Transparent Networks Research Articles

Kill Two Birds with One Stone: Cracking Lithography Technology for High-Performance Flexible Metallic Network Transparent Conductors and Metallic Micronano Sheets.

Flexible electronics have sparked a wide range of exciting applications, such as flexible display technologies, lighting, sensing, etc. Flexible conductors are essential components of flexible electronics and seriously affect efficiency and overall performance. Here, a facile and kill-two-birds-with-one-stone strategy of cracking lithography technology has been proposed to simultaneously fabricate two high-performance flexible conductors, flexible metallic network transparent conductors (f-NTCs) and metallic micronano sheets (MMNSs). The PET substrate flexible transparent conductors (FTCs) based on this strategy yield 88.1% transparency within the visible spectrum and a sheet resistance of 9 Ω/sq. In addition, the FTCs show exceptional mechanical stability, with the sheet resistance remaining virtually unchanged even after 6000 s of bending tests. Subsequently, the remaining MMNSs are recycled to manufacture a Ag paste, showing a very low conductive percolation threshold (∼13%) and excellent flexibility with 140% breaking elongation. After 1000 s of stretching tests, it showed excellent mechanical stability. Furthermore, flexible electroluminescent devices based on the FTCs and sensors fabricated with MMNSs both show excellent performance, demonstrating their potential wide applications in flexible electronics.

Implantable Fluorogenic DNA Biosensor for Stress Detection.

Implantable sensors that can monitor analytes related to cognitive and physiological status have gained significant focus in recent years. We have developed an implantable biosensor to detect dehydroepiandrosterone sulfate (DHEA-S), a biomarker related to stress. The biosensor strategy was based on the principle of forced intercalation (FIT) aptamers designed to detect subtle intramolecular changes during aptamer-target binding events. By incorporating a steroid-specific fluorogenic aptamer into a hydrogel, the sensitivity and biostability of the FIT biosensor fiber were improved, which were essential for designing implantable sensors to monitor biomarker levels in the living body. The polyethylenimine-based hydrogel chosen for this study produced an optically transparent cross-linked network with optimal microstructure, physicochemical, and mechanical properties, making it suitable for optical biosensors. The in vitro studies showed that the biosensor fiber was successfully activated in human serum and skin analogue, providing a linear response to physiological concentrations of the steroid. We believe that this type of implantable platform can be effective in monitoring more complex biomarkers associated with physiological or psychological health.

Transparent sparse graph pathway network for analyzing the internal relationship of lung cancer.

While it is important to find the key biomarkers and improve the accuracy of disease models, it is equally important to understand their interaction relationships. In this study, a transparent sparse graph pathway network (TSGPN) is proposed based on the structure of graph neural networks. This network simulates the action of genes in vivo, adds to prior knowledge, and improves the model's accuracy. First, the graph connection was constructed according to protein-protein interaction networks and competing endogenous RNA (ceRNA) networks, from which some noise or unimportant connections were spontaneously removed based on the graph attention mechanism and hard concrete estimation. This realized the reconstruction of the ceRNA network representing the influence of other genes in the disease on mRNA. Next, the gene-based interpretation was transformed into a pathway-based interpretation based on the pathway database, and the hidden layer was added to realize the high-dimensional analysis of the pathway. Finally, the experimental results showed that the proposed TSGPN method is superior to other comparison methods in F1 score and AUC, and more importantly, it can effectively display the role of genes. Through data analysis applied to lung cancer prognosis, ten pathways related to LUSC prognosis were found, as well as the key biomarkers closely related to these pathways, such as HOXA10, hsa-mir-182, and LINC02544. The relationship between them was also reconstructed to better explain the internal mechanism of the disease.

