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Abstract Combining epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) with an anti‐ vascular endothelial growth factor (VEGF) agent, bevacizumab or ramucirumab, is indicated for advanced lung adenocarcinoma harboring EGFR mutation. This study aimed to show the real‐world data of combination therapy and compare the effectiveness between bevacizumab and ramucirumab in combination with an EGFR‐TKI. This retrospective study enrolled 47 patients diagnosed of stage IV lung adenocarcinoma with exon 19 deletion or L858R point mutation, receiving a first‐line EGFR‐TKI with anti‐VEGF agent, including 34 (72%) and 13 (28%) patients receiving bevacizumab and ramucirumab, respectively. The response rate was similar in both groups (p = 0.38). Patients receiving bevacizumab had similar progression free survival (PFS) as those receiving ramucirumab (median PFS: 21.9 vs. 24.2 months, p = 0.4871); similar finding was noted in overall survival (OS) (median OS: 33.5 months vs. not reached, p = 0.4618). Patients receiving ramucirumab experienced a significantly high‐grade hypertension compared to those receiving bevacizumab (p = 0.0351). Multivariable Cox regression analysis found independent risk factors for worse PFS included poorer ECOG performance status, multiple (≥3) metastatic sites, brain metastasis, and pleural metastasis/effusion, while the type of anti‐VEGF agent was not a risk factor. Pericardial metastasis/effusion was the only one independent risk factor for worse OS. In summary, ramucirumab may have similar effectiveness as bevacizumab in combination with an EGFR‐TKI as first line therapy for advanced lung adenocarcinoma harboring susceptible EGFR mutation. Further large‐scale registry‐based cohort studies may be needed to validate our findings.

With the progress of the times, cars have become an important means of transportation in our lives. However, with new cars of all kinds, road safety issues still cannot be effectively improved. While countries have responded by enforcing traffic laws and using electronic technology to enhance enforcement, challenges still need to be addressed, including blurred images and misjudgments by police agencies, and even exploiting loopholes in traffic enforcement frameworks to evade consequences through bribery or connections. exacerbating an accident and leaving victims and families in the lurch of the aftermath. To solve this problem, we proposed a data traceability law enforcement system based on blockchain and InterPlanetary File System (IPFS). The system ensures rapid traceability and protects law enforcement data from external attacks. The goal of leveraging blockchain’s decentralized nature and smart contracts is to instill accountability and fairness in road safety measures, mitigate the effects of corruption, and pave the way for a safer and more just transportation environment. Our experimental results show that under our stress test of 50 transactions per second, the throughput can be as high as 300 and the maximum delay can reach 2.01s, which is enough to experimentally prove that our system is feasible. Our approach is designed to solve the data corruption problem caused by the centralized server being paralyzed. It applies to all technology enforcement, with high flexibility and scalability for all participants to join or set privileges. we also provide an automated traffic law enforcement system in a smart city. To make the data storage more secure and fair, and not easily damaged by malicious people or tampered with by intentional attackers. This brings a breakthrough to the country’s program security.

In the charity sector, fundraising and transparency have long been key issues. Charity NFT (Non-Fungible Token) auctions, an emerging charity fundraising model integrating blockchain and NFT concepts, bring opportunities and challenges. Blockchain provides distributed data integrity and transparency via cryptography-linked data blocks, while NFTs enable unique digital ownership representation. This study designs a charity NFT auction platform on the Fisco Bcos blockchain, using multi-signature algorithms to ensure NFT authenticity, ECDSA (Elliptic Curve Digital Signature Algorithm) signatures for transaction integrity and traceability, NFTs and virtual currencies to reduce costs, and IPFS (InterPlanetary File System) for storage. The implemented system achieves 2104 TPS throughput with 492-millisecond latency, increasing transaction processing with low latency. Overall, the platform aims to address charity issues like opaque fund flows, high costs, and fake initiatives through the strategic application of blockchain and NFT functionality.

It is challenging to detect arbitrary-shape text accurately and effectively in natural scenes. While many methods have been implemented for arbitrary-shape text detection, most cannot achieve real-time detection or meet practical needs. In this work, we propose a YOLOv6-based detector that can effectively implement arbitrary-shape text detection and achieve real-time detection. We include two additional branches in the neck part of the YOLOv6 network to adapt the network to text detection, and the output side uses the pixel aggregation (PA) algorithm to decouple the PA output to use it as the detection head of the model. Experiments on benchmark Total-Text, CTW1500, ICDAR2015, and MSRA-TD500 showed that the proposed method outperformed competing methods in terms of detection accuracy and running time. Specifically, our method achieved an F-measure of 84.1% at 291.8 FPS for 640 × 640 Total-Text images and an F-measure of 81.5% at 199.6 FPS for 896 × 896 ICDAR2015 incidental text images.

Due to frequent traffic accidents around the world, people often take out car insurance to mitigate their losses and receive compensation in a traffic accident. However, in the existing car insurance claims process, there are problems such as insurance fraud, inability to effectively track and transmit insurance data, cumbersome insurance procedures, and high insurance data storage costs. Since the immutability and traceability features of blockchain technology can prevent data manipulation and trace past data, we have used the Elliptic Curve Digital Signature Algorithm (ECDSA) to sign and encrypt car insurance data, ensuring both data integrity and security. We propose a blockchain and IPFS-based anticounterfeiting and traceable car insurance claims system to improve the above problems. We incorporate the Interplanetary File System (IPFS) to reduce the cost of storing insurance data. This study also attempts to propose an arbitration mechanism in the event of a car insurance dispute.

Mental and emotional issues are the top-level concerns of public health worldwide. These issues surged during Coronavirus (COVID-19) pandemic due to varied medical, social, and personal reasons. The social determinants highlighted in the literature mainly focus on household solutions rather than on increasing the financial wellbeing of individuals, especially for the most vulnerable groups where the psychological distress coming from the social inequalities cannot be entirely treated. Hence, this study attempts to familiarize the financial capability (the financial literacy, attitude, skills and behavior required for effective financial management) construct into public health domain in the times of COVID-19 as a determinant of psychological distress, and also explores the role of gender in it. The study uses Ordinary Least Square (OLS) regression analysis and employs mental distress questions and Organization for Economic Cooperation and Development (OECD) 2018 financial capability toolkit to collect data from a large sample of households from all over Pakistan. It is inferred that the higher the financial capability, the lower the financial and mental distress during COVID-19. Additionally, females are less financially knowledgeable, depict poor financial behaviors, and face more psychological issues than their counterparts. Age and education are also linked to mental stress during COVID-19. Finally, gender plays a moderating role in financial behavior, and financial and mental stress of households. As evident, COVID-19 is not going away soon hence the findings are relevant for policymakers to proactively plan for the pandemic's upcoming waves and help people be better financially equipped to fight against this or any upcoming crisis, and achieve better mental and physical health.

