Nominal Distribution Research Articles

Assessment of the gate rudder system on the cavitation and underwater radiated noise characteristics of a containership

Predictions of model tests and numerical studies (Sasaki et al., 2016), both at model- and full-scale, as well as results of the recent sea trials, have demonstrated that the Gate Rudder System (GRS), a novel energy-saving device, increases propulsive efficiency and reduces fuel consumption of a ship. Despite detailed investigations into the impact of the GRS on powering performance and manoeuvring, studies on its effect on cavitation phenomena and underwater radiated noise (URN) are limited. In this study, the effects of the GRS on cavitation and URN were investigated experimentally and compared to a conventional rudder system (CRS). Cavitation observations and URN measurements were conducted on sister ships -one with the CRS, SAKURA, and the other with the GRS, SHIGENOBU- at Istanbul Technical University Cavitation Tunnel (ITUKAT). A wooden ship model was built to be used in resistance and propulsion tests and then truncated due to the cavitation tunnel's test section length limitation of 5.5 m. Numerical studies were performed for both the original and truncated models at model- and full-scale to assess their wake characteristics. The nominal wake distributions along the CRS and GRS propeller planes were measured using a Laser Doppler Velocimetry (LDV) system. The test results supported the findings of the sea trials, showing that the GRS significantly reduces URN levels by up to 10 dB at high-frequencies, compared to the CRS, due to its effect on propeller loading and cavitation phenomenon.

Null objects, null nominal anaphora and antilogophoricity

Abstract This paper discusses null objects (NOs) in Ibero-Romance. European Portuguese (EP) has both definite and indefinte NOs, but Castillian Spanish (CSpanish) only allows NOs when the antecedent is a bare plural nominal or a mass noun. The paper argues that these differences are related to the distribution of bare nominals in each language and proposes that the same underlying mechanism is at the root of indefinite and definite object drop, namely a rootless [ nP n ] proform. [ nP n ] denotes a contextually salient property, its possible interpretations being derived by general type-shifting operations. In CSpanish, the property denoted by [ nP n ] is interpreted as a restrictive modifier of the predicate and the relevant variable is bound under VP level Existential Closure. Focusing on EP, there are striking similarities between definite NOs and other types of nominal anaphora, including epithets. In particular, like epithets, NOs are subject to an Antilogophoricity Constraint. This affinity between NOs and epithets constitutes a case in favor of the idea that the NO is a base-generated nominal. The difference with respect to CSpanish lies in the possibility of interpreting the null nominal by a choice function, a function maps a property onto an entity that has the property.

Electric Motor Noise Variation Study Due to Air Gap Effect in Electric Vehicles

Motor whine is a crucial factor in the design of electric motors for electric vehicles (EVs). Predicting noise in a virtual environment is challenging due to the many possible variations during sample testing. The air gap between the stator and rotor in each sample is a significant contributor to radiated noise variation in the electric drive unit (DU). A baseline is first established for radiated noise prediction, assuming a nominal uniform airgap distribution, using the front electric DU in General Motors' Hummer EV. This study explains how the sensitivity of critical motor orders changes with static offset direction when the stiffness of DU is non-uniform and varies with radial direction. It also predicts the range of motor airborne noise variation due to offset orientation. Next, the effect of tilt direction on motor noise variation is studied at different torque conditions and motor speeds. Motor noise is found to be sensitive to tilt direction and angle as the DU stiffness changes axially. Additionally, rotor dynamic offset is investigated, which generates sideband orders in addition to motor pole pass orders. A comparison study is conducted to examine various sideband orders and their central frequency.

Quantifying the Dosimetric Impact of Proton Range Uncertainties on RBE-Weighted Dose Distributions in Intensity-Modulated Proton Therapy for Bilateral Head and Neck Cancer.

