Hydrocarbon Prospectivity and Risk Assessment of “Bob” Field Central Swamp Depobelt, Onshore Niger Delta Basin, Nigeria

This paper evaluates the hydrocarbon prospectivity and play risks of “Bob” field in Niger Delta Basin, Nigeria. The aim is to enhance exploration success through improved approach/technique by incorporating risk analysis that previous studies have not fully considered. This approach combines a set of analyses including stratigraphic/structural, amplitude, petrophysical parameter, volumetric and play risk using a suite of well logs and 3D seismic data. Maximum amplitude anomaly map extracted on the surfaces of delineated 3 reservoirs revealed 6 prospects, namely: Dippers, Cranes, Turacos, Nicators, Jacanas and Pelicans with hydrocarbon accumulation. Petrophysical analysis showed ranges of values for porosity, permeability and water saturation of 0.21 to 0.23, 158.96 to 882.39 mD, and 0.07 to 0.11, respectively. The various prospects yielded the following stock tank volumes 12.73, 6.84, 3.84, 11.32, 7.42 and 4.76 Million barrels (Mbls) each respectively in a column of 66 ft reservoir sand in the study area. Play risk analysis results gave: Pelicans and Nicators (low), Turacos and Dippers (moderate), while Jacanas and Cranes show high risk with minimal promise for good oil accumulation. The prospects possess good reservoir petrophysical properties with low to moderate risk, thus, viable for commercial hydrocarbon production, which increases confidence in management decisions for production.

Incorporating conceptual and interpretation uncertainty to mineral prospectivity modelling

Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes.In particular,knowledge-driven approaches(fuzzy logic)use a conceptual mineral systems model from which data proxies represent the critical components.These typically vary based on the scale of study and the type of mineral system being predicted.Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model.In the case study presented,the prospectivity for remobilised nickel sulphide(NiS)in the west Kimberley,Western Australia,is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios.Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered.The first technique grades the strength of structural interpretations on an individual line-segment basis.Gradings are obtained from an objective measure of feature evidence,which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure.Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence.This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty.The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems.In this approach,multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition.With a suite of prospectivity maps,the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite.This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model,robust modelling of prospectivity and significantly reducing the exploration search space for Ni.

A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidenc

Novel mineral prospectivity modelling presented here applies knowledge-driven feature extraction to a datadriven machine learning approach for tungsten mineralisation.The method emphasises the importance of appropriate model evaluation and develops a new Confidence Metric to generate spatially refined and robust exploration targets.The data-driven Random ForestTM algorithm is employed to model tungsten mineralisation in SW England using a range of geological,geochemical and geophysical evidence layers which include a depth to granite evidence layer.Two models are presented,one using standardised input variables and a second that implements fuzzy set theory as part of an augmented feature extraction step.The use of fuzzy data transformations mean feature extraction can incorporate some user-knowledge about the mineralisation into the model.The typically subjective approach is guided using the Receiver Operating Characteristics(ROC)curve tool where transformed data are compared to known training samples.The modelling is conducted using 34 known true positive samples with 10 sets of randomly generated true negative samples to test the random effect on the model.The two models have similar accuracy but show different spatial distributions when identifying highly prospective targets.Areal analysis shows that the fuzzy-transformed model is a better discriminator and highlights three areas of high prospectivity that were not previously known.The Confidence Metric,derived from model variance,is employed to further evaluate the models.The new metric is useful for refining exploration targets and highlighting the most robust areas for follow-up investigation.The fuzzy-transformed model is shown to contain larger areas of high model confidence compared to the model using standardised variables.Finally,legacy mining data,from drilling reports and mine descriptions,is used to further validate the fuzzy-transformed model and gauge the depth of potential deposits.Descriptions of mineralisation corroborate that the targets generated in these models could be undercover at depths of less than 300 m.In summary,the modelling workflow presented herein provides a novel integration of knowledge-driven feature extraction with data-driven machine learning modelling,while the newly derived Confidence Metric generates reliable mineral exploration targets.

