Processing Complexity Research Articles

Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes

Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure.

Efficient side lobe level reduction technique for planar antenna arrays

This paper presents a novel array synthesis technique based on the virtual antenna array (VAA) concept for side lobe level (SLL) reduction of uniform planar antenna arrays (UPAA). The VAA decomposes the (M×N) UPAA into vertical and horizontal uniform linear antenna arrays (V–ULAA and H–ULAA) consisting of M and N antenna elements, respectively. The V-ULAA and H-ULAA are positioned perpendicularly and closely enough apart to form a new (M+N) virtual UPAA. Consequently, the number of antenna elements is significantly reduced, which in turn minimizes the computational complexity and signal processing time required to synthesize the desired radiation pattern. Therefore, instead of reducing the SLL of the entire UPAA, we simply reduce the SLL of each ULAA using the well-known hybrid Method of Moments/Genetic Algorithm (MoM/GA) array synthesis technique, which adjusts both the excitation coefficients and element spacings to achieve low SLL. The MoM/GA results in non-uniform V-LAA and H-LAA having two non-uniform excitation vectors AV and AH, respectively, and much lower SLLs than the corresponding V-ULAA and H-ULAA. Finally, the synthesized excitation vectors AV and AH are used to synthesize either the virtual (M+N) planar antenna array (V-PAA) or the physical (M×N)PAA with more than two-fold reductions in the SLL.

A Functional Magnetic Resonance Imaging Study of Phrase Structure and Subject Island Violations.

In principle, functional neuroimaging provides uniquely informative data in addressing linguistic questions, because it can indicate distinct processes that are not apparent from behavioral data alone. This could involve adjudicating the source of unacceptability via the different patterns of elicited brain responses to different ungrammatical sentence types. However, it is difficult to interpret brain activations to syntactic violations. Such responses could reflect processes that have nothing intrinsically related to linguistic representations, such as domain-general executive function abilities. To facilitate the potential use of functional neuroimaging methods to identify the source of different syntactic violations, we conducted a functional magnetic resonance imaging experiment to identify the brain activation maps associated with two distinct syntactic violation types: phrase structure (created by inverting the order of two adjacent words within a sentence) and subject islands (created by extracting a wh-phrase out of an embedded subject). The comparison of these violations to control sentences surprisingly showed no indication of a generalized violation response, with almost completely divergent activation patterns. Phrase structure violations seemingly activated regions previously implicated in verbal working memory and structural complexity in sentence processing, whereas the subject islands appeared to activate regions previously implicated in conceptual-semantic processing, broadly defined. We review our findings in the context of previous research on syntactic and semantic violations using ERPs. Although our results suggest potentially distinct underlying mechanisms underlying phrase structure and subject island violations, our results are tentative and suggest important methodological considerations for future research in this area.

It all began in Issaquah 50 years ago

Abstract “Somehow scientists still pursue the same questions, if now on higher levels of theoretical abstraction rooted in deeper layers of empirical evidence… To paraphrase an old philosophy joke, science is more like it is today than it has ever been. In other words, science remains as challenging as ever to human inquiry. And the need to communicate its progress… remains as essential now as then.” — Tom Siegfried, Science News 2021 In fact, essential questions about pain have not changed since IASP's creation in Issaquah: what causes it and how can we treat it? Are we any closer to answering these questions, or have we just widened the gap between bench and bedside? The technology used to answer questions about pain mechanisms has certainly changed, whether the focus is on sensory neurons, spinal cord circuitry, descending controls or cortical pain processing. In this paper, we will describe how transgenics, transcriptomics, optogenetics, calcium imaging, fMRI, neuroimmunology and in silico drug development have transformed the way we examine the complexity of pain processing. But does it all, as our founders hoped, help people with pain? Are voltage-gated Na channels the new holy grail for analgesic development, is there a pain biomarker, can we completely replace opioids, will proteomic analyses identify novel targets, is there a “pain matrix,” and can it be targeted? Do the answers lie in our tangible discoveries, or in the seemingly intangible? Our founders could barely imagine what we know now, yet their questions remain.

