Generic Profile Research Articles

Stochastic residential occupancy schedules based on the American Time-Use Survey

The energy performance of residential buildings is closely correlated with occupants’ behavior and their schedules. Moreover, the energy consumption of heating, ventilation, and air conditioning (HVAC) systems, which are by far the biggest contributor to energy consumption in residential buildings, is controlled by the presence or absence of occupants in a building. Thus, accurate occupancy presence and activity profiles are important to determine actual energy demands and corresponding control schedules for residential buildings. Conventionally, building energy simulation tools typically use a single generic and static occupancy profile to represent a building’s occupancy schedule, regardless of day type or household size. However, literature in the field suggests that there is significant potential for improvement to allow for more flexibility and accuracy in the calculation of occupancy. The objective of this study is thus to develop a stochastic building occupancy model and propose it as a realistic replacement for the conventional generic static schedules. This three-state stochastic occupancy model is based on the 2019 American Time-Use Survey (ATUS) and considers the differences among weekdays, Saturdays, and Sundays. In this model, survey respondents are clustered based on the number of residents in their household and a first-order inhomogeneous Markov chain technique is used to generate occupancy presence schedules. The models’ results are then validated against the original ATUS data in terms of state probabilities, state durations, and number of state changes throughout the course of a day.

A Novel Computational Approach for an Improved Expression of the Spectral Content in the Lower Atmospheric Boundary Layer

The paper presents an innovative methodology for the simulation of incoming wind conditions in computational domains that use large eddy simulation (LES) for the evaluation of wind loads on low-rise buildings. Simulating the atmospheric boundary layer has proven to be a challenging process for computational wind engineering, especially to correctly introduce the fluctuations in the high-frequency spectral domain. Experiments in the Concordia University Building Aerodynamics Laboratory were carried out to estimate the evolution of turbulence features in the along-wind direction of a generic profile. Virtual probes were placed inside the computational domain to match the location of those in the wind tunnel and compare mean speed values, turbulence intensity, integral length scales, and spectral content. Synthetic methods were applied for the incoming wind velocity to estimate their capacity to capture the evolution of these turbulence features in the streamwise direction. The inability of these synthetic methods to represent the spectral domain, particularly for heights of interest for low-rise buildings, was examined, and the so-called dynamic terrain method in which velocity time histories are extracted from wind tunnel measurements and reconstructed to fit certain statistical parameters was implemented. The proposed method represents the frequency domain’s spectral content with good agreement with the theoretical von Karman spectrum and the experimental results. Advantages of the method include time efficiency and relative simplicity, which makes it attractive to the practitioners for the design of a neutral atmospheric boundary layer respecting the turbulent wind characteristics.

Shock Formation and Vorticity Creation for 3d Euler

AbstractWe analyze the shock formation process for the 3D nonisentropic Euler equations with the ideal gas law, in which sound waves interact with entropy waves to produce vorticity. Building on our theory for isentropic flows in [3, 4], we give a constructive proof of shock formation from smooth initial data. Specifically, we prove that there exist smooth solutions to the nonisentropic Euler equations which form a generic stable shock with explicitly computable blowup time, location, and direction. This is achieved by establishing the asymptotic stability of a generic shock profile in modulated self‐similar variables, controlling the interaction of wave families via: (i) pointwise bounds along Lagrangian trajectories, (ii) geometric vorticity structure, and (iii) high‐order energy estimates in Sobolev spaces. © 2022 Wiley Periodicals LLC.

