Maximum Probability Research Articles

MRI atlas of the pituitary gland in young female adults.

The probabilistic topography and inter-individual variability of the pituitary gland (PG) remain undetermined. The absence of a standardized reference atlas hinders research on PG volumetrics. In this study, we aimed at creating maximum probability maps for the anterior and posterior PG in young female adults. We manually delineated the anterior and posterior parts of the pituitary glands in 26 healthy subjects using high-resolution MRI T1 images. A three-step procedure and a cost function-masking approach were employed to optimize spatial normalization for the PG. We generated probabilistic atlases and maximum probability maps, which were subsequently coregistered back to the subjects' space and compared to manual delineations. Manual measurements led to a total pituitary volume of 705 ± 88mm³, with the anterior and posterior volumes measuring 614 ± 82mm³ and 91 ± 20mm³, respectively. The mean relative volume difference between manual and atlas-based estimations was 1.3%. The global pituitary atlas exhibited an 80% (± 9%) overlap for the DICE index and 67% (± 11%) for the Jaccard index. Similarly, these values were 77% (± 13%) and 64% (± 14%) for the anterior pituitary atlas and 62% (± 21%) and 47% (± 17%) for the posterior PG atlas, respectively. We observed a substantial concordance and a significant correlation between the volume estimations of the manual and atlas-based methods for the global pituitary and anterior volumes. The maximum probability maps of the anterior and posterior PG lay the groundwork for automatic atlas-based segmentation methods and the standardized analysis of large PG datasets.

Adaptation and establishment of Habrobracon hebetor Say in the population of stored moths pests of date, Ephestia kuehniella Zeller and Plodia interpunctella Hübner

BackgroundHabrobracon hebetor (Hymenoptera: Braconidae) is a larval ectoparasitoid, mainly of pyralid moths of the stored pests of date, Plodia interpunctella Hübner and Ephestia kuehniella Zeller. It has some suitable characteristics for use in biological control of stored moth pests. The aim of this study was to compare the ability of H. hebetor establishment in the population of two important date storage moth pests P. interpunctella and E. kuehniella, and its role in their population fluctuations.ResultsThe population growth trends of two host species increased in the first 6 weeks and in the 17th to 22nd weeks of the 6-month period of conducting tests. The peaks of larval population of the two species were in the 10th and 21st weeks under temperature of 25 ± 2 °C, RH of 60 ± 5% and a photoperiod of 12:12, D:L. The pattern of fluctuation in the parasitism percentage in the larval population of the two moth species host was inconsistent with each other. The parasitism percentage was higher on the larval population of E. kuehniella than larval population of P. interpunctella. In E. kuehniella, the host population change rates were − 0.1 in the 10th and 15th weeks, while the highest rates were 0.1 and 0.8 in the 12th and 3rd weeks. The lowest level of parasitoid balance in the larval population of E. kuehniella and P. interpunctella was recorded in the 11th and 15th weeks, and the highest levels were recorded in the 20th and 24th weeks, respectively. The maximum probability rate of parasitoid non-establishment in E. kuehniella larval population was zero during the entire period and for P. interpunctella until the 22nd week.ConclusionIn fact, there was a strong correlation between establishment success and the severity of host and parasitoid populations. Only in the larval population of P. interpunctella, the maximum probability of failure of establishment was 11.5%, due to the occurrence of the maximum population of parasitized larvae and the minimum ratio of non-parasitic and parasitic larvae.

Most likely balls in Banach spaces: Existence and nonexistence

AbstractWe establish a general criterion for the existence of convex sets of fixed shape as, for example, balls of a given radius, of maximal probability on Banach spaces. We also provide counterexamples, showing that their existence may fail even in some common situations.

