Maximum Probability Of Occurrence Research Articles

A typical meteorological day database of solar terms for simplified simulation of outdoor thermal environment on a long-term

The simulation of the outdoor thermal environment long-term, represented by the annual conditions, can describe outdoor thermal environment characteristics more completely. To simplify the simulation, Typical Meteorological Year (TMY) or Principal Component Analysis (PCA) methods are often used to generate a Typical Meteorological Day (TMD) as input conditions for a simplified simulation of the outdoor thermal environment in the long term. However, the results of these typical days are accidental, the time interval between the typical days is heterogeneous, and these typical days cannot be linked together to represent a data sequence used to describe meteorological features in the long term. By combining knowledge of Solar Terms in the traditional Chinese calendar with the CSWD (the Chinese standard year) method, a meteorological element data sequence of a typical weather day (Solar Terms Typical Meteorological Day, STTMD) can be generated. There are 24 typical days obtained by the STTMD method. In the traditional Chinese calendar, they have a definite time order, and the time interval between the typical days is about 15 days. Connecting these typical days can form a hypothetical meteorological year, which is used to simplify the description of the annual meteorological characteristics with the maximum probability of occurrence. Based on meteorological observation data from 1981 to 2010, the STTMD data series of 61 stations in China were generated. These data were used to form a simple national climate division, and then the meteorological representation of different divisions was analyzed. Meteorological representativeness analysis for these subtions indicated the good accuracy of STTMD data. The method can simplify the numerical simulation process for those studied on long-term and can be extended to applications such as outdoor comfort evaluation and urban building energy consumption.

Species distribution modelling and population genetic analysis ofYushania anceps; an endemic temperate woody bamboo of the Uttarakhand Himalayas

AbstractYushania ancepsis a temperate woody bamboo taxon of high socio-economic importance occurring in the sub-alpine zone of the western Himalayas. This study was carried out to delineate the potential distribution ofY. ancepsin the western Himalayas through species distribution modelling (SDM), and genetic characterization using sequence-tagged microsatellite (STMS) markers. The present study revealed an endemic distribution of this species in the Uttarakhand Himalayas, with an estimated area of 211.59 km2. The maximum probability of occurrence was recorded in the moderately dense forests between the altitudinal ranges of 2500 and 2700 m. The model output was well supported with high values of different statistical measures, such as the AUC (0.911) and Kappa coefficient (K= 0.513). Environmental variables related to precipitation, temperature and topography were identified as the most contributory in current SDM. In addition, diversity measures, namely allelic richness (Ar), expected heterozygosity (He), and fixation index (FST), were calculated in five sampled populations with eight STMS markers, which indicated high genetic diversity (Ar = 4.24; He = 0.689) and little differentiation (FST= 0.062). The diversity maps displayed that the populations located in the Kumaon region captured relatively more genetic diversity than the Garhwal region. Further, genetic clustering and STRUCTURE analysis revealed a substantial level of genetic admixing across the analysed populations, and as a result, no sub-structuring was detected. Due to the rare and endemic distribution ofY. anceps, it requires immediate conservation measures, and the knowledge base generated here will be of paramount importance to forest managers, researchers and policymakers.

Key fault propagation path identification of CNC machine tools based on maximum occurrence probability

In order to revise the deviation caused by ignoring the dynamic character of fault propagation in traditional fault propagation path identification methods, a method based on the maximum occurrence probability is proposed to identify the key fault propagation path. Occurrence probability of fault propagation path is defined by dynamic importance, dynamic fault propagation probability and fault rate. Taking the fault information of CNC machine tools which subject to Weibull distribution as an example, this method has been proven to be reasonable through comparative analysis. Result shows that the key fault propagation path of CNC machine tools is not unique, but changes with time. Before 1000 hours, key fault propagation path is electrical component (E) to mechanical component (M); after 1000 hours, key fault propagation path is auxiliary component (A) to mechanical component (M). This change should be taken into account when developing maintenance strategies and conducting reliability analysis.

