockchain‐based reliable image copyright protection system named BB‐RICP using Hyperledger Fabric. For one thing, the adoption of Fabric mitigates the limitations of other blockchain platforms represented by Ethereum in terms of efficiency, economy, and availability. For another, BB‐RICP successes in achieving integrated copyright lifecycle management. In addition, BB‐RICP achieves enhanced applicability performance by firstly introducing GM algorithms recommended by Chinese national standard and adapting the more consortium blockchain‐oriented practical byzantine fault tolerance algorithm. Moreover, the adoption of spread spectrum watermarking brings convenience to users. Finally, Kubernetes is used to assist in the simulation of BB‐RICP to verify that users can operate reliably in the blockchain network.

The term “metaverse”, a three-dimensional virtual universe similar to the real realm, has always been full of imagination since it was put forward in the 1990s. Recently, it is possible to realize the metaverse with the continuous emergence and progress of various technologies, and thus it has attracted extensive attention again. It may bring a lot of benefits to human society such as reducing discrimination, eliminating individual differences, and socializing. However, everything has security and privacy concerns, which is no exception for the metaverse. In this article, we firstly analyze the concept of the metaverse and propose that it is a super virtual-reality (VR) ecosystem compared with other VR technologies. Then, we carefully analyze and elaborate on possible security and privacy concerns from four perspectives: user information, communication, scenario, and goods, and immediately, the potential solutions are correspondingly put forward. Meanwhile, we propose the need to take advantage of the new buckets effect to comprehensively address security and privacy concerns from a philosophical perspective, which hopefully will bring some progress to the metaverse community.

Abstract Background/objective People living with HIV (PLWH) are prone to mental health problems and evidence indicates that HIV-related stigma can negatively impact mental health-related quality of life. This study explored potential mechanisms between HIV-related stigma and mental health-related quality of life, specifically whether anxiety or depression mediates, and whether social support moderates, the relationship. Method A total of 1197 Chinese PLWH participated in the study. The Berger HIV Stigma Scale, the Hospital Anxiety and Depression Scale (HADS), the 12-item Brief Health Survey (SF-12), and the Multidimensional Scale of Perceived Social Support (MSPSS) were employed. Results HIV-related stigma was negatively associated with mental health-related quality of life. Anxiety and depression partially mediated the relationship between HIV-related stigma and mental health-related quality of life; social support played a moderating role. Conclusions The mental health-related quality of life in PLWH was shown to be indirectly affected by HIV-related stigma through anxiety and depression in China. The negative impact of HIV-related stigma decreased with increased social support.

The continuous increase in the number of transactions is exerting significant pressure on the storage capacity of blockchains. By storing data on multiple blockchains and interacting with each other with cross-chain technology, the storage pressure can be relieved to a large degree. Nevertheless, the feature of immutability may impede the further development of blockchains, e.g., some expired identity information cannot be updated due to immutability. The decentralized chameleon hash function can resolve the issue while ensuring the feature of decentralization. Unfortunately, how to ensure the consistency of dynamic data updating in cross-chain interaction is still a question worth considering. In this paper, we propose a dynamic cross-chain decentralized data consistency verification (DCCV) model, in which we adopt an audit chain and design a dynamic Merkle hash tree to guarantee the consistency of dynamic data updating between the source chain and the target chain. In addition, in order to enhance the relevance of calculations across different smart contracts and improve audit efficiency, we propose a way to develop cross-chain smart contracts collaboratively. Moreover, Cosi protocol and multi-signcryption are utilized to ensure the security and privacy of cross-chain data transmission. Finally, theoretical and experimental analysis demonstrates that DCCV can achieve dynamic data consistency verification in the process of cross-chain interaction.

This is an essay in the field of mineral processing engineering. Vanadium titanium magnetite is an important strategic resource in China. The traditional element measure method is difficult to directly answer the problems such as the promotion space of comprehensive utilization of vanadium titanium magnetite, which is not conducive to the supervision of vanadium titanium magnetite production enterprises and the timely and effective improvement of comprehensive utilization of vanadium titanium magnetite. The separation process characteristics of vanadium titanium magnetite can be directly reflected by the way of element measure and mineral measure, which can effectively promote the saving and comprehensive utilization of vanadium titanium magnetite resources. In order to realize the new measure method, it is necessary to strengthen the research on quantitative measurement method and mineral balance calculation method of vanadium titanium magnetite and to formulate the standard.

Abstract The intellectual properties (IP) protection of deep neural networks (DNN) models has raised many concerns in recent years. To date, most of the existing works use DNN watermarking to protect the IP of DNN models. However, the DNN watermarking methods can only passively verify the copyright of the model after the DNN model has been pirated, which cannot prevent piracy in the first place. In this paper, an active DNN IP protection technique against DNN piracy, called ActiveGuard, is proposed. ActiveGuard can provide active authorisation control, users' identities management, and ownership verification for DNN models. Specifically, for the first time, ActiveGuard exploits well‐crafted rare and specific adversarial examples with specific classes and confidences as users' fingerprints to distinguish authorised users from unauthorised ones. Authorised users can input their fingerprints to the DNN model for identity authentication and then obtain normal usage, while unauthorised users will obtain a very poor model performance. In addition, ActiveGuard enables the model owner to embed a watermark into the weights of the DNN model for ownership verification. Compared to the few existing active DNN IP protection works, ActiveGuard can support both users' identities identification and active authorisation control. Besides, ActiveGuard introduces lower overhead than these existing active protection works. Experimental results show that, for authorised users, the test accuracy of LeNet‐5 and Wide Residual Network (WRN) models are 99.15% and 91.46%, respectively, while for unauthorised users, the test accuracy of LeNet‐5 and WRN models are only 8.92% and 10%, respectively. Besides, each authorised user can pass the fingerprint authentication with a high success rate (up to 100%). For ownership verification, the embedded watermark can be successfully extracted, while the normal performance of DNN models will not be affected. Furthermore, it is demonstrated that ActiveGuard is robust against model fine‐tuning attack, pruning attack, and three types of fingerprint forgery attacks.