Analysis of seepage failure probability for high core rockfill dams during rapid drawdown of reservoir water level

Rapid drawdown (RDD) is an abnormal reservoir flood control operation that may cause seepage failure of high core rockfill dams. In this paper, a novel framework combining Bayesian inference and transient unsaturated seepage simulation is proposed to evaluate the seepage failure probability of the core during RDD. The dynamic hydraulic boundaries utilized in the simulation are first determined by the performance of water release structures. The extreme learning machine (ELM) is then employed as the surrogate model to establish the mapping relationship between material parameters with simulated seepage pressure and hydraulic gradient. The Bayesian inference method is applied to characterize the uncertainty of material parameters based on seepage monitoring data. Finally, the posterior distribution samples of random parameters are utilized to calculate the seepage failure probability of the core. The seepage safety of the core under different drawdown rates is studied with a real 186 m high core rockfill dam. The results reveal that a faster RDD rate can lead to an increased probability of seepage failure. The maximum failure probability is 35.56 % for the four release structures working together (about 0.316 m/h), higher than 23.16 % for the three release structures (about 0.165 m/h). It is also found that the peak of seepage failure probability often occurs in the middle stage of RDD. Therefore, to ensure the safety of the project, it is important to control the RDD rate and carefully monitor the seepage pressure. From the point of view of seepage failure, the proposed method for evaluating the safety of core rockfill dams under RDD is of high guiding significance to the risk assessment of similar projects under extreme conditions.

Single-photon sources based on incomplete binary-tree multiplexers with optimized inputs

We analyze single-photon sources based on minimum-based, maximum-logic output-extended incomplete binary-tree multiplexers assuming different input mean photon numbers in each of the multiplexed units. This approach results in maximal single-photon probabilities higher than those achieved with optimal identical input mean photon numbers. The proposed technique decreases the multiphoton noise of the sources and it is especially advantageous for lower detector efficiencies. We show that assuming different input mean photon numbers in the multiplexed units, single-photon sources based on the analyzed multiplexers outperform those based on asymmetric multiplexers for the major part of the considered parameter range.

Establishment and verification of a prediction model based on clinical characteristics and computed tomography radiomics parameters for distinguishing benign and malignant pulmonary nodules.

The radiographic classification of pulmonary nodules into benign versus malignant categories is a pivotal component of early lung cancer diagnosis. The present study aimed to investigate clinical and computed tomography (CT) clinical-radiomics nomogram for preoperative differentiation of benign and malignant pulmonary nodules. This retrospective study included 342 patients with pulmonary nodules who underwent high-resolution CT (HRCT) examination. We assigned them to a training dataset (n=239) and a validation dataset (n=103). There are 1781 tumor characteristics quantified by extracted features from the lesion segmented from patients' CT images. The features with poor reproducibility and high redundancy were removed. Then a least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation was used to further select features and build radiomics signatures. The independent predictive factors were identified by multivariate logistic regression. A radiomics nomogram was developed to predict the malignant probability. The performance and clinical utility of the clinical-radiomics nomogram was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). After dimension reduction by the LASSO algorithm and multivariate logistic regression, four radiomic features were selected, including original_shape_Sphericity, exponential_glcm_Maximum Probability, log_sigma_2_0_mm_3D_glcm_Maximum Probability, and ogarithm_firstorder_90Percentile. Multivariate logistic regression showed that carcinoembryonic antigen (CEA) [odds ratio (OR) 95% confidence interval (CI): 1.40 (1.09-1.88)], CT rad score [OR (95% CI): 2.74 (2.03-3.85)], and cytokeratin-19-fragment (CYFRA21-1) [OR (95% CI): 1.80 (1.14-2.94)] were independent influencing factors of malignant pulmonary nodule (all P<0.05). The clinical-radiomics nomogram combining CEA, CYFRA21-1 and radiomics features achieved an area of curve (AUC) of 0.85 and 0.76 in the training group and verification group for the prediction of malignant pulmonary nodules. The clinical-radiomics nomogram demonstrated excellent agreement and practicality, as evidenced by the calibration curve and DCA. The clinical-radiomics nomogram combined of CT-based radiomics signature, along with CYFRA21-1 and CEA, demonstrated strong predictive ability, calibration, and clinical usefulness in distinguishing between benign and malignant pulmonary nodules. The use of CT-based radiomics has the potential to assist clinicians in making informed decisions prior to biopsy or surgery while avoiding unnecessary treatment for non-cancerous lesions.