TrojanProbe: Fingerprinting Trojan tunnel implementations by actively probing crafted HTTP requests

Trojan is a well-known hidden tunnel protocol widely used to bypass Internet censorship and thus presents a big challenge to transparent network management and forensics. As claimed by the protocol designer, Trojan maintains its anti-identifiability by proxying real HTTPS/TLS traffic to react to unauthenticated requests, eliminating any subtle differences between the Trojan traffic and the legitimate HTTPS. Despite such a protocol seeming unidentifiable by design, the diverse Trojan implementations adopting very different programming languages will likely have varied coding logic and networking API calls, opening a new door to be identified and fingerprinted from the implementation level. In this paper, we propose TrojanProbe, a new class of active probing methods that can be used to fingerprint Trojan implementations by triggering their identifiable responses. Our basic idea is to audit the source code of the Trojan programs and discover the subtle logic discrepancy compared with the legitimate HTTPS counterparts, to craft specific HTTP requests as probes to trigger these differences for fingerprinting. By this idea, we choose the five most popular open-source Trojan programs off-the-shelf as our targets to audit, covering the majority of Trojan market share and the mainstream programming languages from traditional C++ to the cutting-edge Go and Rust, and design a suite of novel HTTP probes to differentiate them from their web server masquerades. Our probes exploit either the different responding/buffering logic to the malformed HTTP requests and the different HTTP versions, or the varied timeouts set in the different networking APIs by default. To this end, we have conducted extensive experiments to evaluate the TrojanProbe against a comprehensive set of configuration and networking conditions. The experimental results show that our TrojanProbe can effectively fingerprint our selected Trojan targets in most conditions, but leave a single Rust implementation with a minority market occupied that can only be identified in some constraint cases. Despite such an exception, our research sheds light on a new kind of possibility to fingerprint Trojans at their implementation level, even if such a hidden tunnel is widely known as unidentifiable at the protocol level.

Highly stable Ag@Au nanowires micromesh as transparent conductive network for stretchable electrochromic modulator

The stretchable electrochromic modulator is one of the most promising energy-saving optoelectronic devices that can be integrated into smart portable and wearable electronics. A lot of focus has been on developing transparent conductive electrodes of stretchable electrochromic modulators, but they generally suffer from poor chemical stability, low photoelectric balance, and unsatisfactory deformability. To address this challenge, we first develop Ag@Au nanowires (NWs) micromesh-based stretchable transparent conductive electrodes (STCEs) using a self-assembled approach, which displays superior photoelectric performance (8.5 Ω sq-1 with a transmittance of 81.5 %), good chemical stability in harsh electrolyte environment (acidic and alkaline) as well as robust mechanical stability (bending, folding, stretching and twisting). As a demonstration, the grown WO3 on Ag@Au NWs-based STCEs can deliver good electrochromic stability even after bending over 1000 cycles and stretching to 50 %. Moreover, highly soft Zn//WO3 and polyaniline (PANI)//WO3 electrochromic modulators were assembled using flexible Ag@Au micromesh STCEs, which still retain uniform electrochromism in neutral and acidic electrolytes respectively. Definitionally, the strategy of in-situ grown Au shell on the surface of AgNWs micromesh provides an innovative solution to fabricate high-performance STCEs for the related portable smart wearable electronic devices.

Weld seam object detection system based on the fusion of 2D images and 3D point clouds using interpretable neural networks

This study introduces a novel approach that addresses the limitations of existing methods by integrating 2D image processing with 3D point cloud analysis, enhanced by interpretable neural networks. Unlike traditional methods that rely on either 2D or 3D data alone, our approach leverages the complementary strengths of both data types to improve detection accuracy in environments adversely affected by welding spatter and smoke. Our system employs an improved Faster R-CNN model with a ResNet50 backbone for 2D image analysis, coupled with an innovative orthogonal plane intersection line extraction algorithm for 3D point cloud processing. By incorporating explainable components such as visualizable feature maps and a transparent region proposal network, we address the “black box” issue common in deep learning models.This architecture enables a more transparent decision-making process, providing technicians with necessary insights to understand and trust the system’s outputs. The Faster-RCNN structure is designed to break down the object detection process into distinct, understandable steps, from initial feature extraction to final bounding box refinement. This fusion of 2D-3D data analysis and interpretability not only improves detection performance but also sets a new standard for transparency and reliability in automated welding systems, facilitating wider adoption in industrial applications.

