Initial Simulation Research Articles

PEMODELAN DEBIT SUNGAI MENGGUNAKAN SOIL AND WATER ASSESSMENT TOOL DI SUBDAS MAMASA

ABSTRACT
 Issues related to watershed have received primary attention in the past few decades. The hydrological conditions of the watershed area are considered crucial as a source of life. In efforts to achieve sustainable watershed management, understanding the complex hydrological dynamics is very important. The objective of this research is to simulate river discharge using the Soil and Water Assessment Tools (SWAT) model. This study was conducted in the Mamasa Sub-watershed area. The results show that the river discharge in the Mamasa Sub-watershed has increased as it approaches the outlet (downstream) through the accumulation of discharge in larger order rivers. The Mamasa Sub-watershed also experiences a trend of increasing average annual discharge at a rate of 0.74 m3/s per year. Improvement in the discharge simulation results was achieved through a calibration process using 11 parameters. The calibration results indicate that the calibrated discharge has a higher R2 value compared to the initial simulation discharge, showing that the model calibration successfully improved the quality of the expected discharge results to reach 79.50 percent. However, the calibration results still have a low R2 value, influenced by the selection of appropriate parameters and accurate observational data.
 Keywords: Modeling, River discharge, SWAT, Watershed

A Potential Radiomics-Clinical Model for Predicting Failure of Lymph Node Control after Definite Radiotherapy in Locally Advanced Head and Neck Cancer.

Background and Objectives: To optimally predict lymph node (LN) failure after definite radiotherapy (RT) in head and neck cancer (HNC) with LN metastases, this study examined radiomics models extracted from CT images of different periods during RT. Materials and Methods: This study retrospectively collected radiologic and clinical information from patients undergoing definite RT over 60 Gy for HNC with LN metastases from January 2010 to August 2021. The same largest LNs in each patient from the initial simulation CT (CTpre) and the following simulation CT (CTmid) at approximately 40 Gy were indicated as regions of interest. LN failure was defined as residual or recurrent LN within 3 years after the end of RT. After the radiomics features were extracted, the radiomics alone model and the radiomics plus clinical parameters model from the set of CTpre and CTmid were compared. The LASSO method was applied to select features associated with LN failure. Results: Among 66 patients, 17 LN failures were observed. In the radiomics alone model, CTpre and CTmid had similar mean accuracies (0.681 and 0.697, respectively) and mean areas under the curve (AUC) (0.521 and 0.568, respectively). Radiomics features of spherical disproportion, size zone variance, and log minimum 2 were selected for CTpre plus clinical parameters. Volume, energy, homogeneity, and log minimum 1 were selected for CTmid plus clinical parameters. Clinical parameters including smoking, T-stage, ECE, and regression rate of LN were important for both CTpre and CTmid. In the radiomics plus clinical parameters models, the mean accuracy and mean AUC of CTmid (0.790 and 0.662, respectively) were more improved than those of CTpre (0.731 and 0.582, respectively). Conclusions: Both models using CTpre and CTmid were improved by adding clinical parameters. The radiomics model using CTmid plus clinical parameters was the best in predicting LN failure in our preliminary analyses.

Historical footprints and future projections of global dust burden from bias-corrected CMIP6 models

Dust aerosols significantly affect the Earth’s climate, not only as a source of radiation, but also as ice nuclei, cloud condensation nuclei and thus affect CO2 exchange between the atmosphere and the ocean. However, there are large deviations in dust model simulations due to limited observations on a global scale. Based on ten initial Climate Models Intercomparison Project Phase Six (CMIP6) models, the multi-model ensemble (MME) approximately underestimates future changes in global dust mass loading (DML) by 7–21%, under four scenarios of shared socioeconomic pathways (SSPs). Therefore, this study primarily constrains the CMIP6 simulations under various emission scenarios by applying an equidistant cumulative distribution function (EDCDF) method combined with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) datasets based on observation assimilation. We find that the results (19.0–26.1 Tg) for 2000–2014 are closer to MERRA2 (20.0–24.8 Tg) than the initial simulations (4.4–37.5 Tg), with model deviation reduced by up to 75.6%. We emphasize that the DML during 2081–2100 is expected to increase significantly by 0.023 g m–2 in North Africa and the Atlantic region, while decreasing by 0.006 g m–2 in the Middle East and East Asia. In comparison with internal variability and scenario uncertainty, model uncertainty accounts for more than 70% of total uncertainty. When bias correction is applied, model uncertainty significantly decreases by 65% to 90%, resulting in a similar variance contribution to internal variability.

