Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Adaptive Distributed Inference for Multi-source Massive Heterogeneous Data

In this paper,we consider the distributed inference for heterogeneous linear models with massive datasets.Noting that heterogeneity may exist not only in the expectations of the subpopulations,but also in their variances,we propose the heteroscedasticity-adaptive distributed aggregation(HADA)estimation,which is shown to be communication-efficient and asymptotically optimal,regardless of homoscedasticity or heteroscedasticity.Furthermore,a distributed test for parameter heterogeneity across subpopulations is constructed based on the HADA estimator.The finite-sample performance of the proposed methods is evaluated using simulation studies and the NYC flight data.

Insight into the spoilage heterogeneity of meat-borne bacteria isolates with high-producing collagenase

Chilled chicken is inevitably contaminated by microorganisms during slaughtering and processing,resulting in spoilage.Cutting parts of chilled chicken,especially wings,feet,and other skin-on products,are abundant in collagen,which may be the primary target for degradation by spoilage microorganisms.In this work,a total of 17 isolates of spoilage bacteria that could secrete both collagenase and lipase were determined by raw-chicken juice agar(RJA)method,and the results showed that 7 strains of Serratia,Aeromonas,and Pseudomonas could significantly decompose the collagen ingredients.The gelatin zymography showed that Serratia liquefaciens(F5)and(G7)had apparent degradation bands around 50 kDa,and Aeromonas veronii(G8)and Aeromonas salmonicida(H8)had a band around.65 and 95 kDa,respectively.The lipase and collagenase activities were detected isolate-by-isolate,with F5 showing the highest collagenase activity.For spoilage ability on meat in situ,F5 performed strongest in spoilage ability,indicated by the total viable counts,total volatile basic nitrogen content,sensory scores,lipase,and collagenase activity.This study provides a theoretical basis for spoilage heterogeneity of strains with high-producing collagenase in meat.

Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Reservoir heterogeneity analysis using multi-directional textural attributes from deep learning-based enhanced acoustic impedance inversion:A study from Poseidon,NW shelf Australia

Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.

Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data

Environmental assessments are critical for ensuring the sustainable development of human civilization.The integration of artificial intelligence(AI)in these assessments has shown great promise,yet the"black box"nature of AI models often undermines trust due to the lack of transparency in their decision-making processes,even when these models demonstrate high accuracy.To address this challenge,we evaluated the performance of a transformer model against other AI approaches,utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators.We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments,enabling the identification of individual indicators'contributions to the model's predictions.We find that the transformer model outperforms others,achieving an accuracy of about 98%and an area under the receiver operating characteristic curve(AUC)of 0.891.Regionally,the environmental assessment values are predominantly classified as level II or III in the central and southwestern study areas,level IV in the northern region,and level V in the western region.Through explainability analysis,we identify that water hardness,total dissolved solids,and arsenic concentrations are the most influential indicators in the model.Our AI-driven environmental assessment model is accurate and explainable,offering actionable insights for targeted environmental management.Furthermore,this study advances the application of AI in environmental science by presenting a robust,explainable model that bridges the gap between machine learning and environmental governance,enhancing both understanding and trust in AI-assisted environmental assessments.

Individual dynamics and local heterogeneity provide a microscopic view of the epidemic spreading

The COVID-19 pandemic has caused severe global disasters,highlighting the importance of understanding the details and trends of epidemic transmission in order to introduce efficient intervention measures.While the widely used deterministic compartmental models have qualitatively presented continuous “analytical” insight and captured some transmission features,their treatment usually lacks spatiotemporal variation.Here,we propose a stochastic individual dynamical(SID)model to mimic the random and heterogeneous nature of epidemic propagation.The SID model provides a unifying framework for representing the spatiotemporal variations of epidemic development by tracking the movements of each individual.Using this model,we reproduce the infection curves for COVID-19 cases in different areas globally and find the local dynamics and heterogeneity at the individual level that affect the disease outbreak.The macroscopic trend of virus spreading is clearly illustrated from the microscopic perspective,enabling a quantitative assessment of different interventions.Seemingly,this model is also applicable to studying stochastic processes at the “meter scale”,e.g.,human society’s collective dynamics.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Mechanical heterogeneity characterization of coal materials based on nano-indentation experiments

