Framework Of Model Research Articles

Long COVID Definitions and Models of Care : A Scoping Review.

Definitions of long COVID are evolving, and optimal models of care are uncertain. To perform a scoping review on definitions of long COVID and provide an overview of care models, including a proposed framework to describe and distinguish models. English-language articles from Ovid MEDLINE, PsycINFO, the Cochrane Library, SocINDEX, Scopus, Embase, and CINAHL published between January 2021 and November 2023; gray literature; and discussions with 18 key informants. Publications describing long COVID definitions or models of care, supplemented by models described by key informants. Data were extracted by one reviewer and verified for accuracy by another reviewer. Of 1960 screened citations, 38 were included. Five clinical definitions of long COVID varied with regard to timing since symptom onset and the minimum duration required for diagnosis; 1 additional definition was symptom score-based. Forty-nine long COVID care models were informed by 5 key principles: a core "lead" team, multidisciplinary expertise, comprehensive access to diagnostic and therapeutic services, a patient-centered approach, and providing capacity to meet demand. Seven characteristics provided a framework for distinguishing models: home department or clinical setting, clinical lead, collocation of other specialties, primary care role, population managed, use of teleservices, and whether the model was practice- or systems-based. Using this framework, 10 representative practice-based and 3 systems-based models of care were identified. Published literature often lacked key model details, data were insufficient to assess model outcomes, and there was overlap between and variability within models. Definitions of long COVID and care models are evolving. Research is needed to optimize models and evaluate outcomes of different models. Agency for Healthcare Research and Quality. (Protocol posted at https://effectivehealthcare.ahrq.gov/products/long-covid-models-care/protocol.).

Effect of the charge asymmetry and orbital angular momentum in the entrance channel on the hindrance to complete fusion

The hindrance to complete fusion is studied as a function of the charge asymmetry of colliding nuclei and orbital angular momentum of the collision. The formation and evolution of a dinuclear system (DNS) in the heavy-ion collisions at energies near the Coulomb barrier is calculated in the framework of the DNS model. The DNS evolution is considered as nucleon transfer between its fragments. The results prove that a hindrance at formation of a compound nucleus (CN) is related with the quasifission process, which is breakup of the DNS into products instead to reach the equilibrated state of the CN. The role of the angular momentum in the charge (mass) distribution of the reaction products for the given mass asymmetry of the colliding nuclei has been demonstrated. The results of this work have been compared with the measured data for the quasifission yields in the C12+Pb204 and Ca48+Er168 reactions to show the role of the mass asymmetry of the entrance channel. Published by the American Physical Society 2024

Investigating EWOM and halal product knowledge on gen Z’s halal cosmetics purchase intentions in Pakistan

Purpose This study aims to improve the theory of planned behavior (TPB) model by incorporating electronic word of mouth (E-WOM) as a factor influencing attitude (ATT), subjective norms (SN) and perceived behavioral control (PBC). The main goal is to study Generation Z’s intention to purchase halal cosmetics. In addition, the research examines how halal product knowledge (HPK) influences the relationships between ATT, SN, PBC and the purchase intention of halal cosmetics. Design/methodology/approach Data were collected using self-administered questionnaires from two superstores in Karachi, Pakistan, with 265 valid responses. Convergent and discriminant validity analyses were performed to ensure validity and reliability, and variable relationships were assessed using structural equation modeling. Findings The results revealed that EWOM significantly influences ATT, SN and PBC. ATT and PBC both significantly affect purchase intention (PI), while SN has a nonsignificant influence on PI. In addition, the study found HPK only moderates the association between ATT and PI, while moderating influence HPK does not exist in the relationship between (SN and PI) and (PBC and PI). Research limitations/implications This study focuses on the Halal cosmetics industry in Pakistan and explicitly targets Generation Z individuals in a particular cultural environment. The aim is to examine how applying the same research design in different sectors and countries can lead to different results. In addition, the study primarily includes Gen Z consumers of halal cosmetics. Collecting data from other generational groups for future studies, such as generations X and Y, would be interesting. Originality/value This research contributes to the existing literature on halal consumption by introducing the concept of E-WOM as a factor within the TPB model. This study is particularly groundbreaking as it examines how Gen Z Pakistani Muslim consumers’ behavioral intentions toward halal cosmetic products are influenced by their HPK using the TPB model. Additionally, the paper presents an extended TPB modeling framework that could be valuable to researchers, marketers and halal experts.