Modeling Noncondensing Compositional Convection for Applications to Super-Earth and Sub-Neptune Atmospheres

Compositional convection is atmospheric mixing driven by density variations caused by compositional gradients. Previous studies have suggested that compositional gradients of atmospheric trace species within planetary atmospheres can impact convection and the final atmospheric temperature profile. In this work, we employ 3D convection-resolving simulations using Cloud Model 1 (CM1) to gain a fundamental understanding of how compositional variation influences convection and the final atmospheric state of exoplanet atmospheres. We perform 3D initial value problem simulations of noncondensing compositional convection for Earth-air, H2, and CO2 atmospheres. Conventionally, atmospheric convection is assumed to mix the atmosphere to a final, marginally stable state defined by a unique temperature profile. However, when there is compositional variation within an atmosphere, a continuous family of stable end states is possible, differing in the final state composition profile. Our CM1 simulations are used to determine which of the family of possible compositional end states is selected. Leveraging the results from our CM1 simulations, we develop a dry convective adjustment scheme for use in general circulation models (GCMs). This scheme relies on an energy analysis to determine the final adjusted atmospheric state. Our convection scheme produces results that agree with our CM1 simulations and can easily be implemented in GCMs to improve modeling of compositional convection in exoplanet atmospheres.

A role of nurse e-prescription in GPs’ time reorganization

The digitization of Polish health care began before 2000, but recent years, especially the period of the COVID pandemic, accelerated it. The basis of the digital ecosystem of medical services is the e-health system P1. P1 consists of digital services: e-prescription, e-referral, DMR, and applications: Internet Patient Account, MojeIKP, office.gov.pl, as well as solutions improving the processes of planning and implementing health care services.
 One of the most popular e-tools used in medical services is the e-prescription. It was first issued in 2018, and since 2020 it has been a permanent element of e-services. Its advantages are, for example:
 -accelerating and facilitating the process of issuing prescriptions,
 -ensuring the correctness of issuing an e-prescription,
 -this prescription cannot be lost, because it is ""in the patient's phone"" and on his electronic account
 -the doctor has an access to all e-prescriptions filled by the patient
 -it enables the National Health Fund to keep accurate statistics on issued and dispensed prescriptions
 Since January 2016, nurses and midwives in Poland have had the right, with some restrictions, to prescribe basic medicines (on their own) and to continue prescription medicines ordered by a doctor, with the exception of narcotics and psychotropic drugs. That has given an easier access to certain health services for Polish patients.
 Currently, both e-prescriptions and the nurse prescribing, as a systemic solution, allow Polish doctors for saving their time and spending it more effectively on the complex needs of their patients. 
 The aim of this study is to present the data of nursing prescribing as well as to show how nurse e-prescription influences the time saving by doctors.
 The study is based on the statistical data and the initial simulation was carried in Medical and Diagnostic Center LTD. An in-depth analysis may deliver crucial knowledge not only for single doctors, but also health managers and politicians.
 
 The study covers the years 2016-2022 and answers the following research questions:
 1.How many e-prescriptions have been issued since the implementation of the e-prescription by doctors and nurses/midwives?
 2.How has nursing prescription evolved due to digitalization – comparative study of statistical data
 3.How many nurses do regularly issue e-prescriptions (at least once a month)?
 4.What is the possibility of saving time by the doctors when prescription visits are carried out by nurses?
 5.What challenges do e-prescriptions face as a tool that serves the medical personnel, patients and the whole health system?

Evaluation of anesthetic skills acquisition in pre-graduate veterinary students with different grades of anesthetic experience using veterinary simulation exercises.