Support Vector Machine:A Novel Tool for Mineral Prospectivity Mapping

Support Vector Machine(SVM) was demonstrated as a potentially useful tool to integrate multi-variables and to produce a predictive map for mineral deposits. The e 1071,a free R package,was used to construct a SVM with radial kernel function to integrate four evidence layers and to map prospectivity for Gangdese porphyry copper deposits.The results demonstrate that the predicted prospective target area for Cu occupies 20.5%of the total study area and contains 52.4%of the total number of known porphyry copper deposits.The results obtained

Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions

The past two decades have seen a rapid adoption of artificial intelligence methods applied to mineral exploration. More recently, the easier acquisition of some types of data has inspired a broad literature that has examined many machine learning and modelling techniques that combine exploration criteria,or ’features’, to generate predictions for mineral prospectivity. Central to the design of prospectivity models is a ’mineral system’, a conceptual model describing the key geological elements that control the timing and location of economic mineralisation. The mineral systems model defines what constitutes a training set, which features represent geological evidence of mineralisation, how features are engineered and what modelling methods are used. Mineral systems are knowledge-driven conceptual models, thus all parameter choices are subject to human biases and opinion so alternative models are possible.However, the effect of alternative mineral systems models on prospectivity is rarely compared despite the potential to heavily influence final predictions. In this study, we focus on the effect of conceptual uncertainty on Fe ore prospectivity models in the Hamersley region, Western Australia. Four important considerations are tested.(1) Five different supergene and hypogene conceptual mineral systems models guide the inputs for five forest-based classification prospectivity models model.(2) To represent conceptual uncertainty, the predictions are then combined for prospectivity model comparison.(3)Representation of three-dimensional objects as two-dimensional features are tested to address commonly ignored thickness of geological units.(4) The training dataset is composed of known economic mineralisation sites(deposits) as ’positive’ examples, and exploration drilling data providing ’negative’sampling locations. Each of the spatial predictions are assessed using independent performance metrics common to AI-based classification methods and subjected to geological plausibility testing. We find that different conceptual mineral systems produce significantly different spatial predictions, thus conceptual uncertainty must be recognised. A benefit to recognising and modelling different conceptual models is that robust and geologically plausible predictions can be made that may guide mineral discovery.

Prospectivity modeling of porphyry copper deposits: recognition of efficient mono-and multi-element geochemical signatures in the Varzaghan district, NW Iran

The Varzaghan district at the northwestern margin of the Urumieh–Dokhtar magmatic arc, is considered a promising area for the exploration of porphyry Cu deposits in Iran. In this study we identified mono-and multi-element geochemical anomalies associated with Cu–Au–Mo–Bi mineralization in the central parts of the Varzaghan district by applying the concentration–area fractal method. After mono-element geochemical investigations, principal component analysis was applied to ten selected elements in order to acquire a multi-element geochemical signature based on the mineralization-related component. Quantitative comparisons of the obtained fractal-based populations were carried out in accordance with known Cu occurrences using Student's t-values. Then,significant mono-and multi-element geochemical layers were separately combined with related geologic and structural layers to generate prospectivity models, using the fuzzy GAMMA approach. For quantitative evaluation of the effectiveness of different geochemical signatures in final prospectivity models, a prediction-area plot was adapted. The results show that the multi-element geochemical signature of principal component one(PC1) is more effective than mono-element layers in delimiting exploration targets related to porphyry Cu deposits.

Porphyry Copper Mineral Prospectivity Mapping Using Interval Valued Fuzzy Sets Topsis Method in Central Iran

Geospatial Information System (GIS) provide tools to quantitatively analysis and combination of datasets from geological, geophysical, remote sensing and geochemical surveys for decision-making processes. Excellent coverage of well-documented and good quality data enables testing of variable exploration modeling in an efficient way. The study area of this research is the most important part of Cu (Mo) porphyry—type mineralization belt in Iran. There are some well-known porphyry copper deposits in this region like Sarcheshmeh and Meiduk mines, but certainly there are same grounds to search for new porphyry deposits. The risks of developing mineral resources need to be known as accurately as possible, with regarding to all features those are effective in mineralization. These features can be recognized respect to Critical Genetic Factors (CGF’s) using Critical Recognition Criteria (CRC) for each type of mineralization. CGF’s can be employed for designing a Conceptual Genetic Model (CGM). Evidence maps create on the basis of CGM and then integrate together for production of Mineral Prospectivity Map (MPM). This map categorizes the areas based on their exploration importance. There are several techniques for creation of MPM. Interval Valued Fuzzy Sets (IVFSs) TOPSIS method was applied in this research. This method as a knowledge-driven method, allocate appropriate weights to layers on the basis of the effective membership, non membership, and non-certainty. The fundamental concept of TOPSIS is that the chosen alternatives should have the shortest distance from the positive ideal points (A*) and the farthest distance from negative ideal points (A-).

