Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

Antioxidant,antimicrobial,and α-glucosidase inhibitory activities of saponin extracts from walnut(Juglans regia L.) leaves

Objective:To investigate the relationship between triterpenoid saponin content and antioxidant,antimicrobial,and α-glucosidase inhibitory activities of 70%ethanolic,butanolic,aqueous,supernate and precipitate extracts of Juglans regia leaves.Methods:Triterpenoid saponins of different Juglans regia leaf extracts were measured by the vanillin method.Antioxidant activity was evaluated against DPPH and ABTS free radicals.We also assessed α-glucosidase inhibitory and antimicrobial activities of the leaf extracts.Pearson’s correlation coefficient was evaluated to determine the correlation between the saponin content and biological activities.Results:The butanolic extract was most effective against DPPH with an IC50of 6.63μg/mL,while the aqueous extract showed the highest scavenging activity against ABTS free radical with an IC50of 42.27μg/mL.Pearson’s correlation analysis indicated a strong negative correlation (r=-0.956) between DPPH radical scavenging activity (IC50) and the saponin content in the samples examined.In addition,the aqueous extract showed the best α-glucosidase inhibitory activity compared with other extracts.All the extracts had fair antibacterial activity against Bacillus subtilis,Escherichia coli,and Klebsiella pneumoniae except for the aqueous extract.Conclusions:Juglans regia extracts show potent antioxidant,antimicrobial,and α-glucosidase inhibitory activities.There is a correlation between saponin levels in Juglans regia leaf extracts and the studied activities.However,additional research is required to establish these relationships by identifying the specific saponin molecules responsible for these activities and elucidating their mechanisms of action.

Attitudes and understanding of complementary and alternative medicine in cancer care:An exploratory study of patients’perspectives in Karachi,Pakistan

Objective:To evaluate the prevalence and types of complementary and alternative medicine(CAM)modalities among patients with cancer in Karachi,Pakistan.Methods:This descriptive cross-sectional study was conducted from March 2021 to December 2021.Five hundred patients with cancer were invited to participate in the study.Electronic databases,namely,Google scholar,Publons,EMBASE,PubMed,Chinese National Knowledge Infrastructure Database,and ResearchGate was used for questionnaire designed.The self-administered survey included questions on demographic characteristics,education level,socio-economic conditions and information about CAM therapies,prevalence,effectiveness,and common CAM modalities.Statistical analysis was conducted using SPSS software version 22.Results:Out of the 500 invited patients,433(86.6%)successfully completed and returned the questionnaires.In contrast to patients who were with younger,highly educated,professionally active,higher income,and had advanced cancer,time since diagnosis,type of treatment,cancer types and family history are significantly associated with CAM use.The results showed that 59.8%of the participants were acquainted with complementary and/or alternative medicine and considered safe owing to its natural ingredients.The prevalence of CAM usage among cancer patients was 40.9%and the most widely used CAM modality was herbal medicine(27.7%)and dietary supplements(28.8%).Patients used CAM as a complementary therapy to improve the morphological parameter(28.2%),strengthen the immune system(6.8%),and to decrease the side effects of conventional treatment(18.1%).Most of the respondents get the information regarding CAM therapy from the electronic media(43.2%)and the family members(48%)rather than healthcare personnel.Conclusions:Participants used CAM modalities along with the conventional health care practices.Further multicentre studies should be conducted to provide information regarding the usage of CAM therapies and their eventual benefits in patients with cancer.

Multi-Objective Optimization of Traffic Signal Timing at Typical Junctions Based on Genetic Algorithms

With the rapid development of urban road traffic and the increasing number of vehicles,how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities.Therefore,in this paper,a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed.Specifically,a typical urban intersection was selected as the research object,and drivers’acceleration habits were taken into account.What’s more,the shortest average delay time,the least average number of stops,and the maximum capacity of the intersection were regarded as the optimization objectives.The optimization results show that compared with the Webster method when the vehicle speed is 60 km/h and the acceleration is 2.5 m/s^(2),the signal intersection timing scheme based on the proposed Genetic Algorithm multi-objective optimization reduces the intersection signal cycle time by 14.6%,the average vehicle delay time by 12.9%,the capacity by 16.2%,and the average number of vehicles stop by 0.4%.To verify the simulation results,the authors imported the optimized timing scheme into the constructed Simulation of the Urban Mobility model.The experimental results show that the authors optimized timing scheme is superior to Webster’s in terms of vehicle average loss time reduction,carbon monoxide emission,particulate matter emission,and vehicle fuel consumption.The research in this paper provides a basis for Genetic algorithms in traffic signal control.