Remedial Dosing Recommendations for Sirolimus Delayed or Missed Dosages Caused by Poor Medication Compliance in Pediatric Tuberous Sclerosis Complex Patients.

Delayed or missed dosages caused by poor medication compliance significantly affected the treatment of diseases in children. The present study aimed to investigate the influence of delayed or missed dosages on sirolimus pharmacokinetics (PK) in pediatric tuberous sclerosis complex (TSC) patients and to recommend remedial dosages for nonadherent patients. A published sirolimus population PK model in pediatric TSC patients was used to assess the influence of different nonadherence scenarios and recommend optimally remedial dosages based on Monte Carlo simulation. Thirteen nonadherent scenarios were simulated in this study, including delayed 2h, 4 h, 6 h, 8 h, 10 h, 12 h, 14 h, 16 h, 18 h, 20 h, 22 h, 23.5 h, and missed one dosage. Remedial dosing strategies contained 10-200% of scheduled dosages. The optimal remedial dosage was that with the maximum probability of returning the individual therapeutic range. For delayed or missed sirolimus dosages in pediatric TSC patients, when the delayed time was 0-8 h, 8-10 h, 10-18 h, 18-22.7 h, 22.7-24 h, 70%, 60%, 40%, 30%, 20% scheduled dosages were recommended to take immediately. When one dosage was missed, 120% of scheduled dosages were recommended at the next dose. It was the first time to recommend remedial dosages for delayed or missed sirolimus therapy caused by poor medication compliance in pediatric TSC patients based on Monte Carlo simulation. Meanwhile, the present study provided a potential solution for delayed or missed dosages in clinical practice.

Simulations of CO2 emissions peak and abatement potential in China's building operations

The building sector is the key to achieving the goal of emissions peak in China. Understanding the possibility of CO2 emissions peak and abatement potential in building sector is essential to seeking effective carbon-abatement pathways. However, there are differences in emissions from rural, urban, and public buildings, and it is unclear whether different buildings can achieve emissions peak by 2030. We mainly used Monte Carlo simulation to analyze the CO2 emissions peak and abatement potential in building operations. Our study has resulted in three findings. (1) The cumulative emission abatement in the building sector was more than 1 billion from 2000 to 2019, including 379.68 million tons from rural buildings, 383.51 million tons from urban buildings, and 248.85 million tons from public buildings. Compared to rural and urban buildings, the emission abatement performance of public buildings is the poorest. (2) CO2 emissions in the Chinese building sector will achieve its peak of 2519.85–2527.37 million tons in 2026, only under the enhanced low-carbon scenario. It is difficult for the Chinese building sector to achieve its CO2 emissions peak by 2030 under the business-as-usual and low-carbon scenarios. (3) The emission abatement potential with maximum probability in the building sector is 984.61–1005.48 million tons in 2030 under the enhanced low-carbon scenario. Public buildings have the largest emission abatement potential, accounting for about 48% of the total emission abatement potential of the building sector. This study can compensate for the limitations in emissions peak and abatement potential in different building sectors, and assist governments to set effective emission-reduction policies.

Quantile Hedging in the complete financial market under the mixed fractional Brownian motion model and the liquidity constraint

This article proposes a pricing framework for European option that utilizes a Quantile hedging strategy in a complete financial market. The methodology involves applying the long memory Geometric Brownian motion model, utilizing the generalized mixed fractional Girsanov theorem, and incorporating relevant findings related to quasi-conditional expectation. The first step in this framework is to derive the option price formula using the equivalent martingale measure. This approach enables investors to use the entire option price without any restrictions to hedge their contingent claim. Then, we add the initial wealth condition to hedge and determine the option price with the Quantile hedging strategy. To do this, we find the maximum probability of success in complete risk hedging for different amounts of model parameters and then use the probability values to determine the option price. Moreover, we apply the minimum variance algorithms and the error correction method to find the hedge ratio. Finally, we illustrate the hedging strategy and provide numerical results that assist investors in determining the required capital for covering the option risk under market data. This calculation takes into account both the level of risk tolerance and the anticipated return on investment.