Transparent, Self-Healing, and Recyclable Biomass-Based Covalent Adaptable Networks Formed via Magnolol and Dynamic Boronate Ester

Transparent, Self-Healing, and Recyclable Biomass-Based Covalent Adaptable Networks Formed via Magnolol and Dynamic Boronate Ester

Inorganic–organic polymer networks derived from a cyclic siloxane tetrafunctional glycidyl ether resin

AbstractA tetrafunctional glycidyl ether cyclic siloxane epoxy resin (TGTS) has been synthesized, characterized and cured with four aromatic amine hardeners: 1,3‐phenylenediamine (PDA), diethyltoluenediamine (DETDA), 4,4‐diaminodiphenylmethane (DDM) and 1,3‐bis(4‐aminophenoxy)benzene (APB). Each of the cured networks produces transparent and homogeneous networks, although when TGTS is cured with DETDA, reduced compatibility led to lower epoxide consumption, a more heterogenous microstructure and deleterious effects upon properties. Reduced miscibility of DETDA significantly impacts the chemical structure and microstructure of the network, resulting in significant reductions in thermal and mechanical properties but higher UV‐A transmission. The PDA‐, APB‐ and DDM‐cured networks conversely were more miscible and display properties typical of organic–inorganic hybrid networks, such as good mechanical properties at ambient and sub‐ambient temperatures, comparatively high glass transition temperatures, improved resistance to oxidation and lower UV‐A transmission. © 2024 The Author(s). Polymer International published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework.

The growing importance of edge and fog computing in the modern IT infrastructure is driven by the rise of decentralized applications. However, resource allocation within these frameworks is challenging due to varying device capabilities and dynamic network conditions. Conventional approaches often result in poor resource use and slowed advancements. This study presents a novel strategy for enhancing resource allocation in edge and fog computing by integrating machine learning with the blockchain for reliable trust management. Our proposed framework, called CyberGuard, leverages the blockchain's inherent immutability and decentralization to establish a trustworthy and transparent network for monitoring and verifying edge and fog computing transactions. CyberGuard combines the Trust2Vec model with conventional machine-learning models like SVM, KNN, and random forests, creating a robust mechanism for assessing trust and security risks. Through detailed optimization and case studies, CyberGuard demonstrates significant improvements in resource allocation efficiency and overall system performance in real-world scenarios. Our results highlight CyberGuard's effectiveness, evidenced by a remarkable accuracy, precision, recall, and F1-score of 98.18%, showcasing the transformative potential of our comprehensive approach in edge and fog computing environments.

Cryptocurrency fraud detection through classification techniques

Ethereum and its native cryptocurrency, Ether, have played a worthy attention in the development of the blockchain and cryptocurrency space. Its programmability and smart contract capabilities have made it a foundational platform for decentralized applications and innovations across various industries. Because of its anonymous and decentralized structure, the hotheaded expansion of cryptocurrencies in the payment space has created both enormous potential and concerns related to cybercrime, including money laundering, financing terrorism, illegal and dangerous services. As more financial institutions attempt to integrate cryptocurrencies into their networks, there is an increasing need to create a more transparent network that can withstand these kinds of attacks. In this work, we are using different classification techniques, such as logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) for Ethereum fraud detection. The dataset we are using includes rows of legitimate transactions done using the cryptocurrency Ethereum as well as known fraudulent transactions. The “XGBoost” model, which is noteworthy, detects variations that might attract notice and prevent potential issues in this chore.

Blockchain Technology, Structure, and Applications: A Survey

Blockchain-based decentralized technology has gained a lot of attractions and interest in academia and industry. This peer-to-peer and transparent blockchain network makes it an acceptable technology. This decentralized network has many great specifications such as transparency, immutability, irreversibility and auditability. Bitcoin, the first and most successful blockchain application, achieves much success in recent years. This survey article attempts to outline an extensive survey of blockchain technology, investigating its structures, various protocol layers, features, value, challenges, and applications.

