Crossover from 2-dimensional to 3-dimensional aggregations of clusters on square lattice substrates

A Monte Carlo study on the crossover from 2-dimensional to 3-dimensional aggregations of clusters is presented. Based on the traditional cluster-cluster aggregation （CCA） simulation, a modified growth model is proposed. The clusters （including single particles and their aggregates） diffuse with diffusion step length 1 （1≤l≤7） and aggregate on a square lattice substrate. If the number of particles contained in a cluster is larger than a critical size Sc, the particles at the edge of the cluster have a possibility to jump onto the upper layer, which results in the crossover from 2-dimensional to 3-dimensional aggregations. Our simulation results are in good agreement with the experimental findings.

Network Intrusion Detection and Visualization Using Aggregations in a Cyber Security Data Warehouse

The challenge of achieving situational understanding is a limiting factor in effective, timely, and adaptive cyber-security analysis. Anomaly detection fills a critical role in network assessment and trend analysis, both of which underlie the establishment of comprehensive situational understanding. To that end, we propose a cyber security data warehouse implemented as a hierarchical graph of aggregations that captures anomalies at multiple scales. Each node of our proposed graph is a summarization table of cyber event aggregations, and the edges are aggregation operators. The cyber security data warehouse enables domain experts to quickly traverse a multi-scale aggregation space systematically. We describe the architecture of a test bed system and a summary of results on the IEEE VAST 2012 Cyber Forensics data.

Impacts of Aggregation Methods and Trophospecies Number on the Structure and Function of Marine Food Webs

Aggregation of species with similar ecological properties is one of the effective methods to simplify food web researches.However,species aggregation will affect not only the complexity of modeling process but also the accuracy of models’outputs.Selection of aggregation methods and the number of trophospecies are the keys to study the simplification of food web.In this study,three aggregation methods,including taxonomic aggregation(TA),structural equivalence aggregation(SEA),and self-organizing maps(SOM),were analyzed and compared with the linear inverse model–Markov Chain Monte Carlo(LIM-MCMC)model.Impacts of aggregation methods and trophospecies number on food webs were evaluated based on the robustness and unitless of ecological net-work indices.Results showed that aggregation method of SEA performed better than the other two methods in estimating food web structure and function indices.The effects of aggregation methods were driven by the differences in species aggregation principles,which will alter food web structure and function through the redistribution of energy flow.According to the results of mean absolute percentage error(MAPE)which can be applied to evaluate the accuracy of the model,we found that MAPE in food web indices will increase with the reducing trophospecies number,and MAPE in food web function indices were smaller and more stable than those in food web structure indices.Therefore,trade-off between simplifying food webs and reflecting the status of ecosystem should be con-sidered in food web studies.These findings highlight the importance of aggregation methods and trophospecies number in the analy-sis of food web simplification.This study provided a framework to explore the extent to which food web models are affected by dif-ferent species aggregation,and will provide scientific basis for the construction of food webs.

Underwater Pulse Waveform Recognition Based on Hash Aggregate Discriminant Network

Underwater pulse waveform recognition is an important method for underwater object detection.Most existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform features.Sound propagation channels in seawater are time-and space-varying convolutional channels.In the extraction of the waveform features of underwater acoustic signals,the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time-and space-varying convolutional channels to the greatest extent possible.We propose a hash aggregate discriminative network(HADN),which combines hash learning and deep learning to minimize the time-and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform recognition.In the extraction of the hash features of acoustic signals,a discrete constraint between clusters within a hash feature class is introduced.This constraint can ensure that the influence of convolutional channels on hash features is minimized.In addition,we design a new loss function called aggregate discriminative loss(AD-loss).The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash features.Experimental results show that on pool and ocean datasets,which were collected in pools and oceans,respectively,by using acoustic collectors,the proposed HADN performs better than other comparative models in terms of accuracy and mAP.

Small molecules to target tau amyloid aggregation

Protein aggregation has been linked with many neurodegenerative diseases,such as Alzheimer’s disease(AD)or Parkinson’s disease.AD belongs to a group of heterogeneous and incurable neurodegenerative disorders collectively known as tauopathies.They comprise frontotemporal dementia,Pick’s disease,or corticobasal degeneration,among others.The symptomatology varies with the specific tau protein variant involved and the affected brain region or cell type.However,they share a common neuropathological hallmark-the formation of proteinaceous deposits named neurofibrillary tangles.Neurofibrillary tangles,primarily composed of aggregated tau(Zhang et al.,2022),disrupt normal neuronal functions,leading to cell death and cognitive decline.