Anesthetic skills are usually learned through continuous supervision by experienced trainers who observe, advise and challenge students. Current educational techniques rely less on live animal training and include the use of simulations and models for teaching and assessment of surgical and anesthetic skills. To evaluate the development of anesthetic skills of veterinary students having different levels of previous experience using simulation. An additional aim was to evaluate the impact of the simulation training on students with no anesthesia experience. Single group periinterventional and postinterventional study. Initial and final anesthesia simulation training recording were obtained from 53 randomly selected veterinary students. Seven faculty members blinded to previous student anesthesia experience reviewed the simulation recording and scored student performance using a rubric, results were recorded and analyzed. All students participating in an anesthesia and surgery course reached higher proficiency levels on fundamental anesthesia skills regardless of their previous amount of experience with anesthesia. Simulation based learning positively influenced the final score in veterinary students having no previous anesthesia training, suggesting that it is possible for veterinary students to achieve a level of competence in anesthesia skills with simulation-based training. Sample size, group simulation, multiple reviewers bias. Students having no experience with clinical anesthesia demonstrated remarkable improvement in their skills, achieving a score that was similar to students having extensive prior clinical anesthesia experience. Despite this clear improvement students having no prior clinical anesthesia experience required more time to complete all anesthesia tasks and may require more training sessions to acquire the speed demonstrated by peers who had significant prior clinical anesthesia experience. Overall, all participants reached a higher proficiency level performing fundamental anesthesia skills at the end of the course.

A novel technique for delineating the effect of variation in the learning rate on the neural correlates of reward prediction errors in model-based fMRI.

Computational models play an increasingly important role in describing variation in neural activation in human neuroimaging experiments, including evaluating individual differences in the context of psychiatric neuroimaging. In particular, reinforcement learning (RL) techniques have been widely adopted to examine neural responses to reward prediction errors and stimulus or action values, and how these might vary as a function of clinical status. However, there is a lack of consensus around the importance of the precision of free parameter estimation for these methods, particularly with regard to the learning rate. In the present study, I introduce a novel technique which may be used within a general linear model (GLM) to model the effect of mis-estimation of the learning rate on reward prediction error (RPE)-related neural responses. Simulations employed a simple RL algorithm, which was used to generate hypothetical neural activations that would be expected to be observed in functional magnetic resonance imaging (fMRI) studies of RL. Similar RL models were incorporated within a GLM-based analysis method including derivatives, with individual differences in the resulting GLM-derived beta parameters being evaluated with respect to the free parameters of the RL model or being submitted to other validation analyses. Initial simulations demonstrated that the conventional approach to fitting RL models to RPE responses is more likely to reflect individual differences in a reinforcement efficacy construct (lambda) rather than learning rate (alpha). The proposed method, adding a derivative regressor to the GLM, provides a second regressor which reflects the learning rate. Validation analyses were performed including examining another comparable method which yielded highly similar results, and a demonstration of sensitivity of the method in presence of fMRI-like noise. Overall, the findings underscore the importance of the lambda parameter for interpreting individual differences in RPE-coupled neural activity, and validate a novel neural metric of the modulation of such activity by individual differences in the learning rate. The method is expected to find application in understanding aberrant reinforcement learning across different psychiatric patient groups including major depression and substance use disorder.

Research of the WIFI/WIMAX patch type antenna on varying embedded material’s dimensions

The microstrip patch antenna with the feeder gap is improved with nematic liquid crystal of type E7 embedded material, which has replaced the part of dielectric substrate. The investigation is focused on the impact of the geometrical parameters of the fragment of embedded material to the S parameters of the microstrip patch antenna. Authors presents initial simulation results to investigate how embedded material influences and changes typical antenna electrical characteristics. The computer-based modeling is performed with Ansys HFSS®. The embedded material has allowed to control the resonance frequencies and directivity and also to improve the gain of the antenna. The best case is obtained with –21.76 dB return loss at 2.4 GHz frequency when the length and width of the fragment of embedded material were equal to 1 mm and 20 mm respectfully. The designed antenna is characterized by good directivity when all energy is concentrated in main lobe.