Model predictive power control for grid-tied T-type three-level quasi-Z-source inverter with low complexity

Abstract The appealing design known as T-type three-level quasi-Z-source inverters (T2LQZSI) can function in both step-down and step-up modes. Model predictive control for converters has also received a lot of interest. This article suggests a low-complexity model predictive power control for T-type quasi-Z-source inverters with neutral-point voltage (NP-V) balance to address the shortcomings of conventional model predictive control and achieve improved performance. The resulting voltage is produced by using a variety of vectors. For NP-V balance, the tiny vector is employed. In addition, a strong optimum sector selection approach is suggested, which significantly lowers the processing complexity. Results from experiments show how effective and better the suggested technique is.

Assessment of the degree of ripeness of oriental persimmon fruits (Diospyros kaki L.) by tensor stress

The object of the study is persimmon fruits, which occupy an important place among subtropical crops and have a wide development prospect. Since these fruits are difficult to process, they are mainly used fresh. The usefulness of these fruits is associated with their chemical composition. This composition includes biologically active substances, microelements, various mono- and polysaccharides, saturated and unsaturated fatty acids, etc. The complexity of the technological processing of persimmon fruits is associated with its astringent taste, which is determined by the amount of polyphenolic compounds. In general, the strength of raw materials is manifested not only in the degree of ripeness, but also in its technological processing processes, which are the object of the study. From this point of view, the hardness of persimmon fruits acts as a subject of study. Data on the property of fruits and vegetables associated with the stress-hard state is a solution to the problems that arise when expanding the range of finished products. For example, it has been established that at the stage of commercial ripeness, the hardness of persimmon fruits is no more than 12.3 kg/cm2. And this indicator changes downwards over time, i.e. to 1.5÷2.0 kg/cm2. Consequently, the possibility of using fruits for the production of various food products is expanding. The study of raw materials according to the laws of solid state physics is explained by its polymer structure. Therefore, the ripening of raw materials depends on the monomerization of this structure. In such decomposition, a condition is created for the combination of various mono-substances, for example, in persimmon fruits, monophenols combine with monosaccharides, which results in a decrease or disappearance of the tart taste of the raw material. Therefore, determining the degree of raw material ripeness by changes in stress will allow to predict its destruction in advance.

SF6 Gas Humidity Prediction Model Based on Deep Learning

SF6 gas is widely used in Gas Insulated Switchgear(GIS) as an insulating and arc extinguishing medium in electric power industry. However, SF6 gas humidity has a significant impact on the performance and reliability of GIS equipment. The accurate prediction of humidity level is one of the keys to ensure the long-term stable operation of GIS equipment. Traditional humidity prediction methods are often limited by model complexity and data processing ability, which is difficult to meet the actual demand. In this paper, deep learning, as a powerful data-driven approach, shows great potential in gas humidity prediction. An efficient SF6 gas humidity prediction model for GIS equipment is constructed based on deep learning algorithm. The deep learning model can learn complex feature representations from a large number of historical data, and then accurately predict SF6 gas humidity.

Designing Contact Independent High-Performance Low-Cost Flexible Electronics.

Organic semiconductors enable low-cost solution processing of optoelectronic devices on flexible substrates. Their use in contemporary applications, however, is sparse due to persistent challenges in achieving the requisite performance levels in a reliable and reproducible manner. A critical bottleneck is the inefficiency associated with charge injection. Here, large-scale simulations are employed to identify operational windows where key device parameters that are difficult to control experimentally, such as the contact resistance, become less consequential to overall device functionality. This design methodology overcomes injection barrier limitations in organic field-effect transistors (OFETs), leading to high charge carrier mobility and significantly expanding the range of suitable electrode materials. Leveraging this new understanding, all-organic, solution-deposited OFETs are successfully fabricated on flexible substrates. These devices incorporate printed contacts and showcase mobilities exceeding 5cm2Vs-1. These results provide a route for accessing the fundamental limits of material properties even in the absence of ideal contacts - a critical step in establishing reliable structure/property relationships and optimal material design paradigms. While reducing the injection barrier and contact resistance remains critical for achieving high OFET performance, this work demonstrates a path toward consistently achieving high charge carrier mobility through device geometry design, ultimately reducing processing complexity and cost.

An fMRI study of phrase structure and subject island violations.

