Agricultural Investment Project Decisions Based on an Interactive Preference Disaggregation Model Considering Inconsistency

Agricultural investment project selection is a complex multi-criteria decision-making problem,as agricultural projects are easily influenced by various risk factors,and the evaluation information provided by decisionmakers usually involves uncertainty and inconsistency.Existing literature primarily employed direct preference elicitation methods to address such issues,necessitating a great cognitive effort on the part of decision-makers during evaluation,specifically,determining the weights of criteria.In this study,we propose an indirect preference elicitation method,known as a preference disaggregation method,to learn decision-maker preference models fromdecision examples.To enhance evaluation ease,decision-makers merely need to compare pairs of alternatives with which they are familiar,also known as reference alternatives.Probabilistic linguistic preference relations are employed to account for the presence of incomplete and uncertain information in such pairwise comparisons.To address the inconsistency among a group of decision-makers,we develop a pair of 0-1mixed integer programming models that consider both the semantics of linguistic terms and the belief degrees of decision-makers.Finally,we conduct a case study and comparative analysis.Results reveal the effectiveness of the proposed model in solving agricultural investment project selection problems with uncertain and inconsistent decision information.

Soil polygon disaggregation through similarity-based prediction with legacy pedons

Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified.This restricts current applications of the maps in site-specific agricultural management and environmental modelling.We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under-or over-sampled legacy pedon data for the disaggregation.The method consisted of three steps.First,environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors.Second,according to soil types of the pedon sites,the similarities were aggregated to derive similarity distribution for each soil type.Third,a hardening process was performed on the maps to allocate candidate soil types within the polygons.The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba,Canada.Based on 186 independent pedon sites,the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62.The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map,which was commonly used in practice.Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy,indicating that new environmental covariates need to be developed.We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.

Residential PV capacity estimation and power disaggregation using net metering measurements

As the intermittency and uncertainty of photovoltaic(PV)power generation poses considerable challenges to the power system operation,accurate PV generation estimates are critical for the distribution operation,maintenance,and demand response program implementation because of the increasing usage of distributed PVs.Currently,most residential PVs are installed behind the meter,with only the net load available to the utilities.Therefore,a method for disaggregating the residential PV generation from the net load data is needed to enhance the grid-edge observability.In this study,an unsupervised PV capacity estimation method based on net metering data is proposed,for estimating the PV capacity in the customer’s premise based on the distribution characteristics of nocturnal and diurnal net load extremes.Then,the PV generation disaggregation method is presented.Based on the analysis of the correlation between the nocturnal and diurnal actual loads and the correlation between the PV capacity and their actual PV generation,the PV generation of customers is estimated by applying linear fitting of multiple typical solar exemplars and then disaggregating them into hourly-resolution power profiles.Finally,the anomalies of disaggregated PV power are calibrated and corrected using the estimated capacity.Experiment results on a real-world hourly dataset involving 260 customers show that the proposed PV capacity estimation method achieves good accuracy because of the advantages of robustness and low complexity.Compared with the state-of-the-art PV disaggregation algorithm,the proposed method exhibits a reduction of over 15%for the mean absolute percentage error and over 20%for the root mean square error.

Temporal Disaggregation of Time Series Revisited

With the benefits of increased computing power and much improved software,temporal disaggregation is examined.Disaggregation,the process of obtaining high frequency data from low frequency data has been discussed for many years.This study examines three methods which utilize the autoregressive integrated moving average(ARIMA)model in a simulation study comparing parameter estimation,disaggregation mean square error,and forecast mean square error.Finally,the three methods are applied to a real-world time series.

TSILNet:A novel hybrid model for energy disaggregation based on two-stage improved TCN combined with IECA-LSTM

Non-intrusive load monitoring(NILM)technology aims to infer the operation information of electrical appliances from the total household load signals,which is of great significance for energy conservation and planning.However,existing methods are difficult to effectively capture the complex nonlinear features of the power consumption flow,which affects the energy disaggregation accuracy.To this end,this paper designs a method based on temporal convolutional network(TCN),efficient channel attention(ECA),and long short-term memory(LSTM).The method first creatively proposes a two-stage improved TCN(TSTCN),which overcomes its problems of extracting discontinuous information and poor correlation of long-distance information while enhancing the ability to extract high-level load features.Then a novel improved ECA attention mechanism(IECA)is embedded,which is also combined with the skip connection technique to pay channel-weighted attention to important feature maps and promote information fusion.Finally,the LSTM with strong temporal memory capability is introduced to learn the dependencies in the load power sequence and realize load disaggregation.Experiments on two real-world datasets,REDD and UK-DALE,show that the proposed model significantly outperforms other comparative NILM algorithms and achieves satisfactory tracking with the actual appliance operating power.The results show that the mean absolute error(MAE)of all appliances decreases by 18.67%on average,and the F1 score improves by 38.70%.