Demonstrating the Kelvin-Helmholtz Instability Using a Low-Cost Experimental Apparatus and Computational Fluid Dynamics Simulations

A Kelvin-Helmholtz instability is formed when two fluids of different densities exert a shear on one another at their interface when flowing in opposite directions. This paper presents a step-by-step guide for the design of a low-cost, small-scale, experimental tilt tube apparatus and a corresponding computational fluid dynamics (CFD) model that can be used to introduce the Kelvin-Helmholtz instability to undergraduate mechanical engineering students in several courses. A thermal-fluids laboratory course is taken by our fourth-year mechanical engineering students, and the overall variety of experiments has been limited by the cost of commercial teaching equipment. The tilt tube apparatus allows students to induce and record the Kelvin-Helmholtz instability, and no ongoing costs are involved in incorporating this experiment into the course. In our introductory CFD course, students perform CFD simulations as part of the design and analysis process. Developing a two-dimensional (2D) CFD model with two different fluids is well within their capabilities after completing initial software and simulation tutorial exercises and homework. Representative experiments were conducted with fresh water and salt water of different densities, and results showed that both the amplitude of the waves and the amount of time the instability was visible decreased with increasing salt water salinity. Results from a 2D CFD model developed in Ansys Fluent exhibited the same trends as the experimental data.

Exciton Polaritons in Emergent Two-Dimensional Semiconductors.

The "marriage" of light (i.e., photon) and matter (i.e., exciton) in semiconductors leads to the formation of hybrid quasiparticles called exciton polaritons with fascinating quantum phenomena such as Bose-Einstein condensation (BEC) and photon blockade. The research of exciton polaritons has been evolving into an era with emergent two-dimensional (2D) semiconductors and photonic structures for their tremendous potential to break the current limitations of quantum fundamental study and photonic applications. In this Perspective, the basic concepts of 2D excitons, optical resonators, and the strong coupling regime are introduced. The research progress of exciton polaritons is reviewed, and important discoveries (especially the recent ones of 2D exciton polaritons) are highlighted. Subsequently, the emergent 2D exciton polaritons are discussed in detail, ranging from the realization of the strong coupling regime in various photonic systems to the discoveries of attractive phenomena with interesting physics and extensive applications. Moreover, emerging 2D semiconductors, such as 2D perovskites (2DPK) and 2D antiferromagnetic (AFM) semiconductors, are surveyed for the manipulation of exciton polaritons with distinct control degrees of freedom (DOFs). Finally, the outlook on the 2D exciton polaritons and their nonlinear interactions is presented with our initial numerical simulations. This Perspective not only aims to provide an in-depth overview of the latest fundamental findings in 2D exciton polaritons but also attempts to serve as a valuable resource to prospect explorations of quantum optics and topological photonic applications.

Portfolio Analysis of Various Financial Assets

Utilizing Monte Carlo Simulation and annualized data, this paper investigates portfolio establishment with currency, comparing the performance of the portfolio with the performance of equity. Specially, we choose S&P 500 to represent equity, while choosing Gold futures, Bitcoin, and CHF to represent different types of currency. Firstly we use Python to help us do the initial simulation of one million portfolios, analyzing the result of the simulation based on sharp ratio with Excel. We find that there are some regular patterns in the asset allocation of the portfolio and explain the appearance of the strange angle in our simulation. Then to increase the market shares of currency, we advocate adding an extra yield to the currency's annualized return and doing another two simulations on the four assets with a new annual rate of return. We then compare the results of new simulations with the performance of the initial simulation and S&P 500, finding that with extra yield it will be more easier for the portfolio including currency and equity to perform better than initial portfolios. We also find that the regular pattern of asset allocation which is discovered from the initial simulation is inapplicable in new simulations.

Ultrasensitive vibrational resonance induced by small disturbances.

We have found two kinds of ultrasensitive vibrational resonance in coupled nonlinear systems. It is particularly worth pointing out that this ultrasensitive vibrational resonance is transient behavior caused by transient chaos. Considering a long-term response, the system will transform from transient chaos to a periodic response. The pattern of vibrational resonance will also transform from ultrasensitive vibrational resonance to conventional vibrational resonance. This article focuses on the transient ultrasensitive vibrational resonance phenomenon. It is induced by a small disturbance of the high-frequency excitation and the initial simulation conditions, respectively. The damping coefficient and the coupling strength are the key factors to induce the ultrasensitive vibrational resonance. By increasing these two parameters, the vibrational resonance pattern can be transformed from ultrasensitive vibrational resonance to conventional vibrational resonance. The reason for different vibrational resonance patterns to occur lies in the state of the system response. The response usually presents transient chaotic behavior when the ultrasensitive vibrational resonance appears and the plot of the response amplitude vs the controlled parameters shows a highly fractalized pattern. When the response is periodic or doubly periodic, it usually corresponds to the conventional vibrational resonance. The ultrasensitive vibrational resonance not only occurs at the excitation frequency, but it also occurs at some more nonlinear frequency components. The ultrasensitive vibrational resonance as transient behavior and the transformation of vibrational resonance patterns are new phenomena in coupled nonlinear systems.