In principle, functional neuroimaging provides uniquely informative data in addressing linguistic questions, because it can indicate distinct processes that are not apparent from behavioral data alone. This could involve adjudicating the source of unacceptability via the different patterns of elicited brain responses to different ungrammatical sentence types. However, it is difficult to interpret brain activations to syntactic violations. Such responses could reflect processes that have nothing intrinsically related to linguistic representations, such as domain-general executive function abilities. In order to facilitate the potential use of functional neuroimaging methods to identify the source of different syntactic violations, we conducted an fMRI experiment to identify the brain activation maps associated with two distinct syntactic violation types: phrase structure (created by inverting the order of two adjacent words within a sentence) and subject islands (created by extracting a wh-phrase out of an embedded subject). The comparison of these violations to control sentences surprisingly showed no indication of a generalized violation response, with almost completely divergent activation patterns. Phrase structure violations seemingly activated regions previously implicated in verbal working memory and structural complexity in sentence processing, whereas the subject islands appeared to activate regions previously implicated in conceptual-semantic processing, broadly defined. We review our findings in the context of previous research on syntactic and semantic violations using event-related potentials. Although our results suggest potentially distinct underlying mechanisms underlying phrase structure and subject island violations, our results are tentative and suggest important methodological considerations for future research in this area.

The Impact of Emotional Factors on L2 Learners Enjoyment and Anxiety in Foreign Languages

In light of the expansion of globalization and the intensification of cross-cultural communication, there is an increasing necessity to learn a second language (L2). However, many L2 learners face emotional challenges throughout the learning process, which affect their learning experience and outcomes. This paper explores the emotional experiences of Chinese learners of English as a L2 and their impact on learning outcomes. Theoretical frameworks from positive psychology and educational psychology are then employed to investigate the roles of foreign language enjoyment and foreign language anxiety, as well as the moderating effects of classroom environment and social support. The results show that foreign language enjoyment has a positive effect on learning outcomes, whereas foreign language anxiety has a detrimental effect. In the case of semantic-prefix incongruence, high-level learners exhibited stronger interference effects, which indicates the complexity of affective information processing. Therefore, the paper not only deepens the comprehension of emotional factors in foreign language learning, but also provides valuable insights into foreign language teaching practices, which indicates that teachers should prioritize students affective experiences, optimize classroom environments, and offer positive social support to promote their affective engagement and learning outcomes. Future research should further explore the differences in students affective experiences in different cultural contexts to achieve more comprehensive and personalized foreign language teaching goals.

Physical layer security for confidential transmissions in frequency hopping-based downlink NOMA networks

Facing the exponential number of Internet of Things (IoT) devices and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets in terms of providing massive access and ensuring higher spectral efficiency. In this vein, Non-Orthogonal Multiple Access (NOMA) has been widely recognized as one of the advantageous techniques to handle the proliferation of the IoT. Nevertheless, from a security standpoint, enabling a user to decode the signals of the other users, while using Successive Interference Cancellation (SIC), raises serious concerns regarding confidentiality and vulnerability to malicious attacks. Meanwhile, conventional security paradigms, such as upper-layer encryption and sophisticated authentication mechanisms, require high computational complexity and additional processing, which impose an overwhelming burden on energy-efficient IoT devices. Alternatively, Physical layer Security (PLS) has sparked a significant interest as a promising complement to cryptographic techniques. The key idea of PLS is to avail wireless communication properties to secure communications without adding complex encryption mechanisms at higher layers. In this paper, we propose a PLS approach based on a network coding technique to prevent eavesdroppers from decoding users’ information transmitted through a downlink-based NOMA system. This results in correlating the packets to be transmitted with each other, making the interception of a single packet useless. We demonstrate that the eavesdropper’s decoding complexity increases exponentially with the sequence length, making the task intractable for relatively long ones.

Species identification using PCR targeting specific region in cytochrome oxidase I gene for high-value fish product market in China

The Chinese market for high-value fish products suffers from a relatively serious mislabeling phenomenon. This study investigates the current status of products in China’s high-value fish market by collecting 508 online samples comprising seven processing techniques, with a focus on cod, salmon, and tuna, which hold relatively high market shares. A high-salt extraction method that exhibited the best overall performance was applied. Micro-DNA barcode identification traced the species of these products. Of the 508 samples, 201 (39.57 %) did not match their trade labels. Mislabeling was prevalent. There was a greater percentage of mislabeled surimi products (26.97 %) than frozen (0.99 %) or dried products (11.61 %). The likelihood of mislabeling increases with processing complexity. Mislabeling occurred predominantly among similar species of the same genus or purpose, and the phenomenon of substituting low-value meat for high-value fish was also observed. This study informs fish testing and sheds light on the current state of labeling compliance in the high-value fish product market.

