Comprehensive Model Research Articles

Estimation model of potassium content in cotton leaves based on hyperspectral information of multileaf position

Potassium (K) is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution. This indirectly leads to variations in the leaf potassium content (LKC, %) at different leaf positions. However, owing to the interaction between light and leaf age, leaf sensitivity at different positions to this change varies, including the reflection and absorption of the spectrum. How to selecting the optimal monitoring leaf position is an important factor in quickly and accurately evaluation of cotton LKC using spectral remote sensing technology. Therefore, this study proposes a comprehensive multileaf position estimation model based on the vertical distribution characteristics of LKC from top to bottom. This is aimed at achieving an accurate estimation of cotton LKC and optimizing the strategy for selecting the monitored leaf position. Between 2020 and 2021, we collected hyperspectral imaging data of the main stem leaves at different positions from top to bottom (Li, i=1, 2, 3, …, n), during the cotton budding, flowering, and boll setting stages. Vertical distribution characteristics, sensitivity differences, and spectral correlations of LKC at different leaf positions were investigated. Additionally, the optimal range of the dominant leaf position for monitoring was determined. Partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and the entropy weight method (EWM) were used to establish LKC estimation models for single leaf and multileaf positions. The results showed a vertical heterogeneous distribution of cotton LKC, with LKC initially increasing and then gradually decreasing from top to bottom, and the average LKC of cotton reaches its maximum value at flowering stage. The upper leaf position demonstrated greater sensitivity to K and exhibited a stronger correlation with the spectrum. The selected dominant leaf positions for the three growth stages were L1–L5, L1–L4, and L1–L2, respectively. Based on the dominant leaf position monitoring range, the optimal single leaf position models for estimating LKC during the three growth stages were PLSR-L4, PLSR-L1, and SVR-L2, with The coefficient of determination of the validation set (R2val) of 0.786, 0.580, and 0.768, and the root-mean-square error of the validation set (RMSEval) of 0.168, 0.197, and 0.191, respectively. The multileaf position LKC estimation model was constructed by EWM with R2val of 0.887, 0.728, and 0.703, and RMSEval of 0.134, 0.172, and 0.209, respectively. In contrast, the newly developed multileaf position comprehensive estimation model yielded superior results, improving the stability of the model on the basis of high accuracy, especially during the budding and flowering stages. These findings hold significant importance for investigating cotton LKC spectral models and selecting suitable leaf positions for field monitoring.

Performance improvement of a standalone PV system using supercapacitors: modeling and energy management

Standalone photovoltaic (PV) systems are the most common and practical application in remote areas and communities far from the power grid. However, in the case of supplying a pulsating load with only a battery as a storage unit, their performance degraded. Therefore, hybrid electrical energy storage (HEES) systems represent a viable solution. This paper investigates the impact of utilizing a supercapacitor (SC) to work cooperatively with a battery storage unit to enhance the overall system behavior. Two scenarios of battery storage systems with/without SC are considered. A comprehensive modeling and sizing approach is established and presented in detail. Then, an energy management system (EMS) is proposed to enhance the HEES system’s performance. A proportional-integral (PI)-based controller is designed and examined to control the power electronic converters and hence improve energy management. The HEES system operation is simulated and evaluated using MATLAB/Simulink to feed a pulsating load, where the drawn pulsated load current is composed of two components: one component is supplied by battery, and the other component is fed from SC. Finally, the performance of the two hybrid configurations is evaluated in terms of battery voltage and current fluctuations, transient response, and load voltage and current ripples. The obtained results demonstrate the effectiveness of introducing SCs into HEES system.