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Video analytics is an integral part of surveillance cameras. Comparedto video analytics, audio analytics offers several benefits, includingless expensive equipment and upkeep expenses. Additionally, the volume ofthe audio datastream is substantially lower than the video camera datastream,especially concerning real-time operating systems, which makes it lessdemanding of the data channel’s bandwidth needs. For instance, automaticlive video streaming from the site of an explosion and gunshot to the policeconsole using audio analytics technologies would be exceedingly helpful forurban surveillance. Technologies for audio analytics may also be used toanalyze video recordings and identify occurrences. This research proposeda deep learning model based on the combination of convolutional neuralnetwork (CNN) and recurrent neural network (RNN) known as the CNNRNNapproach. The proposed model focused on automatically identifyingpulse sounds that indicate critical situations in audio sources. The algorithm’saccuracy ranged from 95% to 81% when classifying noises from incidents,including gunshots, explosions, shattered glass, sirens, cries, and dog barking.The proposed approach can be applied to provide security for citizens in openand closed locations, like stadiums, underground areas, shopping malls, andother places.

Phytochemical composition and toxicity assessment of Ammi majus L.

Objective:To assess the acute and subacute toxicity as well as the phytochemical composition of two extracts and three fractions of Ammi majus L.Methods:The aqueous extracts were prepared separately by maceration for 48 h and by infusion for 1 h,while the fractions were prepared by the Soxhlet extractor,successively employing cyclohexane,ethyl acetate,and ethanol.The acute toxicity study was carried out in accordance with the OECD N°423 guideline at a single dose(2000 mg/kg)in mice for 14 days.The subacute toxicity study was performed by a daily oral administration of 250 mg/kg 2 for 10 d and 100 mg/kg doses for 28 d.Phytochemical screening was performed using staining and precipitation reactions,while the chemical characterization of some analytes was detected by HPLC-MS/MS analysis.Results:In the acute toxicity study,no signs of toxicity such as convulsion,salivation,diarrhea,sleep and coma were observed during 30 minutes and 14 days,so the lethal dose was higher than 2000 mg/kg for each extract and fraction.The subacute toxicity results showed that at a dose of 250 mg/kg,61.10%of the animals died and the rest developed morbidity.On the other hand,at a dose of 100 mg/kg,all the animals were still alive after 28 days,with no morbidity and the biochemical parameters were normal with no abnormalities in the liver,kidneys and pancreas.Phytochemical screening indicated the presence of flavonoids,tannins,coumarins,and free quinones and the absence of alkaloids and anthocyanins.Conclusions:The extracts and fractions of Ammi majus L.are not toxic in the short and long term with a varied chemical composition.Toxicological tests on animals other than rodents and in the long term(more than 28 days)are needed to further confirm the safety of Ammi majus extracts.

Research on the Spillover Effects of Digital Transformation on the Sustainable Growth of Green Schools

The development of society and the advancement of science and technology have led to the widespread integration of digital transformation in the field of education.However,the current establishment of green schools faces various challenges,including non-environmental building facilities,high renovation costs,low organizational management efficiency,high energy consumption,outdated office tools,and insufficient environmental awareness among teachers and students.Through thorough research and analysis,it becomes evident that digital technology can play a pivotal role in addressing these challenges and contribute to all aspects of green school establishment.The incorporation of digital thinking concepts is essential for the construction of ecologically civilized campuses and inclusive innovation.The process of digital design and transformation proves instrumental in optimizing both software and hardware facilities within the campus,thereby reducing energy consumption.Simultaneously,comprehensive digital teaching management enhances overall efficiency in management and service delivery.Innovative digital teaching and learning models emerge as transformative tools,providing new avenues to create low-carbon,green classrooms for both teachers and students.By exploring the application of digital transformation in establishing green schools and examining the resulting spillover effects,valuable insights can be gained.These insights,in turn,serve as reference points for building diversified digital technology paths on campus and fostering the creation of green schools.

CrossLinkNet: An Explainable and Trustworthy AI Framework for Whole-Slide Images Segmentation

In the intelligent medical diagnosis area,Artificial Intelligence(AI)’s trustworthiness,reliability,and interpretability are critical,especially in cancer diagnosis.Traditional neural networks,while excellent at processing natural images,often lack interpretability and adaptability when processing high-resolution digital pathological images.This limitation is particularly evident in pathological diagnosis,which is the gold standard of cancer diagnosis and relies on a pathologist’s careful examination and analysis of digital pathological slides to identify the features and progression of the disease.Therefore,the integration of interpretable AI into smart medical diagnosis is not only an inevitable technological trend but also a key to improving diagnostic accuracy and reliability.In this paper,we introduce an innovative Multi-Scale Multi-Branch Feature Encoder(MSBE)and present the design of the CrossLinkNet Framework.The MSBE enhances the network’s capability for feature extraction by allowing the adjustment of hyperparameters to configure the number of branches and modules.The CrossLinkNet Framework,serving as a versatile image segmentation network architecture,employs cross-layer encoder-decoder connections for multi-level feature fusion,thereby enhancing feature integration and segmentation accuracy.Comprehensive quantitative and qualitative experiments on two datasets demonstrate that CrossLinkNet,equipped with the MSBE encoder,not only achieves accurate segmentation results but is also adaptable to various tumor segmentation tasks and scenarios by replacing different feature encoders.Crucially,CrossLinkNet emphasizes the interpretability of the AI model,a crucial aspect for medical professionals,providing an in-depth understanding of the model’s decisions and thereby enhancing trust and reliability in AI-assisted diagnostics.