Comparison of CNNs and Vision Transformers-Based Hybrid Models Using Gradient Profile Loss for Classification of Oil Spills in SAR Images

Oil spillage over a sea or ocean surface is a threat to marine and coastal ecosystems. Spaceborne synthetic aperture radar (SAR) data have been used efficiently for the detection of oil spills due to their operational capability in all-day all-weather conditions. The problem is often modeled as a semantic segmentation task. The images need to be segmented into multiple regions of interest such as sea surface, oil spill, lookalikes, ships, and land. Training of a classifier for this task is particularly challenging since there is an inherent class imbalance. In this work, we train a convolutional neural network (CNN) with multiple feature extractors for pixel-wise classification and introduce a new loss function, namely, “gradient profile” (GP) loss, which is in fact the constituent of the more generic spatial profile loss proposed for image translation problems. For the purpose of training, testing, and performance evaluation, we use a publicly available dataset with selected oil spill events verified by the European Maritime Safety Agency (EMSA). The results obtained show that the proposed CNN trained with a combination of GP, Jaccard, and focal loss functions can detect oil spills with an intersection over union (IoU) value of 63.95%. The IoU value for sea surface, lookalikes, ships, and land class is 96.00%, 60.87%, 74.61%, and 96.80%, respectively. The mean intersection over union (mIoU) value for all the classes is 78.45%, which accounts for a 13% improvement over the state of the art for this dataset. Moreover, we provide extensive ablation on different convolutional neural networks (CNNs) and vision transformers (ViTs)-based hybrid models to demonstrate the effectiveness of adding GP loss as an additional loss function for training. Results show that GP loss significantly improves the mIoU and F1 scores for CNNs as well as ViTs-based hybrid models. GP loss turns out to be a promising loss function in the context of deep learning with SAR images.

Modeling revealed the effect of root dynamics on the water adaptability of phreatophytes

Given their high root plasticity, phreatophytes have flexible water use strategies and can dynamically adjust their rooting depth for the effective uptake of water from soils and shallow aquifers. By this strategy, phreatophytes are strongly ecologically resilient to water stress and thus are commonly grown in drylands. In this study, we used a modified soil–hydrological model, Hydrus-1D, with the implementation of a dynamic root scheme to analyze the role of the distribution and dynamics of roots in simulating evapotranspiration (ET) of phreatophytes with declining groundwater levels (GWLs). The results showed that the root mean square error (RMSE) between simulated and observed ET was reduced by approximately 39% using a model with a more authentic root distribution than the generic root profile. Static root schemes cannot portray the adaptation of phreatophytes to GWL changes well, but their performance can be enhanced by introducing a water compensation scheme, which, however, has a weakness related to the difficulties in parameter calibration. The ET was well retrieved by the model with the dynamic root scheme. Its RMSE was 40% to 70% less than those of the static models, and the Nash–Sutcliffe efficiency reached 0.94, demonstrating the importance of root dynamics in simulating phreatophytic root water uptake. In addition, multiscenario estimations showed nonlinear responses of phreatophyte ET to the rate of GWL decrease (RGWD); that is, as the RGWD accelerated, plant adaptation showed three different stages: no water stress, adaptable and unadaptable. The resistance of plants to water stress decreases with decreasing root growth rate. The identified key ecological thresholds for the RGWD provide a reference for ecological protection in arid areas. We highlight the importance of root processes in the plant response to water stress and suggest that more attention should be given to the root adaptation process in Earth system models.

Benzimidazole-carbamate anthelmintics: Perspective candidates for the anticancer drug development.

Cellular oncogenesis involves a complex interplay between the several synchronized, interdependent pathways that collectively determine the pathogenesis and pathophysiology of cancer. Limited therapeutic success with the existing anticancer drugs drew huge interest in the design and development of new pharmacophores with improved clinical efficacy, however despite huge investments in anticancer RD; the average number of Food and Drug Administration-approved anticancer drugs declined since the 1990s. The contemporary anticancer medications possess high attrition rates, bear substantial costs, and experience low efficacy owing to the drug resistance expressed by the aggressive tumors. Mainly, the translation of novel candidate anticancer drugs into clinical practice, their commercialization, and transformation from the bench to bedside require a long timeframe of 10-15 years and capital worth millions of dollars. The repurposing strategy substantially accelerated the anticancer drug development regime as the approved drugs with tested safety and efficacy ensure a minimal risk of failure, and nominal R&D expenses as anticipated for the newly identified candidate drugs yet to enter the clinical trials. In addition, the repurposed drugs ensure a rapid clinical translation due to a validated clinical profile and their ability to target the identified hallmarks and hitherto unknown vulnerabilities of cancer. The flagship project "Repurposing Drugs in Oncology" (ReDO) identified 268 "hard repurposing" noncancer medications as candidate drugs with a promising anticancer profile (https://www.anticancerfund.org/en/redo-db). However, the generic profile of 84% of repurposed drugs in ReDO data set discourages the commercial sponsors from funding the repurposing trials, especially the Phase III efficacy trials that require significant capital.