Diversified soil microbiomes are the key drivers of carbon fixation and plant residue decomposition in forest ecosystems. Revealing the elevation patterns of soil microbial carbon cycling in forests is essential for utilization of forest ecological resources. However, the soil microbial diversity and carbon cycle processes in Platycladus orientalis plantations across different elevations are still unclear. Here, we established a gradient with three elevations (118 m, 300 m, and 505 m) on the Beijing Ming Dynasty Tombs Forest Farm, which is located in Changping District, Beijing. The metagenomics method was applied to study the soil microbiome, with a special focus on the carbon cycle process at each elevation. We found the diversity and composition of the soil microbiomes significantly varied across the elevation gradients. The structure of bacteria and archaea was mainly driven by soil total potassium, pH and NH4+, but the eukaryota had no significant relationship with the environmental factors. The relative abundance of genes involved in microbial carbon fixation and decomposition of organic carbon were also significantly impacted by elevation, with the former showing increasing, u-shaped, or hump trends with increasing elevation, but the latter only showing hump trends. The rTCA cycle and 3-hydroxypropionate pathway were the dominant carbon fixation pathways in the Platycladus orientalis plantations. The elevation gradient shaped the microbial decomposition of plant-derived organic carbon by changing soil properties and, furthermore, led to soil organic carbon stock losses. These findings increase our understanding of soil microbial diversity and the carbon cycle across different elevations and provide a theoretical basis for the utilization of forest ecological resources to promote carbon sequestration.

As the saying goes, one image is worth ten thousand words. Because of the rich visual information carried by the image itself, previewers can easily find what they need. On the other hand, the large amount of detail information carried by the image may invade the privacy of the parties, which needs to be protected. Traditional image encryption schemes can protect privacy, but they indiscriminately erase all visual information, which also gives up the visual usability of the image. Motivated by this, we propose a novel color image privacy-preserving method, which can be regarded as a variant of thumbnail-preserving encryption (TPE). The scheme preserves the rough visual information of the image, and considers the information redundancy in the depth direction for the first time compared with other TPE schemes. Specifically, we propose a TPE scheme combining breadth and depth, and a matching encryption framework is proposed, which is divided into three steps: cube permutation, block substitution, and block permutation. Meanwhile, a prototype is constructed by using a chaotic system and sufficient experiments are carried out with it. The experimental results show that this scheme has excellent properties, can well balance privacy and availability, and has high time efficiency.

With the progress of the times and the development of electronic products, more and more images are captured and stored on multiple cloud servers, which are more likely to exhibit dishonesty because of interdependence rather than semi-honesty and thus how to achieve search over multiple clouds becomes intractable. Moreover, most of the existing research works on content-based image retrieval so far do not consider dishonest clouds and thereby do not support multi-cloud retrieval. On the one hand, a central search server is generally introduced for the convenience of retrieval in the system with multiple cloud servers. However, the central search server may return false or incomplete search results for possible commercial profit, which reduces the retrieval accuracy. On the other hand, cloud servers may destroy the image integrity by deleting or tampering images to save computing and bandwidth resources, resulting in the loss of important information for the image owner and incorrect decryption of the image for the user. In order to address these two problems, we propose a blockchain-assisted privacy-protected similarity image retrieval in the multiple cloud environment, which can not only ensure the accuracy of the retrieval results, but also can verify the integrity of images. The performance of this scheme is evaluated on a real-world dataset, and the results show the high retrieval precision and efficiency of this scheme.

Herbicide residues in farmland soils have attracted a great deal of attention in recent decades. Their accumulation potentially decreases the activity of microbes and related enzymes, as well as disturbs the nitrogen cycle in farmland soils. In previous studies, the influence of natural factors or nitrogen fertilization on the soil nitrogen cycle have frequently been examined, but the role of herbicides has been ignored. This study was conducted to examine the effects of herbicides on NH3 volatilization- and denitrification-related nitrogen loss through three rotation cycles from 2013 to 2016. The four treatments included no urea fertilizer (CK), urea (CN), urea+acetochlor-fenoxaprop-ethyl (AC-FE), and urea+2,4D-dicamba (2,4D-DI) approaches. The results showed that the application of nitrogen fertilizer significantly increased the nitrogen losses from ammonia volatilization and denitrification in the soil. Ammonia volatilization was the main reason for the gaseous loss of urea nitrogen in a wheat–maize rotation system in the North China Plain (NCP), which was significantly higher than the denitrification loss. In the CK treatment, the cumulative nitrogen losses from ammonia volatilization and denitrification during the three crop rotation cycles were 66.64 kg N hm−2 and 8.07 kg N hm−2, respectively. Compared with CK, the nitrogen losses from ammonia volatilization and denitrification under the CN treatment increased 52.62% and 152.88%, respectively. The application of AC-FE and 2,4D-DI significantly reduced the nitrogen gas losses from the ammonia volatilization and denitrification in the soil. Ammonia volatilization reduction mainly occurred during the maize season, and the inhibition rates of AC-FE and 2,4D-DI were 7.72% and 11.80%, respectively, when compared with CN. From the perspective of the entire wheat–maize rotation cycle, the inhibition rates were 5.41% and 7.23% over three years, respectively. Denitrification reduction also mainly occurred in the maize season, with the inhibition rates of AC-FE and 2,4D-DI being 34.12% and 30.94%, respectively, when compared with CN. From the perspective of the entire wheat–maize rotation cycle, the inhibition rates were 28.39% and 28.58% over three years, respectively. Overall, this study demonstrates that herbicides could impact the nitrogen cycle of farmland soil ecosystems via the suppression of ammonia volatilization and denitrification rates, thus reducing gaseous N losses and mitigating global climate change.

The feasibility of dephosphorization using the flotation process and its mechanism of high-phosphorus oolitic hematite were discussed in this paper. The results showed that phosphorus minerals mainly exist in the form of collophane, which can be divided into three types. Block collophane and band collophane could be effectively removed through flotation, while the third type could not be eliminated through physical concentration alone. A lab-made fatty acid was identified as an effective collector for high-phosphorus oolitic hematite, resulting in a product containing 57.67% iron and 0.14% phosphorus with a flotation recovery rate of 82.43%. The study of the flotation mechanism revealed that, in the presence of starch and the lab-made fatty acid, there was both physical absorption and chemisorption occurring on the surfaces of apatite. However, only very weak physical absorption was observed on the surface of hematite.

Privacy concerns may be caused after uploading images into image hosting platforms. For traditional image encryption, images with rich content and meaning are transformed into noise-like encrypted images without any visual information, which can protect privacy from infringement but at the expense of visual usability. Recently, thumbnail-preserving encryption (TPE) was proposed to balance privacy and usability by preserving the thumbnail unchanged after encrypting. On the other hand, a large number of pseudo-random functions with high computational complexity are utilized in the existing TPE schemes, which makes the encryption and decryption time too long. Motivated by this, we deeply analyze the characteristics of TPE and propose a scheme to organically combine TPE with chaotic systems, which reduces the encryption and decryption time exponentially besides ensuring security. The experiments show that the time cost is reduced dozens of times as well as relatively stable compared with the existing schemes. Meanwhile, encrypted images have high visual quality and can resist some commonly used attacks including differential attack and face detection and the balance between privacy and visual usability is achieved.