Turbulence Transitions in Kelvin–Helmholtz Instability “Tube” and “Knot” Dynamics: Vorticity, Helicity, and Twist Waves

We address the sources and dynamics of vorticity and helicity and their relations in transitions to turbulence arising due to Kelvin–Helmholtz instability (KHI) “Tube” and “Knot” (T&K) events. Such events are common in the atmosphere and oceans, and initial numerical simulations reveal that T&K dynamics significantly accelerate turbulence transitions and enhance KHI peak and mean energy dissipation rates. KHI T&K events arise where emerging KH billows exhibit varying wavelengths, phases, amplitudes, and/or discontinuities along their axes. As the KH billows intensify, these regions evolve roughly orthogonal billow cores and induced vortex tubes in close proximity. Their mutual advection as they intensify induces large-amplitude Kelvin vortex waves, or “twist waves”, that arise where locally uniform vortices are distorted by axial or radial advection. The twist waves propagate along, and fragment, the vortex tubes and billow cores, thus accounting for the emergence of helicity and the down-scale energy, enstrophy, and helicity fluxes within the turbulence inertial range. We describe the results of four direct numerical simulations (DNS) addressing KHI T&K dynamics in large and idealized small domains. The large-domain vorticity fields reveal the character and diversity of KHI T&K dynamics, the emergence of twist waves at larger and smaller scales, and their driving of turbulence transitions. Two small-domain DNS exhibit idealized KHI T&K events arising from KH billows that are mis-aligned and that exhibit phase variability along their axes. A third examines the interactions of two vortex tubes in close proximity. These reveal that twist waves drive the character and evolutions of the vorticity and helicity fields.

Soft tissue prediction in orthognathic surgery: Improving accuracy by means of anatomical details.

Three-dimensional virtual simulation of orthognathic surgery is now a well-established method in maxillo-facial surgery. The commercial software packages are still burdened by a consistent imprecision on soft tissue predictions. In this study, the authors produced an anatomically detailed patient specific numerical model for simulation of soft tissue changes in orthognathic surgery. Eight patients were prospectively enrolled. Each patient underwent CBCT and planar x-rays prior to surgery and in addition received an MRI scan. Postoperative soft-tissue change was simulated using Finite Element Modeling (FEM) relying on a patient-specific 3D models generated combining data from preoperative CBCT (hard tissue) scans and MRI scans (muscles and skin). An initial simulation was performed assuming that all the muscles and the other soft tissue had the same material properties (Homogeneous Model). This model was compared with the postoperative CBCT 3D simulation for validation purpose. Design of experiments (DoE) was used to assess the effect of the presence of the muscles considered and of their variation in stiffness. The effect of single muscles was evaluated in specific areas of the midface. The quantitative distance error between the homogeneous model and actual patient surfaces for the midface area was 0.55 mm, standard deviation 2.9 mm. In our experience, including muscles in the numerical simulation of orthognathic surgery, brought an improvement in the quality of the simulation obtained.

Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

IoT cloud computing provides all functions of traditional computing as services through the Internet for the users. Big data processing is one of the most crucial advantages of IoT cloud computing. However, IoT cloud job scheduling is considered an NP-hard problem due to the hardness of allocating the clients’ jobs to suitable IoT cloud provider resources. Previous work on job scheduling tried to minimize the execution time of the job scheduling in the IoT cloud, but it still needs improvement. This paper proposes an enhanced job scheduling mechanism using cat swarm optimization (CSO) with modified distance to minimize the execution time. The proposed job scheduling mechanism first creates a set of jobs and resources to generate the population by randomly assigning the jobs to resources. Then, it evaluates the population using the fitness value, which represents the execution time of the jobs. In addition, we use iterations to regenerate populations based on the cat’s behaviour to produce the best job schedule that gives the minimum execution time for the jobs. We evaluated the proposed mechanism by implementing an initial simulation using Java Language and then conducted a complete simulation using the CloudSim simulator. We ran several experimentation scenarios using different numbers of jobs and resources to evaluate the proposed mechanism regarding the execution time. The proposed mechanism significantly reduces the execution time when we compare the proposed mechanism against the firefly algorithm and glowworm swarm optimization. The average execution time of the proposed cat swarm optimization was 131, while the average execution times for the firefly algorithm and glowworm optimization were 237 and 220, respectively. Hence, the experimental findings demonstrated that the proposed mechanism performs better than the firefly algorithm and glowworm swarm optimization in reducing the execution time of the jobs.