Mineral Prospectivity Mapping Method Integrating Multi-Sources Geology Spatial Data Sets and Case-Based Reasoning

Extracting and synthesizing information from existing and massive amounts of geology spatial data sets is of great scientific significance and has considerable value in its applications. To make mineral exploration less expensive, more efficient, and more accurate, it is important to move beyond traditional concepts and establish a rapid, efficient, and intelligent method of predicting the existence and location of minerals. This paper describes a case-based reasoning (CBR) method for mineral prospectivity mapping that takes spatial features of geology data into account and offers an intelligent approach. This method include a metallogenic case representation that combines spatial and attribute features, metallogenic case-based storage organization, and a metallogenic case similarity retrieval model. The experiments were performed in the eastern Kunlun Mountains, China using CBR and weights-of-evidence (WOE), respectively. The results show that the prediction accuracy of the CBR is higher than that of the WOE.

Geophysical and Hydrogeological Groundwater Prospectivity Mapping in the Kraaipan Granite-Greenstone Terrain, Southeast Botswana

Application of regional geophysical methods for hydrogeological purposes has increased over the last two decades especially in arid and semi-arid areas. A project to map the Kraaipan granite-greenstone terrain in southeast Botswana has recently been undertaken using regional aeromagnetic and gravity data with the aim to map the rocks at depth to understand the geology while the secondary objective was to subsequently assess the mineralization and groundwater potential in the area. An integrated analysis of the aeromagnetic and gravity data and their derived/processed products is hereby investigated for groundwater for drinking and agricultural purposes. The studies include: subsurface characterisation and delineation of structural framework suitable for groundwater exploration and determination of petrophysical relationships used to link the geophysical properties (e.g., density) to hydrological properties (e.g., porosity). The results of interpretation indicate that the rocks are under ~50 m of Kalahari cover and the study area is composed of three aquifers: the extensive hard rock aquifer (granitic and volcanic), the important (fractured) karst aquifer and the minor sedimentary aquifer. The area is dissected by an ENE-to-EW-trending dyke swarm visible on the regional aeromagnetic data and much clearer on high resolution aeromagnetic data. Minor fault and/or dyke elements of NW-SE and NE-SW trend are observed. Spectral analysis reveals three main average ensample interfaces at depths of 0.7 km, 1.99 km and 4.8 km. The linear Euler solutions maps reveal that the majority depths to top of magnetic bodies range from 40 m to 400 m throughout the survey area. The shallowest depths are the most significant one in this case as they probably relate to depth of bedrock and thickness of regolith or thickest sediments. For 2695 existing boreholes analysed, maximum borehole depth is 482 m (mean 108 m), and almost half (1263) were dry with another 972 having low yield (1 - 5 m3/hr) and 432 yielding 6 - 49 m3/hr and only 28 above 50 m3/hr (maximum ~160 m3/hr) and an average water strike of 64 m. There is very little correlation between interpreted hydrogeological features and the existing borehole locations. The study shows the importance of preliminary geophysical investigations before ground borehole siting and drilling in order to improve borehole success rates and/or reduce costs inherent in groundwater projects.

Copper, Zinc, and Lead Mineral Prospectivity Mapping in the North of Tafresh, Markazi Province, Central Iran, Using the AHP-OWA Method

The reason of this research is to identify the favorable areas for copper, zinc, and lead mineralization in the western part of the 1:100,000 Tafresh geological Sheet in the Urmia-Dokhtar structural zone of Iran. Effective data layers for mineralization, such as geology, geochemistry, structures, and satellite images, were analyzed and then integrated using the AHP-OWA method to identify favorable areas. Geochemical stream samples were analyzed by univariate, multivariate, and classical statistical methods and revealed the first, second, and third class anomalies for copper, zinc, and lead in the study region. Detection of hydrothermal alteration zones by Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery in various algorithms, such as Relative absorption Band Depth (RBD), Minimum Noise Fraction (MNF), and Least Square Fit (LS-Fit), shows that argillic, phyllic, propylitic, and iron oxide alterations develop around the faults in the area under study. The favorable areas for copper, zinc, and lead mineralization have been identified by a combination of evidence maps of lithology, faults, dikes, geochemistry, and alteration data layers. Field observations in the area under study have confirmed the results.

