Upcycling municipal solid waste to sustainable hydrogen via two-stage gasification-reforming

As global municipal solid waste(MSW)quantities continue to escalate,serious socio-environmental challenges arise,necessitating innovative solutions.Waste-to-hydrogen(WTH)via two-stage gasification-reforming(TSGR)presents an emergent technology for MSW upcycling,offering to ease waste management burdens and bolster the burgeoning hydrogen economy.Despite early initiatives to advance TSGR technology,a cohesive and critical analysis of cutting-edge knowledge and strategies to enhance hydrogen production remains lacking.This review aggregates literature on MSW upcycling to hydrogen via TSGR,with a focus on optimizing process control and catalytic efficiency.It underscores technological avenues to augment hydrogen output,curtail catalyst costs,and refine system performance.Particularly,the review illuminates the potential for integrating chemical and calcium looping into TSGR processes,identifying opportunities,and pinpointing challenges.The review concludes with a summary of the current state of techno-economic analysis for this technology,presenting outstanding challenges and future research directions,with the ultimate goal of transitioning WTH from theoretical to practical application.

Multi-time scale identification of key kinetic processes for lithium-ion batteries considering variable characteristic frequency

The electrification of vehicles puts forward higher requirements for the power management efficiency of integrated battery management systems as the primary or sole energy supply.In this paper,an efficient adaptive multi-time scale identification strategy is proposed to achieve high-fidelity modeling of complex kinetic processes inside the battery.More specifically,a second-order equivalent circuit model network considering variable characteristic frequency is constructed based on the high-frequency,medium-high-frequency,and low-frequency characteristics of the key kinetic processes.Then,two coupled sub-filters are developed based on forgetting factor recursive least squares and extended Kalman filtering methods and decoupled by the corresponding time-scale information.The coupled iterative calculation of the two sub-filter modules at different time scales is realized by the voltage response of the kinetic diffusion process.In addition,the driver of the low-frequency subalgorithm with the state of charge variation amount as the kernel is designed to realize the adaptive identification of the kinetic diffusion process parameters.Finally,the concept of dynamical parameter entropy is introduced and advocated to verify the physical meaning of the kinetic parameters.The experimental results under three operating conditions show that the mean absolute error and root-mean-square error metrics of the proposed strategy for voltage tracking can be limited to 13 and 16 mV,respectively.Additionally,from the entropy calculation results,the proposed method can reduce the dispersion of parameter identification results by a maximum of 40.72%and 70.05%,respectively,compared with the traditional fixed characteristic frequency algorithms.The proposed method paves the way for the subsequent development of adaptive state estimators and efficient embedded applications.

Lateral femoral tunnel preparation and graft fixation for anterior cruciate ligament reconstruction–A discussion

This article provides a discussion and commentary around the recent advances in arthroscopic anterior cruciate ligament reconstruction(ACLR),with a focus on the aspects of lateral femoral tunnel preparation and graft fixation techniques.The paper explores and comments on a recently published review by Dai et al,titled"Research progress on preparation of lateral femoral tunnel and graft fixation in ACLR",while providing insight into its relevance within the field of ACLR,and recommendations for future research.

A review of data-driven whole-life state of health prediction for lithium-ion batteries:Data preprocessing,aging characteristics,algorithms,and future challenges

Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.

Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.

Glacier area change (1993-2019) and its relationship to debris cover, proglacial lakes, and morphological parameters in the Chandra-Bhaga Basin, Western Himalaya, India

Glacier inventories serve as critical baseline data for understanding the impacts of climate change on glaciers.The present study maps the outlines of glaciers in the Chandra-Bhaga Basin(western Himalaya)for the years 1993,2000,2010,and 2019 using Landsat Thematic Mapper(TM),Enhanced Thematic Mapper(ETM),and Operational Land Imager(OLI)datasets.A total of 251 glaciers,each having an area above 0.5 km^(2),were identified,which include 216 clean-ice and 35 debris-covered glaciers.Area changes are estimated for three periods:1993-2000,2000-2010,and 2010-2019.The total glacierized area was 996±62 km^(2) in 1993,which decreased to 973±70 km^(2) in 2019.The mean rate of glacier area loss was higher in the recent decade(2010-2019),at 0.036 km^(2),compared to previous decades(0.029 km^(2) in 2000-2010 and 0.025 km^(2) in 1993-2000).Supraglacial debris cover changes are also mapped over the period of 1993 and 2019.It is found that the supraglacial debris cover increased by 14.12±2.54 km^(2)(15.2%)during 1993-2019.Extensive field surveys on Chhota Shigri,Panchi II,Patsio,Hamtah,Mulkila,and Yoche Lungpa glaciers were carried out to validate the glacier outlines and supraglacial debris cover estimated using satellite datasets.Controls of various morphological parameters on retreat were also analyzed.It is observed that small,clean ice,south oriented glaciers,and glaciers with proglacial lakes are losing area at faster rates than other glaciers in the basin.

Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

This paper focuses on the task of few-shot 3D point cloud semantic segmentation.Despite some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic discrimination.To tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding network.Concretely,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their wholes.Secondly,we designed a multi-prototype enhancement module to enhance the prototype discriminability.Specifically,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich semantics.Besides,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category similarity.Ablation studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic discrimination.Moreover,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.

A dicistrovirus increases pupal mortality in Spodoptera frugiperda by suppressing protease activity and inhibiting larval diet consumption

Understanding interactions between viruses and their hosts is conducive to enabling better application of viruses as biocontrol agents.Certain viruses carried by parasitic wasps enhance the parasitic efficiency of wasp-larvae by protecting them against the immune system of their Lepidopteran host.However,the relationship between prey pests and viruses found in predatory natural enemies remains unclear.Herein,we report the interaction between Arma chinensis virus-1(AcV-1),originally isolated from a predatory natural enemy,Arma chinensis(Hemiptera:Pentatomidae),and one of its prey species,Spodoptera frugiperda(Lepidoptera:Noctuidae).The results showed that the AcV-1 virus appeared harmful to the novel host S.frugiperda by inhibiting larval diet consumption and increasing pupal mortality.Meanwhile,sequencing data indicated that the virus altered the gene expression profiles of S.frugiperda.KEGG analysis showed that the proteasome and phagosome pathways related to protein degradation and immune response were significantly enriched.Although the expression levels of digestive enzyme genes did not change significantly,the total protease activity of AcV-1 virus-positive individuals was significantly decreased,suggesting that the virus inhibited diet consumption of S.frugiperda via the down-regulation of digestive enzyme activities.These results indicate that a virus initially isolated in a predatory natural enemy can decrease the fitness of its prey species.The virus was found to impact the host proteasome and phagosome pathways related to protein degradation and immunity,providing a potential mechanism to enhance controlling efficiency.

SHT-based public auditing protocol with error tolerance in FDL-empowered IoVs

With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep learning.Federated Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data sets.However,FDL can only guarantee data security and privacy among multiple clients during data training.If the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively affected.In this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs.Specifically,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and recovery.In addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data dynamics.Finally,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear groups.Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.

Evaluating the biosignature potential of ammonium in Proterozoic red beds and implications for the search for life on Mars

Over the past two decades,it has become increasingly apparent that early Mars may once have been warmer,wetter and more habitable for microbial life than it is today,which has spurred discussions about potential biosignatures that may be preserved in Martian sediments.An impediment to this line of research is the pervasive oxidation of Mars’surface due to photochemical oxidants that have likely destroyed remnants of organic matter.Here,we investigate whether nitrogen(N)transferred from biomass to phyllosilicate minerals during diagenesis can be preserved in oxidized mudrocks.We investigate two sequences of terrestrial Proterozoic red beds,namely the Sibley Group(1.4 Ga)in Canada and the Stoer Group(1.2 Ga)in Scotland,and we find enrichments in authigenic N in the range of several tens of ppm in both units.The highest concentrations(ca.100 ppm on average)are found in the most desiccated red beds of the Stoer Group,concurrent with enrichments in potassium(K).We discuss similarities and differences between the two sets of rocks with regards to salinity,pH,biological productivity and K-metasomatism,and we conclude that the ideal mechanism for the preservation of biogenic N in red beds may be in-situ release of ammonium from microbial mats into the clay substrate,possibly facilitated by early diagenetic,biologically induced illitization.Illite and smectite have been observed on Mars,and experiments suggest that Martian waters contained moderate amounts of dissolved K.Hence,it is conceivable that a similar K and N enrichment process could have occurred as to what we document for the Proterozoic,preserving evidence of life that may have survived to the modern day.

Synthesis strategies of hard carbon anodes for sodium-ion batteries

Sodium-ion battery(SIB)is an ideal candidate for large-scale energy storage due to high abundant sodium sources,relatively high energy density,and potentially low costs.Hard carbons,as one of the most promising anodes,could deliver high plateau capacities at low potentials,which boosts the energy densities of SIBs.Their slope capacities have been demonstrated from the defect adsorption of sodium ions,while the plateau capacity depends highly on intercalation and pore filling.Nevertheless,the specific structures of sodium ions stored in hard carbons have not been clarified,namely active sites of adsorption,intercalation,and pore-filling mechanisms.Therefore,delicate synthesis methods are required to prepare hard carbons with controllable specific structures,along with elucidating the precise active sites for enhancing the Na-ion storage performance.To offer databases for future designs,we summarized the synthesis strategies of hard carbon anodes for constructing active sites of plateau capacities.Synthesis methods were highlighted with corresponding influences on the meticulous structures of hard carbons and Na-ion storage behaviors.Last but not least,perspectives were proposed for developing hard carbon anodes from the points of research and practical applications.