To investigate the complex macro-mechanical properties of coal from a micro-mechanical perspective,we have conducted a series of micro-mechanical experiments on coal using a nano-indentation instrument.These experiments were conducted under both dynamic and static loading conditions,allowing us to gather the micro-mechanical parameters of coal for further analysis of its micro-mechanical heterogeneity using the box counting statistical method and the Weibull model.The research findings indicate that the load–displacement curves of the coal mass under the two different loading modes exhibit noticeable discreteness.This can be attributed to the stress concentration phenomenon caused by variations in the mechanical properties of the micro-units during the loading process of the coal mass.Consequently,there are significant fluctuations in the micro-mechanical parameters of the coal mass.Moreover,the mechanical heterogeneity of the coal at the nanoscale was confirmed based on the calculation results of the standard deviation coefficient and Weibull modulus of the coal body’s micromechanical parameters.These results reveal the influence of microstructural defects and minerals on the uniformity of the stress field distribution within the loaded coal body,as well as on the ductility characteristics of the micro-defect structure.Furthermore,there is a pronounced heterogeneity in the micromechanical parameters.Furthermore,we have established a relationship between the macro and micro elastic modulus of coal by applying the Mori-Tanaka homogenization method.This relationship holds great significance for revealing the micro-mechanical failure mechanism of coal.

Editorial:Transcriptome analysis in tumor microenvironment and tumor heterogeneity

Cancer,a disease as intricate as it is devastating,continues to challenge the medical and scientific community[1].Its complex nature is epitomized by the tumor microenvironment and tumor heterogeneity.As we delve deeper into the realms of cancer research,the advent of transcriptome analysis has emerged as a powerful torchbearer,illuminating our understanding of these enigmatic facets of cancer biology[2].

Assessment and Visualization of Ki67 Heterogeneity in Breast Cancers through Digital Image Analysis

The Ki67 index (KI) is a standard clinical marker for tumor proliferation;however, its application is hindered by intratumoral heterogeneity. In this study, we used digital image analysis to comprehensively analyze Ki67 heterogeneity and distribution patterns in breast carcinoma. Using Smart Pathology software, we digitized and analyzed 42 excised breast carcinoma Ki67 slides. Boxplots, histograms, and heat maps were generated to illustrate the KI distribution. We found that 30% of cases (13/42) exhibited discrepancies between global and hotspot KI when using a 14% KI threshold for classification. Patients with higher global or hotspot KI values displayed greater heterogenicity. Ki67 distribution patterns were categorized as randomly distributed (52%, 22/42), peripheral (43%, 18/42), and centered (5%, 2/42). Our sampling simulator indicated analyzing more than 10 high-power fields was typically required to accurately estimate global KI, with sampling size being correlated with heterogeneity. In conclusion, using digital image analysis in whole-slide images allows for comprehensive Ki67 profile assessment, shedding light on heterogeneity and distribution patterns. This spatial information can facilitate KI surveys of breast cancer and other malignancies.

Effects of individual heterogeneity on social contagions

Despite having significant effects on social contagions,individual heterogeneity has frequently been overlooked in earlier studies.To better understand the complexity of social contagions,a non-Markovian model incorporating heterogeneous social influence and adoption thresholds is introduced.For theoretical analysis,a generalized edge-based compartmental theory which considers the heterogeneities of social influence and adoption thresholds is developed.Focusing on the final adoption size,the critical propagation probability,and the phase transition type,social contagions for adoption thresholds that follow normal distributions with various standard deviations,follow various distributions,and correlate with degrees are investigated.When thresholds follow normal distributions,a larger standard deviation results in a larger final adoption size when the information propagation probability is relatively low.However,when the information propagation probability is relatively high,a larger standard deviation results in a smaller final adoption size.When thresholds follow various distributions,crossover phenomena in phase transition are observed when investigating the relationship of the final adoption size versus the average adoption threshold for some threshold distributions.When thresholds are correlated with degrees,similar crossover phenomena occur when investigating the relationship of the final adoption size versus the degree correlation index.Additionally,we find that increasing the heterogeneity of social influence suppresses the effects of adoption threshold heterogeneity on social contagions in three cases.Our theory predictions agree well with the simulation results.

A geochemical perspective on the genesis of Cenozoic basic volcanism in northeastern Turkey:an overview of metasomatism and heterogeneity of the sub-continental lithospheric mantle in a post-collisional setting

The post-collisional Cenozoic basic volcanic rocks in NE Turkey show temporal variations in whole-rock lithophile element and highly siderophile element(HSE)systematics that are mainly associated with the nature of sub-continental lithospheric mantle(SCLM)sources and parental melt generation.So far,the traditional whole-rock lithophile geochemical data of these basic volcanic rocks have provided important constraints on the nature of SCLM sources.Integrated lithophile element and HSE geochemical data of these basic volcanic rocks also reveal the heterogeneity of the SCLM source,which is principally related to variable metasomatism resulting from previous subduction(s)and post-collisional mantle-crust interactions in an extensional setting.Lithophile element geochemical features suggest that the parental magmas have derived from metasomatized spinel-to garnet-bearing SCLM sources for Eocene and Miocene basic volcanic rocks with subduction signatures whereas originated from spinel-to garnet-bearing SCLM sources for Mio-Pliocene and Plio-Quaternary basaltic volcanic rocks without the subduction signature.Lithophile element and HSE geo-chemistry also reveal that Eocene and Miocene basic vol-canic rocks were affected by more pronounced crustal contamination than the basaltic volcanic rocks of Mio-Pliocene and Quaternary.Furthermore,the integrated lithophile element and HSE compositions of these basic volcanic rocks,together with the regional asymmetric lithospheric delamination model,reveal that the compositional variation(especially due to metasomatism)was significant temporally in the heterogeneity of the SCLM sources from which parental magmas formed during the Cenozoic era.