In current clinical practice, intensity-modulated proton therapy (IMPT) head and neck cancer (HNC) plans are generated using a constant relative biological effectiveness (cRBE) of 1.1. The primary goal of this study was to explore the dosimetric impact of proton range uncertainties on RBE-weighted dose (RWD) distributions using a variable RBE (vRBE) model in the context of bilateral HNC IMPT plans. The current study included the computed tomography (CT) datasets of ten bilateral HNC patients who had undergone photon therapy. Each patient's plan was generated using three IMPT beams to deliver doses to the CTV_High and CTV_Low for doses of 70 Gy(RBE) and 54 Gy(RBE), respectively, in 35 fractions through a simultaneous integrated boost (SIB) technique. Each nominal plan calculated with a cRBE of 1.1 was subjected to the range uncertainties of ±3%. The McNamara vRBE model was used for RWD calculations. For each patient, the differences in dosimetric metrices between the RWD and nominal dose distributions were compared. The constrictor muscles, oral cavity, parotids, larynx, thyroid, and esophagus showed average differences in mean dose (Dmean) values up to 6.91 Gy(RBE), indicating the impact of proton range uncertainties on RWD distributions. Similarly, the brachial plexus, brain, brainstem, spinal cord, and mandible showed varying degrees of the average differences in maximum dose (Dmax) values (2.78-10.75 Gy(RBE)). The Dmean and Dmax to the CTV from RWD distributions were within ±2% of the dosimetric results in nominal plans. The consistent trend of higher mean and maximum doses to the OARs with the McNamara vRBE model compared to cRBE model highlighted the need for consideration of proton range uncertainties while evaluating OAR doses in bilateral HNC IMPT plans.

Impact of data transfer between treatment planning systems on dosimetric parameters

PurposeIn radiotherapy it is often necessary to transfer a patient’s DICOM (Digital Imaging and COmmunications in Medicine) dataset from one system to another for re-treatment, plan-summation or registration purposes. The aim of the study is to evaluate effects of dataset transfer between treatment planning systems. Materials and methodsTwenty-five patients treated in a 0.35T MR-Linac (MRidian, ViewRay) for locally-advanced pancreatic cancer were enrolled. For each patient, a nominal dose distribution was optimized on the planning MRI. Each plan was daily re-optimized if needed to match the anatomy and exported from MRIdian-TPS (ViewRay Inc.) to Eclipse-TPS (Siemens-Varian). A comparison between the two TPSs was performed considering the PTV and OARs volumes (cc), as well as dose coverages and clinical constraints. ResultsFrom the twenty-five enrolled patients, 139 plans were included in the data comparison. The median values of percentage PTV volume variation are 10.8 % for each fraction, while percentage differences of PTV coverage have a mean value of −1.4 %. The median values of the percentage OARs volume variation are 16.0 %, 7.0 %, 10.4 % and 8.5 % for duodenum, stomach, small and large bowel, respectively. The percentage variations of the dose constraints are 41.0 %, 52.7 % and 49.8 % for duodenum, stomach and small bowel, respectively. ConclusionsThis study has demonstrated a non-negligible variation in size and dosimetric parameters when datasets are transferred between TPSs. Such variations should be clinically considered. Investigations are focused on DICOM structure algorithm employed by the TPSs during the transfer to understand the cause of such variations.

Modeling of the Combined Effect of the Surface Roughness and Coatings in Contact Interaction

The model of indentation of a spherical punch with a rough surface into a thin elastic layer lying on an elastic half-space has been developed. A numerical-analytical solution is suggested based on the two-scales approach. At macroscale, the integral equation of the second kind is reduced to calculate the nominal contact characteristics, taking into account the surface layer thickness and its mechanical characteristics, as well as additional compliance due to surface roughness calculated from the microscale analysis. The influence of the punch roughness and the surface layer mechanical and geometrical characteristics on the nominal contact pressure distribution, on the nominal contact area and the indentation depth, as well as on the real contact area and pressures at the individual contact spots, is analyzed. The developed contact model can be used to analyze the indentation of the punch into the layered elastic base, taking into account the roughness of the punch surface, and can also be used to give a complete analysis of the roughness effect on the contact process at both scale levels. The results can be used to control the indentation of the rough-coated bodies.