A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification

The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggrega-tion methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodol-ogy. The contribution of this paper is in utilizing the “k- value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.

Pangaean aggregation and disaggregation with evidence from global climate belts

A study of using climate sensitive deposits as a compiled climatic data to locate global climatic belt boundaries through time is developed by the present authors since the 1990s. Global latitudinal belts were presented from Cambrian to Permian as well as the in terval from the early Late Cretaceous to the present. However, during the later Permian and into the Early Cretaceous we noted that the failure of the tropical-subtropical belt to penetrate into the interior of Pangaean resulted in the merging of the two arid belts associated with the northern and southern Hadley Cells into one vast, interior arid region. A Pangaeanic paleogeography dominates and obviously affects the climatic distribution from the Late Permian to Early Cretaceous. We employ the dismission and reoccurrence of the global latitudinal climate belts to determine the aggregation and disaggregation of the Pangaean.

Self-assembled nanochaperones enable the disaggregation of amyloid insulin fibrils

The deposition of highly ordered amyloid fibrils is recognized as a hallmark of amyloidosis diseases such as Alzheimer’s disease and Parkinson’s disease.Disaggregating the amyloid fibrils is considered as one of the effective strategies for the control and treatment of amyloidosis diseases.In this article,by simulating the function of natural molecular chaperones,co-assembled block copolymer micelles with coordination groups of nitrilotriacetic acid(NTA)and hydrophobic microdomains of poly(Nisopropylacrylamide)(PNIPAM)on the surface were used as nanochaperones(n Chaps)to disaggregate amyloid insulin fibrils.Zinc ions chelated by NTA can bind the histidine imidazole residues while the PNIPAM microdomains can interact with the exposed hydrophobic sites on the amyloid insulin fibrils,which synergistically perturb the stability of amyloid insulin fibrils,loosen their structure,and finally promote their disaggregation.A combination of characterizations with fluorescence spectroscopy,transmission electron microscopy(TEM),dynamic hight scattering(DLS),and quartz crystal microbalance(QCM)demonstrated that mature amyloid insulin fibrils were completely disaggregated after incubating with n Chaps for 90 h.This study may provide a promising strategy for the development of n Chaps for the treatment of amyloidosis diseases.

Strategies for Inhibition and Disaggregation of Amyloid-β Fibrillation

Comprehensive Summary Amyloid-β protein(Aβ)is a fatal cause of Alzheimer's disease,which can trigger a series of cytotoxicity by the abnormal aggregation of Aβ in human brain.The strategies for inhibition and disaggregation of Aβ fibrillation are mostly based on the interaction between monomers,oligomers,fibrils,and materials.

A disaggregation approach for estimating high spatial resolution broadband emissivity for bare soils from Landsat surface reflectance

High spatial resolution land surface broadband emissivity(BBE)is not only useful for surface energy balance studies at local scales,but also can bridge the gap between existing coarser resolution BBE products and point-based field measurements.This study proposes a disaggregation approach that utilizes the established BBE–reflectance relationship for estimating high spatial resolution BBE for bare soils from Landsat surface reflectance data.The disaggregated BBE is compared to the BBE calculated from spatial–temporal matched Advanced Spaceborne Thermal Emission and Reflectance Radiometer emissivity product.Comparison results show that better agreement is achieved over relative homogeneous areas,but deteriorated over heterogeneous and cloud-contaminated areas.In addition,field-measured emissivity data over large homogeneous desert were also used to validate the disaggregated BBE,and the bias is 0.005.The comparison and validation results indicated that the disaggregation approach can obtain high spatial resolution BBE with better accuracy for homogeneous area.

Assessment of Future Drought in Southwest China Based on CMIP5 Multimodel Projections

In the last decade, a series of severe and extensive droughts have swept across Southwest China, resulting in tremendous economic losses, deaths, and disruption to society. Consequently, this study is motivated by the paramount importance of as- sessing future changes in drought in Southwest China. Precipitation is likely to decrease over most parts of Southwest China around the beginning of the century, followed by widespread precipitation increases; the increase in potential evapotran- spiration （PET）, due to the joint effects of increased temperature and surface net radiation and decreased relative humidity, will overwhelm the whole region throughout the entire 21st century. In comparative terms, the enhancement of PET will outweigh that of precipitation, particularly under Representative Concentration Pathway （RCP） 8.5, resulting in intensified drought. Generally, the drying tendency will be in the southeast portion, whereas the mountainous region in the northwest will become increasingly wetter owing to abundant precipitation increases. Droughts classified as moderate/severe according to historical standards will become the norm in the 2080s under RCP4.5/RCP8.5. Future drought changes will manifest different characteristics depending on the time scale： the magnitude of change at a time scale of 48 months is nearly twice as great as that at 3 months. Furthermore, we will see that not only will incidences of severe and extreme drought increase dramatically in the future, but extremely wet events will also become more probable.