EEG-FRM: a neural network based familiar and unfamiliar face EEG recognition method.

Recognizing familiar faces holds great value in various fields such as medicine, criminal investigation, and lie detection. In this paper, we designed a Complex Trial Protocol-based familiar and unfamiliar face recognition experiment that using self-face information, and collected EEG data from 147 subjects. A novel neural network-based method, the EEG-based Face Recognition Model (EEG-FRM), is proposed in this paper for cross-subject familiar/unfamiliar face recognition, which combines a multi-scale convolutional classification network with the maximum probability mechanism to realize individual face recognition. The multi-scale convolutional neural network extracts temporal information and spatial features from the EEG data, the attention module and supervised contrastive learning module are employed to promote the classification performance. Experimental results on the dataset reveal that familiar face stimuli could evoke significant P300 responses, mainly concentrated in the parietal lobe and nearby regions. Our proposed model achieved impressive results, with a balanced accuracy of 85.64%, a true positive rate of 73.23%, and a false positive rate of 1.96% on the collected dataset, outperforming other compared methods. The experimental results demonstrate the effectiveness and superiority of our proposed model.

Quantitative assessment of building risks and loss ratios caused by storm surge disasters: A case study of Xiamen, China

China is severely affected by storm surge disasters, which result in substantial economic losses and casualties in coastal regions. Assessing the risk of storm surge disasters can provide valuable insights into the expected losses and severity of future impacts, offering critical foresight for disaster prevention and mitigation strategies. This study assesses the quantitative risk of storm surge disasters, focusing on coastal buildings since they are particularly susceptible to storm surges and frequently bear the brunt such disasters. Xiamen city, China, was used as case study. A high-precision numerical model, using Finite Volume Community Ocean Model (FVCOM), was developed to simulate inundation during storm surges. By referencing historical storm surge records, we defined key parameters for probable maximum typhoon-induced storm surge (PMTSS) in Xiamen. These parameters were used to calculate the corresponding inundation range and water depth distribution within the region. Subsequently, the results were integrated with vulnerability curves that represent the susceptibility of buildings in Xiamen to storm surge-induced damage, enabling the quantitative risk assessment for associated loss risks. The study findings offer valuable guidance for urban planning and functional layout design in coastal areas. Furthermore, the findings contribute to understanding storm surge disaster risks and facilitating informed decision-making processes, ultimately enhancing disaster preparedness and resilience in vulnerable coastal regions.

Trip Purpose Inference and Spatio-Temporal Characterization Based on Anonymized Trip Data: Empirical Study from Dockless Shared Bicycle Dataset in Xi’an, China

The rapid and widespread deployment and applications of the Internet of Things (IoT) and big data technology has generated massive personal travel data. Although most of the data are anonymized, they still provide possibilities for analyzing individual travel behavior. It is worthwhile to explore how to more accurately infer travel purposes based on trajectory data, smart card fare data, or shared bicycle data. In this paper, an improved research framework is proposed for travel purpose inference by applying the gravity model, Bayesian criterion, and spatial clustering method. The gravity model and Bayesian rule are used to calculate the probability of users traveling to nearby POIs (points of interest), and the clustering algorithm is used to identify the locations regularly visited by users. Through the identification of maximum probability POI and regular trips, different travel purposes represented by POI can be distinguished. The results showed that the identification of regular trips could verify and complement the recognition of trip purposes by POIs. Using Xi’an city of China as an example for the study, the results show that: 1) regular trips accounted for 32% of the week’s trips; 2) there are 10% of the trips reflect different trip purposes for the same POI; 3) it is also important to note that 70% of the users have a parking error of fewer than 100 m when going to the exact location. This distance can provide a reference for the study of bicycle commuting. Finally, we analyzed the spatial and temporal distribution characteristics of different trip purposes. The analysis of the spatial and temporal distribution can provide suggestions for the operation of bike-sharing enterprises and the management of regulators.