Generic profile of a long-lived corotating interaction region and associated recurrent Forbush decrease

Context.Corotating interaction regions (CIRs), formed by the interaction of slow solar wind and fast streams that originate from coronal holes (CHs), produce recurrent Forbush decreases, which are short-term depressions in the galactic cosmic ray (GCR) flux.Aims.Our aim is to prepare a reliable set of CIR measurements to be used as a textbook for modeling efforts. For that purpose, we observe and analyse a long-lived CIR, originating from a single CH, recurring in 27 consecutive Carrington rotations 2057–2083 in the time period from June 2007–May 2009.Methods.We studied the in situ measurements of this long-lived CIR as well as the corresponding depression in the cosmic ray (CR) count observed by SOHO/EPHIN throughout different rotations. We performed a statistical analysis, as well as the superposed epoch analysis, using relative values of the key parameters: the total magnetic field strength,B, the magnetic field fluctuations,dBrms, plasma flow speed,v, plasma density,n, plasma temperature,T, and the SOHO/EPHIN F-detector particle count, and CR count.Results.We find that the mirrored CR count-time profile is correlated with that of the flow speed, ranging from moderate to strong correlation, depending on the rotation. In addition, we find that the CR count dip amplitude is correlated to the peak in the magnetic field and flow speed of the CIR. These results are in agreement with previous statistical studies. Finally, using the superposed epoch analysis, we obtain a generic CIR example, which reflects the in situ properties of a typical CIR well.Conclusions.Our results are better explained based on the combined convection-diffusion approach of the CIR-related GCR modulation. Furthermore, qualitatively, our results do not differ from those based on different CHs samples. This indicates that the change of the physical properties of the recurring CIR from one rotation to another is not qualitatively different from the change of the physical properties of CIRs originating from different CHs. Finally, the obtained generic CIR example, analyzed on the basis of superposed epoch analysis, can be used as a reference for testing future models.

Packet Flow Capacity Autonomous Operation Based on Reinforcement Learning.

As the dynamicity of the traffic increases, the need for self-network operation becomes more evident. One of the solutions that might bring cost savings to network operators is the dynamic capacity management of large packet flows, especially in the context of packet over optical networks. Machine Learning, particularly Reinforcement Learning, seems to be an enabler for autonomicity as a result of its inherent capacity to learn from experience. However, precisely because of that, RL methods might not be able to provide the required performance (e.g., delay, packet loss, and capacity overprovisioning) when managing the capacity of packet flows, until they learn the optimal policy. In view of that, we propose a management lifecycle with three phases: (i) a self-tuned threshold-based approach operating just after the packet flow is set up and until enough data on the traffic characteristics are available; (ii) an RL operation based on models pre-trained with a generic traffic profile; and (iii) an RL operation with models trained for real traffic. Exhaustive simulation results confirm the poor performance of RL algorithms until the optimal policy is learnt and when traffic characteristics change over time, which prevents deploying such methods in operators’ networks. In contrast, the proposed lifecycle outperforms benchmarking approaches, achieving noticeable performance from the beginning of operation while showing robustness against traffic changes.