Development of fragility curves by incorporating new spectral shape indicators and a weighted damage index:case study of steel braced frames in the city of Mashhad,Iran

In this study, strong ground motion record （SGMR） selection based on Eta （~/） as a spectral shape indicator has been investigated as applied to steel braced flame structures. A probabilistic seismic hazard disaggregation analysis for the definition of the target Epsilon （ε） and the target Eta （η） values at different hazard levels is presented, taking into account appropriately selected SGMR＇s. Fragility curves are developed for different limit states corresponding to three representative models of typical steel braced frames having significant irregularities in plan, by means of a weighted damage index. The results show that spectral shape indicators have an important effect on the predicted median structural capacities, and also that the parameter r/is a more robust predictor of damage than searching for records with appropriate c values.

Linking individual-tree and whole-stand models for forest growth and yield prediction

Background： Different types of growth and yield models provide essential information for making informed decisions on how to manage forests. Whole-stand models often provide well-behaved outputs at the stand level, but lack information on stand structures. Detailed information from individual-tree models and size-class models typically suffers from accumulation of errors. The disaggregation method, in assuming that predictions from a whole-stand model are reliable, partitions these outputs to individual trees. On the other hand, the combination method seeks to improve stand-level predictions from both whole-stand and individual-tree models by combining them. Methods： Data from 100 plots randomly selected from the Southwicte Seed Source Study of Ioblolly pine （Pinus taedo L） were used to evaluate the unadjusted individual-tree model against the disaggregation and combination methods. Results： Compared to the whole-stand model, the combination method did not show improvements in predicting stand attributes in this study. The combination method also did not perform as well as the disaggregation method in tree-level predictions. The disaggregation method provided the best predictions of tree- and stand-level survival and growth. Conclusions： The disaggregation approach provides a link between individual-tree models and whole-stand models, and should be considered as a better alternative to the unadjusted tree model.

Proanthocyanidins prevent tau protein aggregation and disintegrate tau filaments

Occurrence of neurofibrillary tangles of the tau protein is a hallmark of tau-related neurodegenerative diseases, i.e. Alzheimer's disease(AD) and frontotemporal dementia. The pathological mechanism underlying AD remains poorly understood, and effective treatments are still unavailable to mitigate the disease.Inhibiting of tau aggregation and disrupting the existing fibrils are key targets in drug discovery towards preventing or curing AD. In this study, grape seed proanthocyanidins(GSPs) was found to effectively inhibit the repeat domain of tau(tau-RD) aggregation and disaggregate tau-RD fibrils in a concentrationdependent manner by inhibiting β-sheet formation of tau-RD. In cells, GSPs relieved cytotoxicity induced by tau-RD aggregates. Molecular dynamics simulations indicated that strong hydrogen bonding,hydrophobic interaction and π-π stacking between GSPs and tau-RD protein were major reasons why GSPs had high inhibitory activity on tau-RD fibrillogenesis. These results provide preliminary data to develop GSPs into medicines, foodstuffs or nutritional supplements for AD patients, suggesting that GSPs could be a candidate molecule in the drug design for AD therapeutics.

Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios

Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this study,we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles(MMEs)from the Coupled Model Intercomparison Project Phase 6(CMIP6)under four Shared Socioeconomic Pathway and Representative Concentration Pathway(SSP-RCP)scenarios(SSP126(SSP1-RCP2.6),SSP245(SSP2-RCP4.5),SSP460(SSP4-RCP6.0),and SSP585(SSP5-RCP8.5))during 2015-2100.The bias correction and spatial disaggregation,water-thermal product index,and sensitivity analysis were used in this study.The results showed that the hydrothermal condition is mismatched in the central and southern deserts,whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition.Compared with the historical period,the matched degree of hydrothermal condition improves during 2046-2075,but degenerates during 2015-2044 and 2076-2100.The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions.The result suggests that the optimal scenario in Central Asia is SSP126 scenario,while SSP585 scenario brings further hydrothermal contradictions.This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.

Potential of peptides and phytochemicals in attenuating different phases of islet amyloid polypeptide fi brillation for type 2 diabetes management

Islet amyloid polypeptide(IAPP),or amylin,has been identifi ed as a key factor in the development of type 2 diabetes(T2D).IAPP aggregates,which form amyloid fi brils,contribute to cytotoxicity of the pancreatic β-cells,resulting in loss of function and subsequent reduction in insulin production.As a result,surviving β-cells overcompensate for this reduction of insulin production,further contributing to the loss of function because of increased stress,thus leading to insulin resistance.Endogenously,IAPP monomers function in a variety of roles;however,aggregation renders them non-functional.The use of naturally occurring compounds,including peptides and phytochemicals,has been explored as a way to mitigate or inhibit IAPP fi bril formation.This review discusses the structure,endogenous roles and mechanism of IAPP fi bril formation,recent advances on inhibitors of IAPP fi bril formation,and new insights on the future development and application of foodderived inhibitors towards T2D management.