An experimental study on nonlinear wave dynamics for freak waves over an uneven bottom

The effect of the non-uniform bathymetry on the nonlinear wave dynamics for the freak wave is investigated experimentally, with emphasis on the interrelations between different nonlinear behaviors resulting from various geometric parameters and spectral analysis. Both the frequency modulation and the nonlinear phase coupling can be provoked by the decreasing water depth and weakened after the top peak of the bar, the nonlinear exhibition for transferring energy to high-frequency contents over shoal supports that frequency modulation can reflect nonlinear phase coupling well. The consistent change of the Hilbert energy spectrum and the bicoherence shows that the main nonlinear interaction in the process of wave propagation in shallowing water is quadratic nonlinearity. In addition, the geometric study is conducted to investigate the effect of the water depth on the parametric variations, the research results show that the mean asymmetry and kurtosis change abruptly when the wave approaches the top peak of the bar. As the wave propagates along the water flume, freak waves can be generated at various locations, however, they appear more frequently as waves propagate close to the shallowest water depth, and the maximum probability of occurrence for a freak wave can be up to about 1%.

Modeled Vegetation Community Trajectories: Effects from Climate Change, Atmospheric Nitrogen Deposition, and Soil Acidification Recovery.

Forest understory plant communities in the United States harbor most of the vegetation diversity of forests and are often sensitive to changes in climate and atmospheric deposition of nitrogen (N). As temperature increases from human-caused climate change and soils recover from long term atmospheric deposition of N and sulfur (S), it is unclear how these important ecosystem components will respond. We used the newly developed US-PROPS model - based on species response functions for over 1,500 species - to evaluate the potential impacts of atmospheric N deposition and climate change on species occurrence probability for a case study in the forested ecosystems of the Great Smoky Mountains National Park (GRSM), an iconic park in the southeastern United States. We evaluated six future scenarios from various combinations of two potential recoveries of soil pH (no change, +0.5 pH units) and three climate futures (no change, +1.5, +3.0 deg C). Species critical loads (CLs) of N deposition and projected responses for each scenario were determined. Critical loads were estimated to be low (< 2 kg N/ha/yr) to protect all species under current and expected future conditions across broad regions of GRSM and these CLs were exceeded at large spatial extents among scenarios. Northern hardwood, yellow pine, and chestnut oak forests were among the most N-sensitive vegetation map classes found within GRSM. Potential future air temperature conditions generally led to decreases in the maximum occurrence probability for species. Therefore, CLs were considered "unattainable" in these situations because the specified level of protection used for CL determination (i.e., maximum occurrence probability under ambient conditions) was not attainable. Although some species showed decreases in maximum occurrence probability with simulated increases in soil pH, most species were favored by increased pH. The importance of our study is rooted in the methodology described here for establishing regional CLs and for evaluating future conditions, which is transferable to other national parks in the U.S. and in Europe where the original PROPS model was developed.

Comparative Study of the Es Layer between the Plateau and Plain Regions in China

The lower atmosphere forcing plays an important role in forming the sporadic E (Es) layer in the ionosphere. In this study, a comparative study of the Es layer recorded by ionosondes at the middle latitude regions was carried out between the plateau and plain regions in China. The two ionosonde stations (Zhangye, 39.21°N, 100.54°E and Beijing, 40.25°N, 116.25°E) are located at the Qinghai–Tibet Plateau and North China Plain, respectively. The data during the year 2018 were used to reveal the characteristics of the Es layer. The occurrence probability, the critical frequency (foEs) and the base virtual height (h’Es) were considered in this study. Results show that: (1) The diurnal and seasonal variations of the occurrence probability between these two regions are similar. The maximum occurrence probability is at noontime and in the summer season. However, the Es at Zhangye occurred more frequently than Beijing at nighttime and in winter to early spring. (2) Similar to previous studies, the maximum value of foEs at Beijing mainly occurred in summer. Interestingly, the maximum value is in winter at Zhangye station. (3) The characteristics of the anomaly of the Es layer at Zhangye are mostly consistent with the characteristics of atmospheric gravity waves in the Qinghai–Tibet Plateau. Therefore, compared with observations at Beijing, the anomalies of the Es layer at Zhangye (at night and in winter to spring) might be attributed to gravity waves in the lower atmosphere over the Qinghai–Tibet Plateau.

The propensity of the over-stressed rock masses to different failure mechanisms based on a hybrid probabilistic approach

The simultaneous impact of excavation-induced stress concentration and mining disturbances on deep underground mines/tunnels can result in severe and catastrophic failure like strain bursting. In this regard, the proper measurement of proneness to different rock failure mechanisms has great importance in terms of safety and economics. This study proposes a practical hybrid gene expression programming-based logistic regression (GEP-LR) model, as a multi-class classifier, to detect the failure mechanism (i.e. squeezing, slabbing and strain burst) in hard rock based on four intact rock properties. Three non-linear binary models are developed to predict the occurrence/non-occurrence of each failure mechanism. The logistic regression technique is linked to the developed GEP models to measure the occurrence probability of each failure mechanism. Finally, the failure mechanism that has the maximum probability of occurrence is selected as the predicted output. The performance analysis of the developed model shows that it is efficiently capable of detecting failure mechanisms with high accuracy. The failure mechanism detection models are presented in MATLAB codes to be easily used in practice by engineers/researchers as an initial guide for failure/stability analysis of underground openings. Finally, the validity of the proposed model is further evaluated by new datasets compiled from different studies.