Distributed Denial of Service (DDoS) attacks pose a significant threat to internet and cloud security. Our study utilizes a Poisson distribution model to efficiently detect DDoS attacks with a computational complexity of O(n). Unlike Machine Learning (ML)-based algorithms, our method only needs to set up one or more Poisson models for legitimate traffic based on the granularity of the time periods during preprocessing, thus eliminating the need for training time. We validate this approach with four virtual machines on the CDX 3.0 platform, each simulating different aspects of DDoS attacks for offensive, monitoring, and defense evaluation purposes. The study further analyzes seven diverse DDoS attack methods. When compared with existing methods, our approach demonstrates superior performance, highlighting its potential effectiveness in real-world DDoS attack detection.

Magnetic resonance imaging is the most generally utilized imaging methodology that permits radiologists to look inside the cerebrum using radio waves and magnets for tumor identification. However, it is tedious and complex to identify the tumorous and nontumorous regions due to the complexity in the tumorous region. Therefore, reliable and automatic segmentation and prediction are necessary for the segmentation of brain tumors. This paper proposes a reliable and efficient neural network variant, i.e., an attention-based convolutional neural network for brain tumor segmentation. Specifically, an encoder part of the UNET is a pre-trained VGG19 network followed by the adjacent decoder parts with an attention gate for segmentation noise induction and a denoising mechanism for avoiding overfitting. The dataset we are using for segmentation is BRATS’20, which comprises four different MRI modalities and one target mask file. The abovementioned algorithm resulted in a dice similarity coefficient of 0.83, 0.86, and 0.90 for enhancing, core, and whole tumors, respectively.

The production, testing, and processing of signals without any interpretation is a crucial task with time scale periods in today's biological applications. As a result, the proposed work attempts to use a deep learning model to handle difficulties that arise during the processing stage of biomedical information. Deep Conviction Systems (DCS) are employed at the integration step for this procedure, which uses classification processes with a large number of characteristics. In addition, a novel system model for analyzing the behavior of biomedical signals has been developed, complete with an output tracking mechanism that delivers transceiver results in a low-power implementation approach. Because low-power transceivers are integrated, the cost of implementation for designated output units will be decreased. To prove the effectiveness of DCS feasibility, convergence and robustness characteristics are observed by incorporating an interface system that is processed with a deep learning toolbox. They compared test results using DCS to prove that all experimental scenarios prove to be much more effective for about 79 percent for variations with time periods.

The technology that is most likely to change the corporate world in the next ten years is not social networks, Big Data, cloud computing, robots, not even artificial intelligence, but blockchain [...]

Fog-driven IoT architecture located between IoT devices and the centralized cloud infrastructure is introduced to extend computing, storage and network services to the edge of the Internet and therefore resources and services of the fog nodes are available and are closer to the end user and end device for providing mobility, low latency and location awareness. However, the paradigm of fog computing due to its inherited properties from cloud as inherits its security and privacy concerns such as spoofing, message replay, impersonation, man-in-the middle and physical capturing of IoT devices etc. To address these concerns in fog computing services, in this paper, a lightweight anonymous authentication and secure communication scheme is proposed and it only used secure one-way hash function and bitwise XOR operations when cloud, fog and user mutually authenticate each other. After the successful authentication, both fog-based participants can agree on a session key to encrypt the subsequent communication messages. The security can be ensured during authentication process by using the Burrows-Abadi-Needham (BAN) logic and the performance comparisons with existing schemes demonstrate that the proposed scheme is secure and highly efficient.

G protein-gated inwardly rectifying potassium channels (GIRK) are essential for the regulation of cellular excitability, a physiological function that relies critically on the conduction of K+ ions, which is dependent on two molecular mechanisms, namely selectivity and gating. Molecular Dynamics (MD) studies have shown that K+ conduction remains inefficient even with open channel gates, therefore further detailed study on the permeation events is required. In this study, all-atom MD simulations were employed to investigate the permeation mechanism through the GIRK2 selectivity filter (SF) and its open helix bundle crossing (HBC) gate. Our results show that it is the SF rather than the HBC or the G-loop gate that determines the permeation efficiency upon activation of the channel. SF-permeation is accomplished by a water-K+ coupled mechanism and the entry to the S1 coordination site is likely affected by a SF tilt. Moreover, we show that a 4-K+ occupancy in the SF-HBC cavity is required for the permeation through an open HBC, where three K+ ions around E152 help to abolish the unfavorable cation-dipole interactions that function as an energy barrier, while the fourth K+ located near the HBC allows for the inward transport. These findings facilitate further understanding of the dynamic permeation mechanisms through GIRK2 and potentially provide an alternative regulatory approach for the Kir3 family given the overall high evolutionary residue conservation.

Task offloading could optimize computational resource utilization in edge computing environments. However, how to assign and offload tasks for different behavior users is an essential problem since the systems dynamic, intelligent application diversity, and user personality. With user behavior prediction, this paper proposes soCoM, a semi-online Computational Offloading Model. We explore the user behaviors in sophisticated action space by reinforcement learning for catching unknown environment information. With Dueling Deep-Q Network, both the prediction accuracy of users' behaviors and the server load balance are well-considered, while increasing the computational efficiency and decreasing the resource costing. We propose a dynamic simulation environment of edge computing to demonstrate that user behavior is the critical factor for impacting system utilization. As the action space increasing, Dueling DQN performs better than state-of-art DQN and other improved strategies, and also load balance in multiple different server scenario.

Multi-label classification tackles the problems in which each instance is associated with multiple labels. Due to the interdependence among labels, exploiting label correlations is the main means to enhance the performances of classifiers and a variety of corresponding multi-label algorithms have been proposed. Among those algorithms Classifier Chains (CC) is one of the most effective methods. It induces binary classifiers for each label, and these classifiers are linked in a chain. In the chain, the labels predicted by previous classifiers are used as additional features for the current classifier. The original CC has two shortcomings which potentially decrease classification performances: random label ordering, noise in original and additional features. To deal with these problems, we propose a novel and effective algorithm named LSF-CC, i.e. Label Specific Features based Classifier Chain for multi-label classification. At first, a feature estimating technique is employed to produce a list of most relevant features and labels for each label. According to these lists, we define a chain to guarantee that the most frequent labels that appear in these lists are top-ranked. Then, label specific features can be selected from the original feature space and label space. Based on these label specific features, corresponding binary classifiers are learned for each label. Experiments on several multi-label data sets from various domains have shown that the proposed method outperforms well-established approaches.