Use of Farmers’ Indicators to Evaluate the Sustainability of Cropping Systems on Sloping Land in Yunnan Province, China

Diversity in the biophysical and socio-economic attributes of agricultural systems makes them uniquely niche based. Farmers are expert in local biophysical and socio-economic situations and can contribute in developing pragmatic indicators of agro-environmental development. During evaluation of an agricultural research project in Yunnan, China, local farmers were capable of evaluating the effects of modified technologies on existing cropping systems and discussed their attitudes to the interventions using their own indicators. Farmers＇ response can be grouped into seven major aspects： i） effects on income, ii） effects on production resources, iii） effects on crop management, iv） existing local knowledge about the technology, v） availability of inputs, vi） access to information, and vii） socio-economic conditions of farming households. Farmers concluded that environmental conditions in the experimental catchment in comparison to an adjacent untreated catchment were better in terms of soil and water losses, vegetation cover and natural resources, infrastructures and catchment management, use of environmentally-friendly technologies, and crop productivity. Success in soil and water conservation programmes depends on the efforts of the farmers and other local users and their greater involvement helps to identify more pragmatic indicators. Furthermore, it increases ownership of the programme, enhances interactions with the project scientists, increases farmers＇ awareness of ago-environmental problems and their possible consequences. These development will enable scientists to develop better targeted interventions and increase the likelihood of adoption of tested technologies by local communities. The use of paired adjacent catchments improved evaluation activities and is proposed as good practice for future catchment improvement programmes.

Processed beetroot(Beta vulgaris L.)as a natural antioxidant in mayonnaise:Effects on physical stability,texture and sensory attributes

The oxidative and physical stability of the reformulated mayonnaise with processed beetroot was investigated and compared with a control(mayonnaise without beetroot)and a commercially available product.Processing of beetroot had an impact on the structural integrity of the antioxidants present.Microwaving(960 W for 7 min)was advantageous for preserving the betalain and polyphenol content of beetroot compared to roasting(180◦C for 90 min)and boiling(100◦C for 30 min).The oxidative stability of mayonnaise samples was determined by Rancimat and the thiobarbituric(TBA)assay.The addition of microwaved beetroot significantly enhanced the oxidative stability of mayonnaise at the end of a storage period of 4 weeks(4◦C).Although no significant differences(P>0.05)were detected between the mayonnaise samples containing beetroot and the commercial control,the latter was less susceptible to oxidation during storage.The turbiscan stability index(TSI)revealed that the commercial mayonnaise was less prone to destabilization phenomena.All the textural parameters increased with the incorporation of beetroot.The sensory evaluation revealed that,with the exception of graininess and uniformity,most of the sensory attributes are preserved if not improved with the addition of beetroot.

Analysis and Design of Soils and Terrain Digital Database (SOTER) Management System Based on Object-Oriented Method

A SOTER management system was developed by analyzing, designing, programming, testing, repeated proceeding and progressing based on the object-oriented method. The function of the attribute database management is inherited and expanded in the new system. The integrity and security of the SOTER database are enhanced. The attribute database management, the spatial database management and the model base are integrated into SOTER based on the component object model (COM), and the graphical user interface (GUI) for Windows is used to interact with clients, thus being easy to create and maintain the SOTER, and convenient to promote the quantification and automation of soil information application.

Overcoming ischemia in the diabetic foot:Minimally invasive treatment options

As the global burden of diabetes is rapidly increasing,the incidence of diabetic foot ulcers is continuously increasing as the mean age of the world population increases and the obesity epidemic advances.A significant percentage of diabetic foot ulcers are caused by mixed micro and macro-vascular dysfunction leading to impaired perfusion of foot tissue.Left untreated,chronic limb-threatening ischemia has a poor prognosis and is correlated with limb loss and increased mortality;prompt treatment is required.In this review,the diagnostic challenges in diabetic foot disease are discussed and available data on minimally invasive treatment options such as endovascular revascularization,stem cells,and gene therapy are examined.