The Influence of Profitability, Liquidity, Leverage and Company Size on Dividend Policy

Several food and beverage companies experienced a decrease and increase in the nominal dividend distribution to investors. This research aims to analyze the influence of profitability, liquidity, leverage and company size on dividend policy in food and beverage sub-sector companies listed on the IDX for the 2018-2021 period. The method used is panel data regression analysis obtained from Ordinary Least Square estimation using a fixed effect modelling approach. Profitability is measured by Return on assets (ROA), liquidity by Current Ratio (CR), leverage by Debt-to-Equity Ratio (DER), company size by the natural logarithm of total assets, and dividend policy by Dividend Pay-out Ratio (DPR). The research results show that profitability has a positive effect on dividend policy. Meanwhile, liquidity, leverage and company size have no effect on dividend policy.

Optimal Core Acquisition and Remanufacturing Decisions With Discrete Core Quality Grades

The acquisition of used products (cores) is essential for leveraging the advantages of remanufacturing. Due to limited historical information about cores, it is difficult for the remanufacturers to accurately assess the quality of cores before disassembly and inspection. In this article, we investigate the joint acquisition and remanufacturing problem with a discrete empirical distribution. Under both deterministic and stochastic demands, two integer programming models are developed to obtain operational rules for making optimal decisions. Since the discrete empirical distribution is assumed to be the nominal distribution or asymptotic distribution of the true quality, the error caused by the difference between the estimated discrete empirical distribution and the true distribution cannot be neglected. Thus, we evaluate the error using the Phi-divergence measure, and the corresponding effects of this difference on the resultant acquisition and remanufacturing rules are analyzed. Through numerical examples, we observe that optimal acquisition and remanufacturing decisions are negatively affected by the difference between the estimated distribution and the true distribution, even if the estimated distribution can pass a hypothesis test. Furthermore, to overcome suboptimal outcomes, a multisource information fusion approach is proposed, which can utilize quality information collected from multiple sources to make accurate acquisition and remanufacturing decisions.

Factive Entailments and Clausal Complementation in Karitiana

Attitude verbs describe mental states of their subject in relation to a proposition codified by their clausal complement. In this paper, we describe the behavior of such verbs in Karitiana, a Tupian language spoken in Brazil. Recently, it has been noted in the literature that embedded clauses with nominal features seem to lead to a factive reading of these verbs. In Karitiana, all embedded clauses are nominalized since they lack many clausal features, and conversely exhibit nominal distribution and behavior. We propose a structure with an N head for embedded clauses in Karitiana, which accounts for their morphological behavior and also explains why these constructions behave as strong islands. Judgments of contradiction with attitude verbs of Karitiana were collected and the results show that they fall into two categories: one that obligatorily entails the truth of the embedded clause and another that favors it, but that does not require it obligatorily. This shows that nominalization of embedded clauses seems to be tied to the presence of factive entailments, but this alone is not a sufficient condition to force a factive reading of the matrix attitude verb.

Mutation and Selection Induce Correlations between Selection Coefficients and Mutation Rates.

AbstractThe joint distribution of selection coefficients and mutation rates is a key determinant of the genetic architecture of molecular adaptation. Three different distributions are of immediate interest: (1) the "nominal" distribution of possible changes, prior to mutation or selection; (2) the "de novo" distribution of realized mutations; and (3) the "fixed" distribution of selectively established mutations. Here, we formally characterize the relationships between these joint distributions under the strong-selection/weak-mutation (SSWM) regime. The de novo distribution is enriched relative to the nominal distribution for the highest rate mutations, and the fixed distribution is further enriched for the most highly beneficial mutations. Whereas mutation rates and selection coefficients are often assumed to be uncorrelated, we show that even with no correlation in the nominal distribution, the resulting de novo and fixed distributions can have correlations with any combination of signs. Nonetheless, we suggest that natural systems with a finite number of beneficial mutations will frequently have the kind of nominal distribution that induces negative correlations in the fixed distribution. We apply our mathematical framework, along with population simulations, to explore joint distributions of selection coefficients and mutation rates from deep mutational scanning and cancer informatics. Finally, we consider the evolutionary implications of these joint distributions together with two additional joint distributions relevant to parallelism and the rate of adaptation.