With the rapid development of the social economy, our lives are flooded with all kinds of counterfeit products. The public's attitude of greedy for petty and cheap has encouraged unscrupulous manufacturers to take advantage of the opportunity to provide low-cost counterfeit products, suppress the profits of legitimate manufacturers, and also make the public lose confidence in the quality of the products. At present, the most widely used anti-counterfeiting system based on QR codes on the market. However, existing traceability systems are still mostly built in a centralized manner, and the central agency provides trust guarantees, but the public still has great doubts about the credibility of the central agency. The introduction of blockchain technology can perfectly solve the lack of existing architecture and the environment. In this research, we propose an IoT-based traceable drug anti-counterfeiting management system, a comprehensive plan from drug research and development, certification, production to sales. The framework we propose meets the requirements of information security for data integrity, resistance to replay attacks, irreversible information, and non-repudiation.

In recent years, due to the rapid development of information techniques and network technologies, more and more medical documents have been replaced by electronic files for sharing and transmitting in real time. However, medical data transmitted over public communication channels may suffer from security attacks and privacy threats. Blockchain technology has been gotten many attentions in different areas due to its unique properties such as anonymity, verifiability, immutability and decentralization. In order to secure patient privacy and provide more personal healthcare services, in this paper, we propose a data aggregation scheme based on Blockchain technology for medical environments. Moreover, in order to implement remote medical monitoring, we design a group authentication mechanism for multiple authorized users (such as patient, doctors, caregivers, family and friends) to freely access patient's personal health records. The authorized group members in a group will agree on a group session key and use it to protect patient's sensitive information. In case of a new member joins the medical group or an old member leaves the medical group, the group session key needs to be updated at any time. Finally, the electronic medical system will become more secure, reliable and useful by our proposed scheme.

The photovoltaic (PV) model commonly used in engineering has difficulty in accurately predicting the actual power generation. In this study, a PV model commonly used in engineering was used to establish a PV simulation model and improve it based on experimental data. The influence of the PV output power characteristics and local shading on the power generation efficiency of the PV system was analyzed using MATLAB and the improved model. Aiming at the problem that most maximum power point tracking (MPPT) algorithms have difficulty quickly tracking the maximum power point (MPP) under fast-varying solar irradiation; a polynomial fitting-MPPT (PF-MPPT) algorithm and a simple fitting-MPPT (SPF-MPPT) algorithm based on polynomial fitting were proposed to track the maximum power point under the fast-varying solar irradiation. Finally, an improved MPPT system with the PF-MPPT algorithm was proposed to solve the problem of the significant reduction of the output power of a PV system under fast-varying solar irradiation and local shading. The simulation results showed that under fast-varying solar irradiation, the power-tracking abilities, and stabilities of the proposed two algorithms were similar to those of the perturb and observe (P&O) algorithm, but the tracking speed was over 2.58 times that of the constant voltage tracking (CVT) algorithm and four times that of the P&O algorithm. In addition, under fast-varying solar irradiation and local shading, the speed, ability, and stability of the improved MPPT system with the PF-MPPT algorithm when tracking the maximum power were 9.52, 1.32, and 1.84 times of the MPPT system with the P&O algorithm and 2.18, 1.41, and 2.00 times of the MPPT system with the particle swarm optimization algorithm, respectively.

In recent years, due to the slowdown of the global economy and the instability of the stock market and real estate market, the art market has gradually become the third-largest investment market after the former two. As more and more funds flow into the art market, the two seemingly unrelated industries, art, and finance have increasingly close cooperation due to the growing prosperity of the art market. As a very important part of the process of art financialization, the development of art banks has also received extensive attention from all walks of life. In addition, blockchain, as a technology to jointly maintain reliable databases through trustlessness and decentralization, has grown rapidly in recent years, and is gradually applied in various fields. The advantages of blockchain technology are decentralization, anonymity, immutability, and traceability of stored information. The proposed scheme applies the characteristics of blockchain technology to Art Bank’s art rental system, and will have the following advantages: the use of Hyperledger technology, various art banks being merged into an alliance, and information among alliance members being shared, which is convenient for tenants to comprehensively query a single item. Using blockchain technology, in an environment where there is no central authority, under the premise of preventing the tampering of artwork-related information, ensures that the detailed information of the artwork is shared. Traditional leasing agreements can be compiled into smart contract leasing contracts to automatically run and manipulate data. The proposed protocol satisfies the following security requirements: identities’ mutual authentication, non-repudiation between every two parties, and other major security requirements based on blockchain. When a dispute arises, our proposed scheme also has an arbitration mechanism to clarify responsibilities.

Daily load forecasting is the basis of the economic and safe operation of a power grid. Accurate prediction results can improve the matching of microgrid energy storage capacity allocation. With the popularization of smart meters, the interaction between residential electricity demand and sources and networks is increasing, and massive data are generated at the same time. Previous forecasting methods suffer from poor targeting and high noise. They cannot make full use of the important information of the load data. This paper proposes a new framework for daily load forecasting of group residents. Firstly, we use the singular value decomposition to address the problem of high dimensions of residential electricity data. Meanwhile, we apply a K-Shape-based group residential load clustering method to obtain the typical residential load data. Secondly, we introduce an empirical mode decomposition method to address the problem of high noise of residential load data. Finally, we propose a Bi-LSTM-Attention model for residential daily load forecasting. This method can make full use of the contextual information and the important information of the daily load of group residents. The experiments conducted on a real data set of a power grid show that our method achieves excellent improvements on five prediction error indicators, such as MAPE, which are significantly smaller than the compared baseline methods.

Everyone should be vaccinated, but the eligibility and safety of the vaccine are always overlooked by most people. The outbreak of COVID-19 has led many countries to intensify the development and production of the COVID-19 vaccine. and some countries have even required universal vaccination against this epidemic. However, such popularization of vaccination has also exposed various flaws in vaccine management that existed in the past, and vaccinators have become more concerned about the effectiveness of their vaccinations. In this paper, we propose a blockchain-based traceable vaccine management system. First, the system uses smart contracts to store the records generated during the whole process, from vaccine production to vaccination. Second, the proposed scheme uses the Edwards-curve digital signature algorithm (EdDSA) to guarantee the security and integrity of these data. Third, the system participants can access the corresponding data according to their authority to ensure the transparency of the whole system operation process. Finally, this paper will also conduct a security analysis of the whole system to ensure that the system can resist potential attacks by criminals.