Deep Learning-based Ensemble Approach for Conventional Pap Smear Image Classification

Cervical cancer screening allows the early signs of precancerous abnormalities in the cervix before they develop into invasive cancer. The Pap Smear is a widely used screening for early detection and prevention of cervical cancer. In many remote areas, the number of cytologists available to interpret pap smear screening tests is insufficient. This lack of personnel makes the test interpretation very time-consuming. To address this, deep learning techniques have been employed to detect cervical cancer cells and support cytologists. Therefore, an integrative approach with deep learning models and the ensemble techniques such as the maximum occurrence and the maximum probability score of cervical cells was proposed. The multi-cell assessment of the Pap smear slide allowed aggregate predictions of single cervical cell images using the proposed method. The classification results between pre-trained deep learning models and the proposed method were compared. In the experimental results, the proposed method can achieve an accuracy score of more than 97%, while the best pre-trained deep learning model can attain an accuracy score of more than 85%. Hence, the proposed method may have the potential to assist physicians or cytologists in the classification of cervical cell types for Pap Smear images.

A Binary Object Detection Pattern Model to Assist the Visually Impaired in detecting Normal and Camouflaged Faces

This study presents a novel Binary Object Detection Pattern Model (BODPM) to detect objects with face key points and recognize them using the KERAS dataset. The proximity and accuracy of the recognized items were evaluated using computer vision techniques. The object recognition time interval and duration were recorded and stored permanently in a database, and the information was communicated to the visually impaired user as voice output. The normal face, without wearing a mask, was identified using binary patterns with proximity detection. Camouflaged objects were detected in a maximum probability range of 100%. The proposed method was tested, calculating accuracy and score, and compared with existing models, showcasing remarkable performance. The proposed method of normal and camouflage detection is a novel prediction with proximity analysis of objects in a frame.

Water Properties and Diffusive Convection in the Canada Basin

The aim of this study is to better understand diffusive convection (DC) and its role in the upper ocean dynamic environment and sea ice melting in the Canada Basin. Based on a moored dataset with 6737 profiles collected from August 2003 to August 2011 in the upper layer of the Canada Basin, DC between the warm and salty Atlantic Water (AW) and the colder and less salty Lower Halocline Water (LHW) were investigated. The moorings were designated at four stations: A, B, C, and D, located at the southwestern, southeastern, northeastern, and northwestern parts of the basin, respectively. During the observation period, the temperature, salinity, and depth of the AW and LHW exhibited unique temporal variations. The temperature and salinity of the AW varied among stations, with a decreasing trend from northwest to southeast, consistent with the propagation path of the AW in the Canada Basin. The temperature and salinity of the LHW were similar at all stations. The AW and LHW cores were located between depths of 320–500 m and 160–300 m, respectively, and both gradually deepened over time. Distinct DC staircase structures were observed between the AW and LHW, more pronounced at stations C and D than at stations A and B, which is speculated to be related to eddies at stations A and B during the observation period. The vertical heat fluxes through the DC staircase layer at stations C and D (FHc_C and FHc_D) were estimated using an empirical formula. FHc_C ranged from 0.05 to 0.94 W/m2, and FHc_D ranged from 0.05 to 0.6 W/m2, with the maximum probability value for both at approximately 0.2 W/m2. The effective diffusivities at these two stations (KT_C and KT_D) are similar, ranging from 2 × 10−6 to 3 × 10−5 m2/s, with the highest probability occurring at 6 × 10−6 m2/s. Both the probability density function of the heat flux and the effective diffusivity skewed towards larger values and obey a lognormal distribution, indicating turbulence intermittency of the DC staircase in the Canada Basin. These finding offers new insights into the heat transport and turbulence in the DC staircase, and then bring a deeper understanding of sea ice melting in the Canada Basin.