Psychometric validation of the Korean version of PROMIS 29 Profile V2.1 among patients with lower extremity problems

BackgroundPatients with lower extremity problems (LEP) commonly experience functional loss, pain, decreased range of motion, inadequacy in daily living activities, and structural change in radiographic evaluations. However, the traditional patient-reported outcome measurement which focused on symptoms, had a limited scope of applicability. This study aimed to validate the psychometric properties of the Korean version of PROMIS-29 Profile v2.1 (K-PROMIS-29 V2.1), a multi-dimensional measure for assessing generic profile health-related quality-of-life (HRQoL) in a sample of patients with lower extremity problems (LEP).MethodsParticipants were recruited from the orthopedic outpatient clinics at the Samsung Medical Center in Seoul, South Korea from September to October 2018. Participants completed a survey questionnaire that included the K-PROMIS-29 V2.1 and the SF-36v2. Principal component analysis (PCA) and confirmatory factor analysis (CFA) and Pearson’s correlations were used to evaluate the reliability and validity of the K-PROMIS-29 V2.1.ResultsA total of 299 participants were enrolled in the study and 258 (86%) completed the study questionnaire. The mean age (SD) of the participants was 56.6 (14.5) and 32.3%, 29.8, and 25.2% of the study participants visited outpatient clinics for foot, knee, and hip problems respectively. The Cronbach’s alpha coefficients of 7 sub-domains in K-PROMIS-29 V2.1 ranged from 0.80 to 0.95, indicating satisfactory internal consistency. In CFA, the goodness-of-fit indices were high (CFI = 0.937 and SRMR = 0.061). High to moderate correlations were found between comparable subscales of the K-PROMIS-29 V2.1 and subscales of the SF-36v2 (r = 0.55–0.70).ConclusionsThe K-PROMIS-29 V2.1 is a reliable and valid measure for assessing a broad range of health-related quality-of-life domains in patients with LEP. It would reflect the real-life symptoms experienced by patients with LEP.

Somatostatin receptors scintigraphy prioritization criteria based on net patient epidemiological profile: a bi-centric study

This study aimed to create a generic profile of patients with NETs and to establish the prioritization criteria for somatostatin receptors scintigraphy (SRS), within the NETs diagnostic algorithm, being the first study of this kind conducted in Romania. Material and methods: A bi-centric, retro-prospective study comprising 103 patients with NETs, evaluated through 99mTc-TOC SRS, in two nuclear medicine departments from “Sf. Spiridon” County Clinical Emergency Hospital Iasi, Romania and Center of Nuclear Medicine, University Clinical Center of Kragujevac, Serbia. We analyzed the clinical-paraclinical features of included patients, as a starting point for streamlining the selection of patients for SRS. Results and discussion: The total group of patients included 47.57 % men and 52.43 % women, the age varying between 21-83 years. The most frequent primary NETs were represented by pNETs, and GI-NETs and the symptoms were nonspecific for majority of analyzed patients. Both prospective and retrospective analysis showed that SRS was followed by positive results. For prospectively analyzed patients, due to the used customized sheets, it was extremely easy to identify four SRS prioritization criteria. Conclusions: This study could outline the profile of patients with NETs, as the starting point for streamlining patient selection for SRS. The four prioritization criteria proposed by us could guide the diagnostic algorithm.

Optimal shot peening residual stress profile for fatigue

Shot peening (SP) is frequently used as a palliative measure against metal fatigue in many engineering components. The performance of this surface treatment depends on different factors including the material, shape and loading conditions of the component, as well as process parameters. Fatigue improvement due to SP depends to a great extent upon the in-depth compressive residual stress profile produced in the specimen.In this work, we study the optimum shape for a residual stress profile in terms of fatigue behaviour. For this task, we assume a residual stress profile that is qualitatively similar to that produced by SP in many metals. Based on this generic profile, we analyse the optimum shape for two simple, but noteworthy, fatigue cases: plain fatigue and notch fatigue. The analysis is conducted in the “fatigue damage tolerance design” framework, in which a certain initial defect is assumed to be present in the component under study.

Development and implementation of an enterprise-wide predictive model for early absorption, distribution, metabolismand excretion properties.