A capture-recapture modeling framework emphasizing expert opinion in disease surveillance.

In disease surveillance, capture-recapture methods are commonly used to estimate the number of diseased cases in a defined target population. Since the number of cases never identified by any surveillance system cannot be observed, estimation of the case count typically requires at least one crucial assumption about the dependency between surveillance systems. However, such assumptions are generally unverifiable based on the observed data alone. In this paper, we advocate a modeling framework hinging on the choice of a key population-level parameter that reflects dependencies among surveillance streams. With the key dependency parameter as the focus, the proposed method offers the benefits of (a) incorporating expert opinion in the spirit of prior information to guide estimation; (b) providing accessible bias corrections, and (c) leveraging an adapted credible interval approach to facilitate inference. We apply the proposed framework to two real human immunodeficiency virus surveillance datasets exhibiting three-stream and four-stream capture-recapture-based case count estimation. Our approach enables estimation of the number of human immunodeficiency virus positive cases for both examples, under realistic assumptions that are under the investigator's control and can be readily interpreted. The proposed framework also permits principled uncertainty analyses through which a user can acknowledge their level of confidence in assumptions made about the key non-identifiable dependency parameter.

A Prototype Software Framework for Transferable Computational Health Economic Models and Its Early Application in Youth Mental Health.

We are developing an economic model to explore multiple topics in Australian youth mental health policy. To help make that model more readily transferable to other jurisdictions, we developed a software framework for authoring modular computational health economic models (CHEMs) (the software files that implement health economic models). We specified framework user requirements for: a simple programming syntax; a template CHEM module; tools for authoring new CHEM modules; search tools for finding existing CHEM modules; tools for supplying CHEM modules with data; reproducible analysis and reporting tools; and tools to help maintain a CHEM project website. We implemented the framework as six development version code libraries in the programming language R that integrate with online services for software development and research data archiving. We used the framework to author five development version R libraries of CHEM modules focussed on utility mapping in youth mental health. These modules provide tools for variable validation, dataset description, multi-attribute instrument scoring, construction of mapping models, reporting of mapping studies and making out of sample predictions. We assessed these CHEM module libraries as mostly meeting transparency, reusability and updatability criteria that we have previously developed, but requiring more detailed documentation and unit testing of individual modules. Our software framework has potential value as a prototype for future tools to support the development of transferable CHEMs.Code: Visit https://www.ready4-dev.com for more information about how to find, install and apply the prototype software framework.

Assessment of Repetitive Controlled Ovarian Stimulation (COS) Cycles on Oocyte Donors: Impact on Oocyte Quality and Viable Embryo Yield.

The utilization of donor eggs has broadened the options for Assisted Reproductive Technology (ART) among women facing challenges with egg quantity or quality. Given that donors are typically selected from young and fertile individuals, In Vitro Fertilization with egg donation (IVF-ED) tends to exhibit higher rates of implantation, pregnancy, and live births compared to IVF with the woman's own eggs, especially for females over 35years old. This has led to a projected increase in the demand for IVF-ED, surpassing the number of available donors. Consequently, many centers opt to use oocyte donors for multiple cycles. However, the correlation between repeated Controlled Ovarian Stimulation (COS) cycles and the performance of donors in terms of viable blastocyst stage embryo (VEC) or blastocyst embryo rate is not definitively established and remains of interest. This study aims to explore the preimplantation characteristics of embryo development and oocyte maturation status based on the number of donor COS cycles, employing a Generalized Linear Mixed Model (GLMM) framework. The study encompasses 1965 embryo transfer (ET) cycles involving 399 donors who underwent a minimum of two and a maximum of nine controlled ovarian hyperstimulation (COS) cycles. The findings indicate that, with the patient undergoing six or more cycles of ovarian stimulation, despite a 3.9% increase in both maturation and fertilization rates, there is a corresponding decrease of 4.5% in VEC rate and 4.7% in blastulation rates. In essence, an escalating number of donor COS cycles appears to be associated with a disadvantageous reduction in embryo quality.