Algorithms to Calculate the Most Reliable Maximum Flow in Content Delivery Network

Calculating the most reliable maximum flow(MRMF)from the edge cache node to the requesting node can provide an important reference for selecting the best edge cache node in a content delivery network(CDN).However,SDBA,as the current state-of-the-art MRMF algorithm,is too complex to meet real-time computing needs.This paper proposes a set of MRMF algorithms:NWCD(Negative Weight Community Deletion),SCPDAT(Single-Cycle Preference Deletion Approximation algorithm with Time constraint)and SCPDAP(Single-Cycle Preference Deletion Approximation algorithm with Probability constraint).NWCD draws on the“flow-shifting”algorithm of minimum cost and maximum flow,and further defines the concept of negative weight community.This algorithm continuously deletes the negative weight communities,which can increase reliability while keeping the flow constant in the residual graph.It is proven that when all negative weight communities are deleted,the corresponding maximum flow is the MRMF.SCPDAT tries to approach the optimal solution to the greatest extent possible within the limited time,while SCPDAP tries to reach the probability threshold in the shortest amount of time.Both of these adopt the strategy of first deleting single-cycle communities(which contribute more to the reliability with lower time cost).Experiments show that,compared with SDBA,NWCD combined with the probabilistic pruning achieves an order of magnitude improvement in time cost,while SCPDAT and SCPDAP demonstrate better time performance and increased applicability.

Cadmium Tolerance and Accumulation in Wild Rice Species

When exposed to cadmium(Cd),rice(Oryza sativa L.)suffers a loss in biomass as well as an increased concentration of Cd within the plant.When looking for genes that can reduce Cd accumulation or increase Cd tolerance,the wild relatives of rice are under-utilised resources.In this study,a rapid hydroponic screening system was established using known tolerant and sensitive O.sativa accessions,and found that 10μmol/L Cd concentration appeared to be the optimum for screening Cd tolerance of rice cultivars.

Fine-Grained Multivariate Time Series Anomaly Detection in IoT

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)systems.Multivariate time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and malfunctions.However,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is unknown.Therefore,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators.Accordingly,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)framework.To avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly.Based on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators.Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

Effects of Root Growth of Deep and Shallow Rooting Rice Cultivars in Compacted Paddy Soils on Subsequent Rice Growth

Rice is often grown as multiple seasons in one year,alternating between flooded and upland systems.A major constraint,introduced from the flooded system,is a plough pan that may decrease rooting depth and productivity of follow-on upland rice.Roots penetrating the plough pan under flooded rice system can leave a legacy of weaker root growth pathways.Deeper rooting rice cultivars could have a bigger impact,but no direct evidence is available.To explore whether a deep rather than a shallow rooting rice cultivar grown in a flooded cropping cycle benefited deeper root growth of follow-on rice in an upland,reduced tillage cropping cycle,a simulated flooded paddy in greenhouse was planted with deep(Black Gora) and shallow(IR64) rooting cultivars and a plant-free control.Artificial plough pans were made in between the topsoil and subsoil to form different treatments with no plough pan(0.35 MPa),soft plough pan(1.03 MPa) and hard plough pan(1.70 MPa).After harvest of this ‘first season’ rice,the soil was drained and undisturbed to simulate zero-tillage upland and planted rice cultivar BRRI Dhan 28.The overall root length density(RLD),root surface area,the numbers of root tips and branching of BRRI Dhan 28 did not vary between plough pan and no plough pan treatments.Compared with the shallow rooting rice genotype,the deep rooting rice genotype as ‘first season’ crop produced 19% greater RLD,34% greater surface area and 29% more branching of BRRI Dhan 28 in the subsoil.In the topsoil,however,BRRI Dhan 28 had 28% greater RLD,35% greater surface area and 43% more branching for the shallow rather than deep rooting genotype planted in the ‘first season’.The results suggested that rice cultivar selection for a paddy cycle affects root growth of a follow-on rice crop grown under no-till,with benefits to subsoil access from deep rooting cultivars and topsoil proliferation for shallow rooting cultivars.

Mixing of miscible shear-thinning fluids in a lid-driven cavity

The concentration and velocity fields of two refractive index matched miscible shear-thinning fluids in a lid-driven cavity were investigated by using planar laser-induced fluorescence and particle image velocimetry,as well by computational fluid dynamics.Quantitative analyses show that the results obtained by flow simulations with the species transport model are in good agreement with the experimental results.The effects of different parameters were studied by using the intensity of segregation.For two fluids with the same rheological parameters,the relative amounts of liquids H_(1)/H and the power-law index n dominate the mixing process while the Reynolds number Re plays a marginal role.As for two fluids with density difference,buoyancy has significant influence on the mixing process.The dimensionless group Ar/Re(redefined such as to include shear thinning behavior)is proposed for assessing the effect of buoyancy and rheological properties on the mixing of miscible shear-thinning fluids.