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.

Capability-based IoT access control using blockchain

Internet of Things(IoT)devices facilitate intelligent service delivery in a broad range of settings,such as smart offices,homes and cities.However,the existing IoT access control solutions are mainly based on conventional identity management schemes and use centralized architectures.There are knowm security and privacy limitations with such schemes and architectures,such as the single-point failure or surveillance(e.g.,device tracking).Hence,in this paper,we present an architecture for capability-based IoT access control utilizing the blockchain and decentralized identifiers to manage the identity and access control for IoT devices.Then,we propose a protocol to provide a systematic view of system interactions,to improve security.We also implement a proof-of-concept prototype of the proposed approach and evaluate the prototype using a real-world use case.Our evaluation results show that the proposed solution is feasible,secure,and scalable.

Effect of rain enrichment on soil respiration of Nitraria sphaerocarpa community in a hyperarid area

In order to analyze the effect of rain enrichment on soil respiration rate of a Nitraria sphaerocarpa community, we measured soil respiration rate in bare and vegetated areas in a hyperarid area （Dunhuang） during the growing season. Results show that rain enrichment can increase bare and vegetated soil respiration rates. The more rainfall enrichment, the greater the increment and the longer duration time effect for soil respiration rate. 200% （16 mm） and 300% （24 mm） of rain enrichment can significantly increase bare soil respiration rates by 90% and 106% （P〈0.01）, respectively. By contrast, areas with 100% （8 mm）, 200% （16 mm） and 300% （24 mm） of rain enrichment can significantly increase shrub area respiration rates by 68%, 157% and 205% （P〈0.01）, respectively. The response time of bare and vegetated soil respiration to rainfall enrichment is asynchronous. Response variable of soil respiration in vegetated soil is higher （118%） than in bare soil. There was significant positive correlation between soil respiration rate and soil water content during the growing season （P〈0.01）. For every 1 mm increment of precipitation, soil respiration rate increased by 0.01 and 0.04 pmol/（m2.s）, respectively in vegetated and bare soils.

Enhanced Palladium Adsorption by Immobilized <i>Pichia pastoris</i>: A Reusable Catalyst for Reduction of 4-Nitrophenol

In this paper, the immobilized pellets were prepared by embedding Pichia pastoris (P. pastoris) with sodium alginate and used for the recovery of palladium ions in solution. The catalytic activity of immobilized Pd/P. pastoris for 4-nitrophenol (4-NP) was studied. The results showed that the recovery rate of Pd ions could be significantly increased by immobilized cells. The immobilized Pd/P. pastoris pellets showed good catalytic activity for the reduction of 4-NP, and the catalyst remained good catalytic activity even after multiple reuses.

Recent Advances in Particle Swarm Optimization for Large Scale Problems

Accompanied by the advent of current big data ages,the scales of real world optimization problems with many decisive design variables are becoming much larger.Up to date,how to develop new optimization algorithms for these large scale problems and how to expand the scalability of existing optimization algorithms have posed further challenges in the domain of bio-inspired computation.So addressing these complex large scale problems to produce truly useful results is one of the presently hottest topics.As a branch of the swarm intelligence based algorithms,particle swarm optimization (PSO) for coping with large scale problems and its expansively diverse applications have been in rapid development over the last decade years.This reviewpaper mainly presents its recent achievements and trends,and also highlights the existing unsolved challenging problems and key issues with a huge impact in order to encourage further more research in both large scale PSO theories and their applications in the forthcoming years.

A Perspective of Conventional and Bio-inspired Optimization Techniques in Maximum Likelihood Parameter Estimation

Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.

Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality

Enhancing the terrestrial ecosystem carbon sink(referred to as terrestrial C sink) is an important way to slow down the continuous increase in atmospheric carbon dioxide(CO_(2)) concentration and to achieve carbon neutrality target.To better understand the characteristics of terrestrial C sinks and their contribution to carbon neutrality,this review summarizes major progress in terrestrial C budget researches during the past decades,clarifies spatial patterns and drivers of terrestrial C sources and sinks in China and around the world,and examines the role of terrestrial C sinks in achieving carbon neutrality target.According to recent studies,the global terrestrial C sink has been increasing from a source of (-0.2±0.9) Pg C yr^(-1)(1 Pg=1015g)in the 1960s to a sink of (1.9±1.1) Pg C yr^(-1) in the 2010s.By synthesizing the published data,we estimate terrestrial C sink of 0.20–0.25 Pg C yr^(-1) in China during the past decades,and predict it to be 0.15–0.52 Pg C yr^(-1) by 2060.The terrestrial C sinks are mainly located in the mid-and high latitudes of the Northern Hemisphere,while tropical regions act as a weak C sink or source.The C balance differs much among ecosystem types:forest is the major C sink;shrubland,wetland and farmland soil act as C sinks;and whether the grassland functions as C sink or source remains unclear.Desert might be a C sink,but the magnitude and the associated mechanisms are still controversial.Elevated atmospheric CO_(2) concentration,nitrogen deposition,climate change,and land cover change are the main drivers of terrestrial C sinks,while other factors such as fires and aerosols would also affect ecosystem C balance.The driving factors of terrestrial C sink differ among regions.Elevated CO_(2) concentration and climate change are major drivers of the C sinks in North America and Europe,while afforestation and ecological restoration are additionally important forcing factors of terrestrial C sinks in China.For future studies,we recommend the necessity for intensive and long-term ecosystem C monitoring over broad geographic scale to improve terrestrial biosphere models for accurately evaluating terrestrial C budget and its dynamics under various climate change and policy scenarios.

Internet of Things to network smart devices for ecosystem monitoring

Smart, real-time, low-cost, and distributed ecosystem monitoring is essential for understanding and managing rapidly changing ecosystems. However, new techniques in the big data era have rarely been introduced into operational ecosystem monitoring, particularly for fragile ecosystems in remote areas.We introduce the Internet of Things(IoT) techniques to establish a prototype ecosystem monitoring system by developing innovative smart devices and using IoT technologies for ecosystem monitoring in isolated environments. The developed smart devices include four categories: large-scale and nonintrusive instruments to measure evapotranspiration and soil moisture, in situ observing systems for CO2 and d13 C associated with soil respiration, portable and distributed devices for monitoring vegetation variables, and Bi-CMOS cameras and pressure trigger sensors for terrestrial vertebrate monitoring. These new devices outperform conventional devices and are connected to each other via wireless communication networks. The breakthroughs in the ecosystem monitoring IoT include new data loggers and longdistance wireless sensor network technology that supports the rapid transmission of data from devices to wireless networks. The applicability of this ecosystem monitoring IoT is verified in three fragile ecosystems, including a karst rocky desertification area, the National Park for Amur Tigers, and the oasis-desert ecotone in China. By integrating these devices and technologies with an ecosystem monitoring information system, a seamless data acquisition, transmission, processing, and application IoT is created. The establishment of this ecosystem monitoring IoT will serve as a new paradigm for ecosystem monitoring and therefore provide a platform for ecosystem management and decision making in the era of big data.

Responses of soil respiration to simulated precipitation pulses in semiarid steppe under different grazing regimes

Aims Precipitation pulses and different land use practices(such as grazing)play important roles in regulating soil respiration and carbon balance of semiarid steppe ecosystems in Inner Mongolia.However,the interactive effects of grazing and rain event magnitude on soil respiration of steppe ecosystems are still unknown.We conducted a manipulative experiment with simulated precipitation pulses in Inner Mongolia steppe to study the possible responses of soil respiration to different precipitation pulse sizes and to examine how grazing may affect the responses of soil respiration to precipitation pulses.Methods Six water treatments with different precipitation pulse sizes(0,5,10,25,50 and 100 mm)were conducted in the ungrazed and grazed sites,respectively.Variation patterns of soil respiration of each treatment were determined continuously after the water addition treatments.Important Findings Rapid and substantial increases in soil respiration occurred 1 day after the water treatments in both sites,and the magnitude and duration of the increase in soil respiration depended on pulse size.Significantly positive relationships between the soil respiration and soil moisture in both sites suggested that soil moisture was the most important factor responsible for soil respiration rate during rain pulse events.The ungrazed site maintained significantly higher soil moisture for a longer time,which was the reason that the soil respiration in the ungrazed site was maintained relatively higher rate and longer period than that in the grazed site after a rain event.The significant exponential relationship between soil temperature and soil respiration was found only in the plots with the high water addition treatments(50 and 100 mm).Lower capacity of soil water holding and lower temperature sensitivity of soil respiration in the grazed site indicated that degraded steppe due to grazing might release less CO_(2) to the atmosphere through soil respiration under future precipitation and temperature scenarios.