Pumping-induced Well Hydraulics and Groundwater Budget in a Leaky Aquifer System with Vertical Heterogeneity in Aquitard Hydraulic Properties

In groundwater hydrology,aquitard heterogeneity is often less considered compared to aquifers,despite its significant impact on groundwater hydraulics and groundwater resources evaluation.A semi-analytical solution is derived for pumping-induced well hydraulics and groundwater budget with consideration of vertical heterogeneity in aquitard hydraulic conductivity(K)and specific storage(S_(s)).The proposed new solution is innovative in its partitioning of the aquitard into multiple homogeneous sub-layers to enable consideration of various forms of vertically heterogeneous K or S_(s).Two scenarios of analytical investigations are explored:one is the presence of aquitard interlayers with distinct K or S_(s) values,a common field-scale occurrence;another is an exponentially depth-decaying aquitard S_(s),a regional-scale phenomenon supported by statistical analysis.Analytical investigations reveal that a low-K interlayer can significantly increase aquifer drawdown and enhance aquifer/aquitard depletion;a high-S_(s) interlayer can noticeably reduce aquifer drawdown and increase aquitard depletion.Locations of low-K or high-S_(s) interlayers also significantly impact well hydraulics and groundwater budget.In the context of an exponentially depth-decaying aquitard S_(s),a larger decay exponent can enhance aquifer drawdown.When using current models with a vertically homogeneous aquitard,half the sum of the geometric and harmonic means of exponentially depth-decaying aquitard S_(s) should be used to calculate aquitard depletion and unconfined aquifer leakage.

Effect of Modulus Heterogeneity on the Equilibrium Shape and Stress Field ofαPrecipitate in Ti-6Al-4V

For media with inclusions(e.g.,precipitates,voids,reinforcements,and others),the difference in lattice parameter and the elastic modulus between the matrix and inclusions cause stress concentration at the interfaces.These stress fields depend on the inclusions’size,shape,and distribution and will respond instantly to the evolving microstructure.This study develops a phase-field model concerningmodulus heterogeneity.The effect of modulus heterogeneity on the growth process and equilibrium state of theαplate in Ti-6Al-4V during precipitation is evaluated.Theαprecipitate exhibits strong anisotropy in shape upon cooling due to the interplay of the elastic strain and interfacial energy.The calculated orientation of the habit plane using the homogeneous modulus ofαphase shows the smallest deviation fromthat of the habit plane observed in the experiment,compared to the case where the homogeneous modulus ofβphase is adopted.In addition,the equilibrium volume ofαphase within the systemusing homogeneousβmodulus exhibits the largest dependency on the applied stresses.The stress fields across theα/βinterface are further calculated under the assumption of modulus heterogeneity and compared to those using homogeneous modulus of eitherαorβphase.This study provides an essential theoretical basis for developing mechanics models concerning systems with heterogeneous structures.

The impact of heterogeneity and pore network characteristics on single and multi-phase fluid propagation in complex porous media:An X-ray computed tomography study

This study investigates the impact of pore network characteristics on fluid flow through complex and heterogeneous porous media,providing insights into the factors affecting fluid propagation in such systems.Specifically,high-resolution or micro X-ray computed tomography(CT)imaging techniques were utilized to examine outcrop stromatolite samples of the Lagoa Salgada,considered flow analogous to the Brazilian Pre-salt carbonate reservoirs.The petrophysical results comprised two distinct stromatolite depositional facies,the columnar and the fine-grained facies.By generating pore network model(PNM),the study quantified the relationship between key features of the porous system,including pore and throat radius,throat length,coordination number,shape factor,and pore volume.The study found that the less dense pore network of the columnar sample is typically characterized by larger pores and wider and longer throats but with a weaker connection of throats to pores.Both facies exhibited less variability in the radius of the pores and throats in comparison to throat length.Additionally,a series of core flooding experiments coupled with medical CT scanning was designed and conducted in the plug samples to assess flow propagation and saturation fields.The study revealed that the heterogeneity and presence of disconnected or dead-end pores significantly impacted the flow patterns and saturation.Two-phase flow patterns and oil saturation distribution reveal a preferential and heterogeneous displacement that mainly swept displaced fluid in some regions of plugs and bypassed it in others.The relation between saturation profiles,porosity profiles,and the number of fluid flow patterns for the samples was evident.Only for the columnar plug sample was the enhancement in recovery factor after shifting to lower salinity water injection(SB)observed.