Optimizing sustainable biomass–coal co-firing power plant location problem under ambiguous supply

Promoting biomass–coal co-firing power generation technology in existing coal-fired power plants is a practical and relatively low-cost short-run choice for the coal-fired power industry to reduce carbon emissions and develop sustainably. However, the uncertainty of biomass supply complicates the location of co-firing power plants among existing coal-fired plants. To address the challenge that only limited historical data of uncertain supply are available, this paper proposes a new ambiguity distribution set to characterize the uncertain supply capacity, supply price, and supply quality (i.e., energy conversion coefficient). Accordingly, a novel bi-objective distributionally robust fuzzy optimization model is developed for the sustainable co-firing power plant location (SCPPL) problem to balance conflicting objectives of minimizing total cost and maximizing carbon emission reduction. Subsequently, the computationally tractable counterpart of the proposed model is derived against the worst-case distribution by reformulating the distributionally robust credibility objective and constraints as their solvable equivalent forms. Furthermore, the Pareto optimal solutions of the proposed bi-objective model are generated using the augmented ϵ-constraint method. Finally, a realistic case study of Heilongjiang Province in China is conducted to verify the feasibility and effectiveness of the proposed optimization method. The experimental results demonstrate that the proposed model can hedge against the distributional ambiguity of uncertain parameters and provides a robust co-firing power plant location strategy. By comparison with the nominal distribution model and analysis of the sensitivity of selected parameters, management insights are summarized for decision-makers.

The system of inheritance distribution in South Sulawesi

This article describes the inheritance distribution system of the people of South Sulawesi. This research uses interviews with several community and religious leaders who understand inheritance law. The research sites are Makassar, Pinrang, Palopo and Bone. The theory used is maṣlaḥah mursalah, which analyzes data about the inheritance system, starting from the process of inheritance division and the number of heirs' shares, as well as the interplay between customary law and Islam in the inheritance distribution system in South Sulawesi. Without removing the core of inheritance law, the South Sulawesi people's way of distributing inheritance makes use of the customary law known as ade' in the pangadereng system. There are three types of distribution, depending on whether the family is still together, whether one parent has deceased, or whether both parents have deceased. The nominal distribution varies, sometimes it is equal and other times it is based on fairness, which means that heirs who have invested a lot of money, such as those who go to high school and live well, give up their rights to relatives who are less established or who remain with the parents in the village. According to the benefit principle, men typically receive a plot of land because they have the energy to cultivate it, while women typically receive a house. This study shows how the customary division system emphasizes the common good in order to fulfill a number of maqāṣid al-shari’ah ideals, including the protection of property, honor, and life (soul).

A data-driven robust EVaR-PC with application to portfolio management.

We investigate the robust chance constrained optimization problem (RCCOP), which is a combination of the distributionally robust optimization (DRO) and the chance constraint (CC). The RCCOP plays an important role in modeling uncertain parameters within a decision-making framework. The chance constraint, which is equivalent to a constraint of Value-at-risk (VaR), is approximated by risk measures such as Entropic Value-at-risk (EVaR) or Conditional Value-at-risk (CVaR) due to its difficulty to be evaluated. An excellent approximation requires both tractability and non-conservativeness. In addition, the DRO assumes that we know partial information about the distribution of uncertain parameters instead of their known true underlying probability distribution. In this article, we develop a novel approximation EVaR- PC based on EVaR for CC. Then, we evaluate the proposed approximation EVaR- PC using a discrepancy-based ambiguity set with the wasserstein distance. From a theoretical perspective, the EVaR- PC is less conservative than EVaR and the wasserstein distance possesses many good theoretical properties; from a practical perspective, the discrepancy-based ambiguity set can make full use of the data to estimate the nominal distribution and reduce the sensitivity of decisions to priori knowledges. To show the advantages of our method, we show its application in portfolio management in detail and give the relevant experimental results.