Modelling and performance analysis of free body dynamics of electric vehicles

The modelling and performance analysis of free body dynamics in electric vehicles (EVs) plays a crucial role in understanding and optimizing the vehicle's behavior and performance. This research focuses on accurately modelling the forces and motions acting on the vehicle body, excluding the powertrain components. By considering factors such as vehicle weight, suspension characteristics, tire properties, and aerodynamic forces, a comprehensive mathematical model is developed. This model enables the simulation and analysis of the vehicle's behavior under various operating conditions. The performance analysis involves evaluating key metrics such as vehicle response, stability limits, and ride comfort. The proposed system is compared with the existing electric vehicle in the market. The findings from this research contribute to the design and development of EVs with improved handling, stability, and energy efficiency. Additionally, they inform the development of advanced driver-assistance systems (ADAS) and autonomous driving technologies. Overall, the modelling and performance analysis of free body dynamics in EVs supports the advancement of sustainable and efficient transportation systems.

A comprehensive approach for evaluating salinity stress tolerance in brinjal (Solanum melongena L.) germplasm using membership function value.

Salinity is a major stress factor affecting plant growth and development, which limits the productivity of vegetable crops. Brinjal (Solanum melongena L.), an important vegetable cultivated across the globe is susceptible to salinity stress. In the present study, the salinity tolerance response of 110 brinjal germplasm lines was evaluated at the germination stage using the membership function value (MFV). The MFV is a comprehensive index that integrates salt-tolerance indices of germination parameters. The brinjal germplasms were classified into highly salt-tolerant (>0.79), salt-tolerant (0.79-0.65), moderately salt-tolerant (0.64-0.21), salt-sensitive (0.20-0.07), and highly salt-sensitive (<0.07) based on their mean MFV. Among all the traits examined, germination percentage (0.874) and vigour index-I (0.808) were the most reliable traits for assessing salinity tolerance, showing a higher correlation with mean MFV. Furthermore, a comprehensive mathematical model was developed for evaluating the salt-tolerance of the brinjal germplasm. We validated our model by evaluating the brinjal germplasm at the seedling stage through a hydroponic experiment, and a strong positive correlation was observed between growth parameters at the germination and seedling stages. Salt-tolerant genotypes showed higher chlorophyll content, photosynthetic performance and biomass accumulation with lower canopy temperature (1.57°C) under salinity compared to susceptible genotypes (2.62°C). These findings provide valuable insights into the salinity tolerance of the brinjal germplasm, and identified potential candidates to elucidate the molecular mechanisms and develop salinity-tolerant cultivars. To our knowledge, this is the first report using a mathematical model based on MFV to evaluate the salt-tolerance of any vegetable crop.

School-based sexual and reproductive health education among adolescents in developing countries

The increase in risky sexual behavior among adolescents indicates the need to strengthen sexual and reproductive health. Schools have an essential role, but few studies are related to mapping models of school-based sex and reproductive education for adolescents in developing countries. This scoping review examines school-based sexual and reproductive health education among adolescents in developing countries. A systematic approach was used to search the literature through 5 electronic databases: Science Direct, EBSCO, PubMed, CrossReff, and Proquest. Subsequently, two reviewers conducted a conformity analysis with the inclusion/exclusion criteria, and then the search results that met the requirements were analyzed descriptively. A total of 14 articles met the inclusion criteria. The model of sexual health education in schools comprises digital-based education, peer groups, and comprehensive sex education to improve life skills. Barriers to implementation include culture, traditional norms, organization, commitment, and supporting resources. The supporting factors are student enthusiasm, exciting methods and media, community, and parental support. School-based sexual and reproductive education increases knowledge and attitudes about sexual health. Therefore, there is a need for a comprehensive sexual education model integrated into the school curriculum using attractive media and involving several well-organized stakeholders.

A comprehensive semigray climate model

A climate model is developed for Earth climate history simulations or snapshots of possible conditions on Earthlike exoplanets. It includes estimates for shortwave and longwave optical thickness based on data from Venus, Earth, and Mars; expressions for atmospheric shortwave absorption and surface convective heat loss; climate feedbacks due to water vapor, ice-albedo, clouds, and lapse rate; and a new model for planetary and surface albedo which takes account of surface cover, Rayleigh scattering, and differing wavelength fractions due to primary spectral class. While somewhat complex, it is still orders of magnitude faster than full-spectrum methods or radiative-convective convergence. The model can be modified for use with tidally locked planets, and is here applied to Proxima Centauri b as an example.