Netrin-1 co-cross-linked hydrogel accelerates diabetic wound healing in situ by modulating macrophage heterogeneity and promoting angiogenesis

Diabetic wounds,characterized by prolonged inflammation and impaired vascularization,are a serious complication of diabetes.This study aimed to design a gelatin methacrylate(GelMA)hydrogel for the sustained release of netrin-1 and evaluate its potential as a scaffold to promote diabetic wound healing.The results showed that netrin-1 was highly expressed during the inflammation and proliferation phases of normal wounds,whereas it synchronously exhibited aberrantly low expression in diabetic wounds.Neutralization of netrin-1 inhibited normal wound healing,and the topical application of netrin-1 accelerated diabetic wound healing.Mechanistic studies demonstrated that netrin-1 regulated macrophage heterogeneity via the A2bR/STAT/PPARγsignaling pathway and promoted the function of endothelial cells,thus accelerating diabetic wound healing.These data suggest that netrin-1 is a potential therapeutic target for diabetic wounds.

The heterogeneity of tumor-associated macrophages and strategies to target it

Tumor-associated macrophages(TAMs)are emerging as targets for tumor therapy because of their primary role in promoting tumor progression.Several studies have been conducted to target TAMs by reducing their infiltration,depleting their numbers,and reversing their phenotypes to suppress tumor progression,leading to the development of drugs in preclinical and clinical trials.However,the heterogeneous characteristics of TAMs,including their ontogenetic and functional heterogeneity,limit their targeting.Therefore,in-depth exploration of the heterogeneity of TAMs,combined with immune checkpoint therapy or other therapeutic modalities could improve the efficiency of tumor treatment.This review focuses on the heterogeneous ontogeny and function of TAMs,as well as the current development of tumor therapies targeting TAMs and combination strategies.

Quantifying the impact of earthquakes and geological factors on spatial heterogeneity of debris-flow prone areas:A case study in the Hengduan Mountains

Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significance of this knowledge, a comprehensive quantification of the influence of regional topographical and geological factors on the spatial heterogeneity of debris-flow-prone areas has been lacking. This study selected the Hengduan Mountains, an earthquake-prone region characterized by diverse surface conditions and complex landforms, as a representative study area. An improved units zoning and objective factors identification methodology was employed in earthquake and fault analysis to assess the impact of seismic activity and geological factors on spatial heterogeneity of debrisflow prone areas. Results showed that the application of GIS technology with hydrodynamic intensity and geographical units analysis can effectively analyze debris-flow prone areas. Meanwhile, earthquake and fault zones obviously increase the density of debrisflow prone catchments and make them unevenly distributed. The number of debris-flow prone areas shows a nonlinear variation with the gradual increase of geomorphic factor value. Specifically, the area with 1000 m-2500 m elevation difference, 25°-30° average slope, and 0.13-0.15 land use index is the most favorable conditions for debris-flow occurrence;The average annual rainfall from 600 to 1150 mm and landslides gradient from 16° to 35° are the main causal factors to trigger debris flow. Our study sheds light on the quantification of spatial heterogeneity in debris flow-prone areas in earthquake-prone regions, which can offer crucial support for post-debris flow risk management strategies.

Influence of Transportation Accessibility on Urban-rural Income Disparity and Its Spatial Heterogeneity

Transportation accessibility has been treated as an important means of reducing the urban-rural income disparity.However,only a few studies have examined the effects of different types of transportation accessibility on urban-rural income disparity and their spatial heterogeneity.Based on data from 285 prefecture-level(and above)Chinese cities in 2000,2005,2010,2015,and 2020,this study uses spatial econometric models to examine how highway accessibility and railway accessibility influence the urban-rural income disparity and to identify their spatial heterogeneity.The result reveals that highway accessibility and railway accessibility have‘coreperiphery’ring-like circle structures.The urban-rural income disparity exhibits strong spatial clustering effects.Both highway accessibility and railway accessibility are negatively associated with urban-rural income disparity,and the former having a greater effect size.Moreover,there is a substitution effect between highway accessibility and railway accessibility in the whole sample.Furthermore,these associations differ in geographic regions.In the central region,highway accessibility is more important in reducing the urban-rural income disparity,but its effect is weakened with the increase of railway accessibility.In the western region,railway accessibility has a larger effect on narrowing the urban-rural income disparity,and this effect is strengthened by the increase of highway accessibility.We conclude that improving transportation accessibility is conducive to reducing the urban-rural income disparity but its effect is spatial heterogenetic.Highways and railways should be developed in a coordinated manner to promote an integrated transport network system.