Building an integrated energy system (IES) is an excellent solution to environmental pollution and energy consumption currently. However, there are usually various IES configurations with different performances, and it is usually difficult for the decision-makers to select the most sustainable IES solution among multiple choices. In this paper, we attempt to design an evaluation method that considers the renewable energy and energy system model for evaluating the benefit of an IES. Firstly, this paper constructs an evaluation index system including four dimensions of technology, economy, environment, and society, based on the physical architecture of IES and its benefit influencing factors. It focuses on multiple energy sources or systems, and satisfies the overall evaluation of IES, resulting in accurate benefit evaluation results. Secondly, the subjective weight is determined by combining the triangular fuzzy number (TFN) and the analytic hierarchy process (AHP). The objective weight is determined by the entropy method. The combined weight is obtained by combining the subjective and objective weights, which solves the problem of the weight calculation error caused by the mutual influence between the indexes. Finally, the fuzzy comprehensive evaluation method is used to conduct the benefit evaluation of the IES, which solves the problem that the qualitative index cannot be calculated. This paper takes an IES project in a city in northern China as a case study. Through sensitivity analysis and comparative analysis, the reliability and feasibility of the evaluation results and proposed method are verified. The analysis results show that the model evaluation results are intuitive and consistent with reality, and the data information utilization rate is high.

In recent years, due to the rapid development of Internet of things (IoTs), various physical things (objects) in IoTs are smart enough to make their own decisions without the involvement of humans. The smart devices embedded in a drone can sense, collect, and transmit real-time data back to the controller from a designated environment via wireless communication technologies. The mobility, flexibility, reliability and energy efficiency of drones makes them more widely used in IoT environments such as commercial, military, entertainment applications, traffic surveillance and aerial photography. In a generalized IoD architecture, we have communications among the drones in a flying zone, among the drones and the control server, and also among the drones and authorized user. IoD still has many critical issues that need to be addressed, such as data access being carried out through a public channel and battery operated drones. To address these concerns in IoD communications, in this paper, an efficient authentication and secure communication scheme with privacy preservation is proposed and it only uses secure one-way hash function and bitwise XOR operations when control server, drone and user mutually authenticate each other. After the successful authentication, both IoD-based participants can agree on a common session key to secure the subsequent communication messages. The widely accepted ProVerif and BAN logic analysis have been used to assure that the proposed scheme is provably secure against existing well-known security attacks and ensures privacy. Finally, a comparative analysis is presented to demonstrate the proposed scheme preserves efficiency when compared to existing competitive schemes.

The street sign text information from natural scenes usually exists in a complex background environment and is affected by natural light and artificial light. However, most of the current text detection algorithms do not effectively reduce the influence of light and do not make full use of the relationship between high-level semantic information and contextual semantic information in the feature extraction network when extracting features from images, and they are ineffective at detecting text in complex backgrounds. To solve these problems, we first propose a multi-channel MSER (Maximally Stable Extreme Regions) method to fully consider color information in text detection, which separates the text area in the image from the complex background, effectively reducing the influence of the complex background and light on street sign text detection. We also propose an enhanced feature pyramid network text detection method, which includes a feature pyramid route enhancement (FPRE) module and a high-level feature enhancement (HLFE) module. The two modules can make full use of the network’s low-level and high-level semantic information to enhance the network’s effectiveness in localizing text information and detecting text with different shapes, sizes, and inclined text. Experiments showed that the F-scores obtained by the method proposed in this paper on ICDAR 2015 (International Conference on Document Analysis and Recognition 2015) dataset, ICDAR2017-MLT (International Conference on Document Analysis and Recognition 2017- Competition on Multi-lingual scene text detection) dataset, and the Natural Scene Street Signs (NSSS) dataset constructed in this study are 89.5%, 84.5%, and 73.3%, respectively, which confirmed the performance advantage of the method proposed in street sign text detection.

The subscription economy was born because the relationship between creators and customers is different than it used to be. The era of the creator economy seems to be filled with boundless promise, but at the end of the day, creators are just slaves to tech giants. Neither the control of the content created, nor the money made in their pockets is in complete control of the creator. The blockchain can completely solve these injustices monopolized by enterprises. In the blockchain era, all kinds of creations from music, and video-to-text can be turned into assets that can be purchased and traded through smart contracts. In the music industry, for example, creators do not need to share profits with streaming platforms and record labels and get all the benefits directly. In addition, when the content created is on the chain, every transaction will be recorded on the blockchain, and everyone can inquire about it, avoiding opacity or causing disputes in the future. However, with the structure of the standard blockchain, as long as the registration is successful, each role in the chain will have permanent data access rights, and the flexibility of access control is poor. Therefore, this study proposes a digital media subscription mechanism based on the Hyperledger blockchain architecture combined with proxy re-encryption. We use symmetric and asymmetric cryptography, smart contracts, and algorithms to design our protocol. When the licensee violates the agreement with the creator, the creator can revoke the access rights to the digital media of the licensee at any time, to realize more secure and convenient digital media transmission. The proposed scheme meets various security requirements of blockchain architecture, and we have also applied the BAN logic proof model to evaluate the correctness of the proposed scheme. This study also proposes an arbitration mechanism when the dispute occurs, and performed well in terms of communication and computational costs.

Electronic medical records (EMRs) are extremely private data in the medical industry. Clinicians use the patient data that the EMR stores to quickly assess a patient’s status and save diagnostic information. In the conventional medical model, it is easy for duplicate exams, medical resource waste, or the loss of medical records to happen when a patient is transferred between several medical facilities due to problems with data sharing and exchange, inadequate data privacy, security, confidentiality, and difficulties with data traceability. This paper recommends a Hyperledger Fabric-based strategy to promote the exchange of EMR models. With the use of Hyperledger Fabric, EMR stakeholders can be brought into the channel to facilitate data sharing. Attribute-based access control (ABAC) allows users to design the data access control policy, and the data access control may improve security. Any record stored in the blockchain can be viewed using the Hyperledger Fabric feature and it cannot be altered or destroyed, ensuring data traceability. Through proxy re-encryption, which makes sure that the data is not leaked during data exchange, data secrecy can be ensured. A module for medical tokens has now been added. Many foreign medical institutions currently use the medical token system, and the system described in this paper can use the tokens to pay for some medical expenses. The tokens are obtained by the patient’s initiative to share their EMR with the medical institution for research, which is how many foreign medical institutions currently use the medical token mechanism. This paradigm can encourage the growth of medical data by enabling stakeholders to collaborate and share EMR trust.

As society advances, so does the total number of vehicles on the road, creating a massive consumer market for automobiles. According to statistics, a major portion of today’s traffic difficulties are caused by accidents caused by subpar cars and auto parts. As a result, each country has, over time, enacted equivalent rules and regulations to prevent such tragedies. However, in the face of profit, some people are desperate enough to employ illegal parts and illegally modified cars, and auto fraud is rampant. As a result, we employ the blockchain of the symmetrical Blockchain’s digital ledger and smart contract technology to build a decentralized supply chain system that can identify specific parts. In this study, we design and discuss the proposed system framework by user functions and the flow of parts based on blockchain, and we discuss communication protocols that use the symmetry and asymmetry cryptography, algorithms, properties, and security of the mechanism while providing related analysis and comparing the properties and costs of the system with other studies. Overall, the proposed method has the potential to successfully address the issue of automobile fraud.