Solid polymer electrolytes have demonstrated high promise to solve the safety problems caused by conventional liquid electrolytes in lithium ion batteries. However, the inherent flammability of most polymer electrolyte materials remains unresolved, hence hindering their further industrial application. Addressing this challenge, we designed and constructed a thermal-responsive imide-linked covalent organic framework (COF) bearing ortho-positioned hydroxy groups as precursors, which can conduct a thermal rearrangement to transform into a highly crystalline and robust benzoxazole-linked COF upon heating. Benefiting from the release of carbon dioxide through thermal rearrangement reaction, this COF platform exhibited excellent flame retardant properties. By contrast, classic COFs (e.g., boronate ester, imine, olefin, imide linked) were all flammable. Moreover, incorporating polyethylene glycol and Li salt into the COF channels can produce solid polymer electrolytes with outstanding flame retardancy, high ionic conductivity (6.42 ​× ​10−4 ​S ​cm−1) and a high lithium-ion transference number of 0.95. This thermal rearrangement strategy not only opens a new route for the fabrication of ultrastable COFs, but also provides promising perspectives to designing flame-retardant materials for energy-related applications.

IntroductionDry matter accumulation (DMA) and dry matter partitioning (DMP) are important physiological processes determining crop yield formation. Deep understanding of the DMA and DMP processes and their responses to drought are limited by difficulty in acquiring total root biomass.MethodsPot experiments with treatments quitting and ceasing ear growth (QC) and controlling soil water (WC) during vegetative (VP) and reproductive (RP) growth stages of maize (Zea mays) were conducted in Jinzhou in 2019 and 2020 to investigate the effects of drought and rewatering on DMW and DMP of different organs.ResultsThe response of DMW of reproductive organ to drought was more sensitive than those of vegetative organs, and was maintained after rehydration. Drought during VP (VPWC) reduced more sharply DMW of stalk than of leaves, and that during RP (RPWC) decreased more substantially leaves DMW. The effect of drought on DMPR was inconsistent with that on DMW for each organ. The DMP patterns of maize in different growth stages have adaptability to some level of water stress, and their responses increased with drought severity. Drought increased significantly DMP rates (DMPRs) of vegetative organs and reduced the ear DMPR and harvest index (HI), attributing to the suppressed photosynthates partitioning into ear and dry matter redistribution (DMRD) of vegetative organs, especially for stalk DMRD decreasing 26%. The persistence of drought impact was related to its occurrence stage and degree as well as the duration during rewatering to maturity. The aftereffect of drought during different growth periods on DMP were various, and that of VPWC enlarged and drastically induced the reduction of HI, also was larger than that of RPWC which demonstrated obvious alleviation in the previous responses of DMP and HI. Root-shoot ratio (RSR) increased under VPWC and RPWC and subsequent rehydration.DiscussionThe DMWs of stalk, roots and leaves were affected by VPWC in order from large to small, and were close to or larger than the controls after rehydration, indicating the compensation effect of rewatering after drought. The DMPRs, RSR AND HI are the important parameters in agricultural production, and are often used as the constants, but in fact they vary with plant growth. In addition, the interannual differences in ear and stalk DMPRs in response to drought were probably caused by the difference in degree and occurrence stage of drought, further reflecting the variation in response of allometry growth among organs to the environment. Besides, the persistence of drought impact was related to the occurrence stage and degree of drought, which is also associated with the duration during rewatering to maturity. Notably, the effect of drought on DMW was inconsistent with that on DMPR for each organ meaning that the two variables should be discussed separately. The QC did not affect total DMW but increased RSR, changed and intensified the effect and aftereffect of RPWC on DMP, respectively, indicating that the DMP pattern and its response to drought occur change under the condition of QC.

BackgroundThe patients with chronic low back pain (CLBP) showed impaired postural control, especially in challenging postural task. The dorsolateral prefrontal cortex (DLPFC) is reported to involve in the complex balance task, which required considerable attentional control. The effect of intermittent theta burst stimulation (iTBS) over the DLPFC to the capacity of postural control of CLBP patients is still unknown.MethodsParticipants diagnosed with CLBP received a single-session iTBS over the left DLPFC. All the participants completed the postural control tasks of single-leg (left/right) standing before and after iTBS. The activation changes of the DLPFC and M1 before and after iTBS were recorded by functional near-infrared spectroscopy (fNIRS). The activation pattern of the trunk [transversus abdominis (TrA), superficial lumbar multifidus (SLM)] and leg [tibialis anterior (TA), gastrocnemius medialis (GM)] muscles including root mean square (RMS) and co-contraction index (CCI) during single-leg standing were measured by surface electromyography (sEMG) before and after the intervention. The paired t-test was used to test the difference before and after iTBS. Pearson correlation analyses were performed to test the relationship between the oxyhemoglobin concentration and sEMG outcome variables (RMS and CCI).ResultsOverall, 20 participants were recruited. In the right-leg standing condition, compared with before iTBS, the CCI of the right TrA/SLM was significantly decreased (t = −2.172, p = 0.043), and the RMS of the right GM was significantly increased (t = 4.024, p = 0.001) after iTBS. The activation of the left DLPFC (t = 2.783, p = 0.012) and left M1 (t = 2.752, p = 0.013) were significantly decreased and the relationship between the left DLPFC and M1 was significant after iTBS (r = 0.575, p = 0.014). Correlation analysis showed the hemoglobin concentration of M1 was negatively correlated with the RMS of the right GM (r = −0.659, p = 0.03) and positively correlated between CCI of the right TrA/SLM (r = 0.503, p = 0.047) after iTBS. There was no significant difference in the brain or muscle activation change in the left leg-standing condition between before and after iTBS.ConclusionIntermittent theta burst stimulation over the left DLPFC seems to be able to improve the muscle activation pattern during postural control ability in challenging postural task, which would provide a new approach to the treatment of CLBP.

In the last few decades, the relationship between mathematics and algorithms has become increasingly important and influential in computer science [...]