Background: Accurate prediction of absorption, distribution, metabolism and excretion (ADME) properties can facilitate the identification of promising drug candidates. Methodology & Results: The authorspresent the Janssen generic Target Product Profile (gTPP) model, which predicts 18 early ADME properties, employs a graph convolutional neural network algorithmand was trained on between 1000-10,000 internal data points per predicted parameter. gTPP demonstrated stronger predictive power than pretrained commercial ADME models and automatic model builders. Through a novel logging method, the authors report gTPP usage for more than 200 Janssen drug discovery scientists. Conclusion: The investigators successfullyenabled the rapid and systematic implementation of predictive ML tools across a drug discovery pipeline in all therapeutic areas. Thisexperience provides useful guidance for other large-scale AI/ML deployment efforts.

VIRDENTOPSY: Virtual Dental Autopsy and Remote Forensic Odontology Evaluation.

The identification of human remains relies on the comparison of post-mortem data, collected during the autopsy, with the ante-mortem data gathered from the missing persons’ reports. DNA, fingerprints, and dental data are considered primary identifiers and are usually collected during any human identification process. Post-mortem dental data should be collected and analyzed by forensic odontologists, as a dental autopsy must not be confused with a dental examination. The virdentopsy project was inaugurated in 2020, during the COVID-19 pandemic, to allow the correct process of human remains by collecting dental data from teeth and jaws, which was then transmitted to forensic odontologists remotely for an expert opinion to achieve a generic profile of the unidentified human remains. The post-mortem dental biography is paramount to narrow the search for compatible missing persons but requires knowledge and experience of forensic odontologists. The virdentopsy process uses radiographic imaging (periapical X-rays, CT scans, panoramics), 2D/3D photos and video recording, photogrammetry documentation, 3D scanning, and live streaming where possible. This registered term was created by merging the terms “virtual” and “dental autopsy” but with no commercial benefits. The proposed process combines research topics under the field of the human rights of the dead and humanitarian forensic odontology services. It should enhance and accelerate the human identification process of the deceased, age estimation of the living, analysis of panoramic X-ray images, and be an educational tool for remote live training in forensic odontology and anatomy of skulls. This paper presents an overview of the virdentopsy process in the field of forensic odontology as a remote consultation as well as an educational tool for undergraduates and postgraduates.

Information security education based on job profiles and the e-CF

PurposeThe information security field requires standardised education. This could be based on generic job profiles and a standard competence framework. The question is whether this is possible and feasible. To find out, the author did a case study: developing an information security master curriculum based on a generic PVIB job profile and the underlying competence framework e-CF.Design/methodology/approachThe research is a case study, using Design Science. Starting point is the specification of the learning goals for a cybersecurity master curriculum, using a generic PvIB job profile and the underlying competence framework e-CF. The curriculum has subsequently been developed, using backward design. Thereafter, the curriculum has been submitted for accreditation to test the successfulness of the approach.FindingsA generic job profile and a competence framework such as the e-CF support the development of standardised education. The generic PVIB job profile used works well. The e-CF can be useful, but requires modifications and the introduction of sub-competences. However, the main complaint concerning the e-CF is the use of examples instead of mandatory content.Originality/valueCompetence frameworks are available to formulate job descriptions, and are also suited for developing standardised education. Little research has been done on this. This case study shows that a competence framework is a useful tool for developing standardised education, although the e-CF may not be the most appropriate.

Cuticular Hydrocarbon Profile of Parasitic Beetles, Aethina tumida (Coleoptera: Nitidulidae).