A Two-Layer SD-ANN-CA Model Framework for Multi-Typed Land Use and Land Cover Change Prediction under Constraints: Case Study of Ya’an City Area, Western China

Land use and land cover change (LUCC) prediction of cities in Western China requires higher accuracy in quantitative demand and spatial layout because of complex challenges in balancing relationships between urban constructions and ecological developments. Considering city-level areas and various types of land use and land cover, existing LUCC models without constraint or with only loose demand constraints were impractical in providing evidence of high accuracy and high-resolution predictions in areas facing fierce land competition. In this study, we proposed a two-layer SD-ANN-CA model to simulate and explore the LUCC trend and layout predictions for 2018, 2028, and 2038 in Ya’an City, Western China. The two-layer structure with an upper layer of the SD model and a lower layer of the ANN-CA model, as well as the advantages of all three methods of system dynamics (SD), artificial neural network (ANN), and cellular automata (CA), have allowed us to consider the macro-level demand constraints, meso-level driving factors constraints, and the micro-level spatial constraints into a unified model framework. The simulation results of the year 2018 have shown significant improvement in the accuracy of the ANN-CA model constructed in our earlier work, especially in types of forest land (error-accuracy: 0.08%), grassland (error-accuracy: 0.23%), and construction land (error-accuracy: 0.18%). The layout predictions of all six types of land use in 2028 and 2038 are then carried out to provide visual evidence support, which may improve the efficiency of planning and policy-making processes. Our work may also provide insights into new ways to combine quantitative methods into spatial methods in constructing city-level or even regional-level LUCC models with high resolution.

Debris‐flow entrainment modelling under climate change: Considering antecedent moisture conditions along the flow path

AbstractDebris‐flow volumes can increase along their flow path by entraining sediment stored in the channel bed and banks, thus also increasing hazard potential. Theoretical considerations, laboratory experiments and field investigations all indicate that the saturation conditions of the sediment along the flow path can greatly influence the amount of sediment entrained. However, this process is usually not considered for practical applications. This study aims to close this gap by combining runout and hydrological models into a predictive framework that is calibrated and tested using unique observations of sediment erosion and debris‐flow properties available at a Swiss debris‐flow observation station (Illgraben). To this end, hourly water input to the erodible channel is predicted using a simple, process‐based hydrological model, and the resulting water saturation level in the upper sediment layer of the channel is modelled based on a Hortonian infiltration concept. Debris‐flow entrainment is then predicted using the RAMMS debris‐flow runout model. We find a strong correlation between the modelled saturation level of the sediment on the flow path and the channel‐bed erodibility for single‐surge debris‐flow events with distinct fronts, indicating that the modelled water content is a good predictor for erosion simulated in RAMMS. Debris‐flow properties with more complex flow behaviour (e.g., multiple surges or roll waves) are not as well predicted using this procedure, indicating that more physically complete models are necessary. Finally, we demonstrate how this modelling framework can be used for climate change impact assessment and show that earlier snowmelt may shift the peak of the debris‐flow season to earlier in the year. Our novel modelling framework provides a plausible approach to reproduce saturation‐dependent entrainment and thus better constrain event volumes for current and future hazard assessment.