Embedded direct numerical simulation of ignition kernel evolution and flame initiation in dual-fuel spray assisted combustion

The flame initiation process in dual-fuel spray assisted combustion is presently not fully understood. Here, diesel spray assisted combustion of premixed methane/oxidizer/EGR is explored in the post-ignition phase by scale-resolved simulations. The modified dual-fuel ECN Spray A forms the baseline configuration. An extensive local grid refinement (approaching DNS limit) around one of the first high-temperature ignition kernels is carried out in order to examine the validity of hypothesized flame initiation and deflagration. A high quality LES is used to solve the spray dynamics, while the embedded quasi-DNS (eq-DNS) region offers detailed information on the ignition kernel evolution. The finite-rate chemistry is directly integrated, utilizing 54 species and 269 reactions. Local combustion modes are investigated for the ignition kernel development toward spontaneous ignition and premixed flame propagation using various approaches, including the reaction front displacement speed, energy transport budget, and chemical explosive mode analysis. Furthermore, a new criterion based on reaction flux analysis is introduced, which is compatible with dual-fuel combustion. The spatial and temporal scales associated with the ambient methane consumption and consequent flame initiation are characterized. For the first time in dual-fuel spray assisted simulations, numerical evidence is provided on the initiation of premixed flames, and the corresponding timescale is reported. Particularly, there is a transient mixed-mode combustion phase of approximately 0.2 ms after the spray second stage ignition wherein extinction, ignition fronts, and quasi-deflagrative structures co-exist. After such a transient period, the combustion mode becomes essentially deflagrative. Finally, interactions between turbulence and premixed flame front are characterized mostly in the corrugated regime.

Assessment of summer outdoor thermal comfort in an urban neighborhood with high-rise buildings

The use of urban vegetation and ventilation corridors can be beneficial in terms of outdoor thermal comfort. High-rise buildings modify urban ventilation by redirecting incident wind flow, which can further complicate local thermal conditions. The present study investigates the interaction of building morphology and urban vegetation during heat wave conditions. A coupled multiscale approach is performed that allows for a detailed analysis of the local impact of vegetation and high-rise buildings in an urban neighborhood in Zurich, Switzerland. Existing configuration in the neighborhood with low-rise buildings of mostly uniform height is modified with high-rise buildings. Mesoscale meteorological simulations are employed to drive the flow in building-resolved computational fluid dynamics (CFD) simulations at microscale. The results show significantly lower air temperature around groups of trees and along main streets aligned with the predominant wind direction. However, there are also locations where air temperature increases due to the presence of trees, especially when there is limited exchange with the main ventilation corridor. Initial simulations with high-rise buildings show that they can have a large influence on air temperature in the immediate neighborhood.

Assessing out-of-pocket cost differences in treatment for Medicare-eligible patients with favorable prognosis early-stage Hodgkin lymphoma.