Biochar is commonly used to improve acidic soil and reduce nitrogen loss. However, the impact of biochar on soil nitrogen retention, especially at varying pH levels, is not fully understood. Soil samples were obtained from an acidic red soil citrus orchard. The soil pH was adjusted using CaO, with five levels (4.0, 5.1, 5.8, 6.6, and 7.2), and two biochar doses (0% and 1%) were applied. The study used 15N-Tracer and Ntrace to investigate biochar’s influence on soil nitrogen retention at different pH levels. The results showed that soil amendment with biochar improved gross mineralization rates (TM) and gross NH4+ immobilization rates (TI), except at pH 4.0 for TI. Biochar enhanced heterotrophic nitrification (ONrec) within pH 4.0–7.4, with a threshold for autotrophic nitrification (ONH4) at pH 6.4. The findings revealed biochar’s positive effect on soil nitrogen retention within pH 4.5–6.4. Biochar had a greater impact on TI than TM and inhibited ONH4, potentially enhancing nitrogen retention in this pH range. These results highlight the significance of considering biochar incorporation for improving nitrogen use efficiency and reducing NO3−-N loss in subtropical pomelo orchards.

Soil microbes are the key to revealing the mechanisms driving variation in soil biogeochemical processes. In recent decades, forests in Southeast China have been widely transformed into tea plantations due to the drivers of economic benefits. However, the changes in the soil microbial community and their potential function during the transition from a typical forest ecosystem to tea plantations remain poorly understood. This study investigated the soil microbial community in tea plantation soils with different planting ages, i.e., 6, 12, 23 and 35 years, and in an adjacent woodland control. We discovered that tea planting significantly increased soil bacterial richness (ACE and Chao1) and decreased fungal richness, the diversity of bacteria (Simpson and Shannon) show a trend of initially decreasing and then increasing while there was no significant effect on fungal diversity. After tea planting, the relative abundances of Actinobacteria and Proteobacteria increased by 180.9%–386.6% and 62.3%–97.5%, respectively; the relative abundances of Acidobacteria decreased by 11.4%–66.8%. However, the fungal phyla were not significantly different among different aged tea plantations and woodlands. FAPROTAX and FUNGuild revealed that the transition of natural woodland to tea plantations significantly increased the relative abundances of aerobic_chemoheterotrophy (14.66%–22.69%), chemoheterotrophy (34.36%–37.04%), ureolysis (0.68%–1.35%) and pathogenic fungi (26.17%–37.02%). db-RDA proved that the bacterial community structure was more strongly related to soil pH and available nitrogen (AN), while the main determinants of the fungal community composition were soil pH and soil organic matter (SOM). These findings indicate that tea planting has a strong effect on the soil microbial community and potential function. The change in soil pH during tea planting was the most important factor affecting the soil microbial community, while soil bacteria were more sensitive to tea planting than fungi.

The maturation of wireless connectivity, blockchain (distributed ledger technologies), and intelligent systems has fostered a comprehensive ecosystem for the Internet of Things (IoT). However, the growing volume of data generated by IoT devices creates substantial pressure on blockchain storage and computation capabilities, impeding the further development of the IoT ecosystem. Decentralizing data storage across multiple chains and utilizing cross-chain technology for data exchange eliminates the need for expensive centralized infrastructure, lowers data transfer costs, and improves accessibility. Hence, the issue of computational and storage pressure in blockchain can be improved. Nonetheless, the data of IoT devices are constantly updating, and ensuring consistency for dynamic data across heterogeneous chains remains a significant challenge. To address the aforementioned challenge, we propose a blockchain-based distributed and lightweight data consistency verification model (BDCA), which leverages a batch verification dynamic Merkle hash tree (BV-MHT) and an advanced gamma multi-signature scheme (AGMS) to enable consistent verification of dynamic data while ensuring secure and private data transmission. The AGMS scheme is reliable and robust based on security analysis while the dependability and consistency of BDCA are verified through inductive reasoning. Experimental results indicate that BDCA outperforms CPVPA and Fortress in communication and computation overhead for data preprocessing and auditing in a similar condition, and the AGMS scheme exhibits superior performance when compared to other widely adopted multi-signature schemes such as Cosi, BLS, and RSA. Furthermore, BDCA provides up to 99% data consistency guarantees, demonstrating its practicality.

Soil moisture content (SWC) can change the diversity and composition of soil fungal communities by affecting soil texture and soil nutrients. To explore the response of soil fungal communities to moisture in the grassland ecosystem on the south shore of Hulun Lake, we set up a natural moisture gradient that was subdivided into high (HW), medium (MW), and low (LW) water contents. Vegetation was investigated by quadrat method, and aboveground biomass was collected by the mowing method. Soil physicochemical properties were obtained by internal experiments. The composition of the soil fungal community was determined using high-throughput sequencing technology. The results showed significant differences in soil texture, nutrients, and fungal species diversity under the moisture gradients. Although there was significant clustering of fungal communities in different treatments, the fungal community composition was not significantly different. According to the phylogenetic tree, the Ascomycota and Basidiomycota were the most important branches. The fungal species diversity was smaller when SWC was higher, and in this environment (HW), the fungal-dominant species were significantly related to SWC and soil nutrients. At this time, soil clay formed a protective barrier for the survival of the dominant classes Sordariomycetes and Dothideomycetes and increased their relative abundance. In summary, the fungal community responded significantly to SWC on the southern shore of the Hulun Lake ecosystem in Inner Mongolia, China, and the fungal community composition of the HW group was stable and easier to survive.

The effective recognition of patterns from blurred images presents a fundamental difficulty for many practical vision tasks. In the era of deep learning, the main ideas to cope with this difficulty are data augmentation and deblurring. However, both facing issues such as inefficiency, instability, and lack of explainability. In this paper, we explore a simple but effective way to define invariants from blurred images, without data augmentation and deblurring. Here, the invariants are designed from Fractional Moments under Projection operators (FMP), where the blur invariance and rotation invariance are guaranteed by the general theorem of blur invariants and the Fourier-domain rotation equivariance, respectively. In general, the proposed FMP not only bears a simpler explicit definition, but also has useful representation properties including orthogonality, statistical flexibility, as well as the combined invariance of blurring and rotation. Simulation experiments are provided to demonstrate such properties of our FMP, revealing the potential for small-scale robust vision problems.