Lead and Copper Mineral Prospectivity Mapping in Kalatereshm Area, Based on Exploratory Data Sets Using AHP-Index Overlay Modeling in GIS (Semnan Province, North of Iran)

The Kalatereshm is an area in north of Iran which covers some part of Torud magmatic belt. The area of this belt is about 2000 square kilometers and most of the mines in this particular area are of Copper, lead and Zinc. The Synthesis process is done by the Analysis Hierarchy Process (AHP) and Index Overlay (IO) methods. Of previous studies on the area, various companies providing Geological maps and in particular the company of Jiangxi providing its own geochemical maps can be mentioned. The reasons for doing this research and its innovation in Kalatereshm’s sheet can be justified as to be valuable and the fact that we would be able to save in time and cost by doing so. Previous case studies on this particular region lacked the necessary use of an advanced software and method. The informational layers included geochemical layers (the second and first ratings were given to Copper and Lead respectively by weighting based on AHP method), geology layer (the fourth and second ratings were given to Copper and Lead respectively by weighing based on AHP method), fault layer (the first and fourth ratings were given to Copper and Lead respectively by weighting based on AHP method), satellite imagery layer (the third rating was given to both Copper and Lead by weighting based on AHP method) and the more applicable areas for field exploration and detailed procedures of exploration had been determined (the mentioned ratings were delineated by each element’s respective weight in each layer and their importance in the Synthesis of informational layers).

A Review on Hydrocarbon Prospectivity in Chittagong Hill Tracts and Adjacent Area

Bangladesh is a country with limited sources of energy and since 1990 natural gas has been its main source of energy. Most of the exploration approaches had been conducted onshore especially in the central and eastern part of Bangladesh, particularly in northeastern Sylhet basin. Among the hydrocarbon provinces, the East Delta Hill Tract province is an under explored petroleum province in Bangladesh. An exploratory well drilled in Sitakund anticline was found dry but no reasonable cause was perceived why that well went dry. Although many works had been carried out in Chittagong Hill Tracts but none of them was cumulative and descriptive. In this study, the overall hydrocarbon prospect of the Chittagong Hill Tracts was analyzed by mapping of potential zones on the basis of the evaluation of regional structure and construction of lithocolumn of the prospective zones. The five elements of the petroleum system discussed thoroughly to find overall petroleum prospect of the study area. Source rocks of Chittagong Hill Tracts are mainly Bhuban shale, reservoir rock is sandstone from Bhuban-Bokabil formation, the way of migration path is both through longitudinal and cross fault. The data of source rock and seal is collected from previous researches. Multiple types of traps have been found there. Conventional anticlinal traps which are highly disturbed due to tectonic instability & the core part are shale diapirism. Most of the anticlines are plunging and the nose or plunge area might be prospective to HC for being comparatively less faulted. Broad synclinal areas between tight narrow anticlines are another prospective area for HC. These similar types of synclines are also found in Tripura, India which is a highly prospective area for petroleum and their small anticlinal hums within syncline are also prospective. Some stratigraphic traps have also been found in Tripura from where production has been started already. Both Chittagong Hill Tracts and Tripura Fold Belts are parts of great Arakan Fold Belts, so similar type of structures might be prospective here. Apart from these, Bangladesh is a deltaic country. So stratigraphic trapment like channel sand, pinch-outs is possible. Considering all the elements of petroleum prospectivity of the area and factors discussed above, it is quite clear that Chittagong hill tracts might be the next target for HC exploration program.

PORPHYRY COPPER (-MOLYBDENUM-GOLD) DEPOSIT PROSPECTIVITY IN ZHASHUI-SHANYANG MINERALIZATION BELT,QINLING MT., NW CHINA

The paper focuses on the geological setting, forrning environment, petrochemistry, isotope, etc. of the Yangshanian intermediate-acid porphyry intrusion in ZhashuiShanyang mineralization belt. The alteration type and zone of the intrusion and its host rocks have been studied, and the characteristics of the known copper (-molybdenum-gold) mineralization occurrence have been investigated. Hence the metallogenetic factors and ore prospectivity have been evaluated by comprehensive study of the metallogenic geology and correlation with the rnega-porphyry copper(-molybdenum) deposits in the world.