Mechanical Behavior Based on Aggregates Microstructure of Ultra-high Performance Concrete

We developed ultra-high performance concrete(UHPC)incorporating mullite sand and brown corundum sand(BCS),and the quartz sand UHPC was utilized to prepare for comparison.The properties of compressive strength,elastic modulus,ultrasonic pulse velocity,flexural strength,and toughness were investigated.Scanning electron microscopy and nanoindentation were also conducted to reveal the underlying mechanisms affecting macroscopic performance.Due to the superior interface bonding properties between mullite sand and matrix,the compressive strength and flexural toughness of UHPC have been significantly improved.Mullite sand and BCS aggregates have higher stiffness than quartz sand,contributing to the excellent elastic modulus exhibited by UHPC.The stiffness and volume of aggregates have a more significant impact on the elastic modulus of UHPC than interface performance,and the latter contributes more to the strength of UHPC.This study will provide a reference for developing UHPC with superior elastic modulus for structural engineering.

Case Studies of the Microphysical and Kinematic Structure of Summer Mesoscale Precipitation Clouds over the Eastern Tibetan Plateau

Three cases of microphysical characteristics and kinematic structures in the negative temperature region of summer mesoscale cloud systems over the eastern Tibetan Plateau(TP)were investigated using X-band dual-polarization radar.The time-height series of radar physical variables and mesoscale horizontal divergence δderived by quasi-vertical profiles(QVPs)indicated that the dendritic growth layer(DGL,-20°C to-10°C)was ubiquitous,with large-value zones of K_(DP)(specific differential phase),Z_(DR)(differential reflectivity),or both,and corresponded to various dynamic fields(ascent or descent).Ascents in the DGL of cloud systems with vigorous vertical development were coincident with large-value zones of Z_(DR),signifying ice crystals with a large axis ratio,but with no obvious large values of K_(DP),which differs from previous findings.It is speculated that ascent in the DGL promoted ice crystals to undergo further growth before sinking.If there was descent in the DGL,a high echo top corresponded to large values of K_(DP),denoting a large number concentration of ice crystals;but with the echo top descending,small values of K_(DP) formed.This is similar to previous results and reveals that a high echo top is conducive to the generation of ice crystals.When ice particles fall to low levels(-10℃ to 0℃),they grow through riming,aggregation,or deposition,and may not be related to the kinematic structure.It is important to note that this study was only based on a limited number of cases and that further research is therefore needed.

Straw return increases crop production by improving soil organic carbon sequestration and soil aggregation in a long-term wheat-cotton cropping system

Straw return is a promising strategy for managing soil organic carbon(SOC)and improving yield stability.However,the optimal straw return strategy for sustainable crop production in the wheat(Triticum aestivum L.)-cotton(Gossypium hirsutum L.)cropping system remains uncertain.The objective of this study was to quantify the long-term(10 years)impact of carbon(C)input on SOC sequestration,soil aggregation and crop yields in a wheat-cotton cropping system in the Yangtze River Valley,China.Five treatments were arranged with a single-factor randomized design as follows:no straw return(Control),return of wheat straw only(Wt),return of cotton straw only(Ct),return of 50%wheat and 50%cotton straw(Wh-Ch)and return of 100%wheat and 100%cotton straw(Wt-Ct).In comparison to the Control,the SOC content increased by 8.4 to 20.2%under straw return.A significant linear positive correlation between SOC sequestration and C input(1.42-7.19 Mg ha^(−1)yr^(−1))(P<0.05)was detected.The percentages of aggregates of sizes>2 and 1-2 mm at the 0-20 cm soil depth were also significantly elevated under straw return,with the greatest increase of the aggregate stability in the Wt-Ct treatment(28.1%).The average wheat yields increased by 12.4-36.0%and cotton yields increased by 29.4-73.7%,and significantly linear positive correlations were also detected between C input and the yields of wheat and cotton.The average sustainable yield index(SYI)reached a maximum value of 0.69 when the C input was 7.08 Mg ha^(−1)yr^(−1),which was close to the maximum value(SYI of 0.69,C input of 7.19 Mg ha^(−1)yr^(-1))in the Wt-Ct treatment.Overall,the return of both wheat and cotton straw was the best strategy for improving SOC sequestration,soil aggregation,yields and their sustainability in the wheat-cotton rotation system.

Straw mulching alters the composition and loss of dissolved organic matter in farmland surface runoff by inhibiting the fragmentation of soil small macroaggregates