Gait speed in clinical and daily living assessments in Parkinson\u2019s disease patients: performance versus capacity

Gait speed often referred as the sixth vital sign is the most powerful biomarker of mobility. While a clinical setting allows the estimation of gait speed under controlled conditions that present functional capacity, gait speed in real-life conditions provides the actual performance of the patient. The goal of this study was to investigate objectively under what conditions during daily activities, patients perform as well as or better than in the clinic. To this end, we recruited 27 Parkinson’s disease (PD) patients and measured their gait speed by inertial measurement units through several walking tests in the clinic as well as their daily activities at home. By fitting a bimodal Gaussian model to their gait speed distribution, we found that on average, patients had similar modes in the clinic and during daily activities. Furthermore, we observed that the number of medication doses taken throughout the day had a moderate correlation with the difference between clinic and home. Performing a cycle-by-cycle analysis on gait speed during the home assessment, overall only about 3% of the strides had equal or greater gait speeds than the patients’ capacity in the clinic. These strides were during long walking bouts (>1 min) and happened before noon, around 26 min after medication intake, reaching their maximum occurrence probability 3 h after Levodopa intake. These results open the possibility of better control of medication intake in PD by considering both functional capacity and continuous monitoring of gait speed during real-life conditions.

Anthropogenic drivers of plant species coexistence in selected riparian systems, Kumawu district, Ashanti region (Ghana)

Anthropogenic drivers have long been recognized as influencing species coexistence and the maintenance of biodiversity in riparian ecosystems among others. Despite their important role, riparian zones have been subjected to progressive alteration. This study aimed at evaluating how anthropogenic drivers influence plant species coexistence in riparian zones differing in scale of disturbance – intact zone (Amobia), slightly disturbed zone (Onwame), moderately disturbed zone (Ninian) and degraded zone (Pame), respectively, located in the Kumewu District of Ahanti Region (Ghana). Plots were randomly laid perpendicular to the anthropogenic gradients such as fire, grazing pressure, erosion, farming, animal trampling, bare ground and logging. Number of individuals was counted per plot and the percentage cover estimated per species. Species packing model was employed to quantify how competing species for resources coexisted, while Canonical correspondence analysis (CCA) was used to evaluate the effect of anthropogenic drivers on the variability of species assemblages. High number of species heterogeneity at Amobia riparian zone was a reflection of their tolerance to species coexistence than in the severely disturbed Pame riparian zone. However, species optimum and maximum probability of occurrence tend to be highest in Pame and lowest at Amobia riparian zone. This may be that widespread driver like fire, grazing pressure, farming and logging created conditions in gaps suitable for the recruitment of diverse species, with functional traits facilitating coexistence. These anthropogenic disturbances were responsible for 53.48% variability in species composition and coexistence. Thus, to enhance species coexistence, restoration of severely disturbed Pame, should be of priority concern by park managers.

Diverse interactions of aggregated insulin with selected coumarin dyes: Time dependent fluorogenicity, simulation studies and comparison with thioflavin T

In this study, we have compared neutral coumarin based well-known commercially available probes C6, C7 and C545T for fluorogenic response from the aggregated insulin. The immediate fluorogenic responses were comparatively poor from all the three probes with respect to the previously reported response from thioflavin T (ThT) in the presence of aggregated insulin. Interestingly C6 among the three neutral coumarin derivative showed a significant steady increase of fluorescence intensity with time up to 6 h before reaching the saturation limit. Similar time dependent fluorogenic experiment with C7, C545T and ThT showed comparatively fast saturation within few minutes to 2 h. The molecular docking and simulation studies showed that these neutral probes could be stabilized in the aggregated form of the insulin predominantly by non-covalent weak interactions such as hydrogen bonding, π-π and cation-π interactions. The probability distributions of the dihedral angles between two heterocyclic parts in C6 showed maximum probability of occurrence at 0° and 180°. These probability distributions of the dihedral angles between two heterocyclic parts within all the four fluorophores provided the justification of selective time dependent fluorescence enhancement from C6 in presence of insulin aggregate. The overall fluorogenic enhancement from C6 was comparable to the fluorogenic response from ThT and theoretical study confirmed distinctly different origin of this associated slow time dependent fluorogenic response.