Abstract Owing to climate change, agrometeorological disasters are becoming increasingly complex. Here, we analysed the characteristics of combined agrometeorological disaster (CAD) caused by low temperature in annual rice crops in Liaoning Province, China, from 1961 to 2017. We assessed the repeat occurrence of natural disasters on rice production. The results showed that (1) there were six possible CAD scenarios in a rice growing season. These included two scenarios with one disaster in two periods (OD-1, OD-2), three scenarios with two different disasters (TD-1, TD-2, TD-3) and one with multiple disasters (MD-1). Since 1961, the overall occurrence of the six CAD scenarios showed a downward trend. Among the six scenarios, TD-1 had the greatest distribution and occurred most frequently; (2) three possible single agrometeorological disaster (SAD) scenarios may occur during a rice growing season, delayed cold damage (SAD-d), frost damage at only one stage (SAD-f), sterile-type cold damage at one stage (SAD-s). Since 1961, the SAD-d frequency decreased, whereas, since the mid-1980s, the SAD-f frequency increased; (3) SAD and CAD frequencies showed downward trends, with CAD declining more than SAD. The CAD geographical range and frequency were smaller than those of SAD. Rice damage in SAD-f and OD-1 scenarios showed no significant trend, but appeared to have slightly increased. The main agrometeorological disasters affecting rice production in Liaoning Province were delayed cold damage, frost damage or both; (4) a comparison of the rice yield reduction rates of years in which CAD or SAD occurred in more than 50% of stations in Liaoning Province revealed that the yield reduction rates associated with the former were greater than those associated with the latter. CAD had more types, and the occurrences and impacts were more complicated, than for SAD. Compared with SAD, the effects of CAD may be magnified in rice crops, leading to reduced yields.

Abstract Green synthesis of crystalline porous materials for energy-related applications is of great significance but very challenging. Here, we create a green strategy to fabricate a highly crystalline olefin-linked pyrazine-based covalent organic framework (COF) with high robustness and porosity under solvent-free conditions. The abundant nitrogen sites, high hydrophilicity, and well-defined one-dimensional nanochannels make the resulting COF an ideal platform to confine and stabilize the H3PO4 network in the pores through hydrogen-bonding interactions. The resulting material exhibits low activation energy (Ea) of 0.06 eV, and ultrahigh proton conductivity across a wide relative humidity (10–90 %) and temperature range (25–80 °C). A realistic proton exchange membrane fuel cell using the olefin-linked COF as the solid electrolyte achieve a maximum power of 135 mW cm−2 and a current density of 676 mA cm−2, which exceeds all reported COF materials.

The database of faces containing sensitive information is at risk of being targeted by unauthorized automatic recognition systems, which is a significant concern for privacy. Although there are existing methods that aim to conceal identifiable information by adding adversarial perturbations to faces, they suffer from noticeable distortions that significantly compromise visual perception, and therefore, offer limited protection to privacy. Furthermore, the increasing prevalence of appearance anxiety on social media has led to users preferring to beautify their faces before uploading images. In this paper, we design a novel face database protection scheme via beautification with chaotic systems. Specifically, we construct the adversarial face with better visual perception via beautification for each face in the database. In the training, the face matcher and the beautification discriminator are federated against the generator, prompting it to generate beauty-like perturbations on the face to confuse the face matcher. Namely, the pixel changes produced by face beautification mask the adversarial perturbations. Moreover, we use chaotic systems to disrupt the order of adversarial faces in the database, further mitigating the risk of privacy leakage. Our scheme has been extensively evaluated through experiments, which show that it effectively defends against unauthorized attacks while also yielding good visual results.

The Northeast China maize belt is one of the three major golden maize belts in the world and has been severely affected by climate change, however, the evapotranspiration (ET) partitioning is not clear. It is important to study ET and its components under climate change. In this paper, the water balance model SOILWAT2 was used to estimate ET partitioning in drought and humid years, seasons, and maize growth stages from 1989 to 2018 over rainfed maize farmland. The results indicated that the SOILWAT2 model performed well for the prediction of ET and its partitioning compared with eddy covariance measurements. The mean yearly ET, transpiration (T), soil evaporation (Es), and canopy interception evaporation (Int) were 432.3 mm, 197.6 mm, 204.7 mm and 19.2 mm, respectively, over 30 years. Es/ET was 6.3% lower in drought years than in humid years, T/ET was conversely higher (6.2% higher in drought years). There was no clear difference of Int/ET between humid and drought years. In the growing season, T/ET, Es/ET, and Int/ET varied from 40.0% to 75.0%, 22.8% to 55.7%, and 0.7% to 7.0%, respectively. T/ET decreased along with the growth of maize and was greatest at the greening–jointing stage. Es/ET was smallest at the greening–jointing stage. We found a power function relationship between T/ET, Es/ET, and leaf area index (LAI) and above-ground biomass. Our results indicated that for the rainfed farmland, drought may limit maize yield by increasing water loss of maize through increasing T under climate change conditions. Therefore, securing food yield will depend on increases in water-use efficiency and other adaptive strategies, such as drought-resistant varieties, and irrigation.

Trust and reciprocity promote cooperation and are key elements of a successful social life. This study investigated the framing effects on trust and reciprocity behaviors. Using an iterated one-shot within-subjects design, this study explored how trust and reciprocity decisions changed when the game was framed in terms of a give (i.e., using a standard trust game with a default of no trust) and a take (e.g., using a distrust game with a default of full trust) frame. Participants of both genders first completed the scenario version of the game (Session 1), and then played the roles of trustors (Session 2) and trustees (Session 3) with human and computer-mediated human partners either in the give or take frame. Our results showed increased trust in the give than in the take frame, but only pronounced in direct interaction with human (vs. indirect computer-mediated) partners. Participants also showed higher expectations of return in the give than in the take frame. The actual reciprocity was higher in the give than in the take frame when interacting both with human and computer-mediated human partners. The results contribute to our understanding of the factors that shape trust and reciprocity and emphasize the impact of framing the default.

Machine learning has been pervasively used in a wide range of applications due to its technical breakthroughs in recent years. It has demonstrated significant success in dealing with various complex problems, and shows capabilities close to humans or even beyond humans. However, recent studies show that machine learning models are vulnerable to various attacks, which will compromise the security of the models themselves and the application systems. Moreover, such attacks are stealthy due to the unexplained nature of the deep learning models. In this survey, we systematically analyze the security issues of machine learning, focusing on existing attacks on machine learning systems, corresponding defenses or secure learning techniques, and security evaluation methods. Instead of focusing on one stage or one type of attack, this paper covers all the aspects of machine learning security from the training phase to the test phase. First, the machine learning model in the presence of adversaries is presented, and the reasons why machine learning can be attacked are analyzed. Then, the machine learning security-related issues are classified into five categories: training set poisoning; backdoors in the training set; adversarial example attacks; model theft; recovery of sensitive training data. The threat models, attack approaches, and defense techniques are analyzed systematically. To demonstrate that these threats are real concerns in the physical world, we also reviewed the attacks in real-world conditions. Several suggestions on security evaluations of machine learning systems are also provided. Last, future directions for machine learning security are also presented.