NBA (National Basketball Association) trading cards are a hot collector’s item, with sales increasing rapidly every year. However, with the popularity of online trading, some sellers have started to intentionally and unintentionally sell imitation trading cards, and even PwC (Pricewaterhouse Coopers) is not immune. However, the PSA (Professional Sports Authenticator), which is the authentication agency, is not liable for this. Faced with the above situation, we moved trading cards online and proposed a blockchain-based anti-counterfeit and traceable NBA digital trading card management system, using blockchain technology to protect digital trading cards, and special digital copyright, to move from relying on other regulators to achieve the fight against counterfeit cards and maintain the security of the digital trading card market. Finally, we analyzed the security of the system and compared it with other methods. Our system uses Hyperledger Fabric to share data while protecting corporate privacy. Proxy re-encryption enables secure and trusted access authorization for digital transaction cards. Asymmetric encryption protects the data and uses signatures to achieve traceability and non-repudiation. Overall, our system solves the problem of counterfeiting and traceability that can occur in the digital trading card process from production to purchase.

With the recent rapid increase in the number of motor vehicles on roads, traffic accidents have increased, and emergency reporting processes have become essential. In this paper, a key agreement protocol for a real-time traffic sharing system is proposed for Vehicular Ad-Hoc Networks (VANETs), in which a broadcast center authenticates the legitimacy of a user when they report an emergency and issues a certificate of emergency to other users. This study uses a digital signatures mechanism, key agreement and authentication scheme to satisfy the security requirements for a VANET. Burrows-Abadi-Needham logic (BAN logic) is applied to prove that the proposed scheme achieves secure authentication. The proposed protocol ensures the privacy of the communication between fleets and provides a mechanism for immediate emergency reporting. The experiment results show that the proposed scheme is feasible and meets security requirements.

RF-based gesture sensing and recognition has increasingly attracted intense academic and industrial interest due to its various device-free applications in daily life, such as elder monitoring, mobile games. State-of-the-art approaches achieved accurate gesture sensing by using fine-grained RF signatures (such as CSI, Doppler effect) while could not achieve the same accuracy with coarse-grained RF signatures such as received signal strength (RSS). This paper presents rRuler, a novel feature extraction method which aims to get fine-grained human gesture features with coarse-grained RSS readings, which means rought ruler could measure fine things. In order to further verify the performance of rRuler, we further propose rRuler-HMM, a hidden Markov model (HMM) based human gesture sensing and prediction algorithm which utilizes the features extracted by rRuler as input. We implemented rRuler and rRuler-HMM using TI Sensortag platforms and off-the-shelf (CTOS) laptops in an indoor environment, extensively performance evaluations show that rRuler and rRuler-HMM stand out for their low cost and high practicability, and the average gesture sensing accuracy of rRuler-HMM can achieve 95.71% in NLoS scenario and 97.14% in LoS scenario, respectively, which is similar to the performance that fine-grained RF signatures based approaches could achieve.

Nowadays, multi-label data are ubiquitous in real-world applications, in which each instance is associated with a set of labels. Multi-label learning has attracted significant attentions from researchers and plenty of algorithms have been proposed. Among those algorithms binary relevance (BR) is a widely used framework for multi-label classification. It constructs binary classifiers for each label by means of one-vs-rest style. BR approach is a simple and straight forward way of problem transformation for multi-label learning, but it ignores label correlations totally. Stacking based BR is a feasible way to tackle this problem. The key issue of stacking based BR is how to select label subset to extend the original features for each label. Existing methods of stacking based BR usually select identical label subset for all labels. It may be suboptimal as each label has its own most related label subset. In this paper, a novel stacking based method is introduced to utilize label correlations based on Pareto Optimum for improving the performance of BR. Our method builds a stack of two layers of BR classifiers. At the first layer, a group of binary classifiers are constructed, one for a label. At the second layer, for each label we employ Pareto Optimum to select most related label subset, then augment the original features by the selected label subset. The final binary classifiers for each label are constructed based on their corresponding reconstructed feature space. Comparing to other well-established stacking multi-label learning algorithms in terms of different multi-label classification criteria, experimental results on several multi-label benchmark datasets testify the superiority of the proposed methods.

Smart devices have become an essential part of the architectures such as the Internet of Things (IoT), Cyber-Physical Systems (CPSs), and Internet of Everything (IoE). In contrast, these architectures constitute a system to realize the concept of smart cities and, ultimately, a smart planet. The adoption of these smart devices expands to different cyber-physical systems in smart city architecture, i.e., smart houses, smart healthcare, smart transportation, smart grid, smart agriculture, etc. The edge of the network connects these smart devices (sensors, aggregators, and actuators) that can operate in the physical environment and collects the data, which is further used to make an informed decision through actuation. Here, the security of these devices is immensely important, specifically from an authentication standpoint, as in the case of unauthenticated/malicious assets, the whole infrastructure would be at stake. We provide an updated review of authentication mechanisms by categorizing centralized and distributed architectures. We discuss the security issues regarding the authentication of these IoT-enabled smart devices. We evaluate and analyze the study of the proposed literature schemes that pose authentication challenges in terms of computational costs, communication overheads, and models applied to attain robustness. Hence, lightweight solutions in managing, maintaining, processing, and storing authentication data of IoT-enabled assets are an urgent need. From an integration perspective, cloud computing has provided strong support. In contrast, decentralized ledger technology, i.e., blockchain, light-weight cryptosystems, and Artificial Intelligence (AI)-based solutions, are the areas with much more to explore. Finally, we discuss the future research challenges, which will eventually help address the ambiguities for improvement.

Patients with advanced non-small cell lung cancer (NSCLC) who harbor susceptible epidermal growth factor receptor (EGFR) mutations and are treated with EGFR tyrosine kinase inhibitors (TKIs) show longer progression-free survival (PFS) than those treated with chemotherapy. However, developed EGFR-TKI resistance limits PFS improvements. Currently, combination treatment with EGFR-TKIs and anti-angiogenic agents is considered a beneficial regimen for advanced-stage NSCLC harboring susceptible EGFR mutations. However, several trials reported osimertinib plus bevacizumab failed to show superior efficacy over osimertinib alone. However, subgroup analysis showed significantly longer PFS among patients with a history of smoking over those who never smoked. We performed a comprehensive systematic review and meta-analysis to evaluate the smoking status impact. At the end of the process, a total of 2068 patients from 11 randomized controlled trials (RCTs) were included in our meta-analysis. Overall, combination EGFR-TKI plus anti-angiogenic agent treatment showed significantly better PFS among patients with a smoking history (Hazard Ratio (HR) = 0.59, 95% confidence interval (CI) = 0.48–0.73). Erlotinib-based combination therapy showed positive PFS benefits regardless of smoking status (HR = 0.54, 95%CI = 0.41–0.71 for ever smoker, HR = 0.69, 95%CI = 0.54–0.87 for never smoker). Combination therapy prolonged PFS significantly regardless of ethnicity (HR: 0.64, 95% CI: 0.44–0.93 for Asian RCTs, HR: 0.55, 95% CI: 0.41–0.74 for global and non-Asian RCTs). PROSPERO registration number is CRD42022304198).