Potential Range of Bulbocodium versicolor (Ker-Gawl.) Spreng. (Colchicaceae, Liliopsida) in Russia

The article presents a bioclimatic model of the potential range of Bulbocodium versicolor in European Russia. To build the model, we analyzed a matrix containing 166 B. versicolor localities in the studied region; the analysis was carried out in the SDMtoolbox program using the climatic paramaters from the WorldClim open database. The model demonstrates that, given the available dataset on the modern climatic conditions, B. versicolor may occur in a wider geographical range comprising, at the very least, the Belgorod, Voronezh, Volgograd, Lipetsk, Penza, Rostov and Saratov provinces. Also, within European Russia, the most favorable conditions for B. versicolorare found in most of the Voronezh and Volgograd provinces as well as in some areas of the Right Bank and Left Bank of the Volga River adjacent to the Volga Upland (in the Saratov province). The maximum occurrence probability is 70–100% while the average occurrence probability is 40– 60%. The maximum contribution to the model is made by the precipitation of the warmest and most humid quarter (June–August); a smaller contribution is made by the average temperature of the coldest (December–February) and warmest (June – August) quarters as well as by the average annual precipitation. The least contribution is made by the precipitation of the most humid month (July) and the driest quarter (March–May). Finally, we conclude that bioclimatic model facilitates a better understanding of the geographical distribution of the species in question.

Threshold effects of air pollution and climate change on understory plant communities at forested sites in the eastern United States

Forest understory plant communities in the eastern United States are often diverse and are potentially sensitive to changes in climate and atmospheric inputs of nitrogen caused by air pollution. In recent years, empirical and processed-based mathematical models have been developed to investigate such changes in plant communities. In the study reported here, a robust set of understory vegetation response functions (expressed as version 2 of the Probability of Occurrence of Plant Species model for the United States [US-PROPS v2]) was developed based on observations of forest understory and grassland plant species presence/absence and associated abiotic characteristics derived from spatial datasets. Improvements to the US-PROPS model, relative to version 1, were mostly focused on inclusion of additional input data, development of custom species-level input datasets, and implementation of methods to address uncertainty. We investigated the application of US-PROPS v2 to evaluate the potential impacts of atmospheric nitrogen (N) and sulfur (S) deposition, and climate change on forest ecosystems at three forested sites located in New Hampshire, Virginia, and Tennessee in the eastern United States. Species-level N and S critical loads (CLs) were determined under ambient deposition at all three modeled sites. The lowest species-level CLs of N deposition at each site were between 2 and 11 kg N/ha/yr. Similarly, the lowest CLs of S deposition, based on the predicted soil pH response, were less than 2 kg S/ha/yr among the three sites. Critical load exceedance was found at all three model sites. The New Hampshire site included the largest percentage of species in exceedance. Simulated warming air temperature typically resulted in lower maximum occurrence probability, which contributed to lower CLs of N and S deposition. The US-PROPS v2 model, together with the PROPS-CLF model to derive CL functions, can be used to develop site-specific CLs for understory plants within broad regions of the United States. This study demonstrates that species-level CLs of N and S deposition are spatially variable according to the climate, light availability, and soil characteristics at a given location. Although the species niche models generally performed well in predicting occurrence probability, there remains uncertainty with respect to the accuracy of reported CLs. As such, the specific CLs reported here should be considered as preliminary estimates.

The most-likely skyline problem for stochastic points

For a set O of n points in Rd, the skyline consists of the subset of all points of O where no point is dominated by any other point of O. Suppose that each point oi∈O has an associated probability of existence pi∈(0,1]. The problem of computing the skyline with the maximum probability of occurrence is considered. It is shown that in Rd, d≥3, the problem is NP-hard and that the desired skyline cannot even be well-approximated in polynomial time unless P=NP. In R2, an optimal O(nlog⁡n)-time and O(n)-space algorithm is given.