A deep learning-based approach for statistical robustness evaluation in proton therapy treatment planning: a feasibility study

Objective. Robustness evaluation is critical in particle radiotherapy due to its susceptibility to uncertainties. However, the customary method for robustness evaluation only considers a few uncertainty scenarios, which are insufficient to provide a consistent statistical interpretation. We propose an artificial intelligence–based approach that overcomes this limitation by predicting a set of percentile dose values at every voxel and allows for the evaluation of planning objectives at specific confidence levels. Approach. We built and trained a deep learning (DL) model to predict the 5th and 95th percentile dose distributions, which corresponds to the lower and upper bounds of a two-tailed 90% confidence interval (CI), respectively. Predictions were made directly from the nominal dose distribution and planning computed tomography scan. The data used to train and test the model consisted of proton plans from 543 prostate cancer patients. The ground truth percentile values were estimated for each patient using 600 dose recalculations representing randomly sampled uncertainty scenarios. For comparison, we also tested whether a common worst-case scenario (WCS) robustness evaluation (voxel-wise minimum and maximum) corresponding to a 90% CI could reproduce the ground truth 5th and 95th percentile doses. Main results. The percentile dose distributions predicted by DL yielded excellent agreements with the ground truth dose distributions, with mean dose errors below 0.15 Gy and average gamma passing rates (GPR) at 1 mm/1% above 93.9, which were substantially better than the WCS dose distributions (mean dose error above 2.2 Gy and GPR at 1 mm1% below 54). We observed similar outcomes in a dose-volume histogram error analysis, where the DL predictions generally yielded smaller mean errors and standard deviations than the WCS evaluation doses. Significance. The proposed method produces accurate and fast predictions (∼2.5 s for one percentile dose distribution) for a given confidence level. Thus, the method has the potential to improve robustness evaluation.

Robust metamodel-based simulation-optimization approaches for designing hybrid renewable energy systems

We propose two robust metamodel-based approaches for designing stand-alone hybrid renewable energy systems that combine conventional and renewable energy generators with battery storage. Since the cost and reliability functions of the stochastic system do not have closed-form expressions, they can only be estimated via simulation. In the response surface methodology, the simulation input/output data is used first to locally fit first-degree surrogate functions for each supply scenario to perform a stochastic gradient descent, before a second-degree function is fitted and optimized when the gradient becomes small. In contrast, the global response surface technique globally fits a convex quadratic surrogate function on the simulation input/output data, then uses its minimizer to iteratively refine the search space. To account for estimation errors, a distributional ambiguity set based on a linear ϕ-divergence function is constructed around the nominal probability distribution of supply scenarios, and the expected reliability-adjusted cost of the system is computed using the worst-case distribution in this set. The validity of the proposed approaches and the value of considering distributional ambiguity are demonstrated through a realistic case study of a remote community in Northern Ontario.

From share of choice to buyers' welfare maximization: Bridging the gap through distributionally robust optimization

We study the product design problem where a decision maker selects the features of a product from a set of feasible options. We focus on two widely studied objectives in this field, that is, the share of choice (SOC) and buyers' welfare (BW). The two objectives are vulnerable to different types of customer preference misspecification, that is, deviation from the nominal utility distribution and effects of outliers, respectively. We formulate a distributionally robust optimization (DRO) SOC maximization model and a winsorized BW maximization model to obtain robust solutions to the two problems. Interestingly, we show that the two robust models are equivalent in a certain sense—for appropriate choices of robustness parameters, both models return the same solution. This observation has important ramifications. For instance, it indicates that a product designed to yield higher BW is more robust to the deviation from the nominal utility distribution for the SOC problem, while that with higher SOC is less sensitive to the effect of outliers for the BW problem. Last but not least, we use the equivalence to develop a new approach to solve the DRO model for the SOC problem, using a version of the winsorized BW model. Extensive numerical experiments demonstrate the superior performance of the proposed approach.

Rectangular filled composite members made from high strength materials: Behavior and design of columns and beams