AmazingFT: A Transformer and GAN-Based Framework for Realistic Face Swapping

Current face-swapping methods often suffer from issues of detail blurriness and artifacts in generating high-quality images due to the inherent complexity in detail processing and feature mapping. To overcome these challenges, this paper introduces the Amazing Face Transformer (AmazingFT), an advanced face-swapping model built upon Generative Adversarial Networks (GANs) and Transformers. The model is composed of three key modules: the Face Parsing Module, which segments facial regions and generates semantic masks; the Amazing Face Feature Transformation Module (ATM), which leverages Transformers to extract and transform features from both source and target faces; and the Amazing Face Generation Module (AGM), which utilizes GANs to produce high-quality swapped face images. Experimental results demonstrate that AmazingFT outperforms existing state-of-the-art (SOTA) methods, significantly enhancing detail fidelity and occlusion handling, ultimately achieving movie-grade face-swapping results.

Fourier-based beamforming for 3D plane wave imaging and application in vector flow imaging using selective compounding

Objective. Ultrafast ultrasound imaging using planar or diverging waves for transmission is a promising approach for efficient 3D imaging with matrix arrays. This technique has advantages for B-mode imaging and advanced techniques, such as 3D vector flow imaging (VFI). The computation load of the cross-beam technique is associated with the number of transmit anglesmand receive anglesn. The full velocity vector is obtained using the least square fashion. However, the beamforming is repeatedm × ntimes using a conventional time-domain delay-and-sum (DAS) beamformer. In the 3D case, the collection and processing of data from different beams increase the amount of data that must be processed, requiring more storage capacity and processing power. Furthermore, the large computation complexity of DAS is another major concern. These challenges translate into longer computational times, increased complexity in data processing, and difficulty in real-time applications.Approach. In response to this issue, this study proposes a novel Fourier domain beamformer for 3D plane wave imaging, which significantly increases the computational speed. Additionally, a selective compounding strategy is proposed for VFI, which reduces the beamforming process fromm × ntom(wheremandnrepresent the number of transmission and reception, respectively), effectively shortening the processing time. The underlying principle is to decompose the receive wavefront into a series of plane waves with different slant angles. Each slant angle can produce a sub-volume for coherent or selective compounding. This method does not rely on the assumption that the plane wave is perfect and the results show that our proposed beamformer is better than DAS in terms of resolution and image contrast. In the case of velocity estimation, for the Fourier-based method, only Tx angles are assigned in the beamformer and the selective compounding method produces the final image with a specialized Rx angle.Main results. Simulation studies andin vitroexperiments confirm the efficacy of this new method. The proposed beamformer shows improved resolution and contrast performance compared to the DAS beamformer for B-mode imaging, with a suppressed sidelobe level. Furthermore, the proposed technique outperforms the conventional DAS method, as evidenced by lower mean bias and standard deviation in velocity estimation for VFI. Notably, the computation time has been shortened by 40 times, thus promoting the real-time application of this technique. The efficacy of this new method is verified through simulation studies andin vitroexperiments and evaluated by mean bias and standard deviation. Thein vitroresults reveal a better velocity estimation: the mean bias is 2.3%, 3.4%, and 5.0% forvx,vy, andvz, respectively. The mean standard deviation is 1.8%, 1.7%, and 3.4%. With DAS, the evaluated mean bias is 9.8%, 4.6%, and 6.7% and the measured mean standard deviation is 7.5%, 2.5%, and 3.9%.Significance. In this work, we propose a novel Fourier-based method for both B-mode imaging and functional VFI. The new beamformer is shown to produce better image quality and improved velocity estimation. Moreover, the new VFI computation time is reduced by 40 times compared to conventional methods. This new method may pave a new way for real-time 3D VFI applications.

The Application Progress of Nonthermal Plasma Technology in the Modification of Bone Implant Materials.