One of the foundational and key means of optimizing network service in the field of network security is traffic identification. Various data transmission encryption technologies have been widely employed in recent years. Wrongdoers usually bypass the defense of network security facilities through VPN to carry out network intrusion and malicious attacks. The existing encrypted traffic identification system faces a severe problem as a result of this phenomenon. Previous encrypted traffic identification methods suffer from feature redundancy, data class imbalance, and low identification rate. To address these three problems, this paper proposes a VPN-encrypted traffic identification method based on ensemble learning. Firstly, aiming at the problem of feature redundancy in VPN-encrypted traffic features, a method of selecting encrypted traffic features based on mRMR is proposed; secondly, aiming at the problem of data class imbalance, improving the Xgboost identification model by using the focal loss function for the data class imbalance problem; Finally, in order to improve the identification rate of VPN-encrypted traffic identification methods, an ensemble learning model parameter optimization method based on optimal Bayesian is proposed. Experiments revealed that our proposed VPN-encrypted traffic identification method produced more desirable VPN-encrypted traffic identification outcomes. Meanwhile, using two encrypted traffic datasets, eight common identification algorithms are compared, and the method appears to be more accurate in identifying encrypted traffic.

With the rapid development of power grid informatization, the power system has evolved into a multi-dimensional heterogeneous complex system with high cyber-physical integration, denoting the Cyber-Physical Power System (CPPS). Network attack, in addition to faults, becomes an important factor restricting the stable operation of the power system. Under the influence of network attacks, to improve the operational stability of CPPSs, this paper proposes a CPPS network attack detection method based on ensemble learning. First, to solve the shortcomings of a low detection precision caused by insufficient network attack samples, a power data balancing processing method was proposed. Then, the LightGBM ensemble was constructed to detect network attack events and lock the fault points caused by the attack. At the same time, in the process of gradient boost, the focal loss was introduced to optimize the attention weight of the classifier to the misclassified samples, thus improving the network attack detection precision. Finally, we propose an effective evaluation method of the network attack detection model based on cyber-physical comprehensive consideration. In addition, the cyber-physical power system stability under the action of the network attack detection model is quantitatively analyzed. The experimental results show that the F1 score of network attack detection increases by 16.73%, and the precision increases by 15.67%.

Wireless Underground Sensor Networks (WUSNs) have been showing prospective supervising application domains in the underground region of the earth through sensing, computation, and communication. This paper presents a novel Deep Learning (DL)-based Cooperative communication channel model for Wireless Underground Sensor Networks for accurate and reliable monitoring in hostile underground locations. Furthermore, the proposed communication model aims at the effective utilization of cluster-based Cooperative models through the relay nodes. However, by keeping the cost effectiveness, reliability, and user-friendliness of wireless underground sensor networks through inter-cluster Cooperative transmission between two cluster heads, the determination of the overall energy performance is also measured. The energy co-operative channel allocation routing (ECCAR), Energy Hierarchical Optimistic Routing (EHOR), Non-Cooperative, and Dynamic Energy Routing (DER) methods were used to figure out how well the proposed WUSN works. The Quality of Service (QoS) parameters such as transmission time, throughput, packet loss, and efficiency were used in order to evaluate the performance of the proposed WUSNs. From the simulation results, it is apparently seen that the proposed system demonstrates some superiority over other methods in terms of its better energy utilization of 89.71%, Packet Delivery ratio of 78.2%, Average Packet Delay of 82.3%, Average Network overhead of 77.4%, data packet throughput of 83.5% and an average system packet loss of 91%.

With technological advancement, cloud computing paradigms are gaining massive popularity in the ever-changing technological advancement. The main objective of the cloud computing system is to provide on-demand storage and computing resources to the users on the pay-per-use policy. It allows small businesses to use top-notch infrastructure at low expense. However, due to the cloud resource sharing property, data privacy and security are significant concerns and barriers for smart systems to constantly transfer generated data to the cloud computing resources, which a third-party provider manages. Many encryption techniques have been proposed to cope with data security issues. In this paper, different existing data protection and encryption techniques based on common parameters have been critically analyzed and their workflows are graphically presented. This survey aims to collect existing data encryption techniques widely presented in the literature for smart system data security offloaded to the cloud computing systems under a single umbrella.

Internet of Things (IoT) technology is now widely used in energy, healthcare, services, transportation, and other fields. With the increase in industrial equipment (e.g., smart mobile terminals, sensors, and other embedded devices) in the Internet of Things and the advent of Industry 4.0, there has been an explosion of data generated that is characterized by a high volume but small size. How to manage and protect sensitive private data in data sharing has become an urgent issue for enterprises. Traditional data sharing and storage relies on trusted third-party platforms or distributed cloud storage, but these approaches run the risk of single-node failure, and third parties and cloud storage providers can be vulnerable to attacks that can lead to data theft. To solve these problems, this paper proposes a Hyperledger Fabric blockchain-based secure data transfer scheme for enterprises in the Industrial Internet of Things (IIOT). We store raw data in the IIoT in the InterPlanetary File System (IPFS) network after encryption and store the Keyword-index table we designed in Hyperledger Fabric blockchain, and enterprises share the data by querying the Keyword-index table. We use Fabric’s channel mechanism combined with our designed Chaincode to achieve privacy protection and efficient data transmission while using the Elliptic Curve Digital Signature Algorithm (ECDSA) to ensure data integrity. Finally, we performed security analysis and experiments on the proposed scheme, and the results show that overall the data transfer performance in the IPFS network is generally better than the traditional network, In the case of transferring 5 MB file size data, the transmission speed and latency of IPFS are 19.23 mb/s and 0.26 s, respectively, and the IPFS network is almost 4 times faster than the TCP/IP network while taking only a quarter of the time, which is more advantageous when transferring small files, such as data in the IIOT. In addition, our scheme outperforms the blockchain systems mainly used today in terms of both throughput, latency, and system overhead. The average throughput of our solution can reach 110 tps (transactions are executed per second), and the minimum throughput in experimental tests can reach 101 tps.