The role of nanosize dependence of dislocation and surface roughness concentrations to calculate lattice thermal conductivity in In0.53Ga0.47As nanofilms

ABSTRACT Systematic empirical relations were established for the nanosize-dependent surface roughness, ε ( D ) , dislocations, N D ( D ) , and effective length to examine their effects on the temperature-dependent lattice thermal conductivity (LTC) within the framework of the Morelli-Callaway model for In0.53Ga0.47As alloy films with thicknesses ranging from 10 to 70 and 1400 nm, the latter representing the material’s bulk state. The results were analyzed for film thicknesses of 2, 5, 200, and 500 nm, under the assumption that electrons and impurities are unaffected by nanosize, while a small alloy parameter significantly and sensitively alters the LTC curve in medium and high-temperature regions. According to these new findings, and considering phonon scattering processes, the LTC’s temperature dependence is sufficiently reduced at the nanoscale and fits well with the available reported experimental findings. It was also discovered that the LTC’s peak maximum value and its corresponding temperature are influenced by the film’s dislocation concentration, surface roughness, and thickness. Unravelling the complex interplay between nanosize-dependent dislocation and surface roughness concentrations becomes critical for a thorough knowledge when examining the delicate nuances of LTC in In0.53Ga0.47As nanofilms. Finally, the Morelli-Callaway-based model has been modified to calculate LTC for nanofilms without fitting parameters, provided their related bulk state values are known.

Supporting the Ethical Use of Artificial Intelligence Applications in Universities – A Research Based on Students Opinions

Purpose: The purpose of the research was to identify a feasible way to support the ethical use of AI in universities based on identifying how the AI software application ChatGPT is used in education at higher education institutions and with students in different majors. Study design/methodology/approach: The research methodology consisted of the following stages: (1) a survey based on a questionnaire designed considering the Technology Acceptance Model (TAM) framework to collect students’ opinions on the use of AI ChatGPT; (2) The results of the survey were used to design the materials of the AI Transmedia Campaign, but also to identify the best distribution channels of them; (3) After implementing the AI Transmedia Campaign, a feedback survey was developed, ascertaining the effectiveness of the approach to create an ethical behaviour towards the AI software application in general, and on AI ChatGPT, in particular. Findings: Research shows a gap in regulating the ethical use of AI applications in higher education. Thus, the AI Transmedia Campaign has been well received and appreciated by all categories of students. Originality/value: The research is international and was carried out in higher education institutions in Romania, Greece, and Slovenia (the size of the research sample, 2942, proves the scope of the study). The research results have demonstrated and characterised the students’ behaviour (the cognitive response and the intention to use) towards the use of AI ChatGPT and the utility of the AI Transmedia Campaign realised in the context of the implementation of the RespectNET project (2021-1-IT02-KA220-HED-000027578, https://respectnet.eu/).

Modelling the effect of cascade reservoir regulation on ice-jam flooding

In regulated rivers, dams and their reservoirs are the main means to change the flow conditions, and strategic reservoir operations can be an important measure for mitigating ice-jam flooding (IJF). Previous studies have shown the effect of single reservoir operation on IJF probabilities, but little is known about the effect of cascade reservoir operation on IJF occurrences and severity. This study presents a probabilistic modelling framework that couples machine learning (LSTM, Long Short-Term Memory) and river ice (RIVICE) models to assess the effects of regulated and naturalized flows on IJF backwater levels. The trained LTSM shows good performance in both validation (RMSE = 398.88, R = 0.86 and NSE = 0.67) and testing (RMSE = 366.57, R = 0.84 and NSE = 0.71) periods and the calibrated RIVICE can well simulate backwater levels caused by three IJFs from 2008 to 2010 with total volumes of incoming ice for 0.4 million m3, 0.55 million m3 and 0.32 million m3. The completed framework was used to assess IJF hazard along the Sanhuhekou bend, a reach of the upper Yellow River, whose hydraulic regime is impacted by eight upstream reservoirs. The effect of naturalized and regulated flows on IJF are compared from two aspects. Firstly, comparing the median of naturalized and regulated IJF backwater level ensembles, the results show that regulation increases the IJF backwater levels. Secondly, comparing the extreme values of naturalized and regulated IJF backwater level ensembles, results show that regulation can increase the hazard of IJF inundation when annual exceedance probability (AEP) is less severe than 1:100 and regulation can decrease the hazard of IJF when AEP is more severe than 1:125.