Straw mulching is a widespread practice for reducing the soil carbon loss caused by erosion.However,the effects of straw mulching on dissolved organic matter(DOM)runoff loss from black soil are not well studied.How straw mulching affects the composition and loss of runoff DOM by changing soil aggregates remains largely unclear.Here,a straw mulching treatment was compared to a no mulching treatment(as a control)on sloping farmland with black soil erosion in Northeast China.We divided the soil into large macroaggregates(>2 mm),small macroaggregates(0.25-2 mm),and microaggregates(<0.25 mm).After five rain events,the effects of straw mulching on the concentration(characterized by dissolved organic carbon(DoC)and composition(analyzed by fluorescence spectroscopy)of runoff and soil aggregate DOM were studied.The results showed that straw mulching reduced the runoff amount by 54.7%.Therefore,although straw mulching increased the average DOc concentration in runoff,it reduced the total runoff DOM loss by 48.3%.The composition of runoff DOM is similar to that of soil,as both contain humic-like acid and protein-like components.With straw mulching treatment,the protein-like components in small macroaggregates accumulated and the protein-like components in runoff declined with rain events.Fluorescence spectroscopy technology may help in understanding the hydrological paths of rain events by capturing the dynamic changes of runoff and soil DOM characteristics.A variation partitioning analysis(VPA)indicated that the DOM concentration and composition of microaggregates explained 68.2%of the change in runoff DOM from no mulching plots,while the change in runoff DOM from straw mulching plots was dominated by small macroaggregates at a rate of 55.1%.Taken together,our results demonstrated that straw mulching reduces the fragmentation of small macroaggregates and the loss of microaggregates,thus effecting DOM compositions in soil and reducing the DOM loss in runoff.These results provide a theoretical basis for reducing carbon loss in sloping farmland.

A novel approach to Parkinson's disease treatment with a potentially dual-acting therapeutic agent that targetsα-synuclein aggregation and neuron death

Parkinson's disease(PD),a prevalent neurodegenerative disorder,is chara cterized by the loss of dopaminergic neurons and the aggregation ofα-synuclein protein into Lewy bodies.While the current standards of therapy have been successful in providing some symptom relief,they fail to address the underlying pathophysiology of PD and as a result,they have no effect on disease progression.

A non-probabilistic reliability topology optimization method based on aggregation function and matrix multiplication considering buckling response constraints

A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linear buckling analysis is conducted,and the sensitivity solution of the linear buckling factor is achieved.For a specific problem in linear buckling topology optimization,a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray cells.The aggregation function method is used to consider the high-order eigenvalues,so as to obtain continuous sensitivity information and refined structural design.With cyclic matrix programming,a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is conducted.To maximize the buckling load,under the constraint of the given buckling load,two types of topological optimization columns are constructed.The variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization algorithm.The vertex method and the matching point method are used to carry out an uncertainty propagation analysis,and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic distance.Finally,the differences in the structural topology optimization under different reliability degrees are illustrated by examples.

Design and redesign journey of a drug for transthyretin amyloidosis

The misfolding and subsequent aggregation of proteins into amyloid fibrils underlie the onset of a variety of human disorders collectively known as amyloidosis.Transthyretin(TTR)is one of the>30 amyloidogenic proteins identified to date and is associated with a group of highly debilitating and life-threatening disorders called TTR amyloidosis(ATTR).ATTR comprises senile systemic amyloidosis,which is linked to wild-type(WT)TTR aggregation,and hereditary ATTR,a dominantly inherited disorder caused by the deposition of one of over 130 TTR genetic variants.Senile systemic amyloidosis is a prevalent age-related amyloidosis,affecting up to 25%of the population over 80 years of age,and is characterized by the build-up of TTR fibrils in the myocardium.Regarding hereditary ATTR,the clinical presentation is highly heterogeneous,primarily affecting the peripheral nervous system(familial amyloid polyneuropathy-FAP)or the heart(familial amyloid cardiomyopathy).In rare cases,aggregation develops in the central nervous system,giving rise to a phenotype known as familial leptomeningeal amyloidosis(Carroll et al.,2022).

High-efficiency Carbonation Modification Methods of Recycled Coarse Aggregates

To solve the problem of only surface carbonation and realize high-efficiency carbonation of recycled coarse aggregate,the method of carbonated recycled coarse aggregate with nano materials pre-soaking was first put forward.The carbonation effect of modified recycled coarse aggregate with three different carbonation methods was evaluated,and water absorption,apparent density and crush index of modified recycled coarse aggregate were measured.Combined with XRD,SEM,and MIP microscopic analysis,the high-efficiency carbonation strengthening mechanism of modified recycled coarse aggregate was revealed.The experimental results show that,compared with the non-carbonated recycled coarse aggregate,the physical and microscopic properties of carbonated recycled coarse aggregate are improved.The method of carbonation with nano-SiO_(2) pre-soaking can realize the high-efficiency carbonation of recycled coarse aggregate,for modified recycled coarse aggregate with the method,water absorption is reduced by 23.03%,porosity is reduced by 44.06%,and the average pore diameter is 21.82 nm.The high-efficiency carbonation strengthening mechanism show that the pre-socked nano-SiO_(2) is bound to the hydration product Ca(OH)_(2) of the old mortar with nano-scale C-S-H,which can improve the CO_(2) absorption rate,accelerate the carbonation reaction,generate more stable CaCO_(3) and nano-scale silica gel,and bond to the dense three-dimensional network structure to realize the bidirectional enhancement of nano-materials and pressurized carbonation.It is concluded that the method of carbonation with nano-SiO_(2) pre-soaking is a novel high-efficiency carbonation modification of recycled coarse aggregate.