The popularity and remarkable attractiveness of cryptocurrencies, especially Bitcoin, absorb countless enthusiasts every day. Although Blockchain technology prevents fraudulent behavior, it cannot detect fraud on its own. There are always unimaginable ways to commit fraud, and the need to use anomaly detection methods to identify abnormal and fraudulent behaviors has become a necessity. The main purpose of this study is to use the Blockchain technology of symmetry and asymmetry in computer and engineering science to present a new method for detecting anomalies in Bitcoin with more appropriate efficiency. In this study, a collective anomaly approach was used. Instead of detecting the anomaly of individual addresses and wallets, the anomaly of users was examined. In addition to using the collective anomaly detection method, the trimmed_Kmeans algorithm was used for clustering. The results of this study show the anomalies are more visible among users who had multiple wallets. The proposed method revealed 14 users who had committed fraud, including 26 addresses in 9 cases, whereas previous works detected a maximum of 7 addresses in 5 cases of fraud. The suggested approach, in addition to reducing the processing overhead for extracting features, detect more abnormal users and anomaly behavior.

WBANs (Wireless Body Area Networks) are frequently depicted as a paradigm shift in healthcare from traditional to modern E-Healthcare. The vitals of the patient signs by the sensors are highly sensitive, secret, and vulnerable to numerous adversarial attacks. Since WBANs is a real-world application of the healthcare system, it’s vital to ensure that the data acquired by the WBANs sensors is secure and not accessible to unauthorized parties or security hazards. As a result, effective signcryption security solutions are required for the WBANs’ success and widespread use. Over the last two decades, researchers have proposed a slew of signcryption security solutions to achieve this goal. The lack of a clear and unified study in terms of signcryption solutions can offer a bird’s eye view of WBANs. Based on the most recent signcryption papers, we analyzed WBAN’s communication architecture, security requirements, and the primary problems in WBANs to meet the aforementioned objectives. This survey also includes the most up to date signcryption security techniques in WBANs environments. By identifying and comparing all available signcryption techniques in the WBANs sector, the study will aid the academic community in understanding security problems and causes. The goal of this survey is to provide a comparative review of the existing signcryption security solutions and to analyze the previously indicated solution given for WBANs. A multi-criteria decision-making approach is used for a comparative examination of the existing signcryption solutions. Furthermore, the survey also highlights some of the public research issues that researchers must face to develop the security features of WBANs.

Internet of Things (IoT) device security is one of the crucial topics in the field of information security. IoT devices are often protected securely through firmware update. Traditional update methods have their shortcomings, such as bandwidth limitation and being attackers’ easy targets. Although many scholars proposed a variety of methods that are based on the blockchain technology to update the firmware, there are still demerits existing in their schemes, including large storage space and centralized stored firmware. In summary, this research proposes a highly secure and efficient protection mechanism that is based on the blockchain technology to improve the above disadvantages. Therefore, this study can reduce the need of storage space and improve system security. The proposed system has good performance in some events, including firmware integrity, security of IoT device connection, system security, and device anonymity. Furthermore, we confirm the high security and practical feasibility of the proposed system by comparing with the existing methods.

In recent years, information technology has become a focus of attention. All hardware, software, and communication technologies are growing rapidly. Nowadays, we can't live without technology. We use technology anywhere and anytime, and enjoy the convenience of technology. In particular, all kinds of network services and applications. These communication technologies and services constitute the concept of cloud computing. Cloud computing involves virtualization of service resources on the network; the entire service resources include scheduling, management, maintenance, and so on, which are carried out by specialized personnel. The goal of cloud computing is enabling users to use virtual resource pools maximally and handle large-scale computing problems anytime and anywhere via the network. Robotics is one of the rapidly developing technologies. Although there are numerous research teams working on cloud robots, research on cloud robot systems is still in its infancy and many problems have not yet been considered, such as the issues of robot privatization and communication security. Therefore, we propose a robot cloud service system and focus on four crucial issues: cloud platform central control, robot intelligence technology, robot privatization, and communication security. Finally, we use BAN logic to prove that the proposed scheme achieves mutual authentication and user anonymity. The proposed scheme can also defend against several kinds of attacks, like eavesdropping attack, impersonation attack, parallel session attack, man-in-the-middle attack, and replay attack.

With its explosive development, the Internet has come to offer an increasing multitude of online service applications. In order to better facilitate such services, a cloud computing environment, composed of a large number of processors and memories, high-speed networks, and various applications, has been developed, and continues to grow, providing convenient and quick network services. In this cloud computing environment, each server processor in the environment must cooperate with other processors to satisfy various user demands. As a result, the issue of fault-tolerance needs to be revised in order to ensure the reliability of cloud computing environments. One of the most important issue of fault-tolerance is the Byzantine agreement (BA), which requires that, even if some components are damaged, a set of fault-free service processors are able to agree on a common value. Furthermore, the faulty service processors must be detected and eliminated. Therefore, a fault diagnosis agreement (FDA) issue for such environments must also be revised simultaneously. The goal of FDA is to enable each fault-free service processor to detect/locate a common set of faulty service processors. However, a lot of messages need to be collected to solve the BA and FDA problem in the previous works. Thus, this paper also uses the concept of an early stopping protocol (ESP) to allow its participants to obtain common values early, during different rounds. Furthermore, the result of ESP can then be used to detect/locate the maximum number of faulty service processors with dual failure modes efficiently, using the minimum number of rounds. As a result, the early diagnosis cloud agreement protocol can be proposed to solve the BA, ESP, and FDA problems simultaneously to provide greater computing abilities by enhancing the reliability of a cloud computing environment.

With the popularity of the internet 5G network, the network constructions of hospitals have also rapidly developed. Operations management in the healthcare system is becoming paperless, for example, via a shared electronic medical record (EMR) system. A shared electronic medical record system plays an important role in reducing diagnosis costs and improving diagnostic accuracy. In the traditional electronic medical record system, centralized database storage is typically used. Once there is a problem with the data storage, it could cause data privacy disclosure and security risks. Blockchain is tamper-proof and data traceable. It can ensure the security and correctness of data. Proxy re-encryption technology can ensure the safe sharing and transmission of relatively sensitive data. Based on the above situation, we propose an electronic medical record system based on consortium blockchain and proxy re-encryption to solve the problem of EMR security sharing. Electronic equipment in this process is connected to the blockchain network, and the security of data access is ensured through the automatic execution of blockchain chaincodes; the attribute-based access control method ensures fine-grained access to the data and improves the system security. Compared with the existing electronic medical records based on cloud storage, the system not only realizes the sharing of electronic medical records, but it also has advantages in privacy protection, access control, data security, etc.