Optimal emergency hospitals construction in an unexpected epidemic with considering the interactive effect

AbstractConstructing emergency hospitals is one of the most critical measures to defeat an unexpected epidemic. However, existing operations research (OR) studies rarely consider the interactive effect between the construction of emergency hospitals and the dynamics of epidemic transmission. Inspired by this gap, we propose a new modeling framework for decision‐making in emergency hospital construction. In our optimization model, we address the pandemic evolution functions as constraints. We also consider the heterogeneity among infected individuals, distinguishing between those with mild and severe symptoms, each requiring treatment in different types of emergency hospitals. We formulate the problem as a mixed integer nonlinear programming model. Our model can envision the current and future evolution of the epidemic and the impact of different decisions regarding emergency hospital construction on epidemic development. Simultaneously, it provides the optimal strategy to build hospitals and minimize the total number of untreated patients due to the disease. The proposed model is tested using the Covid‐19 outbreak case in Wuhan. The results can provide precise guidelines for emergency hospitals construction, including timing and capacity, and offer decision boundaries for policymakers considering the uncertainty of disease transmission. Furthermore, our decision‐making framework is general and can be adapted to study other epidemics.

Grappling with the trade-offs of carbon emission trading and green certificate: Achieving carbon neutrality in China

Although our knowledge of national carbon emission trading system and green certificate trading system are powerful incentive instruments that can deliver on increasingly ambitious climate targets in China, there remains an uncertainty of systems' structural reforms. This study builds on and extends a well-established dynamic computable general equilibrium (CGE) model to incorporate carbon trading system and green certificate trading system into the modeling framework, simulating a diverse of system development pathways further allows an exploration of the many possible policy effect. Then, using total factor productivity as a comprehensive indicator to asses policy effectiveness, the evolutionary trend of comprehensive effects under different paths are separately evaluated to discover the reforms’ optimal range. Our work offers main results: First, these instruments provide a price signal. The introduction of a carbon allowance auction drive up carbon prices, while the implementation of a green certificate punishment and the expansion of the trading scope promote an increase in green certificate prices. Second, all policy scenarios that help reduce carbon emission intensity and optimize the power supply structure. However, in achieving the net-zero goal, the green certificate policy incurs more economic costs than the carbon trading policy. Indeed, the combination of multiple policy tools alleviates the decline of social welfare levels. Third, synergism design among policy tools: the focus should be on carbon trading policy from 2021 to 2030, green certificate trading policy from 2030 to 2050, and strengthened policy from 2050 to 2060. Reform measures within policies may need to be introduced in a timely manner. This study offers specific insights and tailored policy proposals to support policymakers in balancing environmental goals with economic and social needs in light of the aforementioned findings.

BPM application in clinical process improvement: a women 'hospital case study

PurposeThe childbirth process is a complex and vital event that requires careful analysis and improvement. This experience can shape a woman's perspective on motherhood and even affect her mental health. Healthcare providers must prioritize improving the birth experience for women. In this interdisciplinary research, a combination of business process modeling (BPM) and medicine have been used with the aim of realizing an improved delivery experience and increased maternal satisfaction.Design/methodology/approachThe data collection of this study was done by observing 518 childbirth processes and interviewing the chief of labor, chief residents, and midwives in the obstetrics and gynecology department of a hospital in Tehran from October 2022 to February 2023.FindingsThe research has been done in four main stages. The first phase is to model the primary process and sub-processes of normal vaginal delivery (NVD). The second phase is validation using expert confirmation and process mining (PM). The third phase is the analysis of the causes of maternal dissatisfaction in labor. The fourth phase of the heuristics redesigning and improving the process, in which for the first time three new categories have been presented including hospital-based, patient-based, and medical technique-based results show BPM intervention effect can be far-reaching in improving patient care and optimizing operational efficiency.Originality/valueThis study is one of only a few to adopt a process-oriented perspective to show how BPM can be used in clinical processes and has specifically examined an essential clinical process, i.e. childbirth.Highlights Developing business process management (BMP) applications in a medical special process related to childbirth as interdisciplinary research.A combination of qualitative and quantitative techniques contains engineering software and management approaches for a Case study, Implementation of BPM lifecycle in the women's hospital in Iran, Tehran, for a clinical process, which is called, normal vaginal delivery (NVD) process for fetal expulsion normally.Modeling NVD clinical process and sub-process for the first time by BPMN2.0 notations in visual paradigm (VP) software and Validation of the made model with process mining (PM), by Disco process mining software. This was done through event log collection from HIS at the hospital.Improving the childbirth process by redesigning heuristics and Introducing two new categories special for clinical process improvement for the first time.Clinical process improvement heuristics obtained in this research are not consistent with the previous seven categories presented in previous studies such as Marlon Dumas' book. Therefore, we have introduced two new heuristics to redesign clinical processes compatible with medical centers, including hospital-based, patient-based, and medical technique-based.Providing a framework for clinical process modeling and improvement containing steps and tools.