Effect of Modification Treatment on Chloride Ions Permeability and Microstructure of Recycled Brick-mixed Aggregate Concrete

The modification methods of pozzolan slurry combined with sodium silicate and silicon-based additive were respectively adopted to treat recycled coarse brick-mixed aggregate(RCBA)in this study.The compressive strength and chloride permeability resistance of recycled aggregate concrete(RAC)before and after modification treatment were tested,and the microstructure of RAC was analyzed by mercury intrusion porosimetry(MIP)and scanning electron microscopy(SEM).The results show that the physical properties of RCBA strengthened by modification treatment are improved,and the compressive strength and chloride permeability resistance of treated RAC are also significantly improved.The modification treatment optimizes the pore size distribution of RAC,which increases the number of gel pores and transition pores,and decreases the number of capillary pores and macro pores.The surface fractal dimension shows a significant correlation with chloride diffusion coefficient,indicating that the variation of chloride permeability of treated RAC is consistent with the microstructure evolution.

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

Unilateral rNurr1-V5 transgene expression in nigral dopaminergic neurons mitigates bilateral neuropathology and behavioral deficits in parkinsonian rats withα-synucleinopathy

Parkinsonism by unilateral,intranigralβ-sitosterolβ-D-glucoside administration in rats is distinguished in that theα-synuclein insult begins unilaterally but spreads bilaterally and increases in severity over time,thus replicating several clinical features of Parkinson’s disease,a typicalα-synucleinopathy.As Nurr1 repressesα-synuclein,we evaluated whether unilateral transfected of rNurr1-V5 transgene via neurotensin-polyplex to the substantia nigra on day 30 after unilateralβ-sitosterolβ-D-glucoside lesion could affect bilateral neuropathology and sensorimotor deficits on day 30 post-transfection.This study found that rNurr1-V5 expression but not that of the green fluorescent protein(the negative control)reducedβ-sitosterolβ-D-glucoside-induced neuropathology.Accordingly,a bilateral increase in tyrosine hydroxylase-positive cells and arborization occurred in the substantia nigra and increased tyrosine hydroxylase-positive ramifications in the striatum.In addition,tyrosine hydroxylase-positive cells displayed less senescence markerβ-galactosidase and more neuron-cytoskeleton markerβIII-tubulin and brain-derived neurotrophic factor.A significant decrease in activated microglia(positive to ionized calcium-binding adaptor molecule 1)and neurotoxic astrocytes(positive to glial fibrillary acidic protein and complement component 3)and increased neurotrophic astrocytes(positive to glial fibrillary acidic protein and S100 calcium-binding protein A10)also occurred in the substantia nigra.These effects followed the bilateral reduction inα-synuclein aggregates in the nigrostriatal system,improving sensorimotor behavior.Our results show that unilateral rNurr1-V5 transgene expression in nigral dopaminergic neurons mitigates bilateral neurodegeneration(senescence and loss of neuron-cytoskeleton and tyrosine hydroxylase-positive cells),neuroinflammation(activated microglia,neurotoxic astrocytes),α-synuclein aggregation,and sensorimotor deficits.Increased neurotrophic astrocytes and brain-derived neurotrophic factor can mediate the rNurr1-V5 effect,supporting its potential clinical use in the treatment of Parkinson’s disease.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.

Differentially Private Support Vector Machines with Knowledge Aggregation

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

Subsoil tillage enhances wheat productivity,soil organic carbon and available nutrient status in dryland fields

Tillage practices during the fallow period benefit water storage and yield in dryland wheat crops.However,there is currently no clarity on the responses of soil organic carbon(SOC),total nitrogen(TN),and available nutrients to tillage practices within the growing season.This study evaluated the effects of three tillage practices(NT,no tillage;SS,subsoil tillage;DT,deep tillage)over five years on soil physicochemical properties.Soil samples at harvest stage from the fifth year were analyzed to determine the soil aggregate and aggregate-associated C and N fractions.The results indicated that SS and DT improved grain yield,straw biomass and straw carbon return of wheat compared with NT.In contrast to DT and NT,SS favored SOC and TN concentrations and stocks by increasing the soil organic carbon sequestration rate(SOCSR)and soil nitrogen sequestration rate(TNSR)in the 0-40 cm layer.Higher SOC levels under SS and NT were associated with greater aggregate-associated C fractions,while TN was positively associated with soluble organic nitrogen(SON).Compared with DT,the NT and SS treatments improved soil available nutrients in the 0-20 cm layer.These findings suggest that SS is an excellent practice for increasing soil carbon,nitrogen and nutrient availability in dryland wheat fields in North China.