With the accelerating trend of global aging, bone damage caused by orthopedic diseases, such as osteoporosis and fractures, has become a shared international event. Traffic accidents, high-altitude falls, and other incidents are increasing daily, and the demand for bone implant treatment is also growing. Although extensive research has been conducted in the past decade to develop medical implants for bone regeneration and healing of body tissues, due to their low biocompatibility, weak bone integration ability, and high postoperative infection rates, pure titanium alloys, such as Ti-6A1-4V and Ti-6A1-7Nb, although widely used in clinical practice, have poor induction of phosphate deposition and wear resistance, and Ti-Zr alloy exhibits a lack of mechanical stability and processing complexity. In contrast, the Ti-Ni alloy exhibits toxicity and low thermal conductivity. Nonthermal plasma (NTP) has aroused widespread interest in synthesizing and modifying implanted materials. More and more researchers are using plasma to modify target catalysts such as changing the dispersion of active sites, adjusting electronic properties, enhancing metal carrier interactions, and changing their morphology. NTP provides an alternative option for catalysts in the modification processes of oxidation, reduction, etching, coating, and doping, especially for materials that cannot tolerate thermodynamic or thermosensitive reactions. This review will focus on applying NTP technology in bone implant material modification and analyze the overall performance of three common types of bone implant materials, including metals, ceramics, and polymers. The challenges faced by NTP material modification are also discussed.

A novel method for tomato stem diameter measurement based on improved YOLOv8-seg and RGB-D data

The automatic acquisition of crop information can promote the rapid development of precision agriculture. Tomatoes, as a representative greenhouse crop, can demonstrate their growth status and overall health could be exhibited by measuring the diameters of their main stems. Automated measurement of the diameters of the trunk stems can not only reduce labor costs, but also enhance efficiency, thereby facilitating the improvement of tomato cultivation and planting management. Therefore, this study proposes a novel method based on an instance segmentation algorithm combined with RGB-D data to measure the diameter of a tomato trunk. First, we utilize the improved YOLOv8-seg to acquire the masks and bounding boxes of the buds and stems. Namely, we replace the SPPF module with Soft-SPPF to enhance the model’s capability to extract multi-scale features. Additionally, we improve the neck layer using cross-stage and weighted feature fusion to enhance the feature fusion effect. Then, we design a method to calculate the diameter of the tomato trunk by utilizing the instance segmentation results and depth information. Specifically, we first obtain the measurement point and the ROI (Region of Interest) to be measured by identifying the intersection between the bounding box of the bud and the straight line fitted by the stem mask in an image. We then filter out irrelevant depth information using the mask within the ROI, optimize the coordinate values of the measurement point, and calculate the main stem diameter. The results demonstrate that the measurements obtained in this study have a RMSE (Root Mean Square Error) of 1.5 mm and a MAPE (Mean Absolute Percentage Error) of 12.37 % compared with the manual measurements. Compared with the method based on object detection, the direct acquisition of contour information for the target instances by the instance segmentation algorithm reduces the algorithmic complexity of the subsequent processing. The proposed method can offer precise and reliable information on the diameter of the main stem of a tomato in real-world scenarios. It can be extended to other types of crops in greenhouses.

Current Tasks in Identifying Invalid Events in Critical Information Infrastructure

The purpose of the study is to develop an approach for identifying and processing invalid events in critical information infrastructure (CII) based on the concepts of taxonomy and categorization. The approach aims to improve the efficiency of identifying, classifying, and managing information security (IS) incidents. The article addresses the current tasks of ensuring the required level of CII protection and minimizing the negative consequences of information security incidents resulting from invalid events. The identification of these events is associated with the complexity of detecting such events, the need to process large volumes of data, insufficient speed in detecting IS events, as well as technological limitations.The relevance of identifying and classifying invalid events in information security, especially for CII, is driven by the need for timely detection and response to incidents that could lead to negative consequences. Understanding the nature and characteristics of such events allows for effective system protection and prevention of significant damage. To enhance the effectiveness of ensuring security, it is necessary to identify the class of invalid events among the numerous information security events by considering the characteristics that define invalid events.The novelty of the proposed approach lies in solving the task of identifying the class of invalid information security events based on taxonomy methods, involving the use of event categorization tools with the attributes of invalid events.Materials and methods. The approach to identifying invalid events in CII, based on the principles of information security event taxonomy, was used to solve the task. It was shown that identifying invalid information security events is directly related to solving the problem of searching for and analyzing their attributes, which represent the characteristics or parameters used to describe and classify security incidents. Based on the key principles of taxonomy, a model of the structure of the set of invalid events was developed to determine the characteristics that can be the basis for classifying invalid events. The process of identifying invalid information security events includes a sequence of stages: taxonomy, categorization, and classification, with appropriate methods and tools implemented at each stage.Results. Approaches to identifying invalid events in CII have been analyzed. Problems related to large data volumes, the complexity of event processing, the considerable time required for their detection, and technological limitations were considered. It was shown that the concept of taxonomy and categorization allows for effective identification and classification of information security incidents, ensuring efficient processing and response. The feasibility of applying taxonomy for describing and identifying the attributes of invalid events was justified, contributing to the development of effective protection strategies and improving security levels. A generalized scheme for processing invalid events was proposed, including a set of interconnected stages of identification, categorization, impact assessment, response, documentation, and analysis. An algorithm for structured description and classification of incidents was developed, allowing for more accurate and timely responses to information security threats.Conclusion. The results obtained increase the effectiveness of solving the task of classifying information security incidents by identifying invalid events, which reduces the level of negative consequences of incidents and enhances the security of CII objects.