Influence of molecular orientation on the photodissociation process of LiH molecules

AbstractRegulation of photodissociation dynamics of oriented LiH molecules in different dissociation channels is proposed based on time dependent quantum wave packet theory. The enhancement of molecular orientation on the photodissociation of LiH is obvious with our theoretical scheme. The results show that the molecular orientation in the ground state has a great effect on the angular distributions of wave packets. By using the proper laser pulses and controlling the polarization direction of the laser pulses, the enhancement of the photodissociation could be realized. After the molecular orientation, an optimal dissociation channel is observed with an improved dissociation probability. Compared with the results without molecular orientation, the maximal dissociation probability is increased by 8.1% in the indirect dissociation channel and 30.7% in the direct dissociation channel. The enhancement effect is more obvious in the direct dissociation channel, which provides a possible method to manipulate the dissociation of LiH molecules experimentally. Additionally, the photodissociation process of LiH also relies on the electric intensity and delay time of two pump pulses.

Effect of lake waves on manoeuvring performance of a model ship

ABSTRACT In this study, we investigate how natural environmental conditions affect a training vessel using a ‘mathematical model of wind and wave’ specific to Lake Silm near Kamionka, Poland. Wind and wave measurements on the lake were conducted. We present the vessel’s trajectory under varying conditions: navigating in restricted waters simulating port conditions with speed readings, and during propeller operation. The control system of the nonlinear autonomous object employed two controllers: a linear matrix inequality controller and an overriding trajectory controller. We assess the impact of nonlinearities in the measured waves. It is shown that the maximum entropy probability distribution conforms more closely to the empirical frequency histogram than the Gaussian distribution. The wave height probability distributions diverge from Rayleigh distributions, indicating slight nonlinear effects. These results enhance the mathematical model of Silm Lake’s wave dynamics.

A probabilistic estimation model for seismic physical portfolio loss of a water supply pipeline system

Losses due to hazards are inevitable and numerical simulations for estimations are complex. This study proposes a model for estimating correlated seismic damages and losses of a water supply pipeline system as an alternative for numerical simulations. The common approach in other research shows average damage spots per mesh estimated statistically independent to one another. Spatially distributed lifeline systems, such as water supply pipelines, are interconnected, and seismic spatial variability affects the damages across the region; thus, spatial correlation of damage spots is an important factor in target areas for portfolio loss estimation. Generally, simulations are used to estimate possible losses; however, these assume each damage behaves independently and uncorrelated. This paper assumed that damages per mesh behave in a Poisson distribution to avoid over-dispersion and eliminate negative losses in estimations. The purpose of this study is to obtain a probabilistic portfolio loss model of an extensive water supply area. The proposed model was compared to the numerical simulation data with the correlated Poisson distribution. The application of the Normal To Anything (NORTA) obtained correlations for Poisson Distributions. The proposed probabilistic portfolio loss model, based on the generalized linear model and central limit theory, estimated the possible losses, such as the Probable Maximum Loss (PML, 90% non-exceedance) or Normal Expected Loss (NEL, 50 % non-exceedance). The proposed model can be used in other lifeline systems as well, though additional investigation is needed for confirmation. From the estimations, a seismic physical portfolio loss for the water supply system was presented. The portfolio was made to show possible outcomes for the system. The proposed method was tested and analyzed using an artificial field and a location-based scenario of a water supply pipeline system. This would aid in pre-disaster planning and would require only a few steps and time.

Systematic review using a spiral approach with machine learning

With the accelerating growth of the academic corpus, doubling every 9 years, machine learning is a promising avenue to make systematic review manageable. Though several notable advancements have already been made, the incorporation of machine learning is less than optimal, still relying on a sequential, staged process designed to accommodate a purely human approach, exemplified by PRISMA. Here, we test a spiral, alternating or oscillating approach, where full-text screening is done intermittently with title/abstract screening, which we examine in three datasets by simulation under 360 conditions comprised of different algorithmic classifiers, feature extractions, prioritization rules, data types, and information provided (e.g., title/abstract, full-text included). Overwhelmingly, the results favored a spiral processing approach with logistic regression, TF-IDF for vectorization, and maximum probability for prioritization. Results demonstrate up to a 90% improvement over traditional machine learning methodologies, especially for databases with fewer eligible articles. With these advancements, the screening component of most systematic reviews should remain functionally achievable for another one to two decades.

Updating probable maximum precipitation for Hong Kong under intensifying extreme precipitation events

Updating probable maximum precipitation for Hong Kong under intensifying extreme precipitation events