Space-associated domain adaptation for three-dimensional mineral prospectivity modeling

Geographical information systems(GIS)are essential tools for mineral prospectivity modeling(MPM).Three-dimensional(3D)MPM is able to learn the association between geological evidence and mineralization in shallow zones and thereby build a prospectivity model for deep zones,making it a desirable technique to target deep-seated orebodies.However,existing 3D MPM methods directly generalize the model learned in shallow zones to the deep zones without attention to model transferability caused by the different metallogenic mechanisms between the two zones.In this study,we aim to robustly transfer the prospectivity model learned from shallow zones to deep zones.We cast the 3D MPM as a domain adaptation problem,which is an important realm of transfer learning.Because the metallogenic mechanism can be closely associated with spatial locations,we specifically focus on domain adaption concerning the spatial locations that are ignored by conventional domain adaptation methods.To measure the spatial-associated domain discrepancy,we propose a novel spatial-associated maximum mean discrepancy(SAMMD),which compares the joint distributions of features and spatial locations across domains.Based on the SAMMD criterion,a deep neural network,referred to as the spatial-associated domain adaptation network,is devised to learn cross-domain but mineralization-indicative features for building prospectivity model that is transferable to deep zones.A case study of the world-class Sanshandao gold deposit,in eastern China,was carried out to validate the effectiveness of the proposed methods.The results show that compared with other leading MPM methods and other domain adaption variants,the proposed method has superior prediction accuracy and targeting efficiency,demonstrating the effectiveness and robustness of the proposed method in targeting deep-seated orebodies in areas with different metallogenic mechanisms and no labeled data.

Liquefaction susceptibility and deformation characteristics of saturated coral sandy soils subjected to cyclic loadings-a critical review

Coral sandy soils widely exist in coral island reefs and seashores in tropical and subtropical regions.Due to the unique marine depositional environment of coral sandy soils,the engineering characteristics and responses of these soils subjected to monotonic and cyclic loadings have been a subject of intense interest among the geotechnical and earthquake engineering communities.This paper critically reviews the progress of experimental investigations on the undrained behavior of coral sandy soils under monotonic and cyclic loadings over the last three decades.The focus of coverage includes the contractive-dilative behavior,the pattern of excess pore-water pressure(EPWP)generation and the liquefaction mechanism and liquefaction resistance,the small-strain shear modulus and strain-dependent shear modulus and damping,the cyclic softening feature,and the anisotropic characteristics of undrained responses of saturated coral sandy soils.In particular,the advances made in the past decades are reviewed from the following aspects:(1)the characterization of factors that impact the mechanism and patterns of EPWP build-up;(2)the identification of liquefaction triggering in terms of the apparent viscosity and the average flow coefficient;(3)the establishment of the invariable form of strain-based,stress-based,or energy-based EPWP ratio formulas and the unique relationship between the new proxy of liquefaction resistance and the number of cycles required to reach liquefaction;(4)the establishment of the invariable form of the predictive formulas of small strain modulus and strain-dependent shear modulus;and(5)the investigation on the effects of stress-induced anisotropy on liquefaction susceptibility and dynamic deformation characteristics.Insights gained through the critical review of these advances in the past decades offer a perspective for future research to further resolve the fundamental issues concerning the liquefaction mechanism and responses of coral sandy sites subjected to cyclic loadings associated with seismic events in marine environments.

Mineral Prospectivity Prediction via Convolutional Neural Networks Based on Geological Big Data

Today’s era of big data is witnessing a gradual increase in the amount of data,more correlations between data,as well as growth in their spatial dimension.Conventional linear statistical models applied to mineral prospectivity mapping(MPM)perform poorly because of the random and nonlinear nature of metallogenic processes.To overcome this performance degradation,deep learning models have been introduced in 3 D MPM.In this study,taking the Huayuan sedimentary Mn deposit in Hunan Province as an example,we construct a 3 D digital model of this deposit based on the prospectivity model of the study area.In this approach,3 D predictor layers are converted from the conceptual model and employed in a 3 D convolutional neural network(3 D CNN).The characteristics of the spatial distribution are extracted by the 3 D CNN.Subsequently,we divide the 22 extracted ore-controlling variables into six groups for contrast experiments based on various combinations and further apply the 3 D CNN model and weight of evidence(WofE)method on each group.The predictive model is trained on the basis of the coupling correlation between the spatial distributions of the variables and the underground occurrence space of the Mn orebodies,and the correlation between different ore-controlling factors.The analysis of 12 factors indicates that the 3 D CNN model performs well in the 3 D MPM,achieving a promising accuracy of up to 100%and a loss value below 0.001.A comparison shows that the 3 D CNN model outperforms the WofE model in terms of predictive evaluation indexes,namely the success rate and ore-controlling rate.In particular,the 1–12 ore-controlling factors selected in experiment 5 provide a significantly better prediction effect than the other factors.Consequently,we conclude that the Mn deposit in the study area is not only related to the stratum and interlaminar anomalous bodies but also to the spatial distribution of the faults.The experimental results confirm that the proposed 3 D CNN is promising for 3 D MPM as it eliminates the interference factors.