Seasonal and interannual variations of ecosystem photosynthetic characteristics in a semi-arid grassland of Northern China

The ecosystem apparent quantum yield(α),maximum rate of gross CO_(2) assimilation(Pmax)and daytime ecosystem respiration rate(R.),reflecting the physiological functioning of ecosystem,are vital photosynthetic parameters for the estimation of ecosystem carbon budget.Climatic drivers may affect photosynthetic parameters both directly and indirectly by altering the response of vegetation.However,the relative contribution and regulation pathway of environmental and physiological controls remain unclear,especially in semi-arid grasslands.We analyzed seasonal and interannual variations of photosynthetic parameters derived from eddy-covariance observation in a typical semi-arid grassland in Inner Mongolia,Northern China,over 12 years from 2006 to 2017.Regression analyses and a structural equation model(SEM)were adopted to separate the contributions of environmental and physiological effects.The photosynthetic parameters showed unimodal seasonal patterns and significantly interannual variations.Variations of air temperature(T,)and soil water content(SWC)drove the seasonal patterns of photosynthetic parameters,while SWC predominated their interannual variations.Moreover,contrasting with the predominant roles of T,onαand Ra,SWC explained more variance of Pmax than T,Results of SEM revealed that environmental factors impacted photosynthetic parameters both directly and indirectly through regulating physiological responses reflected by stomatal conductance at the canopy level.Moreover,leaf area index(LAl)directly affectedα,Pmax and R,and dominated the variation of Pmax.On the other hand,SWC influenced photosynthetic parameters indirectly through LAl and canopy surface conductance(gc).Our findings highlight the importance of physiological regulation on the photosynthetic parameters and carbon assimilation capacity,especially in water-limitedgrassland ecosystems.

Evapotranspiration and soil water relationships in a range of disturbed and undisturbed ecosystems in the semi-arid Inner Mongolia,China

Aims Evapotranspiration(ET)is a key component of water balance and is closely linked to ecosystem productivity.In arid regions,large proportion of precipitation(PPT)is returned to the atmosphere through ET,with only a small amount available to plants.Our objective was to examine the variability in ET–soil water relationship based on a set of ecosystems that are representative for semi-arid Inner Mongolia and its main land use practices.Methods This study used Eddy covariance(EC)data of water vapor(i.e.ET,mm),PPT(mm),soil volumetric water content(VWC,%),root biomass density and soil properties from three paired sites in semi-arid Inner Mongolia:cropland(Cropland-D)versus undisturbed grassland(Steppe-D),grazed grassland(Grazed Steppe-X)versus fenced grassland(Fenced Steppe-X)and poplar plantation(Poplar-K)versus undisturbed shrubland(Shrubland-K).The paired sites experienced similar climate conditions and were equipped with the same monitoring systems.Important Findings The ET/PPT ratio was significantly lower at Cropland-D and Grazed Steppe-X in comparison to the undisturbed grasslands,Steppe-D and Fenced Steppe-X.These differences are in part explained by the lower VWC in the upper soil layers associated with compaction of surface soil in heavily grazed and fallow fields.In contrast,the ET/PPT ratio was much higher at the poplar plantation compared to the undisturbed shrubland because poplar roots tap groundwater.The VWC of different soil layers responded differently to rainfall events across the six study sites.Except for Poplar-K,ETwas significantly constrained by VWC at the other five sites,although the correlation coefficients varied among soil layers.The relative contribution of soil water to ET correlated with the density of root biomass in the soil(R2=0.67,P<0.01).The soil water storage in the upper 50 cm of soil contributed 59,43,64 and 23%of total water loss as ET at Steppe-D,Cropland-D,Shrubland-K and Poplar-K,respectively.Our across-site analysis indicates that the site level of soil water for ET differs between land use and land cover type due to altered root distribution and/or soil physical properties.As a result,we recommend that ecosystem models designed to predict the response of a wide variety of vegetation to climatic variation in arid regions include more detail in defining soil layers and interactions between evaporation,infiltration and root distribution patterns.