Constitutive model and activation recovery performance of Fe-SMA: Experimental and theoretical study

In this study, the static tensile properties, activation recovery properties, and post-activation static tensile properties of the China-made iron-based shape memory alloys (Fe-SMA) were experimentally studied and theoretically analyzed. The material parameters of the Ramberg-Osgood curve are modified based on the static tensile tests of Fe-SMA plates, leading to the proposal of a static tensile constitutive model suitable for Fe-SMA. The applicability analysis of the theoretical model indicates that the calculated curve achieves an accuracy of R2 ≥ 0.9. Subsequent activation tests revealed a locally optimal combination of pre-strain and activation temperature, which is concluded to be associated with the martensite content. Hot-rolled Fe-SMA plates demonstrate local maximum utilization when subjected to a 4% pre-strain and an activation temperature of 300 °C, resulting in a recovery stress of 314 MPa. Moreover, it has been determined that the two activation modes, namely single heating and multiple-cyclic heating, do not significantly impact the final recovery stress of hot-rolled Fe-SMA plates. Therefore, the multiple-cyclic activation of Fe-SMA can be utilized to compensate for prestress loss in practical applications. Furthermore, a comprehensive constitutive model for Fe-SMA is proposed based on the results of the post-activation static tensile tests. This model provides an accurate description of the static-tensile stress-strain relationship of Fe-SMA during pre-straining and after activation. This study focuses on investigating the constitutive relationship and activation recovery performance of Fe-SMA, providing an experimental basis, data support, and theoretical analysis for the practical application and further analysis of Fe-SMA in structural reinforcements.

Information needs for general practitioners on type 2 diabetes in Western countries. A systematic review.

Most people with type 2 diabetes receive treatment in primary care by general practitioners who are not specialised in diabetes. Thus, it is important to uncover the most essential information needs regarding type 2 diabetes in general practice. To identify information needs related to type 2 diabetes for general practitioners. A systematic review focused on literature relating to Western countries. We searched the databases MEDLINE, Embase, PsycInfo and CINAHL from inception to January 2024. Two researchers conducted the selection process, and citation searches were performed to identify any relevant articles missed by the database search. Quality appraisal was conducted with the Mixed Methods Appraisal Tool. Meaning units were coded individually, grouped into categories, and then studies were summarized within the context of these categories using narrative synthesis. An evidence map was created to highlight research gaps. Thirty-nine included studies revealed eight main categories and 37 subcategories of information needs. Categories were organised into a comprehensive hierarchical model of information needs, suggesting that 'Knowledge of guidelines' and 'Reasons for referral' encompass more specific information needs. The evidence map shows geographical distribution of categories and knowledge gaps in qualitative research on management and risk factors. This systematic review provides GPs, policy makers, and researchers with a hierarchical model of information and educational needs for GPs, and an evidence map showing gaps in the current literature. Information needs about clinical guidelines and reasons for referral to specialised care overlapped with needs for more specific information.

Solar Wind Density and Core Temperature Derived from the PSP Quasi-thermal Noise Measurements

Quasi-thermal noise (QTN) spectroscopy is a valuable method to deduce important parameters in space plasma, such as plasma density and temperature, especially when direct particle measurements are not available. The present study develops a new fitting method to fit the QTN spectra observed by the Parker Solar Probe (PSP) with a comprehensive theoretical QTN spectral model. By combining the steepest descent and Levenberg–Marquardt algorithms, the new method is more flexible with initial guess values but still yields reliable solar wind electron density and temperature values. The new method is applied to derive the solar wind density and core temperature from the QTN measurements during 10 encounters of PSP. The electron density and temperature values obtained vary with the radial distance from the Sun as n e ∝ r −2.12 and T e ∝ r −0.71, both of which are consistent with existing models and previous results.