Soil water dynamic is considered an important process for water resource and plantation management in Horqin Sand Land, northern China. In this study, soil water content simulated by the SWMS-2D model was used to systematically analyse soil water dynamics and explore the relationship between soil water and rainfall among micro-landforms (i.e., top, upslope, midslope, toeslope, and bottomland) and 0-200 cm soil depths during the growing season of 2013 and 2015. The results showed that soil water dynamics in 0-20 cm depths were closely linked to rainfall patterns, whereas soil water content in 20-80 cm depths illustrated a slight decline in addition to fluctuations caused by rainfall. At the top position, the soil water content in different ranges of depths (20-40 and 80-200 cm) was near the wilting point, and hence some branches, and even entire plants exhibited diebacks. At the upslope or midslope positions, the soil water content in 20-80 or 80-200 cm depths was higher than at the top position. Soil water content was higher at the toeslope and bottomland positions than at other micro-landforms, and deep caliche layers had a positive feedback effect on shrub establishment. Soil water recharge by rainfall was closely related to rainfall intensity and micro-landforms. Only rainfalls > 20 mm significantly increased water content in > 40 cm soil depths, but deeper water recharge occurred at the toeslope position. A linear equation was fitted to the relationship between soil water and antecedent rainfall, and the slopes and R2 of the equations were different among micro-landforms and soil depths. The linear equations generally fitted well in 0-20 and 20-40 cm depths at the top, upslope, midslope, and toeslope positions (R2 value of about 0.60), with soil water in 0-20 cm depths showing greater responses to rainfall (average slope of 0.189). In 20-40 cm depths, the response was larger at the toeslope position, with a slope of 0.137. In 40-80 cm depths, a good linear fit with a slope of 0.041 was only recorded at the toeslope position. This study provides a soil water basis for ecological restoration in similar regions.

To understand the drought response mechanisms of dry matter partitioning of maize, pot experiments under drought conditions were conducted during the vegetative and reproductive growth periods of maize. The aim was to calculate allometric relationships between the dry matter weight of different organs and their responses to drought. Results showed that allometric relationships between the roots, above-ground plant, and total biomass gradually increased with maize growth approaching maturity under a normal water supply. Drought during the vegetative period reinforced allometric relationships during the growth process and after rewatering and increased the size-dependence of the root shoot ratio. However, drought during the reproductive period weakened them. The ear–shoot allometric relationship was more significant during growth than at later growth under normal conditions but strengthened during later growth in those plants suffering droughts during the vegetative and reproductive stages. The ear–shoot allometric relationship and the size-dependence of harvest index at later growth were significantly enhanced by drought during the reproductive period compared to the vegetative period.

Accurate feature identification of drought disaster events is required for proper risk management in agriculture. This study improved the crop water deficit index (CWDI) by including the daily meteorological, crop development stage, soil moisture content, and yield data for 1981–2020 in northeastern China. Two drought characteristic variables (drought duration and intensity) were extracted using the theory of runs to produce the improved crop water deficit index (CWDIwp). Thresholds for the bivariate indicators were also determined for agricultural drought events of varying severity. A joint distribution model for drought variables was constructed based on five types of Archimedean copulas. The joint probability and the joint recurrence period for agricultural drought events were analyzed for drought events with varying intensities in northeast China. The results suggest that the CWDIwp can reliably identify the onset, duration, and intensity of drought events over the study area and can be used to monitor agricultural drought events. The conditional probability of drought intensity (duration) decreased as the drought duration (intensity) threshold increased, whereas the drought recurrence period increased as the threshold for drought duration and intensity rose. In the period (1981–2020), drought intensity in the three Northeastern provinces showed an increasing trend in the order Jilin Province > Liaoning Province > Heilongjiang Province. The spatial distribution of the joint probability and the joint recurrence period was obvious, and the joint probability showed a decreasing distribution trend from west to east. The distribution trend for the joint probability was opposite to that of the joint recurrence period. Furthermore, the areas with high drought probability values corresponded to the areas with low values for the recurrence period, indicating that the drought occurrence probability was higher, and the recurrence period value was lower in the drought-prone areas. The high-risk drought areas (60–87%) were in western Liaoning and western Jilin, with a recurrence period of 1–3 years, whereas the low-risk areas ( moderate drought > severe drought > extreme drought. The order for the recurrence period was light drought < moderate drought < severe drought < extreme drought. The results provide technical support for disaster prevention and mitigation in drought risk management.

Cloud as a developing technology provides convenient services to sharing and usage of images resource, but brings a threat to images security and privacy authentication. Optical chaos possess distinct superiority for image secure sharing, therefore, based on two mutually coupled semiconductor lasers (MC-SL1 and MC-SL2), we theoretically propose a color image encryption system for secure resource sharing and introduce a watermarking method for images privacy authentication in cloud. In our paper, after achieving chaos synchronization under proper parameters, two MC-SLs are used to transmit encryption/decryption key space generated by chaotic signal of MC-SL1 and user's key. We carry out digital watermarking and encryption of a color image before secure resource sharing in cloud. On the other side of cloud, we make some tests for decryption of color image by received key space and watermark extraction. Meanwhile, we discuss security performance of color image encryption/decryption and key space transmission process, and analyze secure resource sharing of color image in cloud.

Recently, rate-compatible raptor-like quasi-cyclic low-density parity-check (RL-QC-LDPC) codes, which are a typical class of multi-edge type LDPC codes with excellent performance and high flexibility, have been adopted in the technical specification of 5G new radio (5G-NR). In this paper, the rate-compatible RL-QC-LDPC coded interleave division multiple access (IDMA), or called the enhanced IDMA, is developed as non-orthogonal multiple access (NOMA)-based random access solution, and various spectral efficiencies can be flexibly supported thanks to the rate compatible capability. First, 5G-NR LDPC codes are directly incorporated into enhanced IDMA-based random access, which can support a wide range of user loads with relatively low system throughput. Second, to improve the supported user load and system throughput, an edge-classification-based extension (ECE) algorithm is proposed to construct rate-compatible RL-QC-LDPC codes toward enhanced IDMA while taking into consideration the novel structural features of 5G-NR LDPC codes. Based on the two proposed edge-classification methods, the ECE can efficiently reduce the search space of base matrices for each extension round and effectively maintain enough diversity in the selected seed base matrices to facilitate further extension. Multi-edge type density evolution-aided extrinsic information transfer chart is employed to predict the asymptotic performance during the base matrix extension and optimization. As an example, a rate-compatible RL-QC-LDPC code family with fine code-rate granularity is constructed via the proposed ECE. Compared with 5G-NR LDPC coded IDMA, the outage performance of enhanced IDMA with the new code family can be significantly improved at high user load and system throughput for NOMA-based random access.