Feasibility of Fit24, a Digital Diabetes Prevention Program for Hispanic Adolescents: Qualitative Evaluation Study.

Digital health interventions are promising for reaching and engaging high-risk youth in disease prevention opportunities; however, few digital prevention interventions have been developed for Hispanic youth, limiting our knowledge of these strategies among this population. This study qualitatively assessed the feasibility and acceptability of Fit24, a 12-week goal-setting intervention that uses a Fitbit watch (Fitbit Inc) and theoretically grounded SMS text messages to promote physical activity and sleep among Hispanic adolescents (aged between 14 and 16 years) with obesity. After completing the intervention, a subsample of youth (N=15) participated in an in-depth interview. We categorized the themes into dimensions based on participant perspectives using the Practical, Robust Implementation, and Sustainability Model (PRISM) framework. Participants shared positive perceptions of wearing the Fitbit and receiving SMS text messages. Youth were highly engaged in monitoring their behaviors and perceived increased activity and sleep. Almost all youth organically received social support from a peer or family member and suggested the use of a group chat or team challenge for integrating peers into future interventions. However, most youth also expressed the need to take personal responsibility for the change in their behavior. Barriers that impacted the feasibility of the study included the skin-irritating material on the Fitbit watch band and environmental barriers (eg, lack of resources and school schedules), that limited participation in activity suggestions. Additionally, sync issues with the Fitbit limited the transmission of data, leading to inaccurate feedback. Fit24 is a promising approach for engaging Hispanic youth in a diabetes prevention program. Strategies are needed to address technical issues with the Fitbit and environmental issues such as message timing. While integrating peer social support may be desired by some, peer support strategies should be mindful of youth's desire to foster personal motivation for behavior change. Findings from this study will inform future diabetes prevention trials of Fit24 and other digital health interventions for high-risk pediatric populations.

Commodity price volatility, institutions and economic growth: An empirical investigation

AbstractThis article investigates the role of institutional quality in transmitting effects of commodity price volatility to economic growth. To do so, we collect data on 107 primary commodity exporting countries in both developing and developed ones over the period 1976–2015. Our empirical approach is based on Solow growth model framework (Solow, R. M. (1956). The Quarterly Journal of Economics, 70(1), 65) and consists of estimating a dynamic panel model using the two‐step system GMM estimator. Our results show evidence that commodity price booms are associated with good economic performances that are unfortunately wiped out by the negative effects of price volatility in developing commodity‐dependent countries (CDCs). The main channel through which this volatility affects economic growth turns out to be through factor productivity. Finally, we have formally established that the negative effect of price volatility in CDCs is mainly due to the poor quality of institutions in these countries. These results suggest that it is important for commodity‐exporting economies, especially developing CDCs, to work on building strong economic and political institutions to guard against the risk of commodity price volatility.