Mouse Models of Sepsis.

Human sepsis is a complex disease that manifests with a diverse range of phenotypes and inherent variability among individuals, making it hard to develop a comprehensive animal model. Despite this difficulty, numerous models have been developed that capture many key aspects of human sepsis. The robustness of these models is vital for conducting pre-clinical studies to test and develop potential therapeutics. In this article, we describe four different models of murine sepsis that can be used to address different scientific questions relevant to the pathology and immune response during and after a septic event. Basic Protocol 1 details a non-synchronous cecal ligation and puncture (CLP) model of sepsis, where mice are subjected to polymicrobial exposure through surgery at different time points within 2 weeks. This variation in sepsis onset establishes each mouse at a different state of inflammation and cytokine levels that mimics the variability observed in humans when they present in the clinic. This model is ideal for studying the long-term impact of sepsis on the host. Basic Protocol 2 is also a type of polymicrobial sepsis, where injection of a specific amount of cecal slurry from a donor mouse into the peritoneum of recipient mice establishes immediate inflammation and sepsis without any need for surgery. Basic Protocol 3 describes infecting mice with a defined gram-positive or -negative bacterial strain to model a subset of sepsis observed in humans infected with a single pathogen. Basic Protocol 4 describes administering LPS to induce sterile endotoxemia. This form of sepsis is observed in humans exposed to bacterial toxins from the environment. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Non-synchronous cecal ligation and puncture Basic Protocol 2: Cecal slurry model of murine sepsis Basic Protocol 3: Monomicrobial model of murine sepsis Basic Protocol 4: LPS model of murine sepsis.

A Spatial-Temporal Variational Graph Attention Autoencoder Using Interactive Information for Fault Detection in Complex Industrial Processes.

Modern industry processes are typically composed of multiple operating units with reaction interaction and energy-mass coupling, which result in a mixed time-varying and spatial-temporal coupling of process variables. It is challenging to develop a comprehensive and precise fault detection model for the multiple interconnected units by simple superposition of the individual unit models. In this study, the fault detection problem is formulated as a spatial-temporal fault detection problem utilizing process data of multiple interconnected unit processes. A spatial-temporal variational graph attention autoencoder (STVGATE) using interactive information is proposed for fault detection, which aims to effectively capture the spatial and temporal features of the interconnected unit processes. First, slow feature analysis (SFA) is implemented to extract temporal information that reveals the dynamic relevance of the process data. Then, an integration method of metric learning and prior knowledge is proposed to construct coupled spatial relationships based on temporal information. In addition, a variational graph attention autoencoder (VGATE) is suggested to extract temporal and spatial information for fault detection, which incorporates the dominances of variational inference and graph attention mechanisms. The proposed method can automatically extract and deeply mine spatial-temporal interactive feature information to boost detection performance. Finally, three industrial process experiments are performed to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the proposed method dramatically increases the fault detection rate (FDR) and reduces the false alarm rate (FAR).

Design and optimization of a hybrid cooling configuration combining PCM and liquid cooling for Li-ion battery using data-based response surface approximation model

Effective thermal management is crucial for safe operation of electric vehicles. This study proposes a novel hybrid cooling system that using phase change material (PCM) and liquid cooling to address issues such as leakage, non-uniform temperatures under high discharge rates. A comprehensive 3D model was developed to analyze the influence of geometric parameters on the cooling system. Response surface approximation modeling was used to optimize the layout parameters of the PCM. The results show that the hybrid cooling outperforms other methods, maintaining the cell temperature with 35 ℃ and the temperature difference within 4 ℃. The thickness of the PCM is a significant factor, as it increases heat storage capacity and reduces temperature; however, exceeding a certain threshold leads to increased thermal resistance. A non-uniform PCM layout can reduce the temperature difference by 26 %. An optimal PCM thickness of 2.5 mm to 3 mm ensures that the maximum temperature remains below 32.5 ℃, with a temperature difference within 2 ℃. The lowest temperature difference is achieved with a vertical layout height of 70 mm and a horizontal layout thickness of 3 mm. These findings are valuable for enhancing the thermal management of lithium-ion battery.