A cross-chain access control mechanism based on blockchain and the threshold Paillier cryptosystem

With the continuous maturation of blockchain technology and the increasing demands for various industry applications, data sharing and interoperability among different blockchain networks face significant challenges. Research on cross-chain interoperability mechanisms has facilitated data collaboration across organizations and industries, enhancing the value and utility of data. When engaging in cross-chain data interactions, access control ensures the security and privacy of data while promoting collaboration and information exchange among multiple chains. Attribute-based access control can provide fine-grained authorization support, matching complex business scenarios. However, publicly disclosed policies and attributes in a transparent blockchain network may pose privacy and security issues. To address these issues, this paper proposes a cross-heterogeneous multichain data access control scheme based on attributes and threshold homomorphic encryption, achieving fine-grained and secure cross-domain access control in cross-chain networks. This scheme uses the threshold Paillier cryptosystem to encrypt and conceal user attributes and access policies. Through smart contracts, homomorphic differential computation is performed on the ciphertext of policies and attributes, to protect data privacy. This solution leverages private key decryption shares from multiple relay nodes in the cross-chain network to jointly decrypt the computation results, providing secure access control in complex cross-chain scenarios. Security analysis and experimental results demonstrate that the proposed scheme ensures security with reasonable computational overhead, to meet the access control requirements in cross-chain networks.

High efficient employment of hybrid space division multiplexing/time division multiplexing techniques in active/passive optical transparent networks

High efficient employment of hybrid space division multiplexing/time division multiplexing techniques in active/passive optical transparent networks

Synergizing Intelligence: Revolutionizing Supply Chains with Blockchain and AI

In the evolving landscape of supply chain management, the integration of blockchain technology and artificial intelligence (AI) stands as a beacon of innovation, promising to address the perennial challenges of efficiency, transparency, and reliability. This paper presents a comprehensive exploration of how AI can revolutionize blockchain supply chains, offering a synthesis of current research, methodologies, and case studies that highlight the transformative potential of this synergy. The supply chain, a complex network that underpins global trade, is often beleaguered by inefficiencies and vulnerabilities. Blockchain technology, with its decentralized and immutable ledger, has emerged as a solution to enhance traceability and trust. However, it is the infusion of AI that has the potential to catalyze a paradigm shift in supply chain management. AI’s capabilities in data analytics, machine learning, and autonomous decision-making can optimize logistics, predict trends, and automate tasks, thereby elevating the blockchain beyond its current utility. This research adopts a mixed-methods approach, drawing on qualitative insights from industry experts and quantitative data from performance metrics to assess the impact of AI on blockchain supply chains. Through a series of case studies, the paper illustrates the practical applications and challenges of this integration, providing a nuanced understanding of its implications. The findings reveal that AI significantly enhances the efficiency and accuracy of blockchain supply chains, leading to improvements in transaction times, data verification processes, and overall supply chain performance. The discussion delves into the strategic advantages of this integration, such as improved compliance and ethical supply chain practices, while also acknowledging the limitations and challenges that organizations must navigate. In conclusion, the paper posits that the convergence of AI and blockchain holds great promise for the future of supply chains. It offers a roadmap for practitioners looking to harness these technologies and sets forth directions for future research, particularly in the development of sophisticated AI algorithms tailored for blockchain applications and the long-term economic impacts on supply chain management. The study contributes to the broader field by providing empirical evidence and a new perspective on the potential of AI to create more resilient, efficient, and transparent supply networks..

Blockchain Based Atonomys De-Centralised Online Social Network

Online social networks (OSNs) have become integral to daily life, yet the prevalent centralized model of these platforms presents considerable challenges in terms of security, privacy, and management. In response to these concerns, a decentralized architecture underpinned by blockchain technology emerges as a promising solution. This paper delves into the development of an OSN service leveraging blockchain technology to facilitate decentralized operation, thereby addressing the aforementioned issues. Through the utilization of the Interplanetary Filesystem (IPFS), large volumes of low-security data are effectively decentralized, contributing to enhanced data integrity and accessibility. Furthermore, the establishment of a decentralized autonomous organization empowers users with greater autonomy, enabling democratic self-governance of the OSN. This innovative approach not only addresses existing shortcomings but also paves the way for a more resilient, transparent, and user-centric social networking paradigm.