Simple SummarySocial insects use cuticular hydrocarbons for chemical recognition and communication. Cuticular hydrocarbons can also be exploited by parasites to their advantage for undermining host recognition systems. The small hive beetle (SHB) is a parasite of honey bee colonies but can also infest nests of other bee species. However, its chemical profile is still not known. For the first time, the present study investigated the SHB chemical profile and compared it with that of its honey bee host. The results show that the SHB has a low chemical profile that is similar to its honey bee host’s. However, while honey bees had a clear colony-specific chemical profile, SHBs did not. The generic chemical profile of the SHB is most likely linked to its free-flying behaviour in the field as these parasites are known to switch between host colonies, possibly limiting the acquisition of a colony specific chemical profile. Our findings also suggest that SHBs do not exploit any finely tuned chemical strategy to conceal their presence inside host colonies and probably rely on behavioural adaptations.Cuticular hydrocarbons (CHCs) cover insects’ bodies and play important roles in chemical communication, including nestmate recognition, for social insects. To enter colonies of a social host species, parasites may acquire host-specific CHCs or covertly maintain their own CHC profile by lowering its quantity. However, the chemical profile of small hive beetles (SHBs), Aethina tumida, which are parasites of honey bee, Apis mellifera, colonies, and other bee nests, is currently unknown. Here, adults of SHB and honey bee host workers were collected from the same field colonies and their CHC profiles were analysed using GC-MS. The chemical profiles of field-sampled SHBs were also compared with those of host-naive beetles reared in the laboratory. Laboratory-reared SHBs differed in their CHC profiles from field-sampled ones, which showed a more similar, but ten-fold lower, generic host CHC profile compared to host workers. While the data confirm colony-specific CHCs of honey bee workers, the profile of field-collected SHBs was not colony-specific. Adult SHBs often commute between different host colonies, thereby possibly preventing the acquisition of a colony-specific CHC profiles. An ester was exclusive to both groups of SHBs and might constitute an intraspecific recognition cue. Our data suggest that SHBs do not use any finely tuned chemical strategy to conceal their presence inside host colonies and instead probably rely on their hard exoskeleton and defence behaviours.

Comparing antiviral strategies against COVID-19 via multiscale within-host modelling.

Within-host models of COVID-19 infection dynamics enable the merits of different forms of antiviral therapy to be assessed in individual patients. A stochastic agent-based model of COVID-19 intracellular dynamics is introduced here, that incorporates essential steps of the viral life cycle targeted by treatment options. Integration of model predictions with an intercellular ODE model of within-host infection dynamics, fitted to patient data, generates a generic profile of disease progression in patients that have recovered in the absence of treatment. This is contrasted with the profiles obtained after variation of model parameters pertinent to the immune response, such as effector cell and antibody proliferation rates, mimicking disease progression in immunocompromised patients. These profiles are then compared with disease progression in the presence of antiviral and convalescent plasma therapy against COVID-19 infections. The model reveals that using both therapies in combination can be very effective in reducing the length of infection, but these synergistic effects decline with a delayed treatment start. Conversely, early treatment with either therapy alone can actually increase the duration of infection, with infectious virions still present after the decline of other markers of infection. This suggests that usage of these treatments should remain carefully controlled in a clinical environment.

Vertical structure heterogeneity in broadleaf forests: Effects on light interception and canopy photosynthesis

Foliage clumping refers to the aggregation of leaves around branches and tree crowns. It has been shown to influence considerably light interception in forests and is increasingly being used in terrestrial biosphere models (TBMs) to estimate canopy photosynthesis, represented by a single value encompassing all spatial scales (shoot, branch, crown and plot): the clumping factor (Ω). Several studies have pointed to possible vertical variations in foliage clumping, and a recent study confirmed very low clumping factor values (high leaf aggregation) at the top of deciduous broadleaf forests. However complete profiles of clumping factors have never been described in tall forest canopies. As TBMs are currently moving towards multilayer schemes for the computation of photosynthesis, we investigated whether vertical profiles of foliage clumping should be considered within those schemes. We first used ground lidar combined with hemispherical photos to characterise branch level clumping factor (Ωv) at different heights in a deciduous broadleaf forest. We then applied the branch level clumping factor profile to four forests plots where complete 3D structure derived from ground lidar was available to produce vertical profiles (30 cm resolution) for foliage clumping (Ωl) and leaf area index (LAI). We ran radiative transfer simulations using different scenarios for representing vertical structure to assess its effect on canopy photosynthesis (without considering energy balance, temperature, kinetics or water balance). We found that when considering branch level clumping, the plot level clumping factor values were considerably lower (leaves more clumped together) than the average values commonly used for broadleaf forests. Modeling light fluxes using this lower value increased canopy photosynthesis because of a combination of greater light penetration to lower layers and non-linearity in the light-response curve. Considering the vertical profiles of foliage clumping further increased canopy photosynthesis through a greater contribution from shaded leaves in the lower and upper canopy levels. Further, we find that there is an optimal range of value for clumping factor which maximises photosynthesis, and the four sites surveyed (LAIs above 4) display clumping factors values close to this optimum. Solely on the basis of the effect of radiative transfer, we did not find notable differences in canopy photosynthesis when the LAI profile was considered. We find that it may be of interest to consider foliage clumping vertical profiles within TBM multilayer schemes, and we present a generic profile equation for use in dense deciduous forests.