Forecasting of Wind Capacity Ramp Events Using Typical Event Clustering Identification

With the large-scale integration of wind generation into the power grid, violent wind speed fluctuation will cause wind power ramp events that can affect the safe and stable operation of power systems. In this article, a forecasting method for day-ahead ramp events is proposed based on wind speed event definition and profile analysis. Firstly, event-based K-means (EB-K) clustering is used to preprocess historical wind speed. Typical event indexes, such as change rate, amplitude, and time intervals are then extensively used to describe ramp event characteristics and decrease the computational burden for the following event identification within given intervals. Then, the similarity of wind power event set is obtained through empirical probability estimation of successive history ramp events. Typical event clustering identification (TECI) algorithm based on EB-K clustering, wind capacity events, and event cluster profiles is proposed to search the maximum occurrence probability for historical data with the similarity indicator. Finally, a case study on a practical farm in Hebei, China is used to verify the effectiveness and accuracy of wind capacity ramp event forecasting by using TECI.

Residual flow may increase the risk of adverse events in patients received combined catheter ablation and transcatheter left atrial appendage closure for nonvalvular atrial fibrillation: a meta-analysis

BackgroundCatheter ablation (CA) and left atrial appendage closure (LAAC) have been combined into a novel one-stop procedure for patients with atrial fibrillation (AF). However, postoperative complications are relatively common in patients undergoing LAAC; the complications, including residual flow, increase in the risk of bleeding, or other adverse events, are unknown in patients receiving one-stop therapy. Therefore, we tried to evaluate the adverse events of CA and LAAC hybrid therapy in patients with nonvalvular AF.MethodsWe performed a meta-analysis and computer-based literature search to identify publications listed in the PubMed, Embase, and Cochrane library databases. Studies were included if patients received CA and LAAC hybrid therapy and reported adverse events.ResultsOverall 13 studies involving 952 patients were eligible based on the inclusion criteria. In the periprocedural period, the pooled incidence of pericardial effusion was 3.15%. The rates of bleeding events and residual flow were 5.02 and 9.11%, respectively. During follow-up, the rates of all-cause mortality, embolism events, bleeding events, AF recurrence, and residual flow were 2.15, 5.24, 6.95, 32.89, and 15.35%, respectively. The maximum occurrence probability of residual flow events was 21.87%. Bleeding events were more common in patients with a higher procedural residual flow event rate (P = 0.03). A higher AF recurrence rate indicated higher rates of embolism events (P = 0.04) and residual flow (P = 0.03) during follow-up.ConclusionsBleeding events were more common in patients with a higher procedural residual flow event rate. However, combined CA and LAAC therapy is reasonably safe and efficacious in patients with nonvalvular AF. Further studies on the safety and efficacy of CA or LAAC alone are necessary in future.

Why There Are So Many Drowning Accidents Happened at Dadonghai Beach, Hainan, China: Morphodynamic Analysis

ABSTRACT Li Z. and Zhu S. 2018. Why there are so many drowning accidents happened at Dadonghai Beach, Hainan, China: Beach morphodynamic analysis. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 741–745. Coconut Creek (Florida), ISSN 0749-0208. Dadonghai Beach is one of the most popular seaside tourism areas in the tropical zone of China, attracting a large number of tourists from across the world. In the last 10 years, hundreds of beach drowning accidents occurred at Dadonghai Beach. In this paper, a beach safety evaluation model is established based on the beach morphodynamic state model proposed by Masselink and Short (1993). This paper suggests that the low tide bar/rip and barred beaches have the maximum occurrence probability of rip current, presenting the most hazards for swimmers at beaches. Barred dissipative beaches and low tide terrace with rips bea...

Hierarchical model-based object localization for auto-contouring in head and neck radiation therapy planning.

Segmentation of organs at risk (OARs) is a key step during the radiation therapy (RT) treatment planning process. Automatic anatomy recognition (AAR) is a recently developed body-wide multiple object segmentation approach, where segmentation is designed as two dichotomous steps: object recognition (or localization) and object delineation. Recognition is the high-level process of determining the whereabouts of an object, and delineation is the meticulous low-level process of precisely indicating the space occupied by an object. This study focuses on recognition. The purpose of this paper is to introduce new features of the AAR-recognition approach (abbreviated as AAR-R from now on) of combining texture and intensity information into the recognition procedure, using the optimal spanning tree to achieve the optimal hierarchy for recognition to minimize recognition errors, and to illustrate recognition performance by using large-scale testing computed tomography (CT) data sets. The data sets pertain to 216 non-serial (planning) and 82 serial (re-planning) studies of head and neck (H&N) cancer patients undergoing radiation therapy, involving a total of ~2600 object samples. Texture property "maximum probability of occurrence" derived from the co-occurrence matrix was determined to be the best property and is utilized in conjunction with intensity properties in AAR-R. An optimal spanning tree is found in the complete graph whose nodes are individual objects, and then the tree is used as the hierarchy in recognition. Texture information combined with intensity can significantly reduce location error for gland-related objects (parotid and submandibular glands). We also report recognition results by considering image quality, which is a novel concept. AAR-R with new features achieves a location error of less than 4 mm (~1.5 voxels in our studies) for good quality images for both serial and non-serial studies.