15 Background: In the treatment of favorable Hodgkin Lymphoma (HL), Level 1 evidence comparing combined modality therapy (CMT) with chemotherapy alone has persistently demonstrated that obviating radiation therapy (RT) worsens recurrence rates despite no significant overall survival difference. As there remains limited evidence on the granularity of the impact of insurance on HL treatment, this project disaggregates costs by insurance plans to enhance the transparency of out-of-pocket (OOP) cost estimations in Medicaid and/or Medicare-eligible (age 65+) patients. Methods: National Comprehensive Cancer Network guidelines were queried to assess the appropriate therapy for HL. Following initial adriamycin/bleomycin/vinblastine/dacarbazine (ABVD) chemotherapy x 2 cycles with Deauville 1-2 response on PET scan, CMT was defined as involved site RT (20 Gy / 10 fractions), while chemotherapy alone was defined as an additional ABVD x 2. Current Procedural Terminology and Healthcare Common Procedure Coding System codes identified treatments. OOP expenditures, deductibles, and copay/coinsurance were computed for Medicaid, Original Medicare, and Medigap Plan G plans via medicare.gov, medicaid.oh.gov, and cms.gov. Price estimates (from actual prices per insurance plan rather than claims data predictions) assume an Ohio hospital location (zip code = 44106) for all treatments. Total costs were calculated based on all treatments plus a 5-year follow-up (not adjusted for inflation). Treatment charges for chemotherapy alone include initial medical oncology office visit, cost of ABVD x 4, and infusion costs for 4 cycles. Additional treatment charges for CMT include initial radiation oncology consultation, treatment planning, dosimetry calculations, treatment device, simulation and verification, RT delivery, on-treatment visits, medical physics consultation, and annual TSH labs. Results: With Original Medicare plans, patients are faced with an OOP cost of 20% for Medicare Part B claims with no cost cap for approved procedures after the deductible. As such, patients are faced with a total 6-year charge of $1,928.95 for chemotherapy alone, and $2,385.28 for CMT. Under Medigap Plan G, patients encounter an OOP charge of $1,151.14 for chemotherapy alone and $1,176.34 for CMT. Medicaid beneficiaries (assuming all treatments are approved by Medicaid) face no OOP expenses for both treatment plans, as all expenses are covered without limit. Conclusions: By comparing OOP costs between Medicare and Medicaid plans, this model improves cost transparency in the treatment of favorable early-stage HL. Our findings indicate that omitting RT in favor of chemotherapy alone yields a cost savings of 2-24% at a 5-year follow-up. Further studies are required to assess whether these savings compensate for the inferior outcomes achieved by omitting RT in this patient population.

Inhibiting hysteresis and optimizing the performance of perovskite solar cells

We developed a comprehensive Poisson and drift-diffusion solver coupled with a time-dependent ion migration model in the COMSOL simulation software to analyze hysteresis effects and efficiency in perovskite solar cells (PSCs). Initial simulations on PSCs with the structure ITO/SnO2/CH3NH3PbI3/Spiro-OMeTAD/Au revealed suboptimal efficiency of 10.31% due to hysteresis loops near the open-circuit voltage (Voc) caused by interface recombination. Next, we explored the influence of the transport layer's dielectric constant on PSCs performance and hysteresis. The modeling results emphasized the vital role of the transport layer's dielectric constant in determining PSCs hysteresis and power conversion efficiency (PCE). To investigate further, we replaced the hole transport layer (HTL) material with NiO, CuSCN, and CuI. Studied four different transport layer combinations for n-i-p PSCs: SnO2-Spiro-OMeTAD, SnO2–NiO, SnO2–CuSCN, and SnO2–CuI. Notably, the SnO2–CuSCN combination achieved an impressive efficiency of 21.30%. The p-i-n PSCs with a matched dielectric constant PCBM-PTAA transport layer combination achieves higher PCE. It attains a reverse-scan PCE of up to 21.22% with a fill factor (FF) of 85.04%. Our findings underscore the importance of dielectric constant matching in both electron transport layer (ETL) and HTL, highlighting the potential of SnO2–CuSCN and PCBM-PTAA as transport layer materials for achieving high efficiency and minimizing hysteresis in PSCs.

An energy community as a platform for local sector coupling: From complex modelling to simulation and implementation

This paper presents the reference architecture of a model for sustainable local planning and sector coupling based on an energy-community framework. The proposed approach includes four building blocks: (1) the data-acquisition building block - local energy consumption, (2) the building block for modelling and calculations - local energy conversions, (3) the building block for connecting the infrastructure status and needs with development plans of the energy-network operators and aggregators, and (4) the decision-support building block for local environmental, spatial and energy planning. The proposed approach was tested in a real industrial environment and a local community. Initial simulation results confirmed that excess heat from the energy-intensive company has the potential to replace the natural gas that is currently used for the production of heat in a local district-heating system. The natural gas savings in the district-heating system would be 3,299,676 Nm3, as the excess heat could cover as much as 68.4 % of the required heat for the local community. It has also been proven that complex modelling and simulation of a future sustainable solution can contribute to its optimization, understanding, and acceptance by all stakeholders and thereby increase the potential of the project's implementation.