An innovative environmental regulation tool for regional air pollution based on DEA-RFR

Environmental regulation tools are one of the conventional means for pollution control in various countries. For large-scale urbanization regions, reasonable pre-allocation of pollutant emission amount for each area is an effective regulation to improve air quality. Treating pollutant emission quota as a resource element, a comprehensive input-output model has been established based on Data Envelopment Analysis (DEA) method, to evaluate the rationality of pollutant emission allocation and provide adjustment scheme, thus developing an environmental regulation tool. The index system includes factors of economy, environment, energy consumption, demographic and land-use as input indicators, regional GDP and environmental air quality index as output indicators. The regulation tool is applied to the current air pollutant emission management in Henan Province, China. The results show that 7 cities need to further adjust the allocation scheme, among which, Anyang, Xinxiang, Nanyang and Shangqiu are suggested to reduce the emission allocation quotas; Puyang and Zhumadian are suggested to increase the emission allocation quotas; Pingdingshan shall seek optimization paths through collaborative improvement. Further, to verify the effectiveness of the tool, by joint using Random Forest Regression (RFR), a DEA efficiency reassessment is conducted on the obtained new allocation scheme of Nanyang City. The results show that the efficiency of the new allocation scheme has reached optimal. This study provides a practical and effective regulation tool for regional management of pollutant emission, to balance economic growth and environmental protection.

Protracted and Progressive Crustal Melting during Continental Collision in the Pamir and Plateau Growth

Abstract Determining crustal melting in parallel with geodynamic evolution provides critical information on plateau crustal thickening and uplift. Here we investigate the timing and duration of crustal melting through in situ analysis of zircon U–Pb ages, trace elements and Hf–O isotopes, and whole-rock elements and Sr–Nd–Hf isotopes for the granites and high-grade metamorphic rocks from the Pamir Plateau. Zircon dates record protracted crustal melting for both Central Pamir (43–33 and 22–12 Ma) and South Pamir (28–10 Ma). The Pamir Cenozoic granites are characterized by significant elemental and isotopic heterogeneity. The elemental variability within the Pamir Cenozoic granites is dominantly attributed to fractional crystallization of K-feldspar and plagioclase with subordinate biotite from a variably fractionated melt, and the accumulation of early crystallized feldspar during magma ascent. Peritectic mineral entrainment and accessory mineral crystallization had some influence on the geochemical characteristics of the garnet-bearing leucogranite dikes. Zircon Hf isotopes and whole-rock Sr–Nd–Hf isotopes show secular variations for both Central and South Pamir granites. The Central Pamir granites show a mild decrease in whole-rock ƐNd(t) values from Eocene (−4.3 to −4.9) to Miocene (−6.2 to −7.7), and the zircon ƐHf(t) values decrease from c. 40 Ma (+2 to −5) to c. 10 Ma (−4 to −8). In contrast, the South Pamir granites have highly variable whole-rock Sr–Nd–Hf (87Sr/86Sr(i) = 0.7053 to 0.7830; ƐNd(t) = −31.5 to +0.2; ƐHf(t) = −40.0 to +8.2) and zircon Hf isotopes (ƐHf(t) = −31.7 to +7.5) and display a strong decrease in ƐNd(t) and ƐHf(t) values from c. 25 Ma to c. 13 Ma. Geochemical and isotopic data indicate that both the Central and South Pamir experienced crustal melting from juvenile lower crust to ancient lower-middle crustal materials, and Indian crustal materials were incorporated into the melt region of the South Pamir leucogranites from c. 20 Ma. Our study highlights a causal link between a chain of events that includes magma underplating induced by lithosphere thinning and slab breakoff, lithosphere delamination and underthrusting of Indian lithosphere, and formation of the Cenozoic granites in Pamir. This series of processes are incorporated here into a comprehensive model for the geodynamic evolution of the Pamir during the India–Asia collision.