Site-Response Analysis Using the Shear-Wave Velocity Profile Correction Approach

ABSTRACT The traditional approach used to incorporate site response into the ground-motion hazard analysis is to compute a design spectrum for a rock-site condition and then propagate the rock motion from the base of the soil model to the surface. The main limitation with this approach is that it can be inconsistent with the ground-motion models (GMMs) used to develop the input rock motion. The VS profile implicit in the GMMs is unlikely to match the site-specific VS profile (value and gradient), because the GMMs were developed for ground motions from different VS profiles over large regions and are unlikely to match the profile of any one site well. This article presents the VS profile correction method for developing surface ground motions as an alternative to the soil-over-rock approach routinely used in earthquake engineering practice. This approach is similar to the standard soil-over-rock analysis, but uses different input motions and involves performing two site response analyses—one for the generic profile associated with the GMM(s) and one for the site-specific profile—then applying the ratio of the two site response analysis results to correct the design spectrum for the reference site condition developed using the GMMs. Two example applications are included to illustrate the VS profile correction methodology as well as some of the challenges that may arise when doing so.

Optical design and performance of a trifocal sinusoidal diffractive intraocular lens.

Two theoretical sinusoidal diffractive profile models to build up a trifocal intraocular lens (IOL) are analysed. Topographic features of the diffractive zones such as their shape, step height and radii, as well as the energy efficiency (EE) of the foci, depends on the particular model, and are compared to the ones experimentally measured in a trifocal lens that claims to be designed with a generic sinusoidal diffractive profile: the Acriva Trinova IOL (VSY Biotechnology, The Netherlands). The topography of the IOL is measured by confocal microscopy. The EE is experimentally obtained through-focus with the IOL placed in a model eye. The experimental results match very accurately with one of the theoretical models, the optimum triplicator, once that a spatial shift in the sinusoidal profile is introduced in the model.

Mission-oriented performance assessment and optimization of electric multirotors

In recent years, the technical advancement of electric propulsion systems has contributed to the growing applicability of multirotor-type unmanned aerial vehicles from personal hobbies to industrial fields. Industrial fields involve various mission profiles comprising several mission-legs, such as hover and forward flight. Thus, enhancing the applicability of multirotors requires them to be designed for specific mission profiles. This paper presents systematic design and analysis methods for realizing the optimum design of multirotors carrying out user-defined missions. To this end, we integrate a newly developed analysis algorithm for the electric propulsion system, which allows accurate performance assessment according to the combination of the components and flight conditions. Further, a practical drag model is developed for the estimation of drag force during forward flight, where the drag force affects the performance of the entire mission. In addition, a frame weight model is proposed to achieve reliable design results in the conceptual design phase. These models and algorithm constitute a design framework named as CLOUDS. Finally, an exemplary optimization problem is formulated and solved for a generic delivery mission profile. The optimal configurations depending on the number of rotors are obtained using Genetic Algorithm. The optimization results clearly demonstrate the necessity and efficacy of the presented methods, algorithm, and models for designing mission-oriented multirotors in the conceptual design phase.