Cosmological constraints from the redshift-space galaxy skew spectra

Extracting the non-Gaussian information of the cosmic large-scale structure (LSS) is vital in unlocking the full potential of the rich datasets from the upcoming stage-IV galaxy surveys. Galaxy skew spectra serve as efficient beyond-two-point statistics, encapsulating essential bispectrum information with computational efficiency akin to power spectrum analysis. This paper presents the first cosmological constraints from analyzing the full set of redshift-space galaxy skew spectra of the data from the SDSS-III BOSS, accessing cosmological information down to nonlinear scales. Employing the forward modeling framework and simulation-based inference via normalizing flows, we analyze the CMASS-SGC subsample, which constitute approximately 10% of the full BOSS data. Analyzing the scales up to kmax=0.5 h−1 Mpc, we find that the skew spectra improve the constraints on Ωm,Ωb,h, and ns by 34%, 35%, 18%, 10%, respectively, compared to constraints from previous power spectrum multipoles analysis, yielding Ωm=0.288−0.034+0.024, Ωb=0.043−0.007+0.005, h=0.759−0.050+0.104, ns=0.918−0.090+0.041 (at 68% confidence limit). On the other hand, the constraints on σ8 are weaker than from the power spectrum. Including the big bang nucleosynthesis (BBN) prior on baryon density reduces the uncertainty on the Hubble parameter further, achieving h=0.750−0.032+0.034, which is a 38% improvement over the constraint from the power spectrum with the same prior. Compared to the bispectrum (monopole) analysis, skew spectra offer comparable constraints on larger scales (kmax<0.3 h−1 Mpc) for most parameters except for σ8. Published by the American Physical Society 2024

Incorporating Symbolic Discrete Controller Synthesis into a Virtual Robot Experimental Platform: An Implementation with Collaborative Unmanned Aerial Vehicle Robots

We introduce a modeling framework aimed at incorporating symbolic discrete controller synthesis (DCS) into a virtual robot experimental platform. This framework involves symbolically representing the behaviors of robotic systems along with their control objectives using synchronous programming techniques. We employed DCS algorithms through the reactive synchronous environment ReaX to generate controllers that fulfill specified objectives. These resulting controllers were subsequently deployed on the virtual robot experimental platform Simscape. To demonstrate and validate our approach, we provide an implementation example involving collaborative UAV robots.

Stochastic model for migration and breakage of detrital and authigenic fines

Mobilisation of attached particles during flow in rocks occurs in geo-energy processes. Particle mobilisation, their migration through rocks and pore plugging yield significant decline in permeability and well injectivity and productivity. While much is currently known about the underlying mechanisms governing the detachment of detrital particles against attracting electrostatic forces, a critical gap exists in the theoretical understanding of detachment by breakage of widely spread authigenic particles, which naturally grow on rock grains during geological times. Previous works derived micro-scale mechanical equilibrium equations for both detrital and authigenic particles, and the upscaling procedure from particle to pore and core scales for detrital fines. In this paper, for the first time we derive a stochastic model for migration and breakage of authigenic fines and authigenic–detrital mixtures. This allows for core-scale transport modelling based on the particle-scale torque balance. We introduce a novel framework for predictive stochastic detachment modelling by particle–rock bond breakage that integrates the beam theory of elastic particle deformation, strength failure criteria and viscous flow around the attached particle. The analytical expressions for stress maxima and stress diagrams for a single particle allow determining the critical failure stresses, breakage points of the beam and breakage flow velocity. The mathematical model describing lab coreflood includes the maximum retention function for both authigenic and detrital fines. The matching laboratory coreflood data under increasing velocity at micro- and core-scales achieved high matching of the experimental data by the model. High matching validates the upscaling and downscaling procedures derived.

Magnetic dipole transition in proton-deuteron radiative capture at BBN energies within potential model

The pd radiative capture reaction plays a vital role in Big Bang nucleosynthesis and stellar proton-proton chain. The study of the low-energy reaction is challenging in both experiments and theories. Using the framework of potential model, we analyze pd radiative capture below 1 MeV for both electric dipole (E1) and magnetic dipole (M1) transitions. The obtained astrophysical S factors agree well with recent results, especially at energies relevant to sensitive deuterium abundance. The calculated reaction rate shows good agreement, with less than a 5% difference compared to recent works. The extrapolated value for S(0) including both transitions is determined to be 0.211 ± 0.016 eV b. A comparison with experimental data using the χ 2 test reveals the sensitivity of the M1 cross section at low energies to the scattering potential depth.