Biophysical regulations of NEE light response in a steppe and a cropland in Inner Mongolia

Aims Ecosystem carbon models often require accurate net ecosystem exchange of CO_(2)(NEE)light-response parameters,which can be derived from the Michaelis–Menten equation.These parameters include maximum net ecosystem exchange(NEE_(max)),apparent quantum use efficiency(a)and daytime ecosystem respiration rate(R_(e)).However,little is known about the effects of land conversion between steppe and cropland on these parameters,especially in semi-arid regions.To understand how these parameters vary in responses to biotic and abiotic factors under land conversions,seasonal variation of light-response parameters were evaluated for a steppe and a cropland of Inner Mongolia,China,during three consecutive years(2006–08)with different precipitation amounts.Methods NEE was measured over a steppe and a cropland in Duolun,Inner Mongolia,China,using the eddy covariance technique,and NEE light-response parameters(NEE_(max),α and R_(e))were derived using the Michaelis–Menten model.Biophysical regulations of these parameters were evaluated using a stepwise regression analysis.Important Findings The maximum absolute values of NEE_(max) occurred in the meteorological regimes of 15℃<T_(a)<25℃,vapor pressure deficit(VPD)<1 KPa and 0.21 m^(3) m^(-3)<volumetric soil water content at 10 cm(SWC)<0.28 m^(3) m^(-3) for both the steppe and the cropland ecosystems.The variations of α and R_(e) showed no regular variation pattern in different T_(air),VPD and SWC regimes.Under the same regime of T_(air),VPDand SWC,the cropland had higher absolute values of NEE_(max) than the steppe.Canopy conductance and leaf area index(LAI)were dominant drivers for variations in NEE light-response parameters of the steppe and the cropland.The seasonal variation of NEE light-response parameters followed the variation of LAI for two ecosystems.The peak values of all light-response parameters for the steppe and the cropland occurred fromJuly to August.The values of NEE light-response parameters(NEE_(max),α and R_(e))were lower in the driest year(2007).Seasonally averaged NEE light-response parameters for the cropland surpassed those for the steppe.Land conversion from steppe to cropland enhanced NEE light-response parameters during the plant growing period.These results will have significant implications for improving the models on regional NEE variation under climate change and land-use change scenarios.

Carbon and water fluxes in ecologically vulnerable areas in China

Ecologic vulnerable areas(EVAs)are the regions where ecosystems are fragile and vulnerable to sufferfrom degradation with external disturbances,e.g.environmental changes and human activities(Fengetal.2022;Wangetal.2019).EVAsinChina are widely distributed and account for more than 55%China's landarea(Ministryof Ecologyand Environment of the People's Republic of China 2008).Theecosystem in EVAs,chartered with low stability,weak resistance and high vulnerability,has been experiencing significant degradation owing to the impacts of global climate change and human activities(Bai et al.2018;Chen et al.202l;Yuetal.2022).

Population turnover promotes fungal stability in a semi-arid grassland under precipitation shifts

Aims Bacteria and fungi are two primary groups of soil microbes,and their stability determines the persistence of microbial functions in response to a changing environment.Recent studies reported higher fungal than bacterial stability under precipitation alteration,the underlying mechanisms,however,remain elusive.Methods A 3-year precipitation manipulation experiment in a semi-arid grassland was used to compare the bacterial and fungal diversities,including alpha diversity,beta diversity and microbial community composition turnover,in response to precipitation manipulations.A framework is proposed to understand the stability properties of bacteria and fungi under precipitation alteration.We conceived a diagrammatic valley to illustrate microbial stability with the depth representing resistance and the width ecological resilience.Important Findings We found that±60%in precipitation significantly reduced the richness and increased the evenness of bacteria but had trivial impacts on fungi.Precipitation alteration yielded stronger impacts on the variation in alpha diversity of bacteria than fungi,suggesting that the bacterial community is more sensitive to water stress than the fungal community.Moreover,fungi had wider composition turnover than that of bacteria,indicating higher composition variation of fungi than bacteria.The population turnover of fungi,reflected by composition variation,coefficient variation of diversity index and composition turnover,was larger than that of bacteria at both temporal and spatial scales,indicating the population turnover promotes fungal stability.The higher stability of fungal community in tolerating water stress is analogous to a ball in a wide valley that swing substantially but remain close to its steady state;while the lower stability of bacteria community is analogous to a ball that swings slightly but stay far away from its steady state.Our finding that the fungal community had higher stability than bacterial community in a semi-arid grassland might be applicable to other biomes.