A Linked List Encryption Scheme for Image Steganography without Embedding

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information undetectable.To enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover modification.In this paper,a novel encrypted communication scheme for image SWE is proposed.It reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer domains.By employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be identified.The receiver can restore the secretmessage fromthe received image using only the list header position information.The scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover image.It boasts high concealment and security,along with a complete message restoration rate,making it resistant to steganalysis.Furthermore,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of robustness.To validate the proposed framework,practical tests and comparisons are conducted using multiple datasets.The results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey

To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising technique.With the widespread use of IoHT,nonetheless,privacy infringements such as IoHT data leakage have raised serious public concerns.On the other side,blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems.In this survey,a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection.In addition,various types of privacy challenges in IoHT are identified by examining general data protection regulation(GDPR).More importantly,an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented.Finally,several challenges in four promising research areas for blockchain-based IoHT systems are pointed out,with the intent of motivating researchers working in these fields to develop possible solutions.

A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric vehicles.However,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust issues.In this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch center.Based on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social welfare.We use blockchain to record power trading data for trusted pricing and use smart contracts for transaction matching.The simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

A survey on blockchain-based integrity auditing for cloud data

With the rapid advancement of cloud computing,cloud storage services have developed rapidly.One issue that has attracted particular attention in such remote storage services is that cloud storage servers are not enough to reliably save and maintain data,which greatly affects users’confidence in purchasing and consuming cloud storage services.Traditional data integrity auditing techniques for cloud data storage are centralized,which faces huge security risks due to single-point-of-failure and vulnerabilities of central auditing servers.Blockchain technology offers a new approach to this problem.Many researchers have endeavored to employ the blockchain for data integrity auditing.Based on the search of relevant papers,we found that existing literature lacks a thorough survey of blockchain-based integrity auditing for cloud data.In this paper,we make an in-depth survey on cloud data integrity auditing based on blockchain.Firstly,we cover essential basic knowledge of integrity auditing for cloud data and blockchain techniques.Then,we propose a series of requirements for evaluating existing Blockchain-based Data Integrity Auditing(BDIA)schemes.Furthermore,we provide a comprehensive review of existing BDIA schemes and evaluate them based on our proposed criteria.Finally,according to our completed review and analysis,we explore some open issues and suggest research directions worthy of further efforts in the future.

Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach

Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)applications.For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for CE.Firstly,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction problem.Then,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse adaptation.The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.

Privacy preservation in permissionless blockchain:A survey

Permissionless blockchain,as a kind of distributed ledger,has gained considerable attention because of its openness,transparency,decentralization,and immutability.Currently,permissionless blockchain has shown a good application prospect in many fields,from the initial cryptocurrency to the Internet of Things(IoT)and Vehicular Ad-Hoc Networking(VANET),which is considered as the beginning of rewriting our digital infrastructure.However,blockchain confronts some privacy risks that hinder its practical applications.Though numerous surveys reviewed the privacy preservation in blockchain,they failed to reveal the latest advances,nor have they been able to conduct a unified standard comprehensive classification of the privacy protection of permissionless blockchain.Therefore,in this paper,we analyze the specific characteristics of permissionless blockchain,summarize the potential privacy threats,and investigate the unique privacy requirements of blockchain.Existing privacy preservation technologies are carefully surveyed and evaluated based on our proposed evaluation criteria.We finally figure out open research issues as well as future research directions from the perspective of privacy issues.

Chiller faults detection and diagnosis with sensor network and adaptive 1D CNN

Computer-empowered detection of possible faults for Heating,Ventilation and Air-Conditioning(HVAC)subsystems,e.g.,chillers,is one of the most important applications in Artificial Intelligence(AI)integrated Internet of Things(IoT).The cyber-physical system greatly enhances the safety and security of the working facilities,reducing time,saving energy and protecting humans’health.Under the current trends of smart building design and energy management optimization,Automated Fault Detection and Diagnosis(AFDD)of chillers integrated with IoT is highly demanded.Recent studies show that standard machine learning techniques,such as Principal Component Analysis(PCA),Support Vector Machine(SVM)and tree-structure-based algorithms,are useful in capturing various chiller faults with high accuracy rates.With the fast development of deep learning technology,Convolutional Neural Networks(CNNs)have been widely and successfully applied to various fields.However,for chiller AFDD,few existing works are adopting CNN and its extensions in the feature extraction and classification processes.In this study,we propose to perform chiller FDD using a CNN-based approach.The proposed approach has two distinct advantages over existing machine learning-based chiller AFDD methods.First,the CNN-based approach does not require the feature selection/extraction process.Since CNN is reputable with its feature extraction capability,the feature extraction and classification processes are merged,leading to a more neat AFDD framework compared to traditional approaches.Second,the classification accuracy is significantly improved compared to traditional methods using the CNN-based approach.