Counterfeit products are internationally regarded as “the world’s second greatest public health hazards after drugs”. Counterfeiters produce counterfeit brand clothing and then sell them to consumers through unofficial channels; thus, consumers spend a lot of money without getting the value they deserve. With the rise of e-shopping, the safety and security of branded clothing supply chains are also under threat. Counterfeit branded apparel manufacturers generate profits while genuine manufacturers suffer, which ultimately violates the interests of the public. This study proposes a traceable anti-counterfeit management system for branded clothing based on Hyperledger Fabric technology. This system can achieve full traceability of the production information of branded clothing. It uses the unique characteristics of blockchain, such as being unforgeable, traceable, open, and transparent, and collectively ‘maintaining’, to record the specific production processes of the brand clothing, and ensure the authenticity and legitimacy of the production information of brand clothing. The end-user can self-verify the product’s authenticity by sharing the product’s details on the immutable framework. It solves problems surrounding information asymmetry, opaque supply chain data, and easy falsification in the production process of branded clothing in traditional branded clothing supply chains.

Text detection in natural scenes is a current research hotspot. The Efficient and Accurate Scene Text (EAST) detector model has fast detection speed and good performance but is ineffective in detecting long text regions owing to its small receptive field. In this study, we built upon the EAST model by improving the bounding box’s shrinking algorithm to make the model more accurate in predicting short edges of text regions; altering the loss function from balanced cross-entropy to Focal loss; improving the model’s learning ability on hard, positive examples; and adding a feature enhancement module (FEM) to increase the receptive field of the EAST model and enhance its detection ability for long text regions. The improved EAST model achieved better detection results on both the ICDAR2015 dataset and the Street Sign Text Detection (SSTD) dataset proposed in this paper. The precision and F1 scores of the model also demonstrated advantages over other models on the ICDAR2015 dataset. A comparison of the text detection effects between the improved EAST model and the EAST model showed that the proposed FEM was more effective in increasing the EAST detector’s receptive field, which indicates that it can improve the detection of long text regions.

Tobacco products are an addictive commodity. According to the World Health Organization’s (WHO) latest statistics data, tobacco kills more than eight million people each year. In 2003, the WHO proposed the Framework Convention on Tobacco Control (FCTC) to provide an effective framework for the control of tobacco products to governments around the world. In the field of tobacco products, the hardest problem is how to prevent counterfeit tobacco products and smuggling. To solve the problems, we proposed a blockchain-based traceable and verifiable logistics system for tobacco products with global positioning system (GPS) and radio-frequency identification (RFID) Technologies. In this research, we provide an overview of system architecture, and also define the protocol and the smart contract in every phase that stores data into the blockchain center. We realized a decentralized database and authentication system that uses blockchain and smart contract technology; every protocol in every phase was designed to achieve the integrity of data and non-repudiation of message. Every tobacco product’s shipping record will be completed by scanning the RFID tag and retrieving the GPS with a mobile reader, where the record will be updated and validated in the blockchain center. In the end, the security and costs of the system were analyzed, and a comparison was made with the EU’s (European Commission) method. Our system is more flexible for transportation, more secure in the communication protocol, and more difficult to tamper and forge data. In general, the proposed scheme solved the problem of tobacco products counterfeiting and tracking issues.

Several anaplastic lymphoma kinase inhibitors (ALKIs) have demonstrated excellent efficacy on overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and also better adverse effect (AE) profiles compared to cytotoxic chemotherapy in advanced stage anaplastic lymphoma kinase (ALK) rearrangement-positive non-small cell lung cancer (NSCLC) in phase III randomized clinical trials (RCTs). We conducted this systematic review and network meta-analysis to provide a ranking of ALKIs for treatment-naïve ALK-positive patients in terms of PFS, ORR, and AEs. In addition, a sub-group analysis of treatment benefits in patients with baseline brain metastasis was also conducted. Contrast-based analysis was performed for multiple treatment comparisons with the restricted maximum likelihood approach. Treatment rank was estimated using the surface under the cumulative ranking curve (SUCRA), as well as the probability of being the best (Prbest) reference. All next-generation ALKIs were superior to crizotinib in PFS but lorlatinib and brigatinib had increased AEs. The probability of lorlatinib being ranked first among all treatment arms was highest (SUCRA = 93.3%, Prbest = 71.8%), although there were no significant differences in pairwise comparisons with high- (600 mg twice daily) and low- (300 mg twice daily) dose alectinib. In subgroup analysis of patients with baseline brain metastasis, low-dose alectinib had the best PFS (SUCRA = 87.3%, Prbest = 74.9%). Lorlatinib was associated with the best ranking for ORR (SUCRA = 90.3%, Prbest = 71.3%), although there were no significant differences in pairwise comparisons with the other ALKIs. In addition, low-dose alectinib had the best safety performance (SUCRA = 99.4%, Prbest = 97.9%). Lorlatinib and low-dose alectinib had the best PFS and ORR in the overall population and baseline brain metastasis subgroup, respectively. Low-dose alectinib had the lowest AE risk among the available ALKIs. Further head-to-head large-scale phase III RCTs are needed to verify our conclusions.

Recently, private security services have become increasingly needed by the public. The proposed scheme involves blockchain technology with a smart contract. When a private security company signs a contract with a client, they install an Internet of Things (IoTs) device in the client’s house and connect it with the IoT main controller; then, the IoT main controller connects to the security control center (SCC). Once there is an event triggered (e.g., a break-in or fire incident) by the IoTs device, the controller sends a message to the SCC. The SCC allocates a security guard (SG) to the incident scene immediately. After the task is accomplished, the SG sends a message to the SCC. All of these record the messages and events chained in the blockchain center. The proposed scheme makes security event records have the following characteristics: authenticated, traceable, and integral. The proposed scheme is proved by a security analysis with mutual authentication, traceability, integrity, and non-repudiation. The known attacks (e.g., man-in-the-middle attack, replay attack, forgery attack) are avoided by message encryption and a signing mechanism. Threat models in the communication phase can also be avoided. Finally, computation cost, communication performance, and comparison with related works are also discussed to prove its applicability. We also provide an arbitration mechanism, so that the proposed scheme can reduce disputes between private security companies and the client.

In recent years, with the rapid levels of economic development, there have been more and more problems in property inheritance and distribution. In today’s society, people still have many taboos when writing a will. Writing a will not only involves various laws and regulations but also costs a lot of money and time, which can be daunting. However, with the development of the Internet, blockchain technology has gradually been applied to many applications. Blockchain technology uses consensus algorithms to ensure consistency and records transaction information in blocks to ensure the effectiveness of transactions. In this paper, we use the cryptography mechanism to propose an online will system based on blockchain and smart contract technology. The architecture considers effectiveness and cost reduction. By combining this with blockchain technology, will assets are saved in blocks, which provides comprehensive will security and non-tamperable security protection. In addition, combined with a smart contract, it realizes the method of automatic property distribution. At the same time, this mechanism also proposes an arbitration solution when there are disputes over wills, and ensures the integrity of data, public verifiability, unforgeability, nonrepudiation, irreversibility of information, and the ability to resist counterfeiting attacks.