Maser polarization through anisotropic pumping

Context. Polarized emission from masers is an excellent tool to study magnetic fields in maser sources. The linear polarization of the majority of masers is understood as an interplay of maser saturation and anisotropic pumping. However, for the latter mechanism, no quantitative modeling has been presented yet. Aims. We aim to construct a comprehensive model of maser polarization, including quantitative modeling of both anisotropic pumping and the effects of maser saturation on the polarization of masers. Methods. We extended regular (isotropic) maser excitation modeling with a dimension that describes the molecular population alignments, as well as including the linear polarization dimension to the radiative transfer. The results of the excitation analysis yielded the anisotropic pumping and decay parameters, which were subsequently used in one-dimensional proper maser polarization radiative transfer modeling. Results. We present the anisotropic pumping parameters for a variety of transitions from class I CH3OH masers, H2O masers, and SiO masers. SiO masers are highly anisotropically pumped due to them occurring in the vicinity of a late-type star, which irradiates the maser region with a strong directional radiation field. Class I CH3OH masers and H2O masers occur in association with shocks, and they are modestly anisotropically pumped due to the anisotropy of the excitation region. Conclusions. Our modeling constitutes the first quantitative constraints on the anisotropic pumping of masers. We find that anisotropic pumping can explain the high polarization yields of SiO masers, as well as the modest polarization of unsaturated class I CH3OH masers. The common 22 GHz H2O maser has a relatively weak anisotropic pumping; in contrast, we predict that the 183 GHz H2O maser is strongly anisotropically pumped. Finally, we outline a mechanism through which non-Zeeman circular polarization is produced, when the magnetic field changes direction along the propagation through an anisotropically pumped maser.

Proposal of model for prediction of grape processing and spraying time by using IoT smart agriculture sensor data

The grape industry's impact on agriculture and the economy requires precise forecasting for processing and spraying schedules to optimize production. This article introduces an innovative IoT-based model for predicting optimal timings in grape processing and spraying. By integrating real-time environmental and viticultural data, the model improves decision-making, enhancing product quality, reducing energy consumption, and increasing operational efficiency. Crucially, SARIMA predictive algorithms forecast parameters like temperature, humidity, wind speed, and air pressure. This comprehensive model transforms the grape industry, offering advanced decision support and promoting sustainable, resource-efficient production. The research signals a potential shift to precision agriculture, balancing economic viability with environmental stewardship in grapes.

Optimal site selection for geothermal heated greenhouses in cold climates: a fuzzy logic and geographic information system approach

Abstract This paper presents a comprehensive model for selecting optimal sites for geothermal heated greenhouses in the agricultural industry. The research involves two main componentsf: assessing the geothermal energy resource using geological, geochemical and geophysical indicators; and developing a siting model based on fuzzy logic that considers technical, environmental and socio-economic factors. The study focuses on East Azerbaijan province in Iran—a cold region. The results indicate that ~3.24% of the total area of the province is suitable for constructing geothermal greenhouses, resulting in potential savings of ~148 000 and 745 978 m3 of natural gas in areas with high and very high suitability, respectively.

Molecular Dynamics Simulations of Native Protein Charging via Proton Transfer during Electrospray Ionization with Grotthuss Diffuse H3O.