Evaluating the influences of measurement time and frequency on soil respiration in a semiarid temperate grassland

Soil respiration(Soil R)is one of the largest CO2fluxes from terrestrial ecosystems to the atmosphere.The largely seasonal and daily patterns of Soil R in semiarid grassland ecosystems indicate that measurement time and frequency would have significant influences on the assessment of seasonal soil carbon release.Based on a three-year continuous measurement of Soil R in a semiarid grassland,we found that the Soil R value measured at around 10:00 o’clock local time was the closest to its daily mean,while the value at 14:00 o’clock was found to be the highest daily rate.A measurement frequency higher than every 10 days was necessary for estimating the seasonal Soil R and its temperature sensitivity(Q10)reasonably.Our study would be useful as guidelines for manual Soil R measurements and model data selection in semiarid temperate grasslands.

Nitrogen addition amplified water effects on species composition shift and productivity increase

Aims Water and nitrogen(N)are two key resources in dryland ecosystems,but they may have complex interactive effects on the community structure and ecosystem functions.How future precipitation(rainfall vs snowfall)change will impact aboveground net primary production(ANPP)is far from clear,especially when combined with increasing N availability.Methods In this study,we investigated changes in community productivity,abundance and aboveground biomass of two dominant plant functional groups(PFGs),i.e.perennial rhizome grasses(PR)and perennial bunchgrasses(PB)under the impacts of increased precipitation(rainfall vs snowfall)combined with N addition in a semiarid temperate steppe.Important Findings Summer rainfall augmentation marginally increased community ANPP,whereas it significantly increased the abundance and aboveground biomass of PR,but not those of PB.Summer rainfall addition increased the fraction of PR biomass(fPR)while decreased that of PB(fPB).Spring snow addition had no effect on aboveground biomass of either compositional PFG although it marginally increased community ANPP.Nitrogen addition significantly increased community ANPP with greater increase in PR under summer rainfall addition,indicating strong interactive effects on community ANPP largely by enhancing PR biomass.We also found a nonlinear increase in the positive effect of nitrogen addition on productivity with the increased precipitation amount.These findings indicate an amplified impact of precipitation increase on grassland productivity under the accelerated atmospheric N deposition in the future.

Transactional Network Analysis and Money Laundering Behavior Identification of Central Bank Digital Currency of China

With the gradual application of central bank digital currency(CBDC)in China,it brings new payment methods,but also potentially derives new money laundering paths.Two typical application scenarios of CBDC are considered,namely the anonymous transaction scenario and real-name transaction scenario.First,starting from the interaction network of transactional groups,the degree distribution,density,and modularity of normal and money laundering transactions in two transaction scenarios are compared and analyzed,so as to clarify the characteristics and paths of money laundering transactions.Then,according to the two typical application scenarios,different transaction datasets are selected,and different models are used to train the models on the recognition of money laundering behaviors in the two datasets.Among them,in the anonymous transaction scenario,the graph convolutional neural network is used to identify the spatial structure,the recurrent neural network is fused to obtain the dynamic pattern,and the model ChebNet-GRU is constructed.The constructed ChebNet-GRU model has the best effect in the recognition of money laundering behavior,with a precision of 94.3%,a recall of 59.5%,an F1 score of 72.9%,and a microaverage F1 score of 97.1%.While in the real-name transaction scenario,the traditional machine learning method is far better than the deep learning method,and the micro-average F1 score of the random forest and XGBoost models both reach 99.9%,which can effectively identify money laundering in currency transactions.