Discrete Fourier transform (DFT), inverse DFT (IDFT), and circular convolution are important tools for analyzing and designing discrete signals and systems, and are widely used in various industries. In order to pursue faster operational efficiencies or more accurate operational results, engineering calculations are often required to be quick and easy. Therefore, it is necessary to reduce the local computational overhead required to perform DFT, IDFT, and circular convolution. In addition to improving the algorithms themselves, cloud computing outsourcing is also an optional method. In this paper, in response to the new challenges brought by cloud computing outsourcing, we design and propose an efficient and secure cloud computing outsourcing protocol for DFT, IDFT, and circular convolution. Through a theoretical explanation and simulation experiment, we show the efficiency, security, and verifiability of the proposed protocol.

Indoor image features extraction is a fundamental problem in multiple fields such as image processing, pattern recognition, robotics, and so on. Nevertheless, most of the existing feature extraction methods, which extract features based on pixels, color, shape/object parts or objects on images, suffer from limited capabilities in describing semantic information (e.g., object association). These techniques, therefore, involve undesired classification performance. To tackle this issue, we propose the notion of high-level semantic features and design four steps to extract them. Specifically, we first construct the objects pattern dictionary through extracting raw objects in the images, and then retrieve and extract semantic objects from the objects pattern dictionary. We finally extract our high-level semantic features based on the calculated probability and delta parameter. The experiments on three publicly available datasets (MIT-67, Scene15, and NYU V1) show that our feature extraction approach outperforms the state-of-the-art feature extraction methods for indoor image classification, given a lower dimension of our features than those methods.

Images and videos (consisting of successive images) are highly autocorrelated due to the inherent spatial correlations. However, when the data are encrypted for privacy protection, the spatial correlations would be eliminated. Thus, it is difficult for an untrusted third party in the cloud to embed annotation information or auxiliary information in the encrypted media. In this paper, we proposed a novel method to maintain the spatial correlation in the encrypted domain to some extent by using a homomorphic cryptosystem in order to achieve high-quality data hiding. The textured and smooth blocks of the image can be identified in the encrypted domain. With a compressive sensing technology, the LSB layers of smooth blocks can be well-handled to make room for accommodating additional data. In the data hiding process, data hider embeds the additional data into LSBs of the smooth area to preserve high fidelity of the stego-image. On the receiver side, the embedded data can be extracted without any distortion from the encrypted image only by data-embedding key, and also, we can reap the directly decrypted image with a high visual quality only with the encryption key. In the case of the user possesses both the encryption key and the data-embedding key, the additional data can be extracted accurately, and the image recovery with overwhelming probability can be achieved. The vast experimental results manifest that the proposed method not merely has excellent security performance but also maintains the fidelity of the host image while providing considerable embedding capacity, which is superior to other state-of-the-art schemes.

The deep convolutional neural networks (CNNs) have been shown excellent performances for image denoising. However, the denoising CNN model trained with a specific noise level cannot deal with the images which have spatiotemporally variant random noise and low signal-to-noise ratio (SNR), such as seismic images. To this end, we propose a patch-based denoising CNN method, namely PDCNN. Specifically, we cluster the overlapping patches of noisy image into K classes where the image patches have close noise levels in each class, and then choose a suitable model for denoising the corresponding class from a series of well-trained CNN models. By embodying the structural statistics, we propose a CNN model selection criterion with a structural-dependent parameter. In contrast to the manual model selection process, the more accurate CNN model is chosen automatically and effectively. The capability of the PDCNN is demonstrated on synthetic and field seismic images. Experimental results show that the proposed method largely benefits from using multiple CNN models to jointly denoise, and leads to the satisfactory denoising performance in spatiotemporally variant seismic random noise reduction and structural signal preservation.

In recent studies of generative adversarial networks (GAN), researchers have attempted to combine adversarial perturbation with data hiding in order to protect the privacy and authenticity of the host image simultaneously. However, most of the studied approaches can only achieve adversarial perturbation through a visible watermark; the quality of the host image is low, and the concealment of data hiding cannot be achieved. In this work, we propose a true data hiding method with adversarial effect for generating high-quality covers. Based on GAN, the data hiding area is selected precisely by limiting the modification strength in order to preserve the fidelity of the image. We devise a genetic algorithm that can explore decision boundaries in an artificially constrained search space to improve the attack effect as well as construct aggressive covert adversarial samples by detecting “sensitive pixels” in ordinary samples to place discontinuous perturbations. The results reveal that the stego-image has good visual quality and attack effect. To the best of our knowledge, this is the first attempt to use covert data hiding to generate adversarial samples based on GAN.

Observational data on dry matter accumulation (DMA) and dry matter partitioning (DMP) from the experiment of maize sown on four different dates in a normal year (2012) and three drought years (2014, 2015, 2018) were analyzed to assess the impact of drought and the sowing date on DMA and DMP in different above-ground organs. The phenology of maize was more closely related to the sowing date than to drought. In the normal year, the amount of dry matter in different organs differed slightly among sowing dates, except for those at maturity, and increased linearly after jointing: the dry matter of leaves and stalks increased rapidly before tasselling and the milk stage, respectively, and both increased slowly thereafter, whereas the dry matter of the ears increased linearly. In the drought years, DMA was more sensitive to precipitation relative to the normal year and was affected by the sowing date and drought. Specifically, drought lowered the dry matter of the above-ground organs to varying degrees and accentuated that variation in crops sown on different dates. From the view of DMP, a mild drought lowers the stalk DMP rate but increases the rate in ears. The more severe the drought, the smaller the DMP rate in ears and the stronger the inhibition of the transfer of dry matter from stalk to ears, as well as the more significant difference in the DMP pattern among the sowing dates. High temperatures and drought had a significant synergistic effect on DMAs in all the organs relative to that of drought alone, reversely having an inappreciable impact on the DMP pattern. During the three growth periods, namely jointing to tasselling, tasselling to the milk stage, and the milk stage to maturity, DMA during any two adjacent periods had a clear complementary effect, especially before and after tasselling. Dry matters of the above-ground parts in aggregate, of the stalk and of the ears, at the milk and maturity stages were negatively correlated to the degree of drought, and the maximum correlation was for the ears.