The AISC Specification limits the use of steel and concrete materials in filled composite members to concrete strength (fc′) less than or equal to 69 MPa and steel yield strength (Fy) less than or equal to 517 MPa. This paper presents the results of comprehensive analytical investigations conducted using benchmarked numerical models to evaluate the behavior of rectangular filled composite members made from one or both materials exceeding the AISC Specification strength limits. The numerical models are developed and analyzed using the displacement-based formulation of the fiber-based finite element in OpenSees. Prior research has developed effective (phenomenological) stress–strain curves for steel and concrete materials that implicitly account for the effects of steel yielding, local buckling, concrete cracking, crushing and confinement, and the interaction between the two materials in the composite cross-section. This paper first benchmarks the modeling approach and then uses the benchmarked model to conduct analytical parametric studies covering a wide range of parameters, including: member slenderness, section slenderness, section aspect ratio, steel yield stress and concrete compressive strength. The results from the parametric studies are used to evaluate behavior and develop new design equations based on nominal stress distributions over the composite section. The nominal steel stress in compression (Fcr) is expressed as a continuous function of the section slenderness and yield strength, thus eliminating the current AISC Specification’s need for section classification (compact, noncompact and slender). The new design equations are recommended for relaxing the material strength limitations in the next version of the AISC Specification.

Shortfall-Based Wasserstein Distributionally Robust Optimization

In this paper, we study a distributionally robust optimization (DRO) problem with affine decision rules. In particular, we construct an ambiguity set based on a new family of Wasserstein metrics, shortfall–Wasserstein metrics, which apply normalized utility-based shortfall risk measures to summarize the transportation cost random variables. In this paper, we demonstrate that the multi-dimensional shortfall–Wasserstein ball can be affinely projected onto a one-dimensional one. A noteworthy result of this reformulation is that our program benefits from finite sample guarantee without a dependence on the dimension of the nominal distribution. This distributionally robust optimization problem also has computational tractability, and we provide a dual formulation and verify the strong duality that enables a direct and concise reformulation of this problem. Our results offer a new DRO framework that can be applied in numerous contexts such as regression and portfolio optimization.

Wake-adapted ducted propeller for full-scale generic underwater vehicle: parametric study on blade skew for unsteady propeller thrust

A parametric study of blade skew for unsteady propeller thrust in the wake of an underwater vehicle (BB2) is presented. Unsteady thrust forces transmit via the propeller shaft and produce undesirable noise and vibration, particularly at frequencies corresponding to multiples of the blade number. The effectiveness of blade skew in reducing noise and vibration has been reported in previous studies, though not for underwater vehicles. The amplitudes of the dominant harmonics of the unsteady propeller thrust are determined using a Fourier series. The balanced skew reduces the dominant fifth harmonic more significantly than the biased skew at identical total-skew angles. The total-skew angle strongly influences the unsteady propeller thrust magnitude for balanced skew designs, whereas the effect of the maximum forward-skew angle is negligible. Significant fluctuations in one-blade loads are observed when the blade passes through the downstream wake of the vehicle's sail and casing. Increasing the blade total-skew reduces the blade load fluctuation when the blade approaches the top dead centre position, but has limited influence on the fluctuations when the blade moves away from this position. The nominal wake distribution at the propeller plane is analysed, and the fifth harmonic is identified as having the largest amplitude. This suggests that an increase in the number of blades would reduce the unsteady propeller thrust of the full-scale BB2 underwater vehicle.

Two-stage distributionally robust optimization for disaster relief logistics under option contract and demand ambiguity

Disaster relief logistics (DRL) provides adequate relief supplies to victims of natural disasters (e.g., earthquakes and volcanic eruptions). This study explicitly considers supplier selection and inventory pre-positioning corresponding to static preparedness decisions, and post-disaster procurement and delivery associated with dynamic response decisions in actual DRL operations. To tackle issues triggered by shortage and surplus of multi-class relief resources, a flexible option contract is adopted to purchase relief items from suppliers. To measure the risk of demand ambiguity, a worst-case mean-quantile-deviation criterion is introduced to reflect the decision-maker’s risk-averse attitude. To handle the ambiguity in the probability distribution of demand, a novel two-stage distributionally robust optimization (DRO) model is developed for the addressed DRL problem. The proposed DRO model can be transformed into equivalent mixed-integer linear programs when the ambiguity sets incorporate all distributions within L1-norm and joint L1- and L∞-norms from a nominal (reference) distribution. A computational study of earthquakes in Iran is conducted to illustrate the applicability of the proposed DRO model to real-world problems. The experimental results demonstrate that our proposed DRO model has superior out-of-sample performance and can mitigate the effect of Optimization Bias compared to the traditional stochastic programming model. Some managerial insights regarding the proposed approach are provided based on numerical results.