3D Mineral Prospectivity Mapping Based on Deep Metallogenic Prediction Theory:A Case Study of the Lala Copper Mine,Sichuan,China

With the decrease in surface and shallow ore deposits,mineral exploration has focused on deeply buried ore bodies,and large-scale metallogenic prediction presents new opportunities and challenges.This paper adopts the predictive thinking method in this era of big data combined with specific research on the special exploration and exploitation of deep-earth resources.Four basic theoretical models of large-scale deep mineralization prediction and evaluation are explored:mineral prediction geological model theory,multidisciplinary information correlation theory,mineral regional trend analysis theory,and mineral prediction geological differentiation theory.The main workflow of large-scale deep resource prediction in the digital and information age is summarized,including construction of ore prospecting models of metallogenic systems,multiscale 3 D geological modeling,and 3 D quantitative prediction of deep resources.Taking the Lala copper mine in Sichuan Province as an example,this paper carries out deep 3 D quantitative prediction of mineral resources and makes a positive contribution to the future prediction and evaluation of mineral resources.

Recent advances in the study of epitopes,allergens and immunologic cross-reactivity of edible mango

Mango(Mangifera indica L.)is a tropical fruit that is widely consumed as both fresh fruits and processed products around the world.The high incidence of mango allergy,on the other hand,has sparked widespread concern.Therefore,a summary and analysis of the current status and issues in mango allergen research can guide in-depth study on the mechanism of mango allergy and reveal effective desensitization methods.We described the incidence of fruit allergy,as well as the mechanism and clinical symptoms of mango allergy,in this review.We also looked into the structural properties of mango allergens,the effect of processing methods on mango allergens,prediction methods for mango allergen epitopes,and the current state of research on mango cross-reactive allergens and preventive measures.Finally,the research directions and ideas for the future are proposed and discussed.

Plant-based dietary patterns and risk of esophageal cancer:A prospective cohort study spanning 17 years

Objective: Plant-based diets have multiple health benefits for cancers;however, little is known about the association between plant-based dietary patterns and esophageal cancer(EC).This study presents an investigation of the prospective associations among three predefined indices of plant-based dietary patterns and the risk of EC.Methods: We performed endoscopic screening for 15,709 participants aged 40-69 years from two high-risk areas of China from January 2005 to December 2009 and followed the cohort until December 31, 2022. The overall plant-based diet index(PDI), healthful plant-based diet index(h PDI), and unhealthful plant-based diet index(u PDI), were calculated using survey responses to assess dietary patterns. We applied Cox proportional hazard regression to estimate the multivariable hazard ratios(HRs) and 95% confidence intervals(95% CIs) of EC across 3plant-based diet indices and further stratified the analysis by subgroups.Results: The final study sample included 15,184 participants in the cohort. During a follow-up of 219,365person-years, 176 patients with EC were identified. When the highest quartile was compared with the lowest quartile, the pooled multivariable-adjusted HR of EC was 0.50(95% CI, 0.32-0.77) for h PDI. In addition, the HR per 10-point increase in the h PDI score was 0.42(95% CI, 0.27-0.66) for ECs. Conversely, u PDI was positively associated with the risk of EC, and the HR was 1.80(95% CI, 1.16-2.82). The HR per 10-point increase in the u PDI score was 1.90(95% CI, 1.26-2.88) for ECs. The associations between these scores and the risk of EC were consistent in most subgroups. These results remained robust in sensitivity analyses.Conclusions: A healthy plant-based dietary pattern was associated with a reduced risk of EC. Emphasizing the healthiness and quality of plant-based diets may be important for preventing the development of EC.

Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

Understanding and modeling individuals’behaviors during epidemics is crucial for effective epidemic control.However,existing research ignores the impact of users’irrationality on decision-making in the epidemic.Meanwhile,existing disease control methods often assume users’full compliance with measures like mandatory isolation,which does not align with the actual situation.To address these issues,this paper proposes a prospect theorybased framework to model users’decision-making process in epidemics and analyzes how irrationality affects individuals’behaviors and epidemic dynamics.According to the analysis results,irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high.Then,this paper proposes a behavior inducement algorithm to guide individuals’behaviors and control the spread of disease.Simulations and real user tests validate our analysis,and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’behavior.