Highly conductive and transparent metal microfiber networks as front electrodes of flexible thin-film photovoltaics

Simultaneously enhancing the optical transmittance and electrical conductivity of transparent conductors (TCs) applicable in various optoelectronic devices is a long-standing challenge. Herein, we present an innovative approach for fabricating electroplated Ni and Cu microfiber networks (NiMF and CuMF) as highly conductive TCs to realize high efficiency and desired aesthetics in thin-film solar cells and modules. The metal microfibers (MFs) are fabricated using electrospun polyacrylonitrile nanofibers as the template. The large cross-sectional aspect ratio of the metal MF networks remarkably and concurrently improves their electrical conductivity and optical transmittance. Between the NiMF and CuMF TCs, the CuMF sample exhibits a superior figure of merit owing to its exceptionally low electrical resistivity. The metal MF TC is a promising alternative to conventional patterned grids used in flexible Cu(In,Ga)Se2 thin-film solar cells, because it effectively reduces the series resistance, which is advantageous for large-area cells. The CuMF can be successfully employed as a ribbon to maintain the solar cell performance in centimeter-scale cells connected in series. The outstanding performance of the metal MF TCs indicates their potential to eliminate complicated monolithic integration processes or front grids and ribbons in flexible thin-film solar cells and modules.

Across-dimensional optical constellation de-aggregations from QAMs to PAMs in optical transparent networks

In this paper, an across-dimensional optical constellation de-aggregation scheme is proposed and verified. This de-aggregation scheme builds flexible optical connections between core and edge networks and fills the lack of the conversions between quadrature amplitude modulation (QAM) and pulse amplitude modulation (PAM) formats. 10G Baud quadrature phase shift keying/9QAM/16QAM-to-2×on off keying (OOK)/PAM3/PAM4 constellation de-aggregations are achieved. The enabled constellation manipulations contain polarization-assisted quadrature de-multiplexing and bi-directional vector moving. The key research status of QAM/PAM de-aggregations and adopted theoretical derivations are analyzed and expressed in detail. The signal spectrum, constellations, eye diagrams, transfer characteristics curves, error vector magnitudes and bit error ratios (BERs) during the de-aggregation process are calculated to measure the system performances. With the input optical signal-to-noise ratio (OSNR) of 10 dB/15 dB/20 dB, the de-aggregated OOK/PAM3/PAM4 signals show the receiver OSNR of 19.1 dB/21.9 dB/31.2 dB, respectively, at the BER of 10−3. The proposed scheme presents excellent format scalability and has potential applications in flexible all-optical connections, across-dimensional optical transmissions, and optical transparent networks.

Collaborative Planning in a Two-Stage Supply Chain: Transparent Coordination, Distributed Response, Integrated Optimization

In the competitive global market, large manufacturing enterprises are striving to achieve a competitive edge through constructing a transparent collaborative supply chain. One of the important and effective ways is to cooperate with distributed response from suppliers in the operation of the supply chain, such as production planning. This article proposes a new collaborative planning mode in a two-stage supply chain considering the supplier part production and product assembly simultaneously. In this context, a mathematical model is formulated and the integrated optimization method is proposed. To reduce the complexity of the problem, the part production decisions of suppliers are first addressed by some lemmas. Then, the transformation process of the mathematical model is described as the decision variables gradually decrease. On this basis, we further propose a LIMA-GVNDS (less is more approach-general variable neighborhood decomposition search) algorithm to solve the integrated supply chain planning problem for the assembly manufacturing enterprise. The objective is to minimize the total supply chain costs. A new neighborhood structure is designed according to the problem characteristics and a series of variable neighborhood descent (VND) variants are proposed. Some computational experiments are carried out, and the results show that the proposed LIMA-GVNDS algorithm has obtained an average 3.69%, 13.07%, 2.47%, and 2.99% improvement of objective value against the other four meta-heuristic algorithms. The model transformation and the integrated optimization method proposed in this article can provide a reference for large manufacturing enterprises to formulate collaborative planning in a transparent supply chain network.

Transparent Object Depth Perception Network for Robotic Manipulation Based on Orientation-Aware Guidance and Texture Enhancement

Transparent Object Depth Perception Network for Robotic Manipulation Based on Orientation-Aware Guidance and Texture Enhancement

Flexible and transparent gold network electrodes on fluorinated graphene

Flexible, transparent gold electrodes on fluorinated graphene achieve 80.2% transparency, 1.66 Ω □−1 sheet resistance, and excellent durability under bending.