Investigation on the randomly-coupled unit grouping multi-core fibers with different unit arrangement

Randomly-coupled unit grouping multi-core fiber (RC-UG-MCF) is a new fiber structure that combines weakly and strongly coupled multi-core fiber technologies. Compared to the weakly-coupled MCF with a large cladding size, the RC-UG-MCF can shrink the cladding size by grouping the cores. On the other hand, the RC-UG-MCF only maintains a few cores in the strongly-coupled condition, which can reduce the multiple-input multiple- output digital signal processing (MIMO-DSP) complexity compared to the coupled MCFs with full MIMO-DSP for all the cores. In this paper, we intro- duce three types of randomly-coupled unit grouping 12-core fiber, which are randomly-coupled 2-core unit grouping MCF (RC-2CUG-MCF), RC-3CUG- MCF and RC-4CUG-MCF. Subsequently, we compare these three RC-UG- MCFs in terms of group delay spread, inter-unit crosstalk (XT), and cladding size. To achieve an inter-unit XT of −40 dB/100 km, RC-3CUG-MCF can provide a solution to maintain MIMO-DSP scale at the level of 6 × 6 with 180-µm cladding radius. However, the RC-2CUG-MCF has worse inter-unit XT, and the RC-4CUG-MCF has larger MIMO complexity. Based on the analysis, there is a trade-off relationship between the MIMO-DSP scale and the fiber cladding size, assuming that the target inter-unit XT is fixed.

Blockchain-based anonymous authentication and data aggregation for advanced metering infrastructure in smart grid

This paper proposes a blockchain-based scheme, focusing on anonymous identity authentication and data aggregation, for safer and more reliable bidirectional communication between the utility company and power consumers based on Advanced Metering Infrastructure (AMI). Firstly, to cope with the mutual identity authentication between resource-constrained Smart Meters, a lightweight anonymous authentication strategy is designed using Elliptic Curve Cryptography. Meanwhile, a reputation-based consensus protocol is developed to accomplish data aggregation in AMI by using decentralization and non-tampering features of the blockchain. During the communication, in each time slot, the proposed scheme only needs to select a trusted user randomly to undertake the mining node rather than to introduce a third-party in a centralized manner to summarize and record the user-side data into the blockchain. Such scheme can effectively prevent data tampering and also be effective to decrease the processing complexity. The AVISPA tool is adopted to formally evaluate its security. The simulation results show promising performance. The proposed scheme not only can guarantee secure communication but also effectively decrease the computational cost in AMI.

Janus nanocellulose membrane by nitrogen plasma: Hydrophilicity to hydrophobicity selective switch

Cellulose nanofibrils are one of the keystone materials for sustainable future, yet their poor water repellency hinders their push into industrial applications. Due to complexity and poor economical outcome and/or processing toxicity of the existing hydrophobization methods, nanocellulose loses against its antagonist plastic in medical and food industries. Herein, we demonstrate for the first time the “one-side selective water-repellency activation” in nanocellulose membranes by the means of mild N2-plasma treatment, exhibiting lowest wettability after 20 s of treatment. Hydrophobicity and accompanying Janus character were justified by the topological, chemical and structural reorganizations in cellulose nanofibrils. The findings suggest that the mechanism behind the hydrophilic/hydrophobic change primarily relies on the interplay between OH removal and appearance of SiCH3, originating from the polysiloxanes-based substrate, as well as complementary CNH2 groups formation. First-principles calculations show that NH2 groups moderately increase hydrophobicity, while various SiCH3 substitutions wholly change the character of the surface to repel water. Using nitrogen is shown to be crucial, as N(H)Si(CH3)3 groups induce greater hydrophobicity than simple OSi(CH3)3. Finally, the obtained materials absorb water on the hydrophilic side, while remaining hydrophobic on the other, exhibit high tensile strength, and protection against UV light, demonstrating applicability over wide range of applications.