Unraveling the mechanism by which native proteins are charged through electrospray ionization (ESI) has been the focus of considerable research because observable charge states can be correlated to biophysical characteristics, such as protein folding and, thus, solution conformation. Difficulties in characterizing electrosprayed droplets have catalyzed the use of molecular dynamics (MD) to provide insights into the mechanisms by which proteins are charged and transferred to the gas phase. However, prior MD studies have utilized metal ions, primarily Na+, as charge carriers, even though proteins are primarily detected as protonated ions in the mass spectra. Here, we propose a modified MD protocol for simulating discrete Grotthuss diffuse H3O+ that is capable of dynamically altering amino-acid protonation states to model electrospray charging and gaseous ion formation of model proteins, ubiquitin, and myoglobin. Application of the protocol to the evaporation of acidic droplets enables a molecular perspective of H3O+ coordination and proton transfer to/from proteins, which is unfeasible with the metal charge carriers used in previous MD studies of ESI. Our protocol recreates experimentally observed charge-state distributions and supports the charge residue model (CRM) as the dominant mechanism of native protein ionization during ESI. Additionally, our results suggest that protonation is highly specific to individual residues and is correlated to the formation of localized hydrated regions on the protein surface as droplets desolvate. Considering the use of discrete H3O+ instead of Na+, the developed protocol is a necessary step toward developing a more comprehensive model of protein ionization during ESI.

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

The booming development of distributed power sources in power systems has drawn attention to the carrying capacity and stability of the power grid, becoming a key challenge for the power industry. This study aims to develop a comprehensive deep learning model by combining deep recurrent double Q network (DR-DQN) and deep convolutional neural network (DCNN), and use meta-learning to optimize the model as a whole to simultaneously optimize the power grid. Distributed power supply carrying capacity and predicting the voltage fluctuations of the grid. The comprehensive model is designed to consider distributed power capacity optimization and voltage fluctuation prediction holistically. Through the DR-DQN model, the maximum distributed power capacity is determined under different grid conditions and the distributed power configuration of the grid is optimized. At the same time, the DCNN model is used to analyze the power grid time series data and predict the voltage fluctuation of the power grid. The results are presented in graph form, showing trends in maximum capacity and voltage fluctuations under different grid conditions. Experimental results show that the overall model achieves satisfactory results in distributed power capacity optimization and voltage fluctuation prediction. Performance evaluation and comparison highlight the comprehensive model’s excellent performance in terms of prediction accuracy and computational efficiency, providing new possibilities for efficient management and reliable operation of power systems. The successful development of the model provides practical and reliable solutions for the future development of power systems.

Development of a Comprehensive Model for Cancer Symptom Care for Women With Ovarian or Endometrial Cancer

Background: Women with ovarian or endometrial cancer experience multiple symptoms during chemotherapy. Specialized cancer nurses possess specific knowledge and competencies to effectively monitor and manage treatment-related toxicities and provide self-management support. Objective: To describe the conception and development of a comprehensive cancer symptom model of care in an oncological setting for women diagnosed with ovarian or endometrial cancer. Methods: The participatory evidence-based, patient-focused process for guiding the development, implementation, and evaluation of advanced practice nursing roles—the participatory, evidence-based, patient-centered process for advanced practice (PEPPA) framework directed the process. The first 6 steps of this 9-step framework were utilized to incorporate research evidence, engage, and obtain the input of key stakeholders. Results: Stakeholders (n = 27) contributed with specific knowledge, perspectives, and feedback to the entire development process, and several needs were identified. Following structured discussions, a new model of cancer symptom care with elements such as symptom management, electronic patient-reported outcomes, and an expanded nursing role in the form of nurse-led consultations was developed. Conclusions: We effectively utilized the PEPPA framework to design a new cancer symptom model of care, that was agreed upon by key stakeholders. Implications for Practice: This stakeholder-engaged, and evidence-driven process could be used as a template for others wanting to develop a population-specific model of care to improve cancer symptom management. What is Foundational: With the expansion of the cancer nursing role, the new model has the potential to improve the quality of cancer care and health outcomes related to symptom management.