Assessment of Credible Capacity for Intermittent Distributed Energy Resources in Active Distribution Network

The irregularity and randomness of distributed energy sources’ (DERs) output power characteristic usually brings difficulties for grid analysis. In order to reliably and deterministically evaluate intermittent distributed generation’s active power output, a credible capacity index for active distribution network (ADN) is proposed. According to the definition, it is a certain interval that the stochastic active power output of DERs may fall in with larger probability in all kinds of possible dynamic and time varying operation scenarios. Based on the description and analysis on the time varying scenarios, multiple scenarios considered dynamic power flow method for and are proposed. The method to calculate and evaluate credible capacity based on dynamic power flow (DPF) result is illustrated. A study case of an active distribution network with DERs integrated and containing 32 nodes is selected; multiple operation scenarios with various fractal dimension are established and used. Results of calculated credible capacity based on several groups of scenarios have been analyzed, giving the variance analysis of groups of credible capacity values. A deterministic value with the maximum occurrence probability representing credible capacity is given. Based on the same network case, an application of credible capacity to grid extension planning is given, which contributes to expenditure and cost reduction. The effectiveness and significance of the proposed credible capacity and solution method have been demonstrated and verified.

NEEWS: A novel earthquake early warning model using neural dynamic classification and neural dynamic optimization

An Earthquake Early Warning System (EEWS) can save lives. It can also be used to manage the critical lifeline infrastructure and essential facilities. Recent research on earthquake prediction towards the development of an EEWS can be classified into two groups based on a) arrival of P waves and b) seismicity indicators. The first approach can provide warnings within a timeframe of seconds. A seismicity indicator-based EEWS is intended to forecast major earthquakes within a time frame of weeks. In this paper, a novel seismicity indicator-based EEWS model, called neural EEWS (NEEWS), is presented for forecasting the earthquake magnitude and its location weeks before occurrence using a combination of a classification algorithm (CA) based on machine learning concepts and a mathematical optimization algorithm (OA). The role of the CA is to find whether there is an earthquake in a given time period greater than a predefined magnitude threshold and the role of the OA is to find the location of that earthquake with the maximum probability of occurrence. The model is tested using earthquake data in southern California with a combination of four CAs and one OA to find the best EEWS model. The proposed model is capable of predicting strong disastrous events as long as sufficient data are available for such events. The paper provides a novel solution to the complex problem of earthquake prediction through adroit integration of a machine learning classification algorithm and the robust neural dynamics optimization algorithm of Adeli and Park.

Development of a stochastic approach for fatigue life prediction of AlSi12 alloy processed by selective laser melting

Abstract Parts manufactured by selective laser melting (SLM) process possess unique features in terms of surface roughness, microstructure, residual stresses as well as defect distribution. These defects are responsible for failure of the parts in functional applications. When fatigue loading is applied, these defects are the dominant cause of crack initiation, resulting in scatter of fatigue properties. This scatter occurs due to interacting phenomena like defect size, location as well as the magnitude and type of load. For the purpose of investigating the effect of defects on fatigue life performance of AlSi12 manufactured by selective laser melting, a procedure was developed based on the weakest-link theory and Weibull's probability density function. Using various destructive and non-destructive techniques, defects, including remnant porosity and surface roughness, have been characterized in amount, size and location. Therefore fatigue life prediction, relying on equations constituted from crack propagation properties, was carried out. Predicted fatigue life and Weibull's statistical parameters were used to compare the effect of both defect types on fatigue reliability of AlSi12 produced by SLM. The most probable fatigue life for a sample was interpreted based on Weibull probability density function with respect to maximum probability of occurrence. The prediction of numerous possible values enabled an estimation of fatigue scatter to be made. Thus, the findings of this novel approach enabled conclusions about strength and reliability of different SLM AlSi12 configurations and gave a prelude towards application-oriented design of SLM components.