Nitrous oxide (N2O) is a potent greenhouse gas with high global warming potential. Soil acts as sources and sinks of N2O. The mechanism of soil N2O uptake is important for both the net emission of N2O and the N2O concentration in the atmosphere. The use of organic matter as an amendment is an effective management practice for soil fertility worldwide, whereas, its effects on soil N2O uptake remain unclear. To understand the responses of N2O uptake with organic carbon incorporation. A laboratory incubation using 15N2O dilution tracing technology with and without acetylene (C2H2) was conducted. The result showed that gross rate of soil N2O production in Glucose treatment (3.33 g kg−1 glucose incorporated with soil) with and without C2H2 were 497.9 and 639.2 μg N kg−1 soil h−1, which were significantly higher than that in CK (no carbon incorporation) and Straw (3.33 g kg−1 straw incorporated with soil) treatments (25.3 and 29.8 μg N kg−1 soil h−1 and 42.0 and 52.5 μg N kg−1 soil h−1, respectively). The gross rates of N2O uptake was significantly increased by Glucose with 221.2 μg N kg−1 soil h−1, whereas, in CK and Straw treatments, they were 21.6 and 28.3 μg N kg−1 soil h−1. However, the gross rates of N2O uptake were decreased to 2.2, 3.2 and 6.2 μg N kg−1 soil h−1 by C2H2 for CK, Straw and Glucose treatments, which indicated that denitrification could be a key pathway of soil N2O uptake. We also found that soil dissolved organic carbon (DOC) content was an important factor that positively correlated with the gross rates of N2O production and uptake. These results indicate that soil DOC enhances both soil N2O production and uptake, and denitrification is an important pathway of soil N2O uptake.

Watermelon fruit texture and quality are determined by flesh firmness. As a quality trait, flesh firmness is controlled by multigenes. Defining the key regulatory factors of watermelon flesh firmness is of great significance for watermelon genetic breeding. In this study, the hard-flesh egusi seed watermelon PI186490 was used as the male parent, the soft-flesh cultivated watermelon W1-1 was used as the female parent, and 175 F2 generations were obtained from selfing F1. Primary mapping of the major genes controlling center flesh firmness was achieved by bulked-segregant analysis (BSA)-Seq analysis and molecular marker technology. Finally, major genes were delimited in the physical interval between 6,210,787 and 7,742,559 bp on chromosome 2 and between 207,553 and 403,137 bp on chromosome 8. The content of each cell wall component and hormone was measured, and comparative transcriptome analysis was performed during fruit development in watermelon. The protopectin, cellulose, hemicellulose, indole-3-acetic acid (IAA) and abscisic acid (ABA) contents were measured, and paraffin sections were made during the three fruit developmental stages. The results revealed that protopectin, celluloses, and hemicelluloses exhibited similar trends for flesh firmness, while the IAA and ABA concentrations continued to decrease with fruit ripening. Paraffin sections showed that PI186490 cells were more numerous, were more tightly packed, had clearer cell wall edges and had thicker cell walls than W1-1 cells at every developmental stage. Comparative transcriptome analysis was conducted on RNA samples of flesh during fruit development and ripening in W1-1 and PI186490. The results from the localization interval transcriptome analysis showed that Cla016033 (DUF579 family member), which may influence the cell wall component contents to adjust the flesh firmness in watermelon fruit, was different in W1-1 and PI186490 and that Cla012507 (MADS-box transcription factor) may be involved in the regulation of fruit ripening and affect the hardness of watermelon fruit.

Determining the effects of progressive drought (PD) on dry matter production, partitioning, and grain yield of maize will help in designing a suitable strategy for water management. Though influences of drought on maize growth and development have been investigated extensively, few of them focused on the effects of different duration and occurrence stage of PD on yield formation of maize. Six variations of PD, in the form of withholding irrigation for varying lengths of time from jointing or tasselling, were tested in the field, using a mobile rain shelter, in terms of their effects on aboveground biomass accumulation, partitioning, and grain yield in 2015-2016. The results showed that grain yield was significantly reduced by PD during either vegetative or reproductive stage, and the reduction in grain yield from reproductive PD (41.6-46.6%) was greater than that from vegetative PD (18.6-26.2%). The decrease in grain yield was largely caused by the decrease in kernels per ear (r2 = 0.88, P < 0.001). This research implied that guaranteeing water supply for maize during reproductive stage is crucially important to avoid the reduction in kernels per ear and grain yield.

Many smart healthcare applications are adopting cloud to provide services to patients. However, the sensitive data can be disclosed to the authentication server/service provider. Therefore, security and privacy are crucial to its success and deployment at large scale. Patients don't want to disclose their identities to the cloud server. One way to protect their identities from cloud server is anonymous authentication. The authentication process normally involves disclosing users' private information, such as username and password to the authentication server. If the patient can be linked or tracked by the authentication server or malicious adversaries by their requests, their privacy can be breached. Most of the existing privacy preserving health care applications provides anonymity from the adversaries. However, very few of them provide anonymity from the authentication server. In this paper, we have proposed a system which provides complete privacy and anonymity to the users of health care applications from adversaries and the authentication server. In our proposed authentication scheme, we have utilized rotating group signature scheme based on Elliptic curve cryptography to provide anonymity to the patients. To add an extra layer of protection, we have used The Onion Router to provide privacy at the network layer. The performance of our scheme is evaluated by theoretical analysis which demonstrates that it resists various attacks and provides several attractive security features.

Recently, a new color image encryption scheme based on hybrid hyper-chaotic system and cellular automata was proposed. In order to generate different key streams according to different plaintexts, the sum of pixel values of each component of the original color image apart from the secret keys is used to determine the initial value of logistic map for encryption. It was claimed that the scheme can resist kinds of attacks. However, this paper found out three security drawbacks about the original encryption scheme and proposed an effective attack method using chosen-plaintext attack by cryptanalysis. The equivalent permutation key stream can be obtained by adjusting individual pixel values of the chosen plaintexts while keeping the sum of pixel values of each color channel unchanged, and all 256 possibilities of equivalent diffusion key streams can be obtained by using 512 specific chosen plaintexts. In addition, the main stages of the proposed attack method, including breaking diffusion and breaking permutation, are exchangeable. The effectiveness of our method is supported by both theoretical analyses and experimental results.