Virome in healthy pangolins reveals compatibility with multiple potentially zoonotic viruses

Previous studies have identified multiple viruses in dead or severely diseased pangolins,but descriptions of the virome in healthy pangolins are lacking.This poses a greater risk of cross-species transmission due to poor preventive awareness and frequent interactions with breeders.In this study,we investigated the viral composition of 34 pangolins with no signs of disease at the time of sampling and characterized a large number of arthropodassociated viruses belonging to 11 families and vertebrate viruses belonging to eight families,including those with pathogenic potential in humans and animals.Several important vertebrate viruses were identified in the pangolins,including parvovirus,pestivirus,and picobirnavirus.The picobirnavirus was clustered with human and grey teal picobirnaviruses.Viruses with cross-species transmission ability were also identified,including circovirus,rotavirus,and astrovirus.Our study revealed that pangolins are frequently exposed to arthropod-associated viruses in the wild and can carry many vertebrate viruses under natural conditions.This study provides important insights into the virome of pangolins,underscoring the importance of monitoring potential pathogens in healthy pangolins to prevent outbreaks of infectious diseases in domesticated animals and humans.

Distributed scheduling for multi-energy synergy system considering renewable energy generations and plug-in electric vehicles:A level-based coupled optimization method

Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.

Graphene oxide-coated microbottle resonators for relative humidity sensing

Graphene oxide(GO)is a 2D coating material used to improve fiber optics sensors’response to relative humidity.Microbottle resonators(MBRs)have garnered more attention as sensing media structures.An MBR with a 190μm diameter was coated with GO.Then,tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25℃.The MBR showed a higher Q factor before and after GO coating.The sensitivity of 0.115 dB/%RH was recorded with the 190μm GO-coated MBR sample compared to a sensitivity of 0.022 dB/%RH for the uncoated MBR sample.These results show that the MBR can be used in fiber optic sensing applications for environmental sensing.

AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence:Cases,Applications,Issues,and Future Directions

This study explores the potential of Artificial Intelligence(AI)in early screening and prognosis of Dry Eye Disease(DED),aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the promising opportunities,challenges such as diverse diagnostic evidence,complex etiology,and interdisciplinary knowledge integration impede the interpretability,reliability,and applicability of AI-based DED detection methods.The research conducts a comprehensive review of datasets,diagnostic evidence,and standards,as well as advanced algorithms in AI-based DED detection over the past five years.The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques:(1)those with ground truth and/or comparable standards,(2)potential AI-based methods with significant advantages,and(3)supplementary methods for AI-based DED detection.The study proposes suggested DED detection standards,the combination of multiple diagnostic evidence,and future research directions to guide further investigations.Ultimately,the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations,advanced methods,challenges,and potential future perspectives,emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.

AWI-BS: An adaptive weight incentive for blockchain sharding

The sharding technique enables blockchain to process transactions in parallel by dividing blockchain nodes into small groups,each of which handles a subset of all transactions.One of the issues with blockchain sharding is generating a large number of cross-shard transactions that need to be checked on the output shard as well as the destination shard.Our analysis suggests that the processing efficiency of cross-shard transactions is consistent with the barrel effect,i.e.,that efficiency is more dependent on slower processing shard.Most of the existing studies focus on how to deal with cross-shard transactions,but neglecting the fact that the relative independence between sharding results in different incentive costs between sharding.We perform a sharding analysis on 100,000 real transactions data on Ethereum,and the results show that there is a large difference in gas prices between different shards indeed.In this paper,we propose an Adaptive Weight Incentive(AWI)for Blockchain Sharding,which uses adaptive weight in place of traditional incentive,to address the problem of differing incentive costs for each shard.Take Ethereum as an example,AWI-BS computes the weight of a transaction as a function of a combination of the underlying gas price,the latency of the transaction,and the urgency of the transaction.Then the node chooses which transaction to pack based on the AWI-BS.Lastly,we also perform an in-depth analysis of AWI-BS's security and effectiveness.The evaluation indicates that AWI-BS outperforms the other alternatives in terms of transaction confirmation latency,transaction hit rate,and system throughput.