Can a multistage approach improve individual tree mortality predictions across the complex mixed-species and managed forests of eastern North America?

Tree mortality plays a fundamental role in the dynamics of forest ecosystems,yet it is one of the most difficult phenomena to accurately predict.Various modeling strategies have been developed to improve individual tree mortality predictions.One less explored strategy is the use of a multistage modeling approach.Potential improvements from this approach have remained largely unknown.In this study,we developed a novel multistage approach and compared its performance in individual tree mortality predictions with a more conventional approach using an identical individual tree mortality model formulation.Extensive permanent plot data(n=9442)covering the Acadian Region of North America and over multiple decades(1965–2014)were used in this study.Our results indicated that the model behavior with the multistage approach better depicted the observed mortality and showed a notable improvement over the conventional approach.The difference between the observed and predicted numbers of dead trees using the multistage approach was much smaller when compared with the conventional approach.In addition,tree survival probabilities predicted by the multistage approach generally were not significantly different from the observations,whereas the conventional approach consistently underestimated mortality across species and overestimated tree survival probabilities over the large range of DBH in the data.The new multistage approach also predictions of zero mortality in individual plots,a result not possible in conventional models.Finally,the new approach was more tolerant of modeling errors because it based estimates on ranked tree mortality rather than error-prone predicted values.Overall,this new multistage approach deserves to be considered and tested in future studies.

Reservoir quality of fluvial sandstone reservoirs in salt-walled mini- basins： an example from the Seagull field, Central Graben, North Sea, UK

The Triassic fluvial sandstones of the Skagerrak Formation were deposited in a series of salt-walled mini-basins and act as important hydrocarbon reservoirs for several high-pressure, high-temperature（HPHT） fields in the Central Graben, North Sea. The HPHT reservoirs exhibit excellent reservoir quality considering their depth of burial and hence have been of high interest for hydrocarbon exploration. This research uses a multidisciplinary approach to assess the Skagerrak Formation fluvial reservoir quality from the Seagull field incorporating core analysis, petrography, electron microscopy, XRD analysis, fluid inclusion appraisal and burial history modelling. Halokinesis and salt withdrawal at the margin of the saltwalled mini-basin induced early disaggregation bands and fractures at shallow burial and led to increased influx of meteoric water and clay mineral infiltration from overlying sedimentation. The density of disaggregation bands correlates with the occurrence and magnitude of pore-filling authigenic clay minerals, concentrated along the margin of the saltwalled mini-basin. The fluvial channel sandstones of the Skagerrak Formation are subject to strong intra-basinal spatial reservoir quality variations despite diagenesis and low vertical effective stress having played a favourable role in arresting porosity loss.

Probabilistic Seismic Hazard Assessment for Bridgetown-Barbados, Employing Subduction Interface Characteristic Earthquakes

A new probabilistic seismic hazard analysis was performed for the city of Bridgetown, Barbados, West Indies. Hazard computations have been performed using the standard Cornell-McGuire approach based on the definition of appropriate seismogenic sources and expected maximum magnitudes, the authors take into consideration the possibility of large subduction interface earthquakes of magnitude 8.0-9.0 beneath the Barbados accretionary prism via application of a characteristic model and slip rates. The analysis has been conducted using a standard logic-tree approach. Uniform hazard spectra have been calculated for the 5% of critical damping and the horizontal component of ground motion for rock site conditions setting 5 return periods （95, 475, 975, 2,475 and 4,975 years） and spectral accelerations for 34 structural periods ranging from 0 to 3 s. The disaggregation results suggest that the magnitude-distance pair that dominates the hazard yields M 7.4 and 8.6 and a distance of 42.5 km in the Interface Subduction Zone beneath Barbados for the 475 and 975 years RP （return period）, respectively. An event with an M 8.0 at a distance of 107.5 km in the Intraplate Subduction Zone is the second scenario that dominates the hazard for both 475 and 975 years RP.

Selection model of trip time for rural population

Referring to the 1 248 survey data of rural population in 14 provinces of China, the influencing factors of trip time choice were analyzed. Based on the basic theory of disaggregate model and its modelling method, nine grades were selected as the alternatives of trip time, the variables affecting time choice and the method getting their values were determined, and a multinomial logit (MNL) model was developed. Another 1 200 trip data of rural population were selected to testify the model's validity. The result shows that the maximum absolute error of each period between calculated value and statistic is 3.6%